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Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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100 Questions (and Answers) About Research Methods

Neil J. Salkind
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"How do I know when my literature review is finished?"

"What is the difference between a sample and a population?"

"What is power and why is it important?"

In an increasingly data-driven world, it is more important than ever for students as well as professionals to better understand the process of research. This invaluable guide answers the essential questions that students ask about research methods in a concise and accessible way.

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Question #16: Question #16: How Do I Know When My Literature Review Is Finished?

Question #32: How Can I Create a Good Research Hypothesis?

Question #40: What Is the Difference Between a Sample and a Population, and Why

Question #92: What Is Power, and Why Is It Important?

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Questions to test students’ understanding of research methods

How to craft questions for closed book examinations on undergraduate research methods

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Research methods are a compulsory component of many undergraduate programmes. But it is challenging to design good questions for assessing students’ understanding of research methods in closed book exams. There is a fine balance between ensuring the often broad and ambitious course objectives and content are fully reflected in the questions while avoiding asking students to replicate all the research procedures.

In the final exam for our research methods class, we include eight to 10 varied questions. Below are some common types:

Defining key concepts and terminologies

While research methods are not purely technical and procedural, there is a need for students to understand the foundational theories before they proceed further. So, we set basic and factual questions to check students’ fundamental understanding of various research concepts and terminologies. This is not aimed at forcing students to memorise definitions or exact wording. We want students to understand and explain concepts in their own words.

What is a critical case study?
What is the difference between a panel survey and a longitudinal survey?

Giving real-world examples

The best way for students to demonstrate that they understand the research concepts is by giving real-world examples as concrete illustrations or applications. We go further by asking students to use their own daily experiences and observations to illustrate research concepts in their own words. This process helps students to understand and reflect on how the concepts fit into a greater societal context that is far more interesting.

Explain what is a measurement reliability , with an example.
Use an example to illustrate the Belmont Report ethical principle of justice in research involving human subjects.

Application of real-life contexts

Students should be capable of making sense of complex research concepts by applying them in real-life research contexts. But rather than asking them to draft a full research proposal which would involve framing research questions, identifying variables, stating measurement tools, framing research instruments and so on, we can ask questions that focus on just one or two of these components. In this way, we can check whether students understand the crux of various research items.

Construct an example of 2x2 factorial design in a survey experiment that investigates public opinion towards foreign immigration.
Write a research question and explain how you can personally use participant observation to answer the question.

Doing simple mathematical calculations

Although students often feel anxious about the quantitative part, it is essential for them to have a basic understanding of the mathematical or statistical steps and procedures relating to research calculations. To check their understanding of fundamental statistical concepts, we ask them to do or describe simple calculations that are manageable under the time and space constraints of the exam.

Chelsea conducted a survey to study turnout patterns in the last Student Council election. She collected the following data. Consider the respondent’s Grade Point Average, calculate the mean and compare it with the median.
Describe without using any numbers or mathematical symbols the steps for conducting a t-test for the difference in means .

Offering evidence-based commentaries

To facilitate deeper thinking on a variety of research concepts, we ask students to assess and respond to a number of hypothetical research claims and scenarios, using sound reasoning and concrete evidence.

Ren Shen suspects that taking Ginseng Tonic will improve academic performance. He surveys his classmates and finds that those who take it have an average GPA score significantly higher than those who do not. He concludes that taking it will improve GPA scores. Is Ren Shen’s causal conclusion a credible one? Explain.
Suppose you have invented a time machine that allows you to travel between the past and the present. Explain how you may potentially use your machine to solve the fundamental problem of causal inference.

Aligning with real research in the field

To align students’ learning with authentic research, some questions can be rooted in real research. These might include asking students to identify the relevant information in an abstract, summarising the research design, or interpreting the relevant data output and making resulting conclusions. The following research experiment conducted by the team has a number of ethical problems. Identify one of these problems.

Consider the following regression output. Identify the dependent and independent variables. Which variables are statistically significant? How do you know?

Adrian Man-Ho Lam is course tutor in the department of politics and public administration at the University of Hong Kong.

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Research Methodology

Student resources, multiple choice questions.

Research: A Way of Thinking

The Research Process: A Quick Glance

Reviewing the Literature

Formulating a Research Problem

Identifying Variables

Constructing Hypotheses

The Research Design

Selecting a Study Design

Selecting a Method of Data Collection

Collecting Data Using Attitudinal Scales

Establishing the Validity and Reliability of a Research Instrument

Selecting a Sample

Writing a Research Proposal

Considering Ethical Issues in Data Collection

Processing Data

Displaying Data

Writing a Research Report

Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Research Method	Quantitative	Qualitative	Mixed Methods
	To measure and quantify variables	To understand the meaning and complexity of phenomena	To integrate both quantitative and qualitative approaches
	Typically focused on testing hypotheses and determining cause and effect relationships	Typically exploratory and focused on understanding the subjective experiences and perspectives of participants	Can be either, depending on the research design
	Usually involves standardized measures or surveys administered to large samples	Often involves in-depth interviews, observations, or analysis of texts or other forms of data	Usually involves a combination of quantitative and qualitative methods
	Typically involves statistical analysis to identify patterns and relationships in the data	Typically involves thematic analysis or other qualitative methods to identify themes and patterns in the data	Usually involves both quantitative and qualitative analysis
	Can provide precise, objective data that can be generalized to a larger population	Can provide rich, detailed data that can help understand complex phenomena in depth	Can combine the strengths of both quantitative and qualitative approaches
	May not capture the full complexity of phenomena, and may be limited by the quality of the measures used	May be subjective and may not be generalizable to larger populations	Can be time-consuming and resource-intensive, and may require specialized skills
Typically focused on testing hypotheses and determining cause-and-effect relationships	Surveys, experiments, correlational studies	Interviews, focus groups, ethnography	Sequential explanatory design, convergent parallel design, explanatory sequential design

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

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Multimodal Large Language Model Passes Specialty Board Examination and Surpasses Human Test-Taker Scores: A Comparative Analysis Examining the Stepwise Impact of Model Prompting Strategies on Performance

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Background: Large language models (LLMs) have shown promise in answering medical licensing examination-style questions. However, there is limited research on the performance of multimodal LLMs on subspecialty medical examinations. Our study benchmarks the performance of multimodal LLMs enhanced by model prompting strategies on gastroenterology subspecialty examination-style questions and examines how these prompting strategies incrementally improve overall performance. Methods: We used the 2022 American College of Gastroenterology (ACG) self-assessment examination (N=300). This test is typically completed by gastroenterology fellows and established gastroenterologists preparing for the gastroenterology subspecialty board examination. We employed a sequential implementation of model prompting strategies: prompt engineering, Retrieval-Augmented Generation (RAG), five-shot learning, and an LLM-powered answer validation revision model (AVRM). GPT-4 and Gemini Pro were tested. Results: Implementing all prompting strategies improved the overall score of GPT-4 from 60.3% to 80.7% and Gemini Pro from 48.0% to 54.3%. GPT-4's score surpassed the 70% passing threshold and 75% average human test-taker scores unlike Gemini Pro. Stratification of questions by difficulty showed the accuracy of both LLMs mirrored that of human examinees, demonstrating higher accuracy as human test-taker accuracy increased. The addition of the AVRM to prompt, RAG, and 5-shot increased GPT-4's accuracy by 4.4%. The incremental addition of model prompting strategies improved accuracy for both non-image (57.2% to 80.4%) and image-based (63.0% to 80.9%) questions for GPT-4, but not Gemini Pro. Conclusions: Our results underscore the value of model prompting strategies in improving LLM performance on subspecialty-level licensing exam questions. We also present a novel implementation of an LLM-powered reviewer model in the context of subspecialty medicine which further improved model performance when combined with other prompting strategies. Our findings highlight the potential future role of multimodal LLMs, particularly with the implementation of multiple model prompting strategies, as clinical decision support systems in subspecialty care for healthcare providers. Keywords: ChatGPT, Gemini pro, gastroenterology, RAG, prompt engineering, medical specialty examination.

Competing Interest Statement

Conflict of Interest: Jamil S. Samaan declares that they have no conflict of interest. Samuel Margolis declares that they have no conflict of interest. Nitin Srinivasan declares that they have no conflict of interest. Yee Hui Yeo declares that they have no conflict of interest. Rajsavi Anand declares that they have no conflict of interest. Fadi S. Samaan declares that they have no conflict of interest. James Mirocha declares that they have no conflict of interest. Seyed Amir Ahmad Safavi-Naini received non-significant financial compensation as an R&D associate from AryaspCo. Bara El Kurdi declares that they have no conflict of interest. Ali Soroush declares that they have no conflict of interest. Rabindra Watson declares that they have no conflict of interest. Srinivas Gaddam declares that they have no conflict of interest. Joann G. Elmore declares that they have no conflict of interest. Brennan M.R. Spiegel declares that they have no conflict of interest. Nicholas P. Tatonetti declares that they have no conflict of interest.

Funding Statement

Author declarations.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

2022 American College of Gastroenterology (ACG) self-assessment examination. Available at https://education.gi.org/satest/satest_18

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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https://education.gi.org/satest/satest_18

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Published: 24 July 2024

Faculty standardized patients versus traditional teaching method to improve clinical competence among traditional Chinese medicine students: a prospective randomized controlled trial

Meilan Huang 1 , 2 na1 ,
Han Yang 1 , 2 na1 ,
Jing Guo 1 , 2 na1 ,
Xiaoxu Fu 1 , 2 ,
Wangshu Chen 1 , 2 ,
Bin Li 1 , 2 ,
Shan Zhou 1 , 2 ,
Ting Xia 1 , 2 ,
Sihan Peng 1 , 2 ,
Lijuan Wen 3 ,
Xiao Ma 4 ,
Yi Zhang 1 , 2 &
Jinhao Zeng 1 , 2

BMC Medical Education volume 24 , Article number: 793 ( 2024 ) Cite this article

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Standardized patients (SPs) simulation training models have been widely used in various fields, the study of using SPs in Traditional Chinese medicine (TCM) is still a new filed. Previous studies have demonstrated the effectiveness of occupational SP for TCM (OSP-TCM), which has an increasingly problem of high time and financial costs. The faculty SPs for TCM (FSP-TCM) simulation training model may provide a better alternative. This study aims to test and determine whether FSP-TCM simulations are more cost-effective than OSP-TCM and traditional educational models to improve the clinical competence of TCM students.

This study was a single-blind, prospective, randomized controlled trial conducted between February 2023 and October 2023. The participants were randomized into FSP-TCM group, OSP-TCM group and traditionally taught group (TT group) in the ratio of 1:1:1. The duration of this training program was 12 weeks (36 credit hours). Formative and summative assessments were integrated to evaluate the effectiveness of teaching and learning. Three distinct questionnaires were utilized to collect feedback from students, SPs, and teachers at the conclusion of the course. Additionally, analysis of cost comparisons between OSP-TCM and FSP-TCM were performed in the study.

The study comprised a total of 90 students, with no dropouts during the research. In the formative evaluation, students assigned to both the FSP-TCM and OSP-TCM groups demonstrated higher overall scores compared to those in the TT group. Notably, their performance in “physical examination” ( P a = 0.01, P b = 0.04, P c = 0.93) and “comprehensive ability” ( P a = 0.01, P b = 0.006, P c = 0.96) significantly exceeded that of the TT group. In the summary evaluation, both SP-TCM groups students outperforms TT group in the online systematic knowledge test ( P a = 0.019, P b = 0.04, P c = 0.97), the application of TCM technology ( P a = 0.01, P b = 0.03, P c = 0.93) and real-time assessment ( P a = 0.003, P b = 0.01, P c = 0.93). The feedback questionnaire demonstrated that both SP-TCM groups showed higher levels of agreement for this course in “satisfaction with the course” ( P a = 0.03; P b = 0.02) and “enhanced TCM clinical skills” ( P a = 0.02; P b = 0.03) than TT group. The SP questionnaire showed that more FSPs than OSPs in “provided professional feedback” (FSPs: strongly agree 30%, agree 50% vs. OSPs: strongly agree 20%, agree 40%. P = 0.69), and in “gave hints” during the course (FSPs: strongly agree 10%, agree 30% vs. OSPs: strongly agree 0%, agree 10%. P = 0.42). It is noteworthy that FSP-TCM was significantly lower than the OSP-TCM in overall expense (FSP-TCM $7590.00 vs. OSP-TCM $17415.60), and teachers have a positive attitude towards the FSP-TCM.

FSP-TCM training mode showed greater effectiveness than traditional teaching method in improving clinical competence among TCM students. It was feasible, practical, and cost-effective, and may serve as an alternative method to OSP-TCM simulation.

Peer Review reports

Introduction

Standardized patients (SPs) are simulated patients trained based on specific standards and procedures and can simulate various diseases and individual characteristics encountered in real medical scenarios [ 1 ]. They present the clinical features of real patients in a standardized manner, including different conditions, physical signs, symptoms, and needs of patients [ 2 ]. Conventionally, occupational SPs (OSPs) were recruited from the general public, including professional actors and volunteers. These individuals were organized by relevant institutions and underwent training to provide consistent services to educational institutions, including medical schools [ 3 ].

The lack of practical experience among medical students is a common problem in the traditional model of medical education [ 4 ]. Uncertainty of clinical work poses challenges and elicits anxiety during the transition to clinical practice in most medical students [ 5 , 6 ]. However, SPs have reversed this situation. The efficacy of SPs in improving students’ clinical competence, reducing pre-clinical anxiety [ 7 ], and boosting confidence before clinical practice, has been demonstrated and regarded as a useful complement to traditional clinical experiences [ 8 ]. SPs as a situated teaching model, can illustrate medical student deficits in communication skills, thinking, and diagnostic and therapeutic accuracy [ 9 ], and can assess students’ proficiency in clinical skills, facilitating the adjustment and optimization of teaching strategies [ 10 , 11 ]. The diverse advantages of SPs have contributed to their popularity and wide adoption.

Traditional Chinese Medicine (TCM) is increasingly recognized as an important complementary and alternative medicine worldwide, encompassing herbal medicine, acupuncture, moxibustion, and therapeutic massage, etc. [ 12 , 13 ]. TCM plays a vital role in China’s healthcare system, with the reports from the State Administration of TCM indicating that 828,871 TCM practitioners were registered nationwide by 2020, comprising 17.2% of the country’s healthcare professionals ( http://www.natcm.gov.cn/ ). Moreover, over 800 of colleges, universities and technical schools nationwide currently established TCM programs (including 44 TCM colleges and universities, 150 western medicine colleges and universities, 250 non- medical colleges and universities, 39 TCM technical schools, 135 western medicine technical schools and 204 non-medical technical schools). TCM programs are classified as first-tier disciplines by the Ministry of Education, underscoring the Chinese government’s profound commitment to educating and nurturing TCM students. Currently, SPs are widely used in various fields, including nursing, psychology, pharmacy, and clinical training [ 8 , 14 ]. However, the integration of SPs into Chinese medicine education remains an under-explored area. As an independent discipline, TCM has distinctive features [ 15 ]. For example, in terms of clinical skills, TCM emphasizes cultivating student ability of the four diagnostic methods (inspection, auscultation, inquiry, and palpation) and TCM syndrome differentiation [ 16 ]. The disciplinary characteristics differ between TCM and Western medicine, requiring specifically trained SPs of TCM.

Our previous studies have progressively introduced courses involving both OSPs, student standardized patients, and virtual standardized patients based on TCM students feedback to enhance clinical skills [ 12 , 17 , 18 , 19 ]. These studies have demonstrated that OSP-TCM simulated training significantly improves students’ clinical abilities compared to conventional methods [ 18 ]. Nevertheless, the training and utilization of OSPs incur high time and financial costs. Consequently, ongoing efforts are focused on developing a viable, practical, and cost-effective training model. Currently, studies on faculty standardized patients (FSPs) in the field of TCM education are scarce, and the effectiveness of different models to improve students’ clinical competence remains uncertain.

A prospective, single-blind, randomized controlled trial was conducted to evaluate the clinical competency of students from FSP-TCM, OSP-TCM, and traditional teaching groups. Formative and summative assessments, questionnaires, and cost analysis were integrated to evaluate the effectiveness. This study aims to test the hypothesis that FSP-TCM, as compared to OSP-TCM and traditional teaching mode, may be a feasible, practical, and cost-effective mode of training simulation.

Ethics review and approval

This study was registered in the Educational Administration System of CDUTCM (The registration number: 1,130,730), and the Ethics Committee of CDUTCM has approved our study protocol (The grant number: 1,005,510). The study complied with ethical principles and regulations to fully safeguard the rights and interests of all participants [ 20 ]. Curriculum arrangements were made following the “Medical Education Standards of Undergraduate Education- Chinese Medicine” issued by the Chinese Ministry of Education and the “Five-Year Undergraduate Education Guide of TCM” issued by CDUTCM. All participants received comprehensive information before signing the informed consent form and voluntarily agreed to participate in the study.

Trainee recruitment

The screening was performed by sophomore students majoring in TCM (five-year program) who were studying TCM clinical competency training program at CDUTCM in 2023. By reviewing relevant literature and referencing effect sizes [ 18 , 21 ], then using PASS 15 software for sample size estimation, the minimum sample size was determined to be 75.

Inclusion and exclusion criteria

The inclusion criteria were: (1): Sophomore students majoring in TCM (five-year program) studying at CDUTCM in 2023. (2):19–23 years old, male or female. (3): Voluntary confidentiality agreements that are signed and informed consent. (4): Participants had passed the examination of the basic Chinese medicine course and the basic western medicine course. (5): Physically and mentally healthy enough to complete the study.

The exclusion criteria were: (1): Participants who had previously participated in formal training for standardized patients. (2): Trainees who had violated the confidentiality agreement of the course content during the training period. (3) Trainees who were unwilling or unable to complete the training due to the trainees’ own reason.

Randomization and blinding

SPSS 27.0 software was utilized to generate 90 random numbers, which were then randomly split into three groups, each group representing a specific intervention. Researchers assigned random numbers to eligible students, and then used these numbers to allocate the students into different groups. Randomization was conducted by an individual with no exposure to participants, ensuring confidentiality in participant allocation and baseline information throughout the study. The Data were collected and managed by individuals who was blinded to the study. Subsequently, an independent individual conducted the data analysis upon completion of data collection.

Training and qualification of FSP-TCM and OSP-TCM

Volunteers were recruited based on predefined criteria before the start of the study who need to have been teaching in the field for 5 years or more. They underwent physical and mental health assessments and received certification of good health. Additionally, volunteers were obligated to sign confidentiality and informed consent forms and demonstrate availability for training sessions (Supplement 1 ). They completed a rigorous training program with three seasoned SP trainers. The program included lectures delivered by the trainers, group-based skill training, and self-directed learning, which aims to give them a better understanding and presentation of real patient signs and symptoms. Upon completing the training, two seasoned SP instructors conducted volunteer evaluations using a combination of tests and performances. 10 volunteers finally passed the eligibility assessment and were enrolled as FSP-TCM for this study. Furthermore, a cohort of volunteers for OSP-TCM was recruited, trained, and certified following the criteria. There are currently 19 established OSP-TCM practitioners from whom 10 were randomly selected to participate in this study. The training methodology and eligibility assessment criteria are provided in our previous studies [ 18 , 19 ] (Supplement 2 ).

Training curriculum and setting

This study was conducted within the framework of a clinical skills training course in Chinese medicine, which was undertaken during the second semester of the sophomore year. This course comprises 36 credit hours and 12 representative TCM diseases. Throughout a span of 12 weeks, participants engaged in 3 credit hours of training for a target TCM disease each week (see supplement 3 ). The training process for members of TT group consists of six steps. (1) Teachers delivered didactic instruction. (2) Students engaged in open discussion. (3) Students collaborated in pairs to enact doctor-patient communication role-plays. (4) Teachers provide feedback on student performance. (5) Students developed medical histories and Chinese medicine treatment plans. (6) The teacher analyzed and summarized the case.

The FSP and OSP-TCM group were exposed to the same cases used in the control group, and the training process for both groups consisted of six steps [ 17 , 18 ]. (1) The teacher delivered lectures on the diseases involved in each case. (2) Students participated in open discussion. (3) The FSPs or OSPs introduced students to information on the patient’s personal data and disease conditions based on the clinical cases, and demonstrated TCM-specific symptoms and signs via images and medical devices. (4) The SP provided feedback and advice to the student on their consultation and physical examination. (5) Students completed a medical history, (the results of TCM syndrome differentiation and disease), and established a Chinese medicine treatment plan (treatment methods, rules, prescriptions, dosage, and medicine usage). (6) The teacher analyzed and summarized the case. It is worth noting that OSPs is derived from our previous study.

The study started in February 2023 and ended in October 2023. Volunteer recruitment and training were completed prior to the start of the course. The course started in March 2023 and evaluated and examined students at the end of the course. The data was collected from those who did not participate in this study and analyzed by the professionals.

Evaluation of training effectiveness

Formative evaluation.

The formative evaluation utilized a refined adaptation of the mini-Clinical Evaluation Exercise (mini-CEX) used in our previous study [ 22 ], with the assessments conducted on a nine-point scale (Supplement 3 ). This evaluation encompassed five domains employed to assess the clinical competence of the participants: physical examination, medical interview, disease treatment, clinical judgment, and overall performance (eTable 1 in Supplement 4 ). This session took place in the middle of the curriculum (6th week).

Summative evaluation

Online systematic test.

The assessment format for this stage was an online case exam comprising six cases (100-point scale). The first five cases were multiple-choice questions, each with five questions covering disease diagnosis, syndrome differentiation, treatment principles, treatment methods, and major prescriptions. The final case required students to analyze the given case and provide answers to questions on TCM diagnosis, syndrome differentiation, the basis of syndrome differentiation, treatment principles, prescriptions, and analysis of the chosen prescription. The entire exam lasted for 90 min.

Offline clinical skill test

OSPs to obtain information on their medical history. Subsequently, within a 30-minute timeframe, they completed treatment based on syndrome differentiation and medical records. The results of interviews, medical records, and differential diagnosis treatment obtained during this process were evaluated by six TCM professionals not involved in teaching based on pre-established criteria. Evaluation criteria, including the application of TCM skills, written medical records, TCM syndrome differentiation, and therapeutic regimen are provided in eTable 2 in Supplement 4 .

Real time assessment

The Arizona Clinical Interview Rating (ACIR) scale was used to evaluate the communication ability and interviewing skills of the students [ 17 ]. This standardized assessment comprised 20 items, each assigned a numerical value ranging from 1 to 5, where scores of 1 and 5 represented poor and excellent performances, respectively. Once students collected the medical data, OSPs evaluated and assigned scores for each item.

Feedback questionnaire

Three different questionnaire feedback forms were designed to capture the perceptions and opinions of students, SPs, and teachers. After course completion, we administered a questionnaire to the three groups of students. The questionnaire aimed to gather feedback on their attitudes towards the course and identify any benefits they had accrued for optimization of future teaching initiatives. Furthermore, we surveyed 12 participating teachers to ascertain their views and suggestions on the use of FSP-TCM in clinical skills training. Concurrently, we administered a questionnaire to the 20 SP volunteers involved in this study to gain insight into their impressions of the study and their willingness to continue playing the role of an SP. By exploring each participant group’s insights and perspectives, we sought to promote ongoing improvement in both SP training and its practical application.

The cost comparison between OSP-TCM and FSP-TCM

To enable a comparison of cost between the FSP-TCM and OSP-TCM training modes, detailed expenditure records were meticulously documented. These records covered expenses encompassing training expense, qualification authentication, course fees, transportation allowances, retraining expense, re-qualification authentication, medical examinations, and psychological assessments.

Statistical analysis

SPSS 27.0 software were used for processing and analysis the statistical data. In this study, continuous variables were presented as mean ± standard deviation. The normality of the data in each outcome indicator across the three groups was assessed using the Shapiro-Wilk test. In cases where the data deviated from a normal distribution, a rank sum test was employed for statistical analysis. Furthermore, when the data followed a normal distribution, a variance chi-square test was initially conducted prior to performing a one-way ANOVA. Subsequently, if the data met the assumption of variance chi-square, the Tukey test was utilized for further comparative analyses. Conversely, if the assumption of variance chi-square was not met, the Dunnett’ 3 method was employed. Dichotomous variables were analyzed using frequencies and percentages, and statistical significance was assessed using the chi-square test ( P < 0.05 was considered statistically significant).

The basic characteristics of participants

Ninety students participated in this study, and they were randomly divided into three groups: FSP-TCM ( n = 30), OSP-TCM ( n = 30), and TT ( n = 30) in a 1:1:1 ratio, following the principle of randomization. No statistically significant differences among the members of the three groups in terms of sex ( P = 0.72), age ( P = 0.96), basic Chinese medicine courses, and basic Western medicine courses were found. The detailed basic information of the participating students is shown in Table 1 .

The results shown in Fig. 1 indicate a consistent trend across all five dimensions (medical interview, physical examination, clinical judgment, disease treatment, and comprehensive), with slightly higher scores observed in the SP-TCM groups compared to the TT group. Specifically, focusing on medical interviews revealed that data from all three groups followed a normal distribution and showed homogeneity of variance ( P = 0.84). Furthermore, no significant statistical differences among the three groups were found (F = 4.14, P = 0.02).

Flowchart of this study

The OSP-TCM group demonstrated superior performance compared to both FSP-TCM and TT groups in terms of medical interview scores, with significant difference between OSP-TCM and TT groups (TT: mean 6.43 SD 0.94, FSP-TCM: 7.03 SD 0.93, OSP-TCM: mean 7.07 SD 0.98, P a = 0.04, P b = 0.03). However, no statistically significant difference observed between FSP-TCM and OSP-TCM groups ( P c = 0.99). Moreover, in terms of physical examination, the observed variation between TT and FSP-TCM and OSP-TCM groups had statistically significant ( P a = 0.01; P b = 0.04, respectively) (TT: mean 5.73 SD 1.11, FSP-TCM: mean 6.53 SD 0.90, OSP-TCM: mean 6.43 SD 1.19, P c = 0.93).

The same trend was found for comprehensive ability and clinical judgment (Comprehensive ability: TT: mean 6.03 SD 0.718, FSP-TCM: 6.73 SD 1.05, OSP-TCM: mean 6.63 SD 0.72, P a = 0.01, P b = 0.006, P c = 0.96; clinical judgment: TT: mean 5.93 SD 0.83, FSP-TCM: mean 6.53 SD 1.17, OSP-TCM: mean 6.6 SD 0.86, P a = 0.05, P b = 0.02, P c = 0.96). Both SP groups outperformed the TT group in disease treatment scores, and no statistically significant distinction among the three groups (TT: mean 6.17 SD 0.95, FSP-TCM: mean 6.73 SD 1.11, OSP-TCM: mean 6.7 SD 1.09, P a = 0.10, P b = 0.13, P c = 0.99) was found (Fig. 2 ).

The score of formative evaluation. ( A ) Score of medical interview. ( B ) Score of physical examination. ( C ) Score of clinical judgment. ( D ) Disease treatment. ( E ) Score of comprehensive ability

a indicates a significant difference between FSP-TCM and TT group ( P < 0.05)

b indicates a significant difference between OSP-TCM and TT group. ( P < 0.05)

Online systematic knowledge test

Based on the results of the Shapiro-Wilk test, the online systematic knowledge test data of all three groups conformed to a normal distribution. The chi-square test showed that the variances of the three groups were equal ( P = 0.94). The two SP groups scored greater than the control group. The results of one-way ANOVA showed that the online systematic knowledge test data of the three groups were different, with statistically significant differences (F = 4.71, P = 0.01). The FSP-TCM and OSP-TCM groups outperformed the TT group in terms of online systematic knowledge test scores (TT: mean 83.23, SD 3.43; FSP-TCM: mean 85.37, SD 3.14, OSP-TCM: mean 85.57, SD 3.22, P a = 0.04, P b = 0.02). However, no statistically significant differences between the FSP-TCM and OSP-TCM groups were found ( P c = 0.97) as shown in Fig. 2 (Note: P a = FSP-TCM group vs. TT group, P b =OSP-TCM group vs. TT group, P c =FSP-TCM group vs. OSP-TCM group) (Fig. 3 ).

The score of summative evaluation. ( A ) Score of online systematic knowledge test. ( B ) Score of the application of TCM technology. ( C ) Score of written medical records. ( D ) Score of TCM syndrome differentiation and therapeutic. ( E ) Real-time assessment scores

Application of TCM technology

Shapiro-Wilk test indicated that the scores of the application of TCM technology data within all three groups adhered to a normal distribution. Moreover, the results of the chi-square test showed that the variances of the three groups were equal ( P = 0.21). Results of one-way ANOVA showed that the scores of the application of TCM technology data of the three groups were significantly different (F = 4.97, P = 0.009). The scores of FSP-TCM group were better than those of the TT group (TT: mean 82.33, SD 4.37; FSP-TCM: mean 85.07, SD 3.33, P a = 0.01), and those of OSP-TCM group were higher than the TT group (OSP-TCM: mean 84.73, SD 3.18, P b = 0.03), with a statistically significant difference. However, there was no significant difference between the OSP-TCM and FSP-TCM groups ( P c = 0.93).

Scores of written medical records

Shapiro-Wilk test indicated that the scores of written medical records data of all three groups adhered to a normal distribution. The chi-square test showed that the variances of the three groups were equal ( P = 0.29). One-way ANOVA showed that the scores of written medical records data of the three groups were significantly different (F = 6.17, P = 0.003). The scores of the FSP-TCM and OSP-TCM groups were better than that of the TT group (TT: mean 78.77 SD 4.61; FSP-TCM: mean 81.93 SD 3.90, OSP-TCM: mean 82.20 SD 4.09, P a = 0.01, P b = 0.006, P c = 0.97) but no significant difference between the FSP-TCM and TT groups was found. Furthermore, the difference between the FSP-TCM and OSP-TCM groups was not statistically significant.

Scores of the TCM syndrome differentiation and therapeutic regimen

Shapiro-Wilk test indicated that the scores of the TCM syndrome differentiation and therapeutic regimen of all three groups adhered to a normal distribution. The variances of the three groups were equal ( P = 0.74). One-way ANOVA showed that the differences among the three groups were statistically significant (F = 5.944, P = 0.004). The scores of the FSP-TCM and OSP-TCM groups were higher than that of the TT group (TT: mean 85.63 SD 3.39; FSP-TCM: mean 88.27 SD 3.33; OSP-TCM: mean 87.93 SD 2.924; P a = 0.006, P b = 0.02). However, the same trend was found between the FSP-TCM and OSP-TCM groups, with no significant difference ( P c = 0.92).

Real time assessment scores

The Shapiro-Wilk test indicated that the real time assessment scores data of all three groups adhered to a normal distribution. The variances of the three groups were equal ( P = 0.77). One-way ANOVA showed that the differences among the three groups were statistically significant (F = 6.73, P = 0.002). The same trend was observed among the three groups. The scores of the FSP-TCM and OSP-TCM groups were higher than the TT group (TT: mean 82.93 SD 3.41, FSP-TCM: mean 85.67 SD 2.96, OSP-TCM: mean 85.37 SD 3.09; P a = 0.003, P b = 0.01, P c = 0.93).

Student feedback questionnaire analysis

Table 2 summarizes the results of the student questionnaire feedback. The students in the FSP and SP-TCM groups showed higher levels of approval for this course compared to those in the TT group in the “satisfaction with the course”, “confidence in handling clinical work,” (χ²=11.09, P a = 0.03; χ²=10.54, P b = 0.03) and “Motivation to study TCM” (χ²=10.06, P a = 0.04; χ²= 11.399, P b = 0.02). Moreover, trainees in the two SP-TCM groups made greater progress in medical information processing relative to the TT group, in terms of “knowledge of medical history,” (χ²=10.83, P a = 0.03; χ²=11.247, P b = 0.02) “ability to write medical records,”(χ²=15.13, P a = 0.004, χ²=13.07, P b =.01) and “ability to syndrome differentiation and treatment” (χ²=9.51, P a =.05; χ²=12.70, P b =.01).

Students in the FSP and OSP-TCM groups were more positive than those in the TT group regarding the enhancement of doctor-patient communication skills, “interpersonal skills,” (χ²=16.08, P a = 0.003; χ²=10.28, P b = 0.03) and “ability to build harmonious doctor-patient relationships” (χ²=14.78, P a =.005; χ²=11.79, P b =.02). Similar trends were observed for the clinical skills required for Chinese medicine (χ²=11.533, P a =.02; χ²=10.76, P b =.03). However, no significant differences in feedback between students in the FSP and OSP-TCM groups across these 11 items were found.

Feedback questionnaire analysis of SPs

Eight items were set to obtain information about the feelings and self-evaluations of the SP volunteers who participated during the course (Table 3 ). Volunteers of both groups showed a strong willingness to “continue the training course as SP-TCM” (FSPs: strongly agree 40%, agree 50% vs. OSPs: strongly agree 60%, agree 40%, χ²=1.51, P = 0.47). For switching roles, only a small number of FSPs and OSPs thought that there was difficulty (FSPs: agree 10% vs. OSPs: agree 10%, χ²=2.25, P = 0.52), and for “providing a flexible clinical environment,” more than half of the volunteers were in favor (FSPs: strongly agree 20%, agree 50% vs. OSPs: strongly agree 30%, agree 40%, χ²=1.64, P = 0.80).

When evaluating the fidelity of their own performances as SPs compared to real patients, OSPs showed greater recognition compared to FSPs (FSPs: strongly agree 10%, agree 30% vs. OSPs: strongly agree 20%, agree 40%, χ²=1.01, P = 0.91). There was a comparable trend in “presentation of Chinese medicine syndrome” (FSPs: strongly agree 10%, agree 30% vs. OSPs: strongly agree 20%, agree 50%, χ²=2.83, P = 0.59). It was noteworthy that more FSPs than OSPs thought they provided “professional and constructive feedback” during the course (FSPs: strongly agree 30%, agree 50% vs. OSPs: strongly agree 20%, agree 40%, χ²=2.27, P = 0.69). In contrast, more FSPs than OSPs indicated that they gave hints and used medical terminology for students during the course (FSPs: strongly agree 10%, agree 30% vs. OSPs: strongly agree 0%, agree 10%, χ² = 3.867 P = 0.42; χ² = 3.67, P = 0.45).

Analysis of the teacher feedback questionnaire

Analysis of the teacher feedback questionnaire is presented in Table 4 . Overall, teachers who participated in the questionnaire feedback favored FSPs in this course. Ten (83%) teachers expressed willingness to continue using FSP-TCM simulation for clinical training, and 11 out of 12 teachers (91.66%) agreed that the use of FSP-TCM could effectively supplement bedside teaching and reduce teaching costs.

Regarding teaching effectiveness, FSP-TCM simulation improved teaching efficiency of clinical training (strongly agree: 1, 8.33%; agree: 10, 83.3%), enhanced students’ syndrome differentiation and treatment ability (strongly agree: 7, 58.33%; agree, 4 33.33%), improved students’ critical thinking on TCM (strongly agree: 3, 25.0%; agree: 6, 50.0%), and motivation to learn TCM (strongly agree: 8, 66.67%; agree: 3, 25.0%). Regarding the construction of case scripts for FSP-TCM simulation, teachers preferred to various TCM syndromes (strongly agree: 4, 33.33%; agree: 6, 50.0%) rather than different diseases (strongly agree: 1, 8.33%; agree: 2, 16.67%).

Analysis of cost comparison between FSP-TCM and OSP-TCM

The expenses for qualification authentication (one time /per person, $27.60), re-qualification authentication (one time biennially/per person, $27.60), and psychological assessment (one time biennially/per person, $41.40) are identical for both FSPs and OSPs. However, the two models diverge in terms of five key expense categories: training expenses, course fees, traffic allowance, retraining expenses, and medical examinations. Notably, the training and retraining expenses for OSPs are double those of FSPs (training expense: FSPs $345.00 vs. OSPs $690.00; retraining expense: FSPs $69.00 vs. OSPs $138.00). As for course fees, FSPs incur a cost of $6.90 per person per session, significantly lower than the $16.56 per person per session cost for OSPs. This disparity arises from FSPs’ participation being considered a teaching assignment, leading to a reduced classroom fee in comparison to OSPs. Furthermore, as faculty members undergo annual school-organized medical examinations, the cost of medical examinations for FSPs is excluded, with OSPs incurring a fee of $41.40 per person biennially. Additionally, each OSP receives a $6.90 transportation allowance per course attended.

In conclusion, the total cost for FSP-TCM amounts to $7590.00, significantly lower than the total cost of $17415.60 for OSP-TCM, highlighting the cost-effectiveness of the FSP-TCM simulation training model in TCM education (supplement 5 ).

Formative assessment revealed a significant improvement in the overall competence of trainers with the FSP and OSP-TCM groups compared to the TT group, particularly in medical interviews and physical examinations. While no significant differences were noted in clinical judgment and disease treatment, the benefits of employing FSP and OSP training methods became more apparent during summative assessment. Students using FSPs and OSPs showed significant improvements in their knowledge of the system, ability to write medical records, ability to apply Chinese medicine techniques, and accuracy of their diagnoses and treatment.

Furthermore, over 80% of the participants in both the FSP and OSP-TCM groups acknowledged that this course significantly enhanced their proficiency in TCM clinical skills. Unlike traditionally taught methods, SP-TCM simulation utilizes SP as a bridge to construct a “simulated clinical environment” for trainees based on specific case scripts, vividly presenting other monotonous disease characteristics. The SP-TCM method enhances disease concreteness and characteristics, deepens students’ understanding of diseases and medical history, and improves their ability to differentiate and treat syndromes and proficiency in TCM [ 23 , 24 ]. By using SPs instead of real patients, the trainees can obtain diagnostically beneficial information through methods, including “observation, listening, inquiry, and pulse examination,” nurturing their communication and interpersonal skills and preparing them for establishing harmonious doctor-patient relationships in the future [ 25 ]. The training mode using SPs as a substitute for real patients provides a safe environment for students [ 26 ], thereby effectively preventing the risks that students may encounter in a real medical setting [ 27 ].

The advantages of FSPs are not limited to these aspects. FSPs are teachers or doctors who have been teaching for more than 5 years, so compared to OSPs who do not have a medical background, it is easier for them to understand the medical terminology of Chinese medicine, thus greatly reducing the duration of the training cycle (FSPs: 20 of credit hours vs. OSP: 40 of credit hours). Furthermore, FSPs are more professional in giving students constructive feedback during this study compared to OSPs (FSPs: 30% Strongly agree, 50% agree vs. OSPs: 20% strongly agree, 40% agree). Meanwhile, as faculty members, they have lesser job mobility and can continue to participate in simulation training as SPs stably [ 28 ]. OSP training incurs high costs, including appearance fees, transportation allowances, and social insurance [ 29 ]. Conversely, FSPs effectively mitigate these cost-related challenges. The overall expense analysis revealed a significant cost advantage during the research process between FSP-TCM (¥55000.00/$7590.00) and OSP-TCM (¥126200.00/$17415.60). Utilizing FSP-TCM can result in a savings of $9825.6 compared to the OSP-TCM training mode, illustrating significant cost-effectiveness. This advantage primarily stems from differences in training and retraining durations, medical examination, course fees, and transportation allowances among the two groups of volunteers.

However, the results from the SP feedback questionnaire reflected some issues regarding FSPs. Specifically, 40% (10% strongly agree, 30% agree) of these FSPs felt that they unconsciously gave students hints and used medical terminology during simulation training, and 10% of OSPs indicated a similar situation. Some factors may contribute to this phenomenon.

FSPs, as medical faculty, already possess extensive medical knowledge and inevitably encounter a vast array of medical terminology in their daily work. This immersion in the field and habitual influence leads to the unconscious integration of medical terms into their daily communication. Additionally, they assume the role of teachers during their regular teaching activities [ 30 ]. When interacting with students, they may instinctively employ cues to steer them toward the correct answers [ 31 ]. Other studies reported the similar shortcomings of OSPs and SSPs [ 21 , 32 ]. It is noteworthy is that this phenomenon diminished with increased training experience. In the subsequent phase, enhancing FSPs’ understanding of patient roles through targeted training and coaching should be prioritized to diminish the use of medical terminology and jargon.

Limitations

This study recruited teaching staff as FSPs for participation in simulated training of students’ clinical abilities, achieving positive results. However, some limitations of the study warrant consideration. First, FSPs taking on multiple roles during training may encounter difficulties in role switching, resulting in problems for students in implication and use of medical terminology. However, these problems can be overcome through long-term targeted training and guidance. Second, this study is prospective and a follow-up on the clinical abilities of the trainees was lacking, thus providing limited knowledge about the post-training capabilities of the participants. Third, clinical patients exhibit diversity, including in terms of age groups, characteristics of different diseases, and TCM syndromes, which FSPs as teaching staff cannot fully represent.

FSP-TCM training mode showed greater effectiveness than traditional teaching method in improving clinical competence among TCM students. It possesses certain characteristics that render it feasible, practical, and cost-effective, and thus, it presents a viable alternative to OSP-TCM simulation. Further optimization of FSP-TCM to facilitate its promotion and development is necessitated.

Data availability

The data generated or analyzed during this study are available in this published article and its supplementary information files.

Abbreviations

Arizona Clinical Interview Rating

Chengdu University of TCM

Faculty SP for Traditional Chinese medicine

Mini-Clinical Evaluation Exercise

Occupational SP for Traditional Chinese Medicine

Standardized Patients

Traditional Chinese Medicine

Traditionally Taught Group

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Acknowledgements

The authors would like to express their sincere gratitude to the Curriculum Team Training Support Program and Xinglin Top Teacher and Talent Support Program of CDUTCM (Yi Zhang, Han Yang, Bin Li, Xiaoxu Fu, and Xiao Ma). Additionally, the authors extend their deepest appreciation to Jiansheng Wu and Fangming Liao, senior SP trainers from the West China Medical Center of Sichuan University, for their invaluable support, and to all the FSP-TCM volunteers and the OSP-TCM volunteers who participated in this program.

This study was funded by grants JGYB202314 from the Teaching Reform Project of the Chengdu University of Traditional Chinese Medicine (CDUTCM); JG2023-55 from the First Batch of Advanced Courses of Higher Education in Sichuan Province; 2023YB21, JYJX202207, JYJX202201 and JYJX202215 from the Education and Teaching Reform Research Project of Clinical Medical School of CDUTCM.

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Meilan Huang and Han Yang contributed to this article equally and shared the first author.

Authors and Affiliations

Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shierqiao Road, Chengdu, 610072, China

Meilan Huang, Han Yang, Jing Guo, Xiaoxu Fu, Wangshu Chen, Bin Li, Shan Zhou, Ting Xia, Sihan Peng, Yi Zhang & Jinhao Zeng

Clinical Medical School, Chengdu University of Traditional Chinese Medicine, No. 1166 Liutai Avenue, Chengdu, 611137, China

Clinical Skill Center, Clinical Medical School of Chengdu University of Traditional Chinese Medicine, No. 37 Shierqiao Road, Chengdu, 610072, China

School of Pharmacy, Chengdu University of Traditional Chinese Medicine, No. 1166 Liutai Avenue, Chengdu, 611137, China

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Contributions

MH and HY contributed to the drafting and revision of the manuscript. YZ, JZ, and YZ had full access to all the data in the study and was assured of its integrity and accuracy. XF, XX, LW, BL and JG are responsible for statistical methodology and data management. SZ, WC and SP provided technical or material support. RY, TX, XF and JG are responsible for the revision of the manuscript and supervision of the study. XM, JZ and YZ contributed to the supervision of the study. All authors have read and approved the final manuscript. YZ, JZ and BL obtained funding. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiao Ma , Yi Zhang or Jinhao Zeng .

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The Ethics Committee of Chengdu University of Traditional Chinese Medicine approved this study, which strictly adhered to the principles of the Helsinki Declaration (No. 1005510).

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The authors declare no competing interests.

Clinical trial registration

The authors would like to clarify that this study does not fall under the category of clinical intervention study, as confirmed by consultation with the China Clinical Trial Registry ( https://www.chictr.org.cn/ ). Our study, categorized as educational research, does not involve human trials or pharmaceuticals, thus exempting it from requiring a clinical registration ID.

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Huang, M., Yang, H., Guo, J. et al. Faculty standardized patients versus traditional teaching method to improve clinical competence among traditional Chinese medicine students: a prospective randomized controlled trial. BMC Med Educ 24 , 793 (2024). https://doi.org/10.1186/s12909-024-05779-3

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Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study

Authors of this article:

Original Paper

Samia C Akhter-Khan 1, 2 * , MSc ;
Qiushan Tao 2, 3 * , MSc, MD ;
Ting Fang Alvin Ang 2, 4, 5, 6 * , MD ;
Cody Karjadi 2, 4 , MSc ;
Indira Swetha Itchapurapu 3 , MPH ;
David J Libon 7, 8 , PhD ;
Michael Alosco 9, 10 , PhD ;
Jesse Mez 2, 9, 10 , MSc, MD ;
Wei Qiao Qiu 2, 3, 10, 11 , MD, PhD ;
Rhoda Au 2, 4, 5, 6, 9, 10 , PhD

1 Department of Global Health & Social Medicine, King's College London, London, United Kingdom

2 Framingham Heart Study, Boston University School of Medicine, Boston, MA, United States

3 Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, United States

4 Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, United States

5 Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States

6 Slone Epidemiology Center, Boston University School of Medicine, Boston, MA, United States

7 Department of Geriatrics and Gerontology, Rowan University, Glassboro, NJ, United States

8 Department of Psychology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Glassboro, NJ, United States

9 Department of Neurology, Boston University School of Medicine, Boston, MA, United States

10 Alzheimer’s Disease and Chronic Traumatic Encephalopathy Centers, Boston University, Boston, MA, United States

11 Department of Psychiatry, Boston University School of Medicine, Boston, MA, United States

*these authors contributed equally

Corresponding Author:

Wei Qiao Qiu, MD, PhD

Framingham Heart Study

Boston University School of Medicine

72 East Concord Street

Boston, MA, 02118

United States

Phone: 1 6176384336

Email: [email protected]

Background: Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible.

Objective: This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB.

Methods: In this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia.

Results: The study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB ( P <.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P =.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (β=–0.65, 95% CI –1.15 to –0.15; P =.01) and simple motor (β=–0.86, 95% CI –1.43 to –0.30; P =.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model.

Conclusions: The study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral.

Introduction

It is widely shown that cerebrovascular diseases increase the risk of Alzheimer disease (AD) and other dementia [ 1 , 2 ]. Cerebral microbleeds (CMB) are one of such cerebrovascular abnormalities, defined as small chronic brain hemorrhages, likely caused by structural abnormalities of the small vessels of the brain [ 3 ]. The prevalence of CMB is estimated to be as high as 34% in people with ischemic stroke and 60% in people with nontraumatic intracerebral hemorrhage [ 4 ]. CMB have also been associated with cognitive impairment and increased risk for AD development across multiple studies [ 5 , 6 ]. CMB can be divided into 2 subclasses based on their location in the brain, that is, lobar and deep CMB. A recent meta-analysis reported a 75% increased risk of dementia with deep or mixed CMB [ 7 ].

In light of population aging demographics, these figures are concerning, and solutions for early detection of emergent disease and AD risk factors at a preclinical stage to prevent the disease’s development are urgently needed [ 8 ]. Neuroimaging methods, such as brain magnetic resonance imaging (MRI) and computerized axial tomography scans, are valuable tools to detect cerebrovascular pathology. Currently, lobar and deep CMB can only be identified by brain MRI. However, these imaging tools are costly and, in most cases, not accessible in rural areas and low-income contexts. There are over 50 million people estimated to live with dementia and AD worldwide, with the highest increases in lower- and middle-income countries [ 9 ]. The worldwide costs of dementia are estimated to amount to over US $1 trillion [ 10 ]. To drive down the costs and promote early detection of early brain pathology for AD risk, including CMB, which have subtle clinical symptoms, one promising approach is to explore new, inexpensive technologies such as digital neuropsychological assessments coupled with machine learning analytics to detect and screen for cerebrovascular diseases in clinic before applying neuroimaging tools and neuropsychological assessments [ 11 ].

The clock-drawing test (CDT; traditional, paper based) is an easily applicable cognitive test, and prior research has documented that patients with dementia with MRI evidence of vascular disease make more clock-drawing errors than other groups [ 12 - 14 ]. However, the traditional analog, manually scored CDT is limited by its low sensitivity and specificity for different brain diseases, especially at an early stage or where there is subtle brain pathology. Moreover, analog scoring systems are only able to generate a limited number of clock-drawing metrics [ 15 ]. Developing efficient tools to measure cognitive change and brain health, especially in the preclinical stage of AD, is necessary. Recently, the CDT has been transformed through the use of a digital ballpoint pen, replacing a conventional ink pen, and coupled with a dot pattern that provides raw, time-stamped data that capture the full performance sequence (digital clock-drawing test; DCT), thus, generating large, detailed data on cognition that cannot be derived using the traditional CDT [ 11 ]. Recently, the Framingham Heart Study (FHS) analyzed DCTs from 1833 participants using machine learning to detect cognitive nuances. The DCT showed superiority over existing methods such as the Mini Mental Status Examination in detecting early cognitive impairment and characterizing individuals along the AD trajectory [ 16 ]. In addition, the DCT has evidenced its diagnostic value for early screening for mild cognitive impairment (MCI) and AD [ 17 - 19 ]. Further, the DCT was also associated with biomarkers relevant to cognitive impairment and AD, collected by positron emission tomography imaging, for example, amyloid and τ pathology [ 20 ].

Given the important role of CMB in the development of AD, the value of operationalizing putative negative effects on behavior caused by CMB using less costly and more accessible diagnostic tools is evident. Using data from the FHS, we predicted that the DCT would be useful in detecting CMB in older adults without dementia. We also examined whether the DCT could detect CMB in different brain regions, for example, lobar and deep CMB.

Study Sample

FHS is a multigeneration, community-based, prospective cohort study in Framingham, Massachusetts. The FHS offspring cohort (Generation 2) has been longitudinally examined in 9 core examinations, with examinations occurring on average every 4 years between 1971 and 2014. Details about this cohort have been previously described elsewhere [ 21 ]. These participants also had serial neuropsychological and MRI scans on average every 5 years between 1999 and 2019 [ 22 ]. For this study, FHS Generation 2 participants who were over 45 years old and had brain MRI CMB data (2000-2009) and DCT assessments (2011-2018; n=1072) were included. For primary analyses, participants were excluded if they had dementia (n=23) or had a history of stroke (n=29).

Clock-Drawing Measures: the CDT Versus DCT

The manual for administering and scoring the CDT has been previously described [ 23 ]. The CDT score using this analog (hand-scored) method ranged from 0 (no abnormality) to 3 (severe impairment). The DCT was obtained using the digital pen technology from Anoto, and the time-stamped features were processed by Linus Health Inc. Similar to the original paper-pencil CDT, the DCT contains a command and a copy condition [ 20 ]. In brief, the DCT contains multiple objective measurements that were derived from approximately 5000 digital clock drawings using machine learning algorithms to precisely evaluate nuances in performance beyond successful task completion [ 24 ]. Variables from the command and copy versions were combined from the machine learning calculations into an overall command or copy score ranging from 0 to 100. A similar technique was used for the domain-specific subscores measuring drawing efficiency, information processing, simple motor, and spatial reasoning (see previous publications [ 20 , 25 ] and Table S1 in Multimedia Appendix 1 for details).

The FHS MRI protocol and CMB diagnosis criteria have been previously described [ 26 ]. Briefly, participants were imaged by a Siemens 1.5T MRI, using a 3D T1-weighted coronal spoiled gradient-recalled echo sequence. All images were transferred to and processed by the University of California Davis Medical Center without knowledge of clinical information. The MRI scans were conducted between 2000 and 2009 with gradient recalled echo T2-weighted sequences, allowing for the detection of CMB. Using recently published guidelines [ 27 ], CMB were defined as rounded or ovoid hypointense lesions on a T2*-GRE weighted sequence, measuring <10 mm in diameter and surrounded by brain parenchyma over at least half the circumference of the lesion. The presence, number, and location of CMB were determined. Reliability measures for CMB readings have previously been described [ 28 , 29 ]. In line with previous studies [ 29 ], CMB location in the brain was classified into subgroups based on assumed pathophysiology (cerebral amyloid angiopathy and hypertensive vasculopathy), and it was classified into 2 locations: deep and lobar, with each participant potentially having 1 or more CMB. All participants were grouped into 4 subgroups based on assumed pathophysiology (cerebral amyloid angiopathy and hypertensive vasculopathy), which included the no CMB (control) group and groups with lobar-only, deep-only, and mixed (deep+lobar) CMB. White matter hyperintensities (WMHs) were segmented with fluid-attenuated inversion recovery and gray matter with T1-weighted images by semiautomated procedures, as previously described [ 30 ]. WMHs were adjusted for head size (by dividing WMHs by the total cerebral volume).

Statistical Analysis

Baseline characteristics of study participants were evaluated for the total sample and for CMB status. To compare the CMB status groups, 2-tailed t tests were applied for continuous variables, and χ 2 tests or Fisher exact tests were applied for categorical variables. To facilitate standardized comparisons between different scales in the models adjusted for covariates, the digital clock variables were transformed to z scores (mean 0, SD 1), for example, the overall DCT score (percentage range 0%-100%) was rescaled to a z score after logit transformation; all other DCT scores were also rescaled to z scores to standardize the variables that have different value ranges. Robust multivariable linear regression models were applied to assess whether the DCT scores (outcomes variables) were significantly different between different CMB subgroups and no CMB (control) group. All models were adjusted for age, sex, education, WMHs, and the time difference between brain MRI and DCT. A sensitivity analysis was conducted by including subjects with dementia (n=23), or a history of stroke (n=29) that were excluded from the main models. To test whether the DCT facet scores could distinguish different CMB subgroups from no CMB (control group), we also calculated the area under the curve (AUC) of a receiver operating characteristic (ROC) curve analysis based on multinomial classification models that were adjusted for main confounding factors such as age, sex, time between examinations, and MCI. The AUC, ranging from 0.5 to 1, is a key metric for evaluating a classifier’s ability to distinguish between positive and negative outcomes. A value nearing 1 indicates a highly accurate model, while an AUC of 0.5 suggests performance equivalent to random chance, indicating no predictive power. The results were shown as beta estimates (β) with 95% CIs. Statistical significance was indicated by a P value <.05 (2-tailed tests). All statistical analyses were performed using R (version 4.2.1; R Foundation for Statistical Computing).

In our data analysis, we used several R packages and functions. Specifically, we used the rlm function from the MASS library for robust linear model fitting [ 31 ], the lmrob function from the robustbase library for MM-type estimator calculation in linear regression [ 32 ], and the sandwich library for robust SE estimation in nonlinear models [ 33 ]. Additionally, the pROC library facilitated ROC curve analysis and AUC value calculation [ 34 ].

Ethical Considerations

The study was conducted per the Declaration of Helsinki, and ethical approval was provided by Boston University’s Institutional Review Board (H-40620). All participants provided informed consent, and data was de-identified.

The 1020 participants from the FHS Generation 2 sample were on average aged 72 (SD 7.9) years, and 57.35% (n=585) of them were female ( Table 1 ). Among them, 64 (6.27%) participants had at least 1 CMB. Participants with CMB were more likely to be older ( P <.001), have MCI ( P =.02), and have greater WMHs ( P <.001). There were no differences between the participants with and without CMB for the traditional, analog-scored CDT, in either the command or copy condition. By contrast, participants with CMB showed a significantly lower overall combined command or copy DCT score ( P =.01) than those without CMB ( Table 1 ).

Table 1 shows that participants with CMB showed worse performance on several command and copy DCT domains. Specifically, participants with CMB scored lower on the command spatial reasoning ( P =.03), copy drawing efficiency ( P =.03), and information processing ( P =.01) subscales. We further divided participants with CMB into those who had CMB exclusively in lobar regions (46/64, 72%), those who had CMB only in deep regions (11/64, 17%), and those who had mixed lobar+deep CMB (7/64, 11%) and used the DCT domain score to examine CMB ( Table 2 ). After adjusting for covariates, there were no statistically significant differences in the overall DCT or domain scores between participants with any CMB ( P >.05), compared to those without CMB. However, when analyzing the subscales, participants with deep-only CMB had lower scores on the command simple motor subscale than the reference group (β=–0.86, 95% CI –1.43 to –0.30; P =.003). Additionally, participants with mixed (lobar+deep) CMB had lower scores on the command spatial reasoning subscale than the reference group (β=–1.70, 95% CI –2.29 to –1.11; P <.001). In contrast, participants with lobar CMB did not show associations with any domain score under both command and copy conditions of the DCT. The results were similar when including participants with dementia or stroke in the analysis (Table S2 in Multimedia Appendix 1 ).

Robust multivariate linear regression models were applied with DCT scores (overall score and domains) and CMB subtypes (lobar-only, deep-only, and any deep). The reference group were participants without CMB (n=956). The overall DCT score (percentage) was rescaled to a z score (mean 0, SD 1) after logit transformation and other DCT scores were rescaled to z scores (mean 0, SD 1) after logit transformation. Participants with dementia or stroke were excluded from the analysis (see sensitivity analysis with inclusion in Table S2 in Multimedia Appendix 1 ). All models were adjusted for age, sex, education, MCI, WMHs, and the time difference between brain MRI and the DCT. The results are shown as standardized β coefficients (β) with 95% CI. P values for statistical significance are indicated.

Next, we used all facets to explore the diagnostic potential of the DCT for CMB, with the subtypes of CMB as the predicted outcomes (lobar, deep, or mixed). In addition to including all facets in the analysis, we investigated which specific facet score had a relationship with the 3 CMB subtypes (ie, lobar-only, deep-only, and mixed; Tables S2 and S3 in Multimedia Appendix 1 ). Some features from the command condition were mainly associated with deep CMB, whereas the features from the copy condition were more likely to be associated with lobar CMB ( Figure 1 ). For example, in the command condition, the features including vertical spatial placement, horizontal spatial placement, clock face circularity, component placement, max speed, initiation speed, average speed, ink length, and drawing size were associated with deep CMB but not with lobar CMB. Whereas oscillatory motion was lower among participants with lobar CMB, this feature was higher among participants with deep CMB in the command condition. In the copy condition, the features long latency count, stroke count conformity, and noise were positively associated with lobar-only CMB, but not with deep-only CMB. The mixed (lobar+deep) CMB was significantly associated with lower oscillatory motion, lower horizontal spatial placement, higher clock face circularity, and higher component placement scores in the command condition and higher noise score in the copy condition ( Figure 1 and Tables S3 and S4 in Multimedia Appendix 1 ).

In the ROC curve analysis, the results indicated that DCT facet scores alone demonstrated discrimination, as evidenced by the AUCs between individuals without CMB and those with lobar CMB (AUC 0.74, 95% CI 0.66-0.82), deep CMB (AUC 0.80, 95% CI 0.63-0.98), and mixed CMB (AUC 0.89, 95% CI 0.68-1.00; Figure 2 A). After adjusting for sex and age, the AUC values were further improved. The AUC (95% CI) values for individuals without CMB and those with lobar CMB, deep CMB, or mixed CMB were 0.77 (0.71-0.84), 0.85 (0.73-0.98), and 0.97 (0.95-0.99), respectively ( Figure 2 B). Further adjustment for the time difference between brain MRI and DCT measurement and the prevalence of MCI at the brain MRI scan yielded similar AUC values for individuals without CMB and those with lobar CMB (0.76, 95% CI 0.69-0.83), deep CMB (0.88, 95% CI 0.78-0.98), or mixed CMB (0.98, 95% CI 0.96-1.00; Figure 2 C). Notably, the highest AUC was observed for the mixed CMB group, followed by a modest increase for the deep CMB group, and the lobar CMB group when compared to the no CMB group, highlighting the enhanced discriminatory ability of DCT facets in identifying different CMB subtypes after adjusting for relevant covariates.

Characteristics				Overall (N=1020)		CMB status (no=0 and yes=1)
						No (n=956)		Yes (n=64)		values
Age (years), mean (SD)				72 (7.9)		71 (7.8)		76 (8.2)
Female, n (%)				585 (57)		551 (58)		34 (53)		.78
											.26
	High school or less			246 (24)		224 (23)		22 (34)
	Some college			316 (31)		295 (31)		21 (33)
	College or higher			458 (45)		437 (46)		21 (33)
Mild cognitive impairment, n (%)				43 (4)		36 (4)		7 (11)
White matter hyperintensities, mean (SD)				0.0 (1.0)		–0.04 (1.0)		0.49 (1.0)

	Command clock, median (IQR)			0 (0-3)		0 (0-2)		0 (0-3)		.36
	Copy clock, median (IQR)			0 (0-2)		0 (0-1)		0 (0-2)		.30
, mean (SD)
	Overall DCT score			76 (20)		76 (20)		68 (23)


		Drawing efficiency	62 (10)		62 (10)		60 (12)		.41
		Simple motor	63 (8.1)		63 (8.0)		63 (9.0)		.89
		Information processing	60 (9.9)		60 (9.9)		59 (8.8)		.83
		Spatial reasoning	65 (17)		65 (17)		59 (20)

		Drawing efficiency	61 (8.4)		61 (8.3)		59 (9.2)
		Simple motor	61 (6.7)		61 (6.7)		60 (7.4)		.80
		Information processing	61 (11)		61 (11)		57 (11)
		Spatial reasoning	67 (18)		68 (17)		62 (21)		.06

a MRI: magnetic resonance imaging.

b CMB: cerebral microbleed.

c t tests ( df =1018) were applied for continuous variable comparisons between 2 groups (cerebral microbleed versus no cerebral microbleed). χ 2 (female df =1, education df =2) tests were used for categorical variables, while Fisher exact tests were used in cases of low frequency. P values for statistical significance are shown.

d Significant P values are italicized.

e Due to the skewed distributions, we performed a Kruskal-Wallis rank sum test for the traditional clock-drawing test.

f DCT: digital clock-drawing test.

DCT		Lobar-only (n=46)		Deep-only (n=11)			Mixed (lobar+deep; n=7)
		β (95% CI)	value	β (95% CI)	value	β (95% CI)		value
Overall DCT score		0.05 (–0.19 to 0.29)	.67	–0.36 (–0.84 to 0.12)	.15	–0.29 (–0.90 to 0.32)		.35

	Drawing efficiency	0.20 (–0.05 to 0.45)	.12	–0.65 (–1.15 to –0.15)		–0.28 (–0.90 to 0.35)		.39
	Simple motor	0.25 (–0.03 to 0.53)	.08	–0.86 (–1.43 to –0.30)		0.52 (–0.19 to 1.23)		.15
	Information processing	0.04 (–0.22 to 0.31)	.75	–0.13 (–0.66 to 0.39)	.62	–0.29 (–0.95 to 0.37)		.39
	Spatial reasoning	–0.06 (–0.30 to 0.18)	.60	–0.12 (–0.59 to 0.35)	.60	–1.70 (–2.29 to –1.11)

	Drawing efficiency	–0.18 (–0.45 to 0.09)	.19	–0.05 (–0.57 to 0.47)	.86	0.05 (–0.60 to 0.71)		.88
	Simple motor	0.08 (–0.20 to 0.36)	.57	–0.18 (–0.73 to 0.38)	.54	0.48 (–0.22 to 1.18)		.18
	Information processing	–0.22 (–0.49 to 0.05)	.11	–0.14 (–0.67 to 0.40)	.62	–0.09 (–0.76 to 0.58)		.79
	Spatial reasoning	–0.01 (–0.29 to 0.26)	.92	–0.22 (–0.77 to 0.32)	.43	–0.16 (–0.85 to 0.52)		.64

a DCT: digital clock-drawing test.

b Significant P values are italicized.

This cross-sectional study in the FHS reveals the potential of the DCT as a cost-effective and objective screening tool for detecting CMB in different brain regions. Unlike the traditional CDT, the DCT offers detailed insights into cognitive function and demonstrates significant associations with CMB, particularly deep and mixed subtypes. The study highlights the limitations of traditional cognitive tests in detecting subtle brain abnormalities such as CMB and underscores the DCT’s value in early prediction of dementia risk.

Principal Findings

To our knowledge, our study is the first to evidence that the DCT reveals detailed, nuanced, and hidden information and can serve as a potentially useful screening tool to detect the presence of CMB in different brain regions. As CMB is a risk factor for AD [ 28 ] but is accompanied by no or very subtle clinical symptoms, it is costly for clinicians to use neuroimaging to detect CMB. In comparison, the DCT is more cost-effective and can be completed by patients within a couple of minutes with minimal assistance. Another advantage of the DCT is that it objectively captures detailed brain functions through automated digital collection and analysis, while the manual traditional counterpart depends on the subjective assessment by trained testers. Using the DCT may be useful for in future clinical practice for early screening and detection of CMB, triggering interventions that can delay the progress of the disease or prevent AD onset [ 35 ].

Comparison to Prior Work

Whereas the traditional CDT has low sensitivity and specificity to screen or diagnose participants with CMB, the DCT scores were significantly associated with CMB in our study, especially deep and mixed CMB, including across 3 different measurement levels (ie, overall score, domains, and facets of the DCT). Previous studies have reported inconsistent associations between CMB and the traditional CDT. For example, whereas 1 study found that CMB were a risk factor for low performance on the CDT [ 36 ], 2 others did not find a relationship [ 37 , 38 ]. Another study that did not exclude participants with dementia illustrated that lobar, but not deep, CMB were associated with CDT [ 39 ]. It is possible that CDT’s crude measurements contain mixtures of different cognitive functions that cannot be broken down into more detailed measurements such as facet scores of DCT and thus may not be able to clearly detect fine brain abnormalities like CMB.

Strengths and Limitations

There were several strengths in our study. First, in our study, the diagnostic discriminability using the DCT facet scores was able to differentiate those with and without CMB, especially the mixed subtype, independent from MCI. Second, the DCT is simple, can be self-administered, and could serve as a screening test before administering costly neuroimaging tests. Third, our study found that participants with lobar CMB and deep CMB had different DCT performance patterns. Whereas the command condition was more strongly associated with deep or mixed CMB, the copy condition may be more associated with lobar CMB. Since deep but not lobar CMB have been identified as risk factors for dementia development in previous studies [ 28 ], the DCT may be a valuable tool for the early prediction of dementia risk. By capturing multiple facets of cognitive function as well as their fine-grained interrelations, the DCT affords substantially more sensitive analyses compared to typical measures of domain-specific cognitive functions that are observed in isolation (or aggregated in sum scores that overlook fine-grained interrelations).

This study had several limitations. First, the sample size of deep and mixed CMB cases is relatively small, potentially limiting the generalizability of findings and statistical power to detect associations. Future studies should aim for larger and more diverse cohorts. Second, the associative study design restricts the ability to establish causal relationships between variables, despite efforts to control for confounding factors. Future research could benefit from longitudinal or interventional approaches to explore causality further. Lastly, the lack of diversity in the FHS cohort may limit the applicability of findings to broader populations. Future studies should strive to include more diverse cohorts to enhance generalizability and reduce potential biases.

Future Directions

Large cohorts with multiethnicities should be used to confirm that the DCT and other digital tools detect CMB and similar pathologies in different brain regions and can serve as a cost-effective screening tool to better identify people at risk earlier in the preclinical stages of the disease [ 11 ]. More importantly, user-oriented assessment devices such as the DCT may promote objectivity and equity within public health research, especially in underserved populations.

Acknowledgments

We thank Lilly Buck (MSc) and Leon Li (PhD) for their helpful feedback on an earlier draft of this paper. This work was supported by the National Heart, Lung, and Blood Institute contract (N01-HC-25195) and by grants from the National Institute of Neurological Disorders and Stroke (NS-17950) and the National Institute on Aging (AG-08122, 016495, 068753: RA, and AG-09899: WQQ).

Data Availability

The data sets generated or analyzed during this study are available through the Framingham Heart Study central database. For detailed information on data access, please visit the Framingham Heart Study Brain Aging Program (FHS-BAP) Data Access Portal [ 40 ].

Authors' Contributions

SCA-K and QT contributed equally to drafting this paper, study design, and data analysis. TFAA and WQQ contributed to the study design and substantial revision of this paper. RA contributed to the initiation of digital data collection in the Framingham Heart Study, data quality control, data sharing, and review and revision of this paper. CK managed digital data used in this study. ISI, DJL, MA, and JM contributed to the review and revision of this paper.

Conflicts of Interest

RA is a scientific advisor to Signant Health, Novo Nordisk, and Davos Alzheimer’s Collaborative. JM receives grants from the NIH (National Institutes of Health) and DOD (U.S. Department of Defense). All other authors have no conflict of interest to declare.

The descriptions of each subdomain of digital clock-drawing test (DCT) scores.

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Abbreviations

Alzheimer disease

area under the curve

clock-drawing test

cerebral microbleed

digital clock-drawing test

Framingham Heart Study

mild cognitive impairment

magnetic resonance imaging

receiver operating characteristic

white matter hyperintensity

Edited by T Leung; submitted 17.01.23; peer-reviewed by K Johnson, F Abdulla; comments to author 24.01.24; revised version received 18.03.24; accepted 31.03.24; published 29.07.24.

©Samia C Akhter-Khan, Qiushan Tao, Ting Fang Alvin Ang, Cody Karjadi, Indira Swetha Itchapurapu, David J Libon, Michael Alosco, Jesse Mez, Wei Qiao Qiu, Rhoda Au. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.07.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Open access
Published: 26 July 2024

Improving Clerkship to Enhance Patients’ Quality of care (ICEPACQ): a baseline study

Kennedy Pangholi 1 ,
Enid Kawala Kagoya 2 ,
Allan G Nsubuga 3 ,
Irene Atuhairwe 3 ,
Prossy Nakattudde 3 ,
Brian Agaba 3 ,
Bonaventure Ahaisibwe 3 ,
Esther Ijangolet 3 ,
Eric Otim 3 ,
Paul Waako 4 ,
Julius Wandabwa 5 ,
Milton Musaba 5 ,
Antonina Webombesa 6 ,
Kenneth Mugabe 6 ,
Ashley Nakawuki 7 ,
Richard Mugahi 8 ,
Faith Nyangoma 1 ,
Jesca Atugonza 1 ,
Elizabeth Ajalo 1 ,
Alice Kalenda 1 ,
Ambrose Okibure 1 ,
Andrew Kagwa 1 ,
Ronald Kibuuka 1 ,
Betty Nakawuka 1 ,
Francis Okello 2 &
Proscovia Auma 2

BMC Health Services Research volume 24 , Article number: 852 ( 2024 ) Cite this article

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Proper and complete clerkships for patients have long been shown to contribute to correct diagnosis and improved patient care. All sections for clerkship must be carefully and fully completed to guide the diagnosis and the plan of management; moreover, one section guides the next. Failure to perform a complete clerkship has been shown to lead to misdiagnosis due to its unpleasant outcomes, such as delayed recovery, prolonged inpatient stay, high cost of care and, at worst, death.

The objectives of the study were to determine the gap in clerkship, the impact of incomplete clerkship on the length of hospital stay, to explore the causes of the gap in clerkship of the patients and the strategies which can be used to improve clerkship of the patients admitted to, treated and discharged from the gynecological ward in Mbale RRH.

Methodology

This was a mixed methods study involving the collection of secondary data via the review of patients’ files and the collection of qualitative data via key informant interviews. The files of patients who were admitted from August 2022 to December 2022, treated and discharged were reviewed using a data extraction tool. The descriptive statistics of the data were analyzed using STATA version 15, while the qualitative data were analyzed via deductive thematic analysis using Atlas ti version 9.

Data were collected from 612 patient files. For qualitative data, a total of 8 key informant interviews were conducted. Social history had the most participants with no information provided at all (83.5% not recorded), with biodata and vital sign examination (20% not recorded) having the least number. For the patients’ biodata, at least one parameter was recorded in all the patients, with the greatest gap noted in terms of recording the nearest health facility of the patient (91% not recorded). In the history, the greatest gap was noted in the history of current pregnancy (37.5% not provided at all); however, there was also a large gap in the past gynecological history (71% not recorded at all), past medical history (71% not recorded at all), past surgical history (73% not recorded at all) and family history (80% not recorded at all). The physical examination revealed the greatest gap in the abdominal examination (43%), with substantial gaps in the general examination (38.5% not recorded at all) and vaginal examination (40.5% not recorded at all), and the vital sign examination revealed the least gap. There was no patient who received a complete clerkship. There was a significant association between clerkships and the length of hospital stay. The causes of the gap in clerkships were multifactorial and included those related to the hospital, those related to the health worker, those related to the health care system and those related to the patient. The strategies to improve the clerkship of patients also included measures taken by health care workers, measures taken by hospitals and measures taken by the government.

Conclusion and recommendation

There is a gap in the clerkships of patients at the gynecological ward that is recognized by the stakeholders at the ward, with some components of the clerkship being better recorded than others, and no patients who received a complete clerkship. There was a significant association between clerkships and the length of hospital stay.

The following is the recommended provision of clerkship tools, such as the standardized clerkship guide and equipment for patient examination, continuous education of health workers on clerkships and training them on how to use the available tools, the development of SOPs for patient clerkships, the promotion of clerkship culture and the supervision of health workers.

Peer Review reports

Introduction

A complete clerkship is the core upon which a medical diagnosis is made, and this depends on the patient’s medical history, the signs noticed on physical examination, and the results of laboratory investigations [ 1 ]. These sections of the clerkship should be completed carefully and appropriately to obtain a correct diagnosis; moreover, one part guides the next. A complete gynecological clerkship comprises the patient’s biodata, presenting complaint, history of presenting complaint, review of systems, past gynecological history, past obstetric history, past medical history, past surgical history, family history, social history, physical examination, laboratory investigation, diagnosis and management plan [ 2 , 3 ].

History taking, also known as medical interviews, is a brief personal inquiry and interrogation about bodily complaints by the doctor to the patient in addition to personal and social information about the patient [ 4 ]. It is estimated that 70-90% of a medical diagnosis can be determined by history alone [ 5 , 6 ]. Physical examination, in addition to the patient’s history, is equally important because it helps to discover more objective aspects of the disease [ 7 ]. The investigation of the patient should be guided by the findings that have been obtained on history taking and the physical examination [ 1 ].

Failure to establish a good complete and appropriate clerkship for patients leads to diagnostic uncertainties, which are associated with unfavorable outcomes. Some of the effects of poor clerkship include delayed diagnosis and inappropriate investigations, which lead to unnecessary expenditures on irrelevant tests and drugs and other effects, such as delayed recovery, prolonged inpatient stays, high costs of care and, at worst, death [ 8 , 9 ]. Despite health care workers receiving training in medical school about the relevance of physical examination, this has been poorly practiced and replaced with advanced imaging techniques such as ultrasounds, CT scans, and MRIs, which continue to make health care services unaffordable for most populations in developing countries [ 6 ]. In a study conducted to determine the prevalence and classification of misdiagnosis among hospitalized patients in five general hospitals in central Uganda, 9.2% of inpatients were misdiagnosed, and these were linked to inadequate medical history and examination, as the most common conditions were the most commonly misdiagnosed [ 9 ].

At Mbale RRH, there has been a progressive increase in the number of patients included in the gynecology department, which is expected to have compromised the quality of the clerkships that patients receive at the hospital [ 10 ]. However, there is limited information about the quality and completeness of clerkships for patients admitted to and treated at Mbale RRH. The current study therefore aimed to determine the gap in patient clerkships and the possible causes of these gaps and to suggest strategies for improving clerkships.

Methods and materials

Study design.

This was a baseline study, which was part of a quality improvement project aimed at improving the clerkships of patients admitted and treated at Mbale RRH. This mixed cross-sectional survey employing both quantitative and qualitative techniques was carried out from August 2022 to December 2022. Both techniques were employed to triangulate the results and address the gap in clerkship using quantitative techniques. Then, qualitative methods were used to explain the reasons for the observed discrepancy, and strategies to improve clerkship were suggested.

Study setting

The study was carried out in Mbale RRH, at the gynecologic ward. The hospital is in Mbale Municipal Council, 214 km to the east of the capital city of Kampala. It is the main regional referral hospital in the Elgon zone in eastern Uganda, a geographic area that borders the western part of Kenya. The Mbale RRH serves a catchment population of approximately 5 million people from 16 administrative districts. It is the referral hospital for the districts of Busia, Budaka, Kibuku, Kapchorwa, Bukwo, Butaleja, Manafwa, Mbale, Pallisa, Sironko and Tororo. The hospital is situated at an altitude of 1140 m within a range of 980–1800 m above sea level. Over 70% of inhabitants in this area are of Bantu ethnicity, and the great majority are part of rural agrarian communities. The Mbale RRH is a government-run, not-for-profit and charge-free 470-bed capacity that includes four major medical specialties: Obstetrics and Gynecology, Surgery, Internal Medicine, and Pediatrics and Child Health.

Study population, sample size and sampling strategy

We collected the files of patients who were admitted to the gynecology ward at Mbale RRH from August 2022 to December 2022. All the files were selected for review. We also interviewed health workers involved in patient clerkships at the gynecological ward. For qualitative data, participants were recruited until data saturation was reached.

Data collection

We collected both secondary and primary data. Secondary data were collected by reviewing the patients’ files. We identified research assistants who were trained in the data entry process. The data collection tool on Google Forms was distributed to the gadgets that were given to the assistants to enter the data. The qualitative data collection was performed via key informant interviews of the health workers involved in the clerkship of the patients, and the interviews were performed by the investigators. The selection of the participants was purposive, as we opted for those who clerk patients. After providing informed consent, the interview proceeded, with a voice recorder used to capture the data collected during the interview process and brief key notes made by the interviewer.

Data collection tool

A data abstraction tool was developed and fed into Google Forms, which were used to collect information about patients’ clerkships from patients’ files. The tool was developed by the investigators based on the requirements of a full clerkship, and it acted as a checklist for the parameters of clerkships that were provided or not provided. The validity of this tool was first determined by using it to collect information from ten patients’ files, which were not included in the study, and the tool was adjusted accordingly. The tool for collecting the qualitative information was an interview guide that was developed by the interviewer and was piloted with two health workers. Then, the guide was adjusted before it was used for data collection.

Variable handling

The dependent variable in the current study was the length of hospital stay. This was calculated from the date of admission and the date of discharge. There were two outcomes: “prolonged hospital stay” and “not prolonged”. A prolonged hospital stay was defined as a hospital stay of more than the 75 th percentile, according to a study conducted in Ethiopia [ 9 ]. This duration was more than 5 (five) days in the current study. The independent variables were the components of the clerkship.

Data analysis

Data analysis was performed using STATA version 15. Univariate, bivariate and multivariate analyses were performed. Continuous variables were summarized using measures of central tendency and measures of dispersion, while categorical variables were summarized using frequencies and proportions. Bivariate analysis was performed using chi-square or Fischer’s exact tests, one-way ANOVA and independent t tests, with the level of significance determined by a p value of <= 0.2. Multivariate analysis was performed using logistic regression, and the level of significance was determined by a p value of <=0.05.

Qualitative data were analyzed using Atlas Ti version 9 via deductive thematic analysis. The audio recordings were transcribed, and the transcripts were then imported into Atlas Ti.

Qualitative

The files of a total of 612 patients were reviewed.

The gap in the clerkships of patients

Patient biodata.

As shown in Fig. 1 below, at least one parameter under patient biodata was recorded for all the patients. The largest gap was identified in the recording of the nearest health facility of the patient, where 91% of the patients did not have this recorded, and the smallest gap was in the recording of the name and age, where less than 1% had this not recorded.

The gap in patients’ biodata

Compliance, HPC and ROS

As shown in Fig. 2 below, the largest gap here was in recording the history of presenting complaint, which was not recorded in 32% of the participants. The least gap was in the review of systems, where it was not recorded in only 10% of the patients.

Gap in the presenting of complaints, HPCs and ROS

As shown in Fig. 3 below, the past obstetric history had the greatest gap in recording the gestational age at delivery of each pregnancy (89% not recorded), while the least gap was in recording the number of pregnancies (43% not recorded). In terms of the history of current pregnancy, the greatest gap was in recording whether hematinics were given to the mother (92% not recorded), while the least gap was in recording the date of the first day of the last normal menstrual period (LNMP) (44% not recorded). On other gynecological history, the largest gap was in recording the history of gynecological procedures (88% not recorded), while the least gap was in the history of abortions (73% not recorded). In the past medical history, the largest gap was in terms of history of medication allergies and history of previous admissions (86% not recorded), and the smallest gap was in terms of history of chronic illnesses (72% not recorded). In the past surgical history, the largest gap was in the history of trauma (84% not recorded), while the least gap was in the history of blood transfusion (76% not recorded). In terms of family history, there was a greater gap in the family history of twin pregnancies (86% not recorded) than in the family history of familial illnesses (83% not recorded). In terms of social history, neither alcohol intake nor smoking were recorded for 84% of the patients.

Gap in history

Physical examination

As shown in Fig. 4 below, the least recorded vital sign was oxygen saturation (SPO2), with 76% of the patients’ SPO2 not being recorded, while blood pressure was least recorded (21% not recorded). On the general examination, checking for edema had the greatest gap (63% not recorded), while checking for pallor had the least gap (45% not recorded). On abdominal examination, auscultation had the greatest gap (76% not recorded), while inspection of the abdomen had the least gap (56% not recorded). On vaginal examination, the greatest difference was in examining the vaginal OS (57% not recorded), while the least difference was in checking for vaginal bleeding (47% not recorded).

Gap in physical examination

Investigations, provisional diagnosis and management plan

As shown in Fig. 5 below, the least common investigation was the malaria test (76% not performed), while the most common investigation was the CBC test (41% not performed). Provisional diagnosis was not performed in 20% of the patients. A management plan was not provided for approximately 4-5 of the patients.

Gap in the provisional diagnosis and management plan

Summary of the gap in clerkships

As shown in Fig. 6 below, most participants had a social history with no information provided at all, while biodata and vital sign examinations had the least number of participants with no information provided at all. There was no patient who had a complete clerkship.

Summary of the gaps in clerkships

Days of hospitalization

The days of hospitalization were not normally distributed and were positively skewed, with a median of 3 [ 2 , 5 ] days. The mean days of hospitalization was 6.2 (±11.1). As shown in Fig. 7 below, 20% of the patients had prolonged hospitalization.

Duration of hospitalization

The effect of the clerkship gap on the number of days of hospital stay

As shown in Tables 1 and 2 below, the clerkship components that had a significant association with the days of hospitalization at the bivariate level included vital examination, abdominal examination, history of presenting complaint and treatment plan.

As shown in Table 3 , the only clerkship component that had a significant association with the days of hospitalization at the multivariate level was abdominal examination. People who had partial abdominal examinations were 1.9 times more likely to have prolonged hospital stays than those who had complete abdominal examinations.

Qualitative results

We conducted a total of 8 key informant interviews with the following characteristics as shown in table 4 below.

The qualitative results are summarized in Table 5 below.

The quality of clerkships on wards

It was reported that both the quality and completeness of clerkships on the ward are poor.

“…most are not clerking fully the patients, just put in like biodata three items name, age address, then they go on the present complaint, diagnosis then treatment; patient clerkship is missing out some important information…” (KIISAMW 2)

It was, however, noted that the quality of a clerkship depends on several factors, such as who is clerking, how sick the patient is, the number of patients to be seen that particular day and the number of hours a person clerks.

“…so, the quality of clerkship is dependent on who is clerking but also how sick the patient is…” (KIIMO 3)

Which people usually clerk patients on the ward?

The following people were identified as those who clerking patients, midwives, medical students, junior house officers, medical officers and specialists.

“…everyone clerks patients here; nurses, midwives, doctors, medical students, specialists, everyone as long as you are a health care provider…” (KIIMO 2)

Causes of the gaps in clerkships

These factors were divided into factors related to health workers, hospital-related factors, health system-related factors and patient-related factors.

Hospital-related factors

The absence of clerkship tools such as a standardized clerkship guide and equipment for the examination of patients, such as blood pressure machines, thermometers, and glucometers, among others, were among the reasons for the poor clerkships of the patients.

…of course, there are other things like BP machines, thermometers; sometimes you want to examine a patient, but you don’t have those examining tools…” (KIIMO 1)

The tools that were available were plain, and they play little role in facilitating clerkships. They reported that they end up using small exercise books with no guidance for easy clerkship and with limited space.

“…most of our tools have these questions that are open ended and not so direct, so the person who is not so knowledgeable in looking out for certain things may miss out on certain data…” (KIIOG 1)

The reluctance of some health workers to clerk patients fully was also reported to be because it is the new normal, and everyone follows a bandwagon to collect only limited information from patients because there is no one to follow up or supervise.

“…you know when you go to a place, what you find people doing is what you also end up doing; I think it is because of what people are doing and no one is being held accountable for poor clerkship…” (KIIMO 3)

The absence of specialized doctors in the OPD department forces most patients, even stable patients, to be managed by the OPD to crowd the ward, making complete clerkships for all patients difficult. Poor triaging of the patients was also noted as one of the causes of poor clerkship, as emergency cases are mixed with stable cases.

“…and this gyn ward is supposed to see emergency gynecological cases, but you find even cases which are supposed to be in the gyn clinic are also here; so, it creates large numbers of people who need services…” (KIIMO 1)

Clerkships being performed by the wrong people were also noted. It was emphasized that it is only a medical doctor who can perform good clerkships for patients, and any other cadres who perform clerkships contribute to poor clerkships on the ward.

Health worker-related factors

A poor attitude of health workers was reported, and it was found that many health workers consider complete clerkship to be a practice that is performed by people who do not know what they look for to make a diagnosis.

A lack of knowledge about clerkships is another factor that has been reported. Some health workers were reported to forget some of the components of clerkship; hence, they end up recording only what they remember at the time of clerkship.

A lack of confidence by some health workers and students that creates fear of committing to making a diagnosis and drawing a management plan was reported to hinder some of them from doing a complete clerkship of the patients.

“…a nurse or a student may clerk, but they don’t know the diagnosis; so, they don’t want to commit themselves to a diagnosis…” (KIIMO 2)

Some health workers reported finding the process of taking notes while clerking tedious; hence, they collected only limited information that they could write within a short period of time.

Health system-related factors

Understaffing of the ward was noted to cause a low health worker-to-patient ratio. This overworked the health workers due to the large numbers of patients to be seen.

“…due to the thin human resource for health, many patients have to be seen by the same health worker, and it becomes difficult for one to clerk adequately; they tend to look out for key things majorly…” (KIIOG 1)

It was noted that in the morning or at the start of a shift, the clerkship can be fair, but as the day progresses, the quality of the clerkship decreases due to exhaustion.

“…you can’t clerk the person you are seeing at 5 pm the same way you clerked the person you saw at 9 am…” (KIIMO 3)

The large numbers of patients were also associated with other factors, such as the inefficient referral system, where patients who can be managed in lower health facilities are also referred to Mbale RRH. It was also stated that some patients do not understand the referral system, causing self-referral to the RRH. Other factors that contributed to the poor referral system were limited trust of the patients, drug stockouts, limited skilled number of health workers, and limited laboratory facilities in the lower health facilities.

“…so, everyone comes in from wherever they can, even unnecessary referrals from those lower health facilities make the numbers very high…” (KIIMO 1)

Patient-related factors

It was reported that the nature of some cases does not allow the health worker to collect all the information from such a patient, for example, the emergency cases. However, some responders stated the emergent nature of the cases to be a contributor to the complete clerkship of such a patient, as the person clerking such a case is more likely to call for help, so they must have enough information on the patient. Additionally, they do not want to fill the gap in the care of this critical patient.

“…usually, a more critical patient gets a more elaborate clerkship compared to a more stable one, where we will get something quick…” (KIIMO 3)

The poor health of some patients makes them unable to afford the files and books where clerkship notes are to be taken.

“…a patient has no money, and they have to buy books where to write, then you start writing on ten pages; does it make sense...” (KIIMO 2)

Strategies to improve patients’ clerkships

These were divided into measures to be taken by the health workers, those to be taken by the hospital leadership and those to be taken by the government.

Measures to be taken by health workers.

Holding each other accountable with respect to clerkship quality and completeness was suggested, including providing feedback from fellow health workers and from the records department.

…like everyone I think should just be held accountable for their clerkship and give each other feedback…” (KIIMO 3)

It was also suggested that medical students be mentored by senior doctors on the ward on the clerkship, and they should clerk the patients and present them to the senior doctors for guidance on the diagnosis and the management plan. This approach was believed to save time for senior doctors who may not have obtained time to collect information from patients and to facilitate the learning of students, most importantly ensuring the complete clerkship of patients.

“…students can give us a very good clerkship if supervised well, then we can discuss issues of diagnosis, the investigations to be done and the management…” (KIIMO 1)

Changes in the attitudes of health workers toward clerkships were suggested. This was also encouraged for those who work in laboratories to be able to perform the required investigations to guide diagnosis and management.

“…our lab has the equipment, but they need to change their attitude toward doing the investigations…” (KIIMO 1)

Measures to be taken by hospital leaders

The provision of tools to be used in clerkships was suggested as one of the measures that can be taken. Among the tools that were suggested include the following: a standardized clerkship guide, equipment for examination of the patients, such as blood pressure machines, and thermometers, among others. It was also suggested that a printer machine be used to print the clerkship guide to ensure the sustainability and availability of the tools. An electronic clerkship provision was suggested so that the amount of tedious paperwork could be reduced, especially for those who are comfortable with it.

“…if the stakeholders, especially those who have funds, can help us to make sure that these tools are always available, it is a starting point…” (KIIOG 1)

Continuous education of the clinicians about clerkships was suggested in the CMEs, and routine morning meetings were always held in the ward. Then, it was suggested that clinicians who clerked patients the best way are rewarded to motivate them.

“…for the staff, we can may be continuously talking about it during our Monday morning meetings about how to clerk well and the importance of clerking…” (KIIOG 1)

They also suggested providing a separate conducive room for the examination of patients to ensure the privacy of the patient, as this will ensure more detailed examination of the patients by the clinicians.

It was also suggested that more close supervision of the clerkship be performed and that a culture of good clerkship be developed to make clerkship a norm.

“…as leaders of the ward and of the department, we should not get tired to talk about the importance of clerkship, not only in this hospital but also in the whole country…” (KIIOG 1)

Proper record-keeping was also suggested, for people clerking to be assured that information will not be discarded shortly.

“…because how good is it to make these notes yet we can’t keep them properly...” (KIIMO 2)

It was also suggested that a records assistant be allocated to take notes for the clinicians to reduce their workload.

Coming up with SOPs, for example, putting different check points that ensure that a patient is fully clerked before the next step

“…we can say, before a patient accesses theater or before a mother enters second stage room, they must be fully clerked, and there is a checklist at that point…” (KIIOG 1)

Measures to be taken by the government

Improving the staffing level is strongly suggested to increase the health worker-to-patient ratio. This, they believed would reduce the workload off the health workers and allow them to give more time to the patients.

“…we also need more staffing for the scan because the person who is there is overwhelmed…” (KIIMO 1)

Staff motivation was encouraged through the enhancement of staff salaries and allowances. It was believed that it would be easy for these health workers to be supervised when they are motivated.

“…employ more health workers, pay them well then you can supervise them well…” (KIIMO 1)

Providing refresher courses to clinicians was also suggested so that they could be updated during the clerkship process.

Streamlining the referral system was also suggested through the use of lower health facilities so that some minor cases can be managed in those facilities to reduce the overcrowding of patients in the RRH.

“…we need to also streamline the referral system, the way people come to the RRH; some of these cases can be handled in the lower health facilities; we need to see only patients who have been referred…” (KIIMO 2)

The qualitative results are further summarized in Fig. 8 below.

Scheme of the clerkship of patients, including the causes of the clerkship gap and the strategies to improve the clerkship at Mbale RRH

Discussion of results

This study highlights a gap in the clerkships of patients admitted, treated, and discharged from the gynecological ward, with varying gaps in the different sections. This could be because some sections of the clerkship are considered more important than others. A study performed in Turkey revealed that physicians tended to record more information that aided their diagnostic tasks [ 11 ]. This is also reflected in the qualitative findings where participants expressed that particular information is required to make the diagnosis and not everything must be collected.

Biodata for patients were generally well recorded, and name and age were recorded for almost all the patients. A similar finding was found in the UK, where 100% of the patients had their personal details fully recorded [ 12 ]. Patient information should be carefully and thoroughly recorded because it enables health workers to create good rapport with patients and creates trust [ 13 ]. This information is also required for every interaction with the patient at the ward.

The presenting complaint, history of presenting complaint and the review of systems were fairly recorded, with each of them missing in less than 40% of the patients. The presence of a complaint is crucial in every interaction with the patient to the extent that a diagnosis can rarely be made without knowing the chief complaint [ 14 , 15 ]. This applies to the history of presenting complaint as well [ 16 ]. For the 30% who did not have the presenting complaint recorded, this could mean that even the patient’s primary problem was not given adequate attention.

In the history, the greatest gap was noted in the history of current pregnancy, where many parameters were not recorded in most patients. This is, however, expected since the study was conducted on a gynecological ward, where only a few pregnant women are expected to visit, as they are supposed to go to their antenatal clinics [ 17 ]. However, there was also a large gap in past gynecological history, which is expected to be fully explored in the gynecology ward. A good medical history is key to obtaining a good diagnosis, in addition to a good clinical examination [ 3 , 18 ]. Past obstetric history, past medical history, past surgical history, and family history also had large gaps, yet they are very important in the management of these patients.

The abdominal parameters, especially the pulse rate and blood pressure, were the least frequently recorded during the physical examination, and vital signs were most often recorded. However, there were substantial gaps in the general examination and vaginal examination. The least gap in vital sign examination is close monitoring, which is performed for most patients admitted to the ward due to the nature of the patients, some of whom are emergency patients [ 19 ].

Among the investigations, 29% of patients were not investigated. The least commonly performed investigations were pelvic USS and malaria tests, while complete blood count (CBC) was most commonly performed. Genital infections are among the most common reasons for women’s visits to health care facilities [ 20 ]. Therefore, most women in the gynecological ward are suspected to have genital tract infections, which could account for why CBC is most commonly performed.

The limited number of other investigations, such as pelvic ultrasound scans, underscore the relative contribution of medical history and physical examination to laboratory investigations and imaging studies aimed at making a diagnosis [ 1 ]. However, this would also highlight the system challenges of limited access to quality laboratory services in low- and middle-income countries [ 21 ]. This was also highlighted by one of the key informants who reported that the USS staff is available on some and not all days. This means that on days where the ultrasound department does not work, USS is not performed, even when needed.

We found that 20% of patients experienced prolonged hospitalization. This percentage is lower than the 24% reported in a study conducted in Ethiopia [ 22 ]. However, this study was conducted in a surgical ward. The median length of hospital stay was the same as that in a study conducted in Eastern Sudan among mothers following cesarean delivery [ 23 ]. A prolonged hospital stay has a negative impact not only on patients but also on the hospital [ 24 , 25 ]. Therefore, health systems should aim to reduce the length of hospital stay for patients as much as possible to improve the effectiveness of health services.

At the multivariate level, abdominal examination was significantly associated with length of hospital stay, with patients whose abdominal examination was not complete being more likely to have a prolonged hospital stay. This underscores the importance of good examination in the development of proper management plans that improve the care of patients, hence reducing the number of days of hospital stay [ 5 , 26 ].

There is a gap in the clerkships of patients at the gynecological ward, which is recognized by the stakeholders at the ward. Some components of clerkships were recorded better than others, with the reasoning that clerkships should be targeted. There were no patients who received a complete clerkship. There was a significant association between clerkships and the length of hospital stay. The causes of the gap in clerkships were multifactorial and included those related to the hospital, those related to the health worker, those related to the health care system and those related to the patient. The strategies to improve the clerkship of patients also included measures taken by health care workers, measures taken by hospitals and measures taken by the government.

Recommendations

Clerkship tools, such as the standardized clerkship guide and equipment for patient examination, were provided. The health workers were continuously educated on clerkships and trained on how to use the available tools. The development of SOPs for patient clerkships, the promotion of clerkship culture and the supervision of health workers.

Strengths of the study

A mixed study, therefore, allows for the triangulation of results.

Study limitations

The quantity of quantitative data collected, being secondary, is subject to bias due to documentation errors. We assessed the completeness of clerkship without considering the nature of patient admission. We did not record data on whether it was an emergency or stable case, which could be an important cofounder. However, this study gives a good insight into the status of clerkship in the gynecological ward and can lay foundation for future research into the subject.

Availability of data and materials

The data and materials are available upon request from the corresponding author via the email provided.

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Authors and affiliations.

Faculty of Health Science, Busitema University, P.O. Box 1460, Mbale, Uganda

Kennedy Pangholi, Faith Nyangoma, Jesca Atugonza, Elizabeth Ajalo, Alice Kalenda, Ambrose Okibure, Andrew Kagwa, Ronald Kibuuka & Betty Nakawuka

Institute of Public Health Department of Community Health, Busitema University, faculty if Health Sciences, P.O. Box 1460, Mbale, Uganda

Enid Kawala Kagoya, Francis Okello & Proscovia Auma

Seed Global Health, P.O. Box 124991, Kampala, Uganda

Allan G Nsubuga, Irene Atuhairwe, Prossy Nakattudde, Brian Agaba, Bonaventure Ahaisibwe, Esther Ijangolet & Eric Otim

Department of Pharmacology and Therapeutics, Busitema University, Faculty of Health Science, P.O. Box 1460, Mbale, Uganda

Department of Obstetrics and Gynecology, Busitema University, Faculty of Health Sciences, P.O. Box 1460, Mbale, Uganda

Julius Wandabwa & Milton Musaba

Department of Obstetrics and Gynecology, Mbale Regional Referral Hospital, P.O. Box 921, Mbale, Uganda

Antonina Webombesa & Kenneth Mugabe

Department of Nursing, Busitema University, Faculty of Health Sciences, P.O. Box 1460, Mbale, Uganda

Ashley Nakawuki

Ministry of Health, Plot 6, Lourdel Road, Nakasero, P.O. Box 7272, Kampala, Uganda

Richard Mugahi

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Contributions

P.K came up with the concept and design of the work and coordinated the team to work K.E.K and A.P helped interpretation of the data O.F and O.A helped in the analysis of data N.A.G, A.I, N.P, W.P, W.J, M.M, A.W, M.K, N.F, A.J, A.E, M.R, K.A, K.A, A.B, A.B, I.E, O.E, N.A, K.R, N.B substantially revised the work.

Corresponding author

Correspondence to Kennedy Pangholi .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted according to the Declaration of Helsinki and in line with the principles of Good Clinical Practice and Human Subject Protection. Prior to collecting the data, ethical approval was obtained from the Research Ethics Committee of Mbale RRH, approval number MRRH-2023-300. The confidentiality of the participant information was ensured throughout the research process. Permission was obtained from the hospital administration before the data were collected from the patients’ files, and informed consent was obtained from the participants before the qualitative data were collected. After entry of the data, the devices were returned to the principal investigator at the end of the day, and they were given to the data entrants the next day.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Pangholi, K., Kagoya, E.K., Nsubuga, A.G. et al. Improving Clerkship to Enhance Patients’ Quality of care (ICEPACQ): a baseline study. BMC Health Serv Res 24 , 852 (2024). https://doi.org/10.1186/s12913-024-11337-w

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Received : 25 September 2023

Accepted : 22 July 2024

Published : 26 July 2024

DOI : https://doi.org/10.1186/s12913-024-11337-w

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Faculty standardized patients versus traditional teaching method to improve clinical competence among traditional Chinese medicine students: a prospective randomized controlled trial

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