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A Systematic Literature Review of Health Information Systems for Healthcare

Ayogeboh epizitone, smangele pretty moyane, israel edem agbehadji.

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Correspondence: [email protected] ; Tel.: +27-(0)73-310-9150

Received 2023 Feb 27; Revised 2023 Mar 20; Accepted 2023 Mar 25; Collection date 2023 Apr.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded.

Keywords: health information system, information system, knowledge management, healthcare

1. Introduction

Health information systems (HIS) are critical systems deployed to help organizations and all stakeholders within the healthcare arena eradicate disjointed information and modernize health processes by integrating different health functions and departments across the healthcare arena for better healthcare delivery [ 1 , 2 , 3 , 4 , 5 , 6 ]. Over time, the HIS has transformed significantly amidst several players such as political, economic, socio-technical, and technological actors that influence the ability to afford quality healthcare services [ 7 ]. The unification of health-related processes and information systems in the healthcare arena has been realized by HIS. HIS has often been contextualized as a system that improves healthcare services’ quality by supporting management and operation processes to afford vital information and a unified process, technology, and people [ 7 , 8 ]. Several authors assert this disposition of HIS, alluding to its remarkable capabilities in affording seamless healthcare [ 9 ]. Haux [ 10 ] modestly chronicled HIS as a system that handles data to convey knowledge and insights in the healthcare environment. Almunawar and Anshari [ 7 ] incorporated this construed method to describe HIS to be any system within the healthcare arena that processes data and affords information and knowledge. Malaquias and Filho [ 11 ] accentuated the importance of HIS in the same light, highlighting its emergence to tackle the need to store, process, and extract information from the system data for the optimization of processes, enhancing services provided and supporting decision making.

HIS’s definition was popularized by Lippeveld [ 12 ], and reported to be an “integrated effort to collect, process, report and use health information and knowledge to influence policy-making, programme action and research”. Over the course of time, this definition has been adopted and contextualized countlessly by many authors and the World Health Organization (WHO) [ 3 , 8 , 13 , 14 , 15 ]. Although Haule, Muhanga [ 8 ] claimed the definition of HIS varies globally, in actuality, the definition has never changed from its inception, but on the contrary, it has been conceptualized over various contexts. Malaquias and Filho [ 11 ] reiterated this definition in the extant literature. These scholars affirmed HIS as “a set of interrelated components that collect, process, store and distribute information to support the decision-making process and assist in the control of health organizations” [ 11 ]. The same definition is adopted in this paper, and HIS is construed as “a system of interrelated constituents that collect, process, store and distribute data and information to support the decision-making process, assist in the control of health organizations and enhance healthcare applications”. However, it is paramount to note that HIS is broad. In many instances, the definition is of minimal relevance due to its associated incorporation with external applications related to health developments and policy making [ 16 ]. Hence, emphasis should not be placed on the definition but on its contribution to all facets of health development.

The current state of HIS is considered to be inadequate despite its numerus deployment of HIS that has been driven by its potential benefit to uplift healthcare and revolutionize its processes [ 17 , 18 ]. The persistence of many constraints and resistance to technology has resulted to the incapacitation of HIS in the attainment of its objectives. The extant literature reveals several challenges in different categories, such as the inadequacy of human resources and technological convergence within the healthcare [ 18 ], highlighting the evidence of limitations of HIS that restrict their utilization and deployment within the healthcare. Although several authors identified the unique disposition of HIS in integrating care and unifying the health process, these perspectives seems to be marred by the presence of barriers [ 17 , 19 ]. Garcia, De la Vega [ 17 ] alleged that the current HIS deployment is characterized by fragmentation, update instability, and lack of standardization that limit its potential to aid healthcare. Congruently, several authors associated the lack of awareness of HIS potential, the underuse HIS, inadequate communication network, and security and confidentiality concerns among the barriers limiting HIS [ 20 ]. Thus, the need for this paper is set forth: to uncover current and pertinent insights on HIS deployment as a concerted effort to strengthen it and augment its healthcare delivery capabilities. This paper comprehensively explores the extant literature systematically with respect to the overarching objective: to ascertain value insights pertaining to HIS holistically from literature synthesis. To achieve this goal, the following research questions are investigated: What has been the development of the HIS since its conception? How has HIS been deployed? Finally, how does HIS enable information and knowledge management in healthcare?

In this paper, an overview HIS from the extant literature in relation to the health sector is presented with associated related work. It is essential to point out that in spite of the surplus of research work conducted on health information systems, there are still many challenges confronting it within the healthcare area that necessitate the need for this study [ 5 ]. Therefore, the extant literature is explored in this paper systematically to uncover current and pertinent insights surrounding the deployment of the HIS, an integrated information system (IS) for healthcare. This paper is structured into five sections. The paper commences with an introductory background that presents the contextualization of HIS for healthcare, followed by a methodology that details the method and material used in this study. The next section, which is the discussion, presents the discourse of HIS evolution that highlights its progress to date, its structural deployment, and the information system and knowledge management within the healthcare arena as mediated by HIS. The last part of this study focuses on the conclusion that summarizes the discussion presented in this paper.

2. Material and Method

In this paper, a systematic review is conducted to synthesize the extant literature and analyze the content to ascertain the value disposition of HIS in relation to healthcare delivery. Preceding this review, the used of search engines was employed to retrieve related research publications that fit the study scope and contexts. The main database used was the Web of Science . Other databases such as SCOPUS and Google Scholar were also used to obtain additional relevant work associated with the context. For inclusion criteria, only articles containing references to the keywords HIS, information, healthcare, and related healthcare systems were analyzed scrupulously. Research work that did not have these references, did not constitute a journal or conference-proceeding work, and were not written in the English language were excluded. Figure 1 , the PRISMA flow statement, illustrates the methodological phases of this research along with the exclusion and inclusion criteria that were implemented for the study synthesis.

Prisma flow Statement.

3. Discussion

3.1. the evolution of health information systems.

The concept of enhancing healthcare applications has always been the foundation of HIS, which posits that the intercession of information systems with business processes affords better healthcare services [ 7 , 21 ]. According to Almunawar and Anshari [ 7 ], many determinants, such as technological, political, social and economic, have enormously influenced the nature of the healthcare industry. The technological determinant, particularly the computerized component, is thought to be deeply ingrained in the enactment and functioning of HIS. According to Panerai [ 16 ], this single attribute can be held solely responsible for HIS letdowns rather than its accomplishment.

The ownership of HIS has been contested in the literature, with some authors claiming that HIS belongs to the IT industries [ 22 ]. While IT has enabled many developments in various industries, it has also resulted in many dissatisfactions. Recently, there has been an insurgence from many industries, particularly the healthcare industries, who acknowledge the role of IT in optimizing and enhancing health initiatives but want appropriation of their integrated IS. However, according to the definition of HIS, it is presented as “a set of interconnected components that collect, process, store, and distribute information to support decision-making and aid in the control of health organizations”; thus, the disposition of HIS was established. Without bias, the development of HIS was conceived due to unavoidable changes and transformations within the global space.

A good representation and consolidation of this dispute are within the realization that there is a co-existence of different related and non-related components in a system. In this case, the HIS is an entrenched system with several features, including technologies. Panerai [ 16 ] supported this notion and theorized HIS to be broad, stating that the relevance of its definition is contextual. In the study, HIS was reiterated as any kind of “structured repository of data, information, or knowledge” that can be used to support health care delivery or promote health development [ 16 ]. Thus, maintaining a rigid definition is of minimal practical use because many HIS instances are not directly associated with health development, such as the financial and human resource modules. Moreover, several different HIS examples are categorized according to the functions they are dedicated to serving within the healthcare arena. They highlight the instances of the existence of outliers that are not regarded as the normal HIS even though they contain health determinants data, such as socioeconomic and environmental, which can be used to formulate health policies.

The development of HIS over the years has led many to believe they are solely computer technology. This notion has contributed dramatically to the misconception of the origin of HIS and the lack of peculiarity between the HIS conceptual structure and implemented HIS technology. The literature dates back the origin of HIS, which can be associated with the first record of mortality in the 18th century, revealing their existence to be 200 years or older than the invention of computers [ 16 ]. This demonstrates the emergence of digitalized HIS from the availability of commercialized episodes of “electronic medical records” EMR records in the 1970s [ 23 ]. Namageyo-Funa, Aketch [ 24 ] commended the advancement of technologies in the healthcare arena, recounting the implementation of digitalized HIS that significantly revolutionized the recording and accessing of health information. A study by Lindberg, Venkateswaran [ 25 ] highlighted an instance of HIS transition from paper based to digitally based, revealing a streamlined workflow that revolutionized health care applications in the healthcare arena. This HIS transition over the course of time has led to increased adoption of it within the health care arena. Tummers, Tekinerdogan [ 26 ] highlighted the landmark of HIS from its transition to digitalization and reported a current trend in healthcare that has now been extended with the inclusion of block chain technology within the healthcare arena. Malik, Kazi [ 27 ] assessed HIS adoption in terms of technological, organizational, human, and environmental determinants and reported a variation of different degrees of utilization. Despite these facts, the extant literature maintains the need for a resilient and sustainable HIS for health care applications within the healthcare arena at all levels [ 18 , 27 , 28 ].

Figure 2 illustrates the successful adoption of HIS amidst the significant determinants of its effectiveness. From the Figure 2 , the technological, organizational, human, and environmental determinants are the defining concepts along with individual sub-determinants in each domain that influence HIS adoption. At the technological level, the need for digitalization drives HIS adoption, especially for stakeholders such as clinicians and decision makers. The administrative, management, and planning functions are the driving actors within the organization level that endorse the implementation of HIS. The environmental and human determinants are more concerned with the socio-technical components that have been regarded as complex drivers for HIS adoptions. Perceptions, literacy, and usability are known forces within these categories that necessitate the adoption of HIS in many healthcare arenas.

Effective health information system associations with the driving adoption determinants. Source: [ 27 ].

3.2. HIS Structural Deployment

HIS’s unified front is geared toward assimilating and disseminating health gen to enhance healthcare delivery. HIS consists of different sub-systems that serve several actors within the healthcare arena [ 29 ]. These sub-systems are dedicated to specific tasks that perform various functions such as civil registrations, disease surveillance, outbreak notices, interventions, and health information sharing within the healthcare arena. It also supports and links many functions and activities within the healthcare environment, such as recording various data and information for stakeholders, scheduling, billing, and managing. Stakeholders are furnished with health information from diverse HIS scenarios. These include but are not limited to information systems for hospitals and patients, health institution systems, and Internet information systems. Sligo, Gauld [ 30 ] regarded HIS as a panacea within the healthcare ground that improves health care applications. Despite all the limitless capabilities of HIS, it has been reported to be asymmetrical, lacking interactions within subsystems [ 1 , 18 ]. Many decision making methods and policies rely on good health information [ 31 ]. According to Suresh and Singh [ 32 ], the HIS enables stakeholders such as the government and all other players in the healthcare arena to have access to health information, which influences the delivery of healthcare. The sundry literature further reveals accurate health information to be the foundation of decision making and highlights the decisive role of the human constituent [ 29 , 31 , 33 , 34 ].

Furthermore, HIS can be classified into two cogs in today’s era: the computer-related constituent that employs ICT-related tools and the non-computer component, which both operate at different levels. These levels include strategic, tactical, and operational. The deployment of HIS at the strategic level offers intelligence functions such as intelligent decision support, financial estimation, performance assessment, and simulation systems [ 3 , 35 ]. At the tactical level, managerial functions are performed within the system, while at the operational level, functions including recording, invoicing, scheduling, administrative, procurement, automation, and even payroll are carried out. Figure 3 shows the three levels within the healthcare system where HIS deployment is utilized.

Levels of HIS deployment: source authors.

3.3. Health Information Systems Benefits

HIS, as an interrelated system, houses several core processes and branches in the healthcare arena, affording many benefits. Among these are the ease of access to patients and medical records, reduction of costs and time, and evidence-based health policies and interventions [ 8 , 21 , 36 , 37 , 38 ]. Several authors revealed the benefits of HIS to be widely known and influential within the healthcare domain [ 38 ]. Furthermore, many health organizations are drawn to HIS because of these numerous advantages [ 22 , 39 ]. Moreover, investment in HIS has enabled effective decision making, real-time comprehensive health information for quality health care applications, effective policies in the healthcare arena, scaled-up monitoring and evaluation, health innovations, resource allocations, surveillance services, and enhanced governance and accountability [ 36 , 40 , 41 , 42 ]. Ideally, HIS is pertinent for data, information, and broad knowledge sharing in the healthcare environment. HIS critical features are now cherished due to their incorporation with diverse technology [ 16 , 43 ]. The extant literature reveals the role of HIS to extend beyond its reimbursement. Table 1 presents a summarized extract of various HIS benefits as captured in the literature and some of its core enabling components or instances.

HIS core enabling components and its benefits.

3.4. Information System and Knowledge Management in the Healthcare Arena

The presence of modernized information systems (IS) in the healthcare arena is alleged by scholars to be a congested domain that seldom fosters stakeholders’ multifaceted and disputed relationships [ 48 ]. On the other hand, it is believed that a significant amount of newly acquired knowledge in the field of healthcare is required for the improvement of health care [ 49 ]. Ascertaining and establishing the role of IS and knowledge management is an important step in the development of HIS for healthcare. Flora, Margaret [ 5 ] posited that efficient IS and data usage are crucial for an effective healthcare system. Bernardi [ 50 ] alleged that the underpinning inkling of a “robust and efficient” HIS enables healthcare stakeholders such as managers and providers to leverage health information to commendably plan and regulate healthcare, which could result in enhanced survival rates. As a result, it is imperative to ground these ideas within the context of the healthcare industry to provide a foundation for developing a robust and sustainable HIS for use in the context of health care applications.

3.4.1. Information System

The assimilation and dissimilation of health information and data within the healthcare system is an important task that influences healthcare outcome. Within the healthcare setting, IS plays a significant role in the assimilation and dissimilation of health information needed by healthcare stakeholders. Many continents endorse the deployment of IS mainly to consolidate mutable information from different sources within the systems. The primary objective for these systems’ deployment has been centered on bringing together unique and different components such as institutions, people, processes, and technology in the system under one umbrella [ 5 , 51 ]. An overview of the extant literature reveals that this has rarely been easy, as integration within this system has always been difficult in many contexts. In the context of HIS, many reported the integration phenomena to be problematic, attributing this to the global transformation within the healthcare arena [ 52 , 53 ]. This revolution, coupled with the advancement of the healthcare arena, has resulted in the need for robust allied health IS systems that incorporates different IS and information technology [ 5 , 22 ]. These allied health information systems are necessary to consolidate independent information systems within their healthcare arena use to enhance healthcare applications [ 54 , 55 ]. Organizations in the healthcare arena expect these systems to be sustainable and resilient; however, in order to satisfy these requirements, an integrated information system is needed to unify all independent, agile, and flexible health IS to mitigate challenges for HIS [ 56 ].

An aligned HIS that is allied is essential, as it supports health information networks (HIN) that subsequently enhance and improve healthcare applications [ 44 , 57 ]. Thus, many organizations within the healthcare settings are fine-tuning their HIS to be resilient and sustainable. However, the realization of a robust information system within the healthcare arena is challenging and depends on the flow of information as a crucial constituent for suave and efficient functioning [ 58 , 59 ].

3.4.2. Knowledge Management

The process of constructing value and generating a maintainable edge for an industry with capitalization on building, communicating, and knowledge applications procedures to realize set aspirations is denoted as knowledge management [ 60 ]. The literature reveals knowledge management as an important contributor to organizational performance through its knowledge-sharing capabilities [ 61 ]. In the healthcare industry, there is a high demand for knowledge to enhance healthcare applications [ 49 , 62 ]. Several studies reported that the deployment of knowledge management in the healthcare arena is set to enhance healthcare treatment effectiveness [ 49 , 58 , 61 ]. Many stakeholders such as governments, World Health Organization (WHO), and healthcare workers rely on the management of healthcare knowledge to complement healthcare applications. According to Kim, Newby-Bennett [ 61 ], the focus of knowledge management is to efficaciously expedite knowledge sharing. However, integrating knowledge from different sources is challenging and requires an enabler [ 61 ].

The HIS is an indispensable enabler of health knowledge generated from amalgamated health information within the healthcare arena [ 63 , 64 , 65 ]. Dixon, McGowan [ 66 ] asserted that efficacious modifications in the healthcare arena are made possible by knowledge codification and collaboration from information technologies. Similarly, some authors have pinpointed information and communication technologies within the healthcare arena to be a major determinant in the attainment of a sustainable health system development [ 58 ]. The knowledge management relationship with HIS is considered complementary and balanced, as it enables the availability of knowledge that can be shared. The importance of knowledge management is relevant for the realization of an enhanced healthcare application via HIS. Soltysik-Piorunkiewicz and Morawiec [ 58 ] claimed that the information society effectively uses HIS as an information system for management, patient knowledge, health knowledge, healthcare unit knowledge, and drug knowledge. The authors herein demonstrated how HIS facilitates knowledge management in the healthcare sector to improve healthcare applications.

The role of HIS as an integrated IS and key enabler of healthcare knowledge management highlights its potential within the healthcare arena. From the conception of HIS and the records of its evolution, significant achievements have been attained that are demonstrated at different levels of its structural deployment. HIS deployment in several settings of healthcare have positively influenced clinical processes and patients’ outcomes [ 17 ]. Globally, the need for HIS within the healthcare system is critical in the enhancement of healthcare. Many healthcare actions are dependent on the use of HIS [ 67 , 68 , 69 ]. This demand is substantiated by the offerings of HIS in tackling the transformation and digitalization confronting the healthcare system. However, despite the need for HIS and its potential within healthcare, several barriers limit its optimization. Some authors posited the role and involvement of healthcare professionals such as physicians to be important measure that is paramount to decreasing the technical and personal barriers sabotaging HIS deployment [ 20 ]. Nonetheless, the design of HIS is accentuated on augmenting health and is considered to be lagging behind in attaining quality healthcare [ 70 ].

Although there are equal blessings as well as challenges with HIS deployment, this study appraisal of HIS highlights its capabilities and attributes that enhance healthcare in many ways. From its conception, HIS has evolved significantly to enable the digitalization of many healthcare processes. Its deployment structurally has facilitated many healthcare applications at all levels within the health system where it has been implemented. Many benefits such as ease of access to medical records, cost reduction, data and information management, precision medicine, and autonomous and intelligent decisions have been enabled by HIS deployment. Primarily, HIS is the core enabler of the healthcare information system and knowledge management within the healthcare arena. Ascertaining the attributes and development of HIS is a paramount to driving its implementation and realizing its potential. Many deployments of HIS can be anchored on this study as a reference for planning and executing HIS implementation. The extant literature points out the need for the role of technology such HIS to be ascertained, as little is known in this regard, which as a result has adversely influenced healthcare coordination [ 19 ]. Additionally, among the barriers of HIS, the presence of inadequate planning that fails to cater to the needs of those adopting it hinders the optimization of these systems within the healthcare arena [ 71 ]. Cawthon, Mion [ 72 ] associated the lack of health literacy incorporation in deployed HIS to increased cost and poorer health outcomes. Hence, the insight from this study can be incorporated and associated with HIS initiatives to mitigate these issues. Thus, the findings of this study can be employed to strategize HIS deployment and plans as well as augment its potential to enhance healthcare. Furthermore, the competency of healthcare stakeholders such as patients can be enhanced with the findings of this study that accentuate the holistic representation of HIS in the dissimilation and assimilation of health data and information.

4. Conclusions

In the healthcare information and knowledge arena, assimilation and dissemination is a facet that influences healthcare delivery. The conception and evolution of HIS has positioned this system within the healthcare arena to arbitrate information interchange for its stakeholders. HIS deployment within healthcare has not only enabled information and knowledge management, but it has also enabled and driven many healthcare agendas and continues to maintain a solidified presence within the healthcare space. However, its deployment and enactment globally has been marred and plagued with several challenges that hinder its optimization and defeat its purpose. Phenomena such as the occurrences of pandemics such as COVID-19, which are uncertain, and the advancement of technology that cannot be controlled have caused disputed gradients regarding the positioning of HIS. These phenomena have not only influenced the adoption of HIS but have also limited its ability to be fully utilized. Although much research on HIS has been conducted, the presence of these phenomena and many other inherent challenges such as fragmentation and cost still maintain a constant, prominent presence, which has led to the need for this study.

Consequently, the starting point for this study was to provide insight and expertise regarding the discourse of HIS for healthcare applications. This paper presents current and pertinent insights regarding the deployment of the HIS that, when adopted, can positively aid its employment. This paper investigated the existing HIS literature to accomplish the objective set forth in the introduction. This study’s synthesis derived key insights relevant to the holistic view of HIS through a thorough systematic review of the various extant literature on HIS and healthcare. According to the study’s findings, HIS are critical and foundational in the drive of information and knowledge management for healthcare. The contribution of HIS to healthcare has been and continues to be groundbreaking since its conception and through its consequent evolution. Nevertheless, despite the presence of some limitations that are external and inherent, it is claimed to have transformed and changed healthcare from the start. Similarly, the evaluation of the current HIS is expected to impact its adoption and strengthen its implementation within the global healthcare space, which is greatly desired. These findings are of great importance to the healthcare stakeholders that directly and indirect interact with HIS. Additionally, scholars and healthcare researchers can benefit from this study by incorporating the findings in future works that plan HIS for healthcare.

Author Contributions

Conceptualization, A.E.; methodology, A.E.; software, A.E.; validation, A.E.; formal analysis, A.E.; investigation, A.E.; resources, A.E.; data curation, A.E.; writing—original draft preparation, A.E.; writing—review and editing, A.E.; visualization, A.E.; supervision, S.P.M. and I.E.A.; project administration, A.E., S.P.M. and I.E.A.; funding acquisition, A.E., S.P.M. and I.E.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare there are no conflict of interest.

Funding Statement

This research received no external funding.

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• Published: 02 September 2024

Common data quality elements for health information systems: a systematic review

• Hossein Ghalavand 1 ,
• Saied Shirshahi 2 ,
• Alireza Rahimi 2 ,
• Zarrin Zarrinabadi 1 &
• Fatemeh Amani 3  

BMC Medical Informatics and Decision Making volume  24 , Article number:  243 ( 2024 ) Cite this article

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Metrics details

Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems.

A literature review was conducted and search strategies run in Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references. We found 760 papers, excluded 314 duplicates, 339 on abstract review and 167 on full-text review; leaving 58 papers for critical appraisal.

Current review shown that 14 criteria are categorized as the main dimensions for data quality for health information system include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added. Accuracy, Completeness, and Timeliness, were the three most-used dimensions in literature.

Conclusions

At present, there is a lack of uniformity and potential applicability in the dimensions employed to evaluate the data quality of health information system. Typically, different approaches (qualitative, quantitative and mixed methods) were utilized to evaluate data quality for health information system in the publications that were reviewed. Consequently, due to the inconsistency in defining dimensions and assessing methods, it became imperative to categorize the dimensions of data quality into a limited set of primary dimensions.

Peer Review reports

Appropriate planning in the health sector relies on the existence of accurate data and the quality of the data must be continuously controlled. The World Health Organization has tried to ensure the quality of health data by providing a toolkit. This toolkit supports countries to assess and improve the quality of health data [ 1 , 2 ].

The existence of accurate, complete, and timely data plays an important role in health care management [ 3 , 4 , 5 ]. Data quality is often only considered a component of the effectiveness of health information systems, and hiding the value of data quality in other parts of the health field can lead to incorrect decision-making [ 6 , 7 , 8 , 9 ]. Previous studies have confirmed that data quality is a multidimensional concept. Data quality assessment requires familiarity with different subjective and objective criteria and both subjective perceptions of people and objective measurements of information must be addressed [ 10 , 11 ]. Qualitative evaluations of subjective data reflect the needs and experiences of stakeholders, and objective evaluations reflect the needs of managers and stakeholders [ 12 ].

Adverse effects on the quality of care, increasing costs, creating liability risks, and reducing the benefits of investing in health information systems can be identified as the negative effects of poor-quality data [ 13 , 14 , 15 , 16 ]. Defects in data quality can lead to incorrect diagnosis and intervention in health care [ 4 , 13 , 17 , 18 ]. The quality of healthcare depends on the existence of quality data, which ultimately leads to a significant impact on customer satisfaction [ 13 , 19 ].

Data quality in health information systems has a complex structure and consists of several dimensions and some critical factors performance such as environmental and organizational, technical and behavioral affected on data quality in health information system [ 20 , 21 , 22 ]. As we mentioned later, previous studies have sporadically reported some data quality elements in health information systems. There is no comprehensive agreement on its dimensions and there is no unique accepted definition of data quality among researchers for health information systems. However, there is still a lack of a review compiling and synthesizing all elements introduced in the literature. In this study, a more comprehensive understanding of the elements for quality of data in health information systems has been done using a systematic review method. The findings of this study can provide opportunities for health policy maker to become familiar with various data quality elements in health information. This systematic review specifically answered the following research questions:

1- What are the common data quality elements for health information systems?

2- What are the roles of common data quality elements to improve the performance of health information systems?

In this review, we used a systematic approach to retrieve the relevant research studies. Our reporting strategy follows the PRISMA guidelines [ 23 ].

Eligibility criteria

In this study the inclusion criteria were: (1) Data quality components were showcased within a health information system; (2) published from the year 2003 to 2024; (3) empirical studies that answered the research questions or tested the hypothesis and conducted on specific health system The exclusion criteria were: (1) Research that did not outline data quality dimensions in health management systems; (2) Content presented in a format other than a scientific article such as Conference papers, book sections, and …; (4) Methodologies deemed to be deficient in terms of quality; (5) Publication language not in English; and (7) The full text was unavailable.

Information sources

The literature search was conducted between September and October 2023, using the following five electronic scientific databases: Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references.

Search strategy

This study used a systematized review approach to identify common data quality elements for health information systems. The following keywords were used in the search strategy: Data quality, Health, clinic, Hospital, Medical, Information system. The keywords chosen were searched using various combinations and in the fields of title, abstract, subject, and keyword. We considered the search features in each database and used the Boolean operators (AND, OR) to combine and search selected keywords. An example of the search strategy was given in Table  1 .

Study selection

All the results were imported into EndNote reference management software. The duplicate and non-journal papers were removed. Next, the title and abstract of the remaining articles were screened to detect subject relevance with the research objectives. The selected articles were analyzed based on the inclusion and exclusion criteria. Finally, the reference lists of all identified articles were searched for additional studies. Two researchers undertook the screening of titles and abstracts obtained through the searches. A sample of just over 20% of articles was double screened in order to assess the level of agreement between the researchers. Disagreements were resolved through discussion or consultation with a third researcher.

Data collection process

Data extraction was completed independently by two assessors. The data were extracted from including four sections: bibliographic information, methodology, and the data quality elements investigated, and key findings. Each study was treated as a single unit of analysis and the relevant information in each study was extracted using a designated data extraction form.

Information was extracted from each included study (including first author, title, publication date, type of study, methodology, processes of knowledge management that were studied and selected results). We emphasize the results of selected papers that have reported elements for assessment data quality in health information systems.

Risk of bias in individual studies

In this study, we used the Joanna Briggs Institute (JBI) checklist [ 24 ] for quality assessment. The authors assessed the included studies with a further random examination by two independent reviewers. The results of the quality assessment were compared any disagreements between the reviewers were addressed through discussion or by involving a third reviewer.

Synthesis of results

In this review, by adopting similar identifies elements as broader themes, the results of the included studies were analyzed and categorized. Finally, the homogeneous data quality elements in health information systems were synthesized and described.

Risk of bias within studies

The JBI checklist was applied to all 58 studies; none were excluded based on quality assessment and all studies were rated as unclear or high risk of bias. In 16% of studies, we cannot find “statement locating the researcher culturally or theoretically” and in 37%, “influence of the researcher on the research” is not addressed.

The search for systematic reviews identified 734 references published between 2003 and 2024. Title and abstract review selected 167 references for full text review. In the analysis, it was found that 68 papers did not address research questions or test hypotheses, 32 papers lacked discussion on data quality dimensions in health management systems, and nine documents presented content in a format other than a scientific article.

Out of the 58 selected paper for final review, 42 were released between 2013 and 2024 [ 1 , 4 , 5 , 7 , 8 , 9 , 10 , 11 , 14 , 15 , 16 , 17 , 18 , 21 , 22 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Thirteen papers looked at information quality [ 7 , 11 , 14 , 27 , 28 , 29 , 31 , 37 , 52 , 54 , 55 , 56 ], five at content quality [ 7 , 15 , 21 , 43 , 50 ], and thirty-six at data quality [ 4 , 5 , 10 , 14 , 17 , 20 , 21 , 27 , 28 , 29 , 31 , 32 , 33 , 36 , 37 , 42 , 43 , 44 , 47 , 49 , 50 , 51 , 52 , 53 , 55 , 57 , 58 , 59 , 60 ]. None of the publications, however, made a distinction between “data” and “information,” or between “data quality” and “information quality.” As a result, “information quality” and “data quality” were used synonymously [ 21 ]. The search results and the study selection process are presented in Fig.  1 .

Flow diagram of study selection process

Evaluating the quality of the data was the primary goal of the reviewed studies [ 4 , 5 , 10 , 13 , 14 , 15 , 17 , 18 , 19 , 20 , 21 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 55 , 56 , 57 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ].Two paper focused on information quality in health systems [ 11 , 52 ]. Methods for evaluating the quality of data were presented in eight publications [ 10 , 20 , 21 , 35 , 38 , 41 , 51 , 52 ], 19 publications tended to conduct on the health information [ 5 , 8 , 10 , 11 , 16 , 17 , 20 , 21 , 22 , 26 , 31 , 37 , 42 , 47 , 49 , 50 , 51 , 55 , 57 , 60 , 66 ] and eight paper focus on health or medical records as an information system in health context [ 13 , 19 , 25 , 38 , 44 , 45 , 64 , 67 ].

To describe data quality, the studies employed a total of 57 dimensions. The first data quality attribute for health information system that was most often used was accuracy [ 4 , 5 , 15 , 17 , 19 , 28 , 29 , 32 , 33 , 34 , 37 , 41 , 43 , 45 , 46 , 49 , 51 , 53 , 59 ], second is completeness [ 4 , 5 , 20 , 28 , 29 , 30 , 41 , 44 , 45 , 46 , 48 , 49 , 51 , 52 , 53 , 56 ], and third most-frequently criterion is timeliness [ 5 , 28 , 41 , 44 , 45 , 51 ]. Table  2 displays the common dimensions of data quality in health information systems that derived from existing literature.

Data accuracy measures the extent to which information accurately represents the objects or events. The accuracy of the information that is gathered, utilized, and stored is assessed through data accuracy. It is imperative for records to serve as a dependable source of information and to facilitate the generation of valuable insights through analysis. Maintaining high data accuracy guarantees that records and datasets meet the standards for reliability and trustworthiness, allowing for their use in decision-making and various applications [ 4 , 5 , 17 , 28 , 29 , 32 , 34 ]. Correctness, precision, free of error, validity, believability and integrity are common terms that use for describe data accuracy [ 21 ]. Data believability relates to whether the data is regarded as being true, real, and credible. Data believability is based on user’s perceptions [ 1 , 36 , 40 ].

Data consistency is the state in which all copies or instances of data are identical across various information systems. This uniformity is crucial in maintaining the accuracy, currency, and coherence of data across different platforms and applications. It is essential for instilling trust in users accessing the data. Implementing data validation rules, employing data standardization techniques, and utilizing data synchronization processes are some strategies to uphold data consistency. By ensuring data consistency, organizations can provide users with reliable information for making informed decisions, streamline operations, minimize errors, and enhance efficiency [ 9 , 45 , 48 , 51 , 52 , 65 ].

Data security is the practice of protecting information from corruption, theft, or unauthorized access throughout its life cycle. This involves safeguarding hardware, software, storage devices, and user devices, as well as implementing access controls, administrative controls, and organizational policies. By utilizing tools and technologies that enhance visibility of data usage, such as data masking, encryption, and redaction, organizations can ensure the security of their data. Moreover, data security assists organizations in streamlining auditing procedures and complying with data protection regulations, ultimately reducing the risk of cyber-attacks, human error, and insider threats [ 5 , 48 , 56 ]. Secure access, safe, confidentiality and privacy are common terms that use for describe data security [ 21 ].

Data timeliness denotes the currency and availability of data at the required time for its intended use. This is critical for enabling health organizations to make swift and accurate decisions based on the most up-to-date information. The timeliness of data has an impact on data quality as it determines the reliability and usefulness of information systems. Moreover, timely data can lead to cost savings as organizations can utilize real-time data to effectively manage inventories, optimize delivery routes, and coordinate with suppliers, thus reducing the risk of stock outs, minimizing delivery delays, and ensuring smooth operations [ 5 , 25 , 28 , 41 , 44 , 45 , 51 ].

Completeness of data refers to the extent to which information includes all necessary elements and observations for a specific purpose. This factor enhances the integrity and reliability of analyses, preventing gaps in understanding and supporting more robust decision-making processes. In a complete dataset, all variables relevant to the presentation of information should be present and fully populated with valid data values. Any missing, incorrect, or incomplete entries in the dataset can compromise the quality of analyses, interpretations, and decisions based on that data [ 4 , 5 , 9 , 28 , 29 , 30 , 41 , 44 , 45 , 52 ]. Coverage, comprehensiveness, appropriate amount, adequate, appropriate amount of data and integrity are common terms that use for describe data completeness [ 21 ]. The amount of data indicates the extent of data sets obtained for analysis and processing. In present-day information systems, these sets of data are frequently observed to be escalating in size, reaching capacities such as terabytes and petabytes [ 4 , 29 , 50 , 57 ].

Data reliability pertains to the uniformity of data across various records, programs, or platforms, as well as the credibility of the data source. Reliable data remains consistently accurate, while unreliable data may not always be valid, making it challenging to ascertain its accuracy. Consequently, organizations cannot depend on unreliable data for decision-making. Data reliability, also referred to as data observability, represents the trustworthiness of data and the insights derived from it for enabling sound decision-making. Reliability is characterized by two other fundamental elements of data quality include accuracy and consistency [ 9 , 49 , 53 , 57 , 59 , 65 ].

Data accessibility refers to the ease with which users can locate, retrieve, comprehend, and utilize data within an organization’s information systems. This is crucial in the modern digital landscape, where data is valuable for decision-making, strategic planning, and operational efficiency. Ensuring data accessibility involves creating an environment where data is available, understandable, and usable by individuals with varying levels of technical expertise. This approach is closely tied to data democratization, which aims to break down silos and make data available across different levels and departments of an organization. A well-implemented data accessibility strategy ensures that data is not locked away in isolated information systems but is integrated and accessible, contributing to a more informed and agile organizational structure. The ultimate goal is to empower users to leverage data in their daily tasks and decision-making processes, thus fostering a data-driven culture [ 4 , 26 , 29 , 33 , 50 , 57 ].

Data Objectivity refers to the extent to which data is free from personal biases, emotions, and subjective interpretations. Objective data is verifiable, reliable, and accurate, meaning that it can be verified independently by multiple parties. In other words, objective data is based on facts rather than opinions or judgments. In the context of information systems, data objectivity is crucial because it enables organizations to make informed decisions based on accurate and reliable information. Objective data helps to reduce errors, inconsistencies, and uncertainties, ensuring that business processes are efficient, effective, and compliant with regulatory requirements. Data objectivity in information systems is often hindered by biases in data collection, data quality issues, information overload, and lack of standardization. Biases may arise from human error, sampling errors, or deliberate data manipulation during the collection process. Inaccuracies, inconsistencies, and incompleteness resulting from poor data quality can compromise the objectivity of the information. The overwhelming amount of data available can make it challenging to differentiate between objective and subjective information. Inconsistencies in data representation and interpretation may occur due to the use of different systems or formats [ 36 , 41 , 44 , 45 , 46 ].

Data relevancy is an aspect of data quality that determines whether the data used or generated are relevant to add to the new target system and how usable it is for users [ 9 , 29 , 45 , 48 , 51 ]. Ease of operation, Usability, applicable, utility, Usefulness, Perceived usefulness and importance are common terms that use for describe data relevancy [ 21 ]. The concept of data usability revolves around a user’s ability to obtain meaningful information from various systems. When data is stored in text files that demand prolonged and intricate processing before it can be analyzed, its usability is limited. Conversely, data that is conveniently displayed on a performance dashboard for immediate interpretation is classified as highly usable [ 4 , 25 , 29 , 45 , 48 , 50 ]. The concept of data usefulness denotes the level at which data, post-analysis, aligns with the intended purpose within a given context for its user or consumer. In most cases, data usefulness is attained when all criteria related to data quality, such as dependability, thoroughness, uniformity, and others, are fulfilled [ 43 , 50 , 52 ].

Data Understandability refer to the level at which data exhibits qualities that facilitate understanding and analysis by users, and are presented in relevant languages, symbols, and measurements within a defined context of utilization [ 22 , 34 , 37 , 46 ]. Interpretability, ease of understanding, granularity and transparency are common terms that use for describe data understandability [ 21 ].

Data navigation refers to the process of searching, locating, and extracting relevant data from a vast pool of information to support decision-making, problem-solving, or analysis. It involves the utilization of different techniques and tools to navigate through extensive data, identify patterns, trends, and correlations, and present the information in a meaningful and actionable way. The success of data navigation is contingent upon several dimensions, including technical, domain knowledge, systems, methodological, and human dimensions. The technical dimension involves mastering programming languages like SQL and Python, utilizing data visualization software such as Tableau and Power BI, and implementing data mining techniques like machine learning algorithms. Domain knowledge dimension stresses the importance of expertise in specific fields. Information system dimension highlights the role of databases, data warehouses, cloud storage platforms, and other technologies in facilitating data navigation by storing, managing, and providing access to data. Methodological dimension focuses on statistical analysis, data mining techniques, and data visualization methods as key approaches to navigating data. Lastly, human dimension recognizes the significance of communication skills, collaboration, and critical thinking in the process of data navigation [ 4 , 50 , 65 , 68 ].

Data reputation is the evaluation of the trustworthiness, reliability, and credibility of data in an information system. It signifies the extent to which stakeholders, such as users, decision-makers, and other systems, perceive the data as accurate, reliable, and complete. Within an information system, data reputation plays a crucial role in decision-making, trust, system performance, and data sharing [ 42 , 60 , 61 ].

The concept of data efficiency revolves around an organization’s effectiveness in maximizing the value obtained from its data, while simultaneously minimizing the resources essential for processing, storing, and up keeping that data. Put simply, data efficiency focuses on streamlining the collection, storage, analysis, and utilization of data to meet objectives. When considering an information system, data efficiency can be examined from various angles, such as efficiency in data acquisition, storage, processing, analysis, visualization, security, retention, and archiving [ 7 , 28 , 29 , 48 ].

Data value-added pertains to the process of refining raw data into more useful, meaningful, and valuable information that can support decision-making, drive business outcomes, and create a competitive advantage. This process involves extracting insights, patterns, or trends from large datasets and presenting them in a manner that is easy to understand and act upon. By prioritizing these dimensions of data value-added within an information system, organizations can ensure that their data is transformed into valuable insights that support informed decision-making and drive business outcomes [ 5 , 22 , 25 , 45 ].

In a few papers, the concept of “fitness for use” was applied to data quality [ 6 , 55 , 69 ]. Two viewpoints can be used to characterize data quality: (1) the inherent quality of the data elements and set, and (2) how the set satisfies the needs of the user. The definition provided by the International Standards Organization best captures the accepted meaning of data quality, which is “the totality of features and characteristics of an entity that bears on its ability to satisfy stated and implied needs” [ 4 , 15 , 28 , 33 , 53 ].

Current review study identified 14 common dimensions for data quality in health information system. In related research data quality dimensions classified on four dimensions include: intrinsic (accuracy, objectivity, reputation), contextual timeliness, completeness, and relevancy), representational (representational format, understandability, consistency), and accessibility (accessibility, security) categories [ 53 , 60 , 69 , 70 , 71 ]. There exists a certain level of intersection between the aspects of data quality recognized in this review and those research in prior classifications of data quality.

Previous literature has often discussed intrinsic data quality in terms of the absence of defects, as indicated by various dimensions such as accuracy, perfection, freshness, and uniformity [ 72 ]. and “completeness, unambiguity, meaningless and correctness” [ 54 , 73 , 74 ]. The Canadian Institute for Health Information put forth a set of 69 quality criteria, organized into 24 quality characteristics, and further classified into 6 quality dimensions: accuracy, timeliness, comparability, usability, relevance, and privacy & security [ 58 , 71 ]. Research on data quality has primarily concentrated on recognizing general quality traits like accuracy, currency, completeness, correctness, consistency, and timeliness as fundamental aspects of data quality applicable across different fields. Nevertheless, existing reviews reveal a lack of consensus regarding the conceptual framework and definition of data quality [ 70 , 73 ]. However, our pervious review shows there is a lack of consensus conceptual framework and definition for data quality [ 1 , 71 ].

In this study, the three most-frequently used dimensions of data quality were accuracy, completeness and timeliness, respectively. This arrangement is somewhat different from previous literature in which the three most-frequently used dimensions were arranged in the order of completeness, accuracy, and timeliness, respectively [ 43 , 51 , 53 ]. Furthermore, the absence of a precise definition of the data quality dimensions led to complexities in evaluating them. The definitions of dimensions and their associated metrics were occasionally based on intuition, past experiences, or the underlying goals. These results indicate that data quality is a multi-faceted phenomenon. Likewise, other scholars argue that data quality is a multi-dimensional notion [ 5 , 28 , 38 , 52 , 61 ].

The Health Information Systems heavily rely on data, as they perform essential functions like generation, compilation, analysis, synthesis, communication, and data application to support decision-making. The literature frequently evaluates the dimensions of data quality, but there is currently a lack of consistency and potential generalizability in using these dimensions and methods to assess data quality in Health Information Systems. In this review of the literature, the data quality for health information system were examined and identified 14 common dimension include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added.

The quality of data in health information systems is indispensable for healthcare institutions to make well-informed decisions and provide patients with optimal care. Accurate and timely data assists healthcare organizations and professionals in identifying patterns, predicting outcomes, and enhancing patient results. Conversely, inadequate data quality in healthcare or other data-related issues can lead to inaccurate diagnoses, inappropriate treatments, and harm to patients. To ensure data quality in healthcare, organizations must prioritize investments in data governance, data management, and data analysis tools, while also maintaining a continuous process of monitoring and improving data quality in health information systems.

It is essential to have high-quality data in order to ensure the safe and dependable delivery of healthcare services. Health facility data plays a crucial role in monitoring performance. While various organizations may prioritize different aspects of data quality, it is important to acknowledge that no health data, regardless of its source, can be deemed flawless. All data are susceptible to various limitations related to data quality, including missing values, bias, measurement error, and human errors in data entry and computation. These limitations are associated with technical, behavioral, and organizational factors [ 75 ].

This study has limitations. Firstly, the number of articles with complete data was relatively small. Secondly, assessing the quality of some studies were difficult because the quality assessment criteria were not clearly identified. We have proposed four fundamental implications to inspire future research. Firstly, it is crucial for researchers to give equal attention to all dimensions of data quality, as these dimensions can have both direct and indirect effects on data quality outcomes. Secondly, researchers should aim to evaluate the existing data quality models and frameworks through a combination of mixed methods and case study designs. Thirdly, it is important to identify the underlying causes of data quality issues in health information systems. Lastly, efforts should be made to develop interventions that can effectively address and prevent data quality issues from occurring.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

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This study was supported by Abadan University of medical sciences, Research code: 1557.

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Hossein Ghalavand and Saied Shirshahi Conceived the study, prepared the analysis plan, conducted the analysis, and prepared the draft manuscript. Alireza Rahimi, Zarrin Zarrinabadi and Fatemeh Amani Conceived the study, prepared the analysis plan, performed the literature search, screening for study inclusion/exclusion, and risk of bias assessment, conducted the analysis, and prepared the draft manuscript. All authors contributed to the final version of the manuscript.

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Ghalavand, H., Shirshahi, S., Rahimi, A. et al. Common data quality elements for health information systems: a systematic review. BMC Med Inform Decis Mak 24 , 243 (2024). https://doi.org/10.1186/s12911-024-02644-7

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A comprehensive set of evaluation criteria for District Health Information Systems (DHISs) in South Africa (SA) have been developed. The criteria are organised in the following eight categories: philosophy and objectives, policy and procedures, functionality, facilities and equipment, DHIS management and staffing, user/patient interaction, staff development and education, and evaluation and quality improvement. A handbook of evaluation criteria has been

Bulletin of the World Health Organization, 2005

Public health decision-making is critically dependent on the timely availability of sound data. The role of health information systems is to generate, analyse and disseminate such data. In practice, health information systems rarely function systematically. The products of historical, social and economic forces, they are complex, fragmented and unresponsive to needs. International donors in health are largely responsible for the problem, having prioritized urgent needs for data over longer-term country capacity-building. The result is painfully apparent in the inability of most countries to generate the data needed to monitor progress towards the Millennium Development Goals. Solutions to the problem must be comprehensive; money alone is likely to be insufficient unless accompanied by sustained support to country systems development coupled with greater donor accountability and allocation of responsibilities. The Health Metrics Network, a global collaboration in the making, is inten...

BMC medical informatics and decision making, 2005

American public policy makers recently established the goal of providing the majority of Americans with electronic health records by 2014. This will require a National Health Information Infrastructure (NHII) that is far more complete than the one that is currently in its formative stage of development. We describe a conceptual framework to help measure progress toward that goal. The NHII comprises a set of clusters, such as Regional Health Information Organizations (RHIOs), which, in turn, are composed of smaller clusters and nodes such as private physician practices, individual hospitals, and large academic medical centers. We assess progress in terms of the availability and use of information and communications technology and the resulting effectiveness of these implementations. These three attributes can be studied in a phased approach because the system must be available before it can be used, and it must be used to have an effect. As the NHII expands, it can become a tool for ...

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Online Journal of Public Health Informatics

Yearbook of Medical Informatics, 2010

Journal of Biomedical Informatics, 2007

International Journal of Medical Informatics, 2009

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Health information system and health care applications performance in the healthcare arena: a bibliometric analysis.

1. Introduction

2. materials and methods, 2.1. search strategy and inclusion criteria, 2.2. study selection, 3. results and discussion, 4. conclusions, author contributions, institutional review board statement, informed consent statement, acknowledgments, conflicts of interest.

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Epizitone, A.; Moyane, S.P.; Agbehadji, I.E. Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis. Healthcare 2022 , 10 , 2273. https://doi.org/10.3390/healthcare10112273

Epizitone A, Moyane SP, Agbehadji IE. Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis. Healthcare . 2022; 10(11):2273. https://doi.org/10.3390/healthcare10112273

Epizitone, Ayogeboh, Smangele Pretty Moyane, and Israel Edem Agbehadji. 2022. "Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis" Healthcare 10, no. 11: 2273. https://doi.org/10.3390/healthcare10112273

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