Next Article in Journal
Investigating Offensive Language Detection in a Low-Resource Setting with a Robustness Perspective
Previous Article in Journal
Aspect-Based Sentiment Analysis of Patient Feedback Using Large Language Models
Previous Article in Special Issue
Trends and Challenges towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
BDCC
Volume 8
Issue 12
10.3390/bdcc8120168
BDCC-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review

by
Varadraj P. Gurupur
1,*,
Giang Vu
1,
Veena Mayya
1,2 and
Christian King
1
1
Center for Decision Support Systems and Informatics, School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA
2
Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2024, 8(12), 168; https://doi.org/10.3390/bdcc8120168
Submission received: 19 September 2024 / Revised: 28 October 2024 / Accepted: 13 November 2024 / Published: 25 November 2024
(This article belongs to the Special Issue Applied Data Science for Social Good)
Browse Figures
Review Reports Versions Notes

Abstract

:
Over the past two decades, there has been an enormous growth in the utilization of electronic health records (EHRs). However, the adoption and use of EHRs vary widely across countries, healthcare systems, and individual facilities. This variance poses several challenges for seamless communication between systems, leading to unintended consequences. In this article, we outline the primary factors and issues arising from the absence of standards in EHRs and dental record implementation, underscoring the need for global standards in this area. We delve into various scenarios and concepts that emphasize the necessity of global standards for healthcare systems. Additionally, we explore the adverse outcomes stemming from the absence of standards, as well as the missed opportunities within the healthcare ecosystem. Our discussions provide key insights on the impacts of the lack of standardization.

1. Overview of the Problem

The use of longitudinal health records, albeit in the form of paper documents, began in the early 1900s, establishing standards for documenting information. The emergence of computer technology in the 1960s and 1970s facilitated the development of electronic health records (EHRs) [1]. The Institute of Electrical and Electronics Engineers (IEEE) [2] defines an EHR as “Data pertaining to an individual’s health record available in a suitable electronic form that makes it machine-interpretable”. Figure 1 illustrates a schematic representation of a standard EHR. Although an EHR is created by an EHR system, it is perceived differently from the system that created it. This type of approach has been rarely conceived in the past due to the differences in thinking between engineers, technicians, and clinicians. The structuring of patient information depends on the governing processes used within a healthcare system. Additionally, the protocols and standards are implemented individually by healthcare environments, but there are no clear standards for communications between EHR systems from a global perspective.
The exclusion of oral health information from most EHRs represents a critical gap in obtaining comprehensive patient medical history and achieving comprehensive patient care. The absence of oral health information could result in missed opportunities for early detection and timely intervention, especially in cases where oral health issues impact overall health [3]. Incorporating dental records into EHRs would enable a more comprehensive approach to patient care, leading to accurate diagnoses, improved treatment plans, and better patient outcomes. Additionally, this integration would encourage greater collaboration among healthcare providers across different specialties, facilitating a more effective management of patient health. The lack of standardized terminologies and coding systems in the dental field poses a significant barrier to this integration, resulting in fragmented and inconsistent records that are challenging to incorporate into the broader EHR framework. Therefore, it is imperative to adopt standardized coding and establish a dedicated dental problem list in EHRs to ensure a more integrated and effective healthcare system, ultimately improving patient health outcomes and promoting comprehensive, collaborative care.
Consequently, the research objectives delineated in this review article are as follows:
  • Present evidence in the form of data and existing research to ascertain the need for appropriate standards in the development and implementation of electronic health records and electronic dental records.
  • Identify key advances in information technology that can facilitate the aforementioned objective on standards.
It is to be noted that here the authors perceive an electronic dental record as a type of electronic health record.

2. Current State of EHR-Related Standards

The quality of EHR has five dimensions: completeness, correctness, concordance, currency, and plausibility [4]. Lewis et al. [5] expanded this framework by incorporating the additional dimensions of conformance and bias. Figure 2 illustrates a schematic representation of the common dimensions of data quality in EHRs and examples of their violations. Data completeness, a critical aspect of data quality, defines the degree to which all necessary healthcare data are present in EHRs and accessible for a particular clinical task. Figure 3 depicts a conceptual model for achieving data completeness in the healthcare system [6].
Young and Smith [7] discuss healthcare standards in relation to quality, safety, and person-centered care. They emphasize the direct relationship between healthcare quality and the services offered. The authors advocate focus on a human-centered approach in healthcare, emphasizing the acceptance of standards and evaluation metrics. Many healthcare standards and protocols revolve around data collection, reporting, and associated evaluations.
The Agency for Health Research and Quality (AHRQ) [8] emphasizes the importance of effective communication in improving patient safety. Particularly in emergency rooms, effective communication can greatly enhance patient safety. Health information technology, such as EHRs, can play a crucial role in improving this communication. This underscores the necessity of establishing standards for data storage and communication protocols associated with EHRs. A recent study found no significant differences in workload or performance between groups using EHR and those using paper-based systems [9]. This suggests that EHRs do not impose additional burdens and effectively replicate real-world conditions.
The HITECH Act of 2009 [10] imposes the use of EHRs as a replacement for paper-based health records in the United States. Specifically, the regulation compels the meaningful use of EHRs within the healthcare ecosystems. This statute also reconfirms the imposition of the Health Insurance Portability and Accountability Act (HIPAA) introduced in 1996 in a more sophisticated and rigid manner. This is mainly because with the introduction of electronic records, access, update, creation, deletion, and modifications can be tracked and logged for any necessary investigations, thereby enabling privacy, confidentiality, and other attributes associated with their security. The introduction of such statutes bolstered the creation and advancement of the economics associated with healthcare security. While the strength and effectiveness of security systems associated with healthcare are debatable, the need for their use has only increased in recent years.
Health data breaches pose significant challenges to healthcare stakeholders. Pool et al. [11] systematically review the existing literature on the complex nature of health data breaches, examining their contributing factors and consequences. There are three critical security aspects in the digital healthcare ecosystem: secure access control, data sharing, and data storage [12]. Each of these aspects has its own challenges, emphasizing the need for improvements to ensure the security and privacy of patient information within the healthcare system. To overcome these privacy and security challenges, healthcare systems must adhere to several security and privacy standards to ensure the confidentiality, integrity, and security of patient’s health information. Some of the key standards for ensuring privacy and security in healthcare ecosystems include HIPAA in the US [13,14,15], which sets standards for protecting sensitive patient information, and the General Data Protection Regulation (GDPR) in the European Union [16], which governs data privacy and security for personal data. The HITECH Act [10] promotes the secure use of EHRs alongside HIPAA regulations. The Personal Information Protection and Electronic Documents Act (PIPEDA) [17] in Canada outlines standards for handling personal information, including health data. The international ISO/IEC 27001 [18] standard also offers a framework for managing information security across various sectors, including healthcare. Together, these standards form a comprehensive framework for protecting patient data and ensuring the secure management of health information worldwide.
Furthermore, the Fast Healthcare Interoperability Resource (FHIR), introduced by Health Level 7 (HL7), intends to establish interoperability between EHR systems. FHIR is a federal mandate within the United States, and all EHR vendors are required to adhere to this standard [19]. However, FHIR is broad in nature and needs more specific standards that can serve as a supplement to it [20]. This clearly establishes the need for variations in specifics of the implementation of certain standards and the need to introduce newer details based on time, place, and circumstances.
The use of electronic health records has the potential to improve clinical documentation [21]. Providing training and education to clinical staff in the effective use of EHRs has critical implications on clinical documentation, and likely improves healthcare outcomes and patient satisfaction. There are three different types of communications within the healthcare ecosystems: human-to-human, human-to-machine, and machine-to-machine [22]. The implementation of appropriate and effective standards with EHRs improves machine-to-machine communication and human-to-machine communication, thereby mitigating the probability of errors. The lack of communication standards can result in incomplete data and other adverse effects.
OpenEHR has been pivotal in enabling clinical documentation improvement (CDI). According to Garde et al. [23], OpenEHR archetypes foster semantic interoperability with healthcare systems. Ubiquitious computing enables the integration of several computing devices to achieve the objective/s. Here, the authors indicate the necessity of structured data entry. Moreover, as Moraes et al. [24] indicate, OpenEHR facilitates the use of agent-based systems to enable pervasive computing. Critically, it is important to enable communication between heterogeneous electronic health record systems. These systems may differ internally in their functions and details of data storage. Moraes et al. highlight the reuse of legacy healthcare systems by suitably connecting them with newer systems and facilitating communications between them.This process also makes use of ontologies in a distributed environment. Therefore, we can conclude that OpenEHR is an important piece in terms of current standards associated with electronic health record systems. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers, Names, and Codes (LOINC), and RxNorm [25] are important terminologies used for communication between different systems within the healthcare ecosystem. They are mainly used in communicating diagnoses and prescriptions between systems.
In their work, Glicklich and Leavy [26] emphasize the need for data standards in clinical data to ensure interoperability. According to the US Office of the National Coordinator, interoperability is defined as “the ability of a system to exchange electronic health information with and use electronic health information from other systems without special effort on the part of the user”. The authors outline the categories of standards required for clinical data, which include semantics, syntax, transport, security, and services. The chapter underscores the complexity of achieving interoperability and its associated hierarchy. Key insights from this work include the need for developers of standards to consider both the syntax and semantics of healthcare data, as well as the close link between data security, communication, and healthcare data standards. More recently, Chinnasamy et al. [27] proposed a secure access control system using smart contracts to enhance EHR sharing between patients and healthcare providers. Their cloud-based, blockchain framework enables the accurate transmission of EHRs via mobile cloud settings. It effectively detects and blocks unauthorized access, ensuring patient confidentiality and system security.
The article by Richesson [28] makes a thought-provoking connection between data standards and learning systems. It references a study conducted by the New York University Langone Health System, which identifies activities with varying degrees of impact. It emphasizes the importance of embracing universally accepted data standards as a means of ensuring impactful activities. The article underlines the limited use of standard specifications for representing data in EHRs. It stresses the significance of adhering to proper standards when coding or recording data within the EHR system. The author argues that using appropriate data standards can enhance clinicians’ effectiveness, enabling them to innovate and improvise. The article posits a correlation between the adoption of standards and the advancement of healthcare systems’ missions.
As outlined by Schulz et al. [29], the following are the key organizations involved in developing standards for EHRs: Federative Committee on Anatomical Terminology (FCAT), Health Level 7 (HL7), Integrating the Healthcare Enterprise (IHE), International Organization for Standardization (ISO), National Electrical Manufacturers Association (NEMA), openEHR Foundation, Regenstrief Institute, PCHAlliance (Personal Connected Health Alliance), SNOMED International (formerly known as the International Health Terminology Standards Development Organisation), World Health Organization (WHO), and World Organization of Family Doctors (WONCA). Depending on the scenario in discussion, some of these organizations may be more important and impactful than others. Schulz et al. [29] describe the eStandards initiative funded by the European Commission, which incorporates an evidence-based roadmap for implementing eHealth. The European Electronic Health Record Exchange (EHRxF) recommendation outlines principles and guidelines for creating and adopting specifications that help healthcare providers securely access and share EHRs [30]. Bonacina et al. [31] highlight areas that need to be addressed for the European EHRxF to enhance shared decision-making for patients.
In the early 2000s, there was no widely accepted standardized terminology for dental diagnoses and treatment outcomes, creating a significant challenge in the field of dentistry [32]. At that time, the commonly used coding system, the International Classification of Diseases (ICD-9) [33], offered only limited coding options for dentistry, making it insufficient for accurately diagnosing dental conditions. In response, several studies delved into developing alternative coding systems tailored specifically for dental practice. Notable proposals included SNODENT [34,35], EZCode [36], and the Oral Health and Disease Ontology [37]. However, navigating the literature on these dental coding systems can be complex, as many proposed classifications have undergone multiple redefinitions or renaming processes over time. In some instances, they have even been merged with other coding systems to better align with the specific needs of dentistry [38]. Table 1 provides an overview of key healthcare data standards organizations and their respective purposes.
The practices utilized in radiology, along with the implementation of Digital Imaging and Communication in Medicine (DICOM), also play a crucial role in image-based communication. DICOM was created by a joint committee created by the American College of Radiology and the National Electrical Manufacturers Association. DICOM enables easier interchange of images between systems. It is to be noted that medical images are a critical part of electronic health records and dental records.
While these standards attempt interoperability between systems and are critical in communication between systems, the criteria and metrics required to quantify the quality use of these standards are necessary. In a particular instance, there was a lack of integration between the EHR system used by a hospital and the system utilized in the individual clinics of a private healthcare provider. As a result, patients were unable to access their records and medical history at the respective facilities, leading to confusion and inconvenience. This situation highlights the importance of seamless communication and integration between systems within a healthcare enterprise.

3. Standardization Leading to Learning Systems

The Agency for Healthcare Research and Quality (AHRQ) [39] describes a learning health system as one that systematically integrates internal data and experiences with external evidence, applying this knowledge in practice. Figure 4 illustrates the features of the learning health cycle within a successful learning health system. This approach leads to higher quality, safer, and more efficient patient care, while also enhancing the work environment within healthcare delivery organizations. It is essential to establish common standards for communication within the healthcare system to facilitate the development of a learning system that relies on two crucial pieces of information: the expected outcome and the perceived outcome. When a significant disparity exists between these two outcomes, it is necessary to investigate the flow of data and associated processes. Often, this gap is caused by either the unavailability of necessary data or issues with the implementation of required processes.
Easterling et al. [40] propose a transformation of the traditional healthcare delivery system into a learning health system. Their comprehensive review outlines key components of a learning healthcare system, including organizational learning, evidence translation, knowledge building, clinical data analysis, and stakeholder engagement. They also highlight the essential conditions necessary for a healthcare system to become a learning system, such as a skilled workforce, efficient data systems, investment in learning health system initiatives, and a supportive organizational culture. This research emphasizes the need to integrate innovation, quality, and safety to continuously enhance healthcare outcomes. Effective communication and standardized processes could facilitate the implementation of these concepts.
Menear et al. [41] discuss the transformation of a traditional healthcare system into a learning system as a means to create a value-based healthcare system. The emphasis is on developing a value-based care system rather than just implementing a learning system. This underscores the significance of enhancing the quality of care. While various approaches like evidence-based medicine, leveraging advanced information systems, and process improvement are crucial for improving care quality, effective communication within the healthcare delivery system becomes increasingly essential for providers to accomplish this objective.
The establishment of dental standardization is pivotal to the development and evolution of learning healthcare systems. Currently, these standards are primarily utilized by academic practitioners [38]. It has been emphasized that standardized EHRs can analyze health trends, track dental service utilization, evaluate care delivery, and identify disparities between treatment needs and the services offered. Therefore, it is imperative for private practitioners also to embrace these standards. Improving the efficiency of standardized data capture is crucial, as it not only enhances data quality but also contributes to better usability and seamless integration with clinical workflows.

4. The Adverse Implications of Disorganization Within the Digital Health Ecosystem

A digital health ecosystem can be defined as “a network of digital health communities consisting of interconnected [42], interrelated and interdependent digital health species, including healthcare stakeholders, healthcare institutions and digital healthcare devices situated in a digital health environment, who adopt the best-demonstrated practices that have been proven to be successful, and implementation of those practices through the use of information and communication technologies to monitor and improve the wellbeing and health of patients, to empower patients in the management of their health and that of their families”. EHRs are regarded as a fundamental building block of the digital health ecosystem [43], and are shared across various organizations, so they must comply with recognized interoperability standards [43]. The existing literature [44,45,46] provides a clear explanation of incomplete EHRs and their potential causes, along with a focus on methods to identify the factors contributing to their incompleteness [47]. The absence of effective communication and standardized protocols often leads to disorganization. The inadequate design and misuse of EHR systems can lead to errors that compromise data integrity, potentially endangering patient safety and reducing the quality of care [48]. Moeenian et al. [49] argue that adaptive and innovative healthcare organizations can significantly reduce the possibility of disorganization. This recent observation aligns with our discussion on learning health systems. It suggests that improvements and evolutionary changes may occur in specific details related to standards and that, sometimes, it may be beneficial for standards to have a more general nature with specific details determined by time, place, and circumstances. For instance, the COVID-19 pandemic, being an unforeseen situation, necessitated adaptability not only for healthcare organizations but also for non-healthcare organizations.
Mamlin and Tierney [50] discuss the role of technology and communication in streamlining the healthcare system. The focus is on integrating EHRs with external devices and systems. The authors advocate for an integrated system that combines regular ambulatory practice with telemedicine and social media. They also acknowledge the use of social media for patient communication, raising important considerations related to trustworthiness and confidentiality breaches. Therefore, the establishment and enforcement of security standards are deemed essential.
Tsou et al. [51] investigated the adverse effects of the copy-and-paste feature in EHRs. While this functionality improves usability by allowing healthcare providers to input text like test results and exam data, maintain consistent medication lists, and enhance documentation efficiency, it also has drawbacks. Specifically, it can lead to longer, poorly organized, and less accurate notes due to the inclusion of redundant, outdated, or inconsistent information. The improper use of the copy-and-paste feature, particularly without sufficient education and controls, can lead to inaccurate documentation, posing risks to patient safety and potentially resulting in medical errors or fraudulent claims [52].
In their work, Lewis et al. [5] emphasize the necessity of establishing scalable and adaptable guidelines to enhance the efficiency, transparency, comparability, and interoperability of EHR data quality assessments. Although automation may aid in standardizing this process, a universally accepted approach is currently lacking. This point is also underscored in the review by Ozonze et al. [53], where the authors highlight the lack of clarity in data quality assessments due to the heterogeneity of EHR data and the difficulties in devising practical measurements.
Existing publicly available EHR datasets predominantly emphasize specific aspects of diagnosis or particular use cases rather than offering a comprehensive dataset that could facilitate complete clinical decision-making. For instance, datasets like Medical Information Mart for Intensive Care (MIMIC-III) [54] and the eICU research database [55] are tailored towards intensive care unit (ICU) diagnostics, focusing on patient vitals and ICU-related outcomes. Imaging-based diagnostics datasets, such as those involving chest X-rays [56,57], computed tomography (CT) [58,59,60], or magnetic resonance imaging (MRI) scans [61,62,63], are common but typically limited to imaging data and associated annotations. Similarly, datasets like those from the UK Biobank [64] prioritize statistical inference based on demographics for disease prediction and the role of genomics in understanding disease risks and outcomes. Precision medicine datasets [65,66,67,68] often concentrate on linking clinical data to specific treatments, particularly in oncology, where treatment plans are based on genetic markers. Despite these resources, there is a gap in publicly available datasets that integrate all these facets into a single, holistic EHR dataset that could support full-spectrum clinical decision-making. Such a dataset would need to combine detailed patient histories, diagnostics, treatments, and outcomes in a way that empowers clinicians to rely solely on the EHR dataset for decision-making and base their decisions on concrete evidence.
Furthermore, missing data within the available EHRs significantly contribute to data incompleteness, which negatively impacts the healthcare ecosystem. Figure 5 presents an example from the eICU research database [55], highlighting the extent of missing values across various features. In this specific instance, 28 out of the 43 features analyzed have more than 50% missing values. The absence of critical information can create gaps in patient records, hindering the ability to make comprehensive and informed clinical decisions. This disorganization can disrupt the continuity of care, as healthcare providers may lack essential data needed for accurate diagnosis, treatment planning, and the monitoring of patient progress.

5. Implications of Healthcare Data Standards on Public Health

The importance of data standardization in public health decision-making is emphasized by Hufstedler et al. [69]. They discuss the utilization of the Clinical Data Interchange Standards Consortium (CDISC) for storing clinical and non-clinical research data, which plays a crucial role in pharmaceutical decision-making. Moreover, Facile et al. [70] elaborate on the application of CDISC in collecting real-world data and real-world experience. Hufstedler et al. [69] also explain the significance of data standardization in achieving semantic interoperability and introduce the FAIR (findability, accessibility, interoperability, and reusability) principles, which are indispensable for avoiding chaos and addressing communication gaps. Nevertheless, the successful realization of these standards necessitates their universal implementation, as their isolated application cannot resolve the issues at a global level.
In their research, Dagliati et al. [71] discuss the significance of enhanced collaboration in health information technology and EHRs to address the challenges posed by the COVID-19 pandemic. They underscore the importance of leveraging internationally shared EHRs to effectively combat the pandemic. The researchers also stress that global cooperation and the interoperability of EHRs among different countries are vital in responding to pandemics and epidemics and can also support medical tourism. However, the lack of interoperability within specific states or provinces often hinders these efforts. This lack of interoperability adversely affects the promotion of public health, as seamless data flow and information exchange are fundamental to healthcare systems worldwide.
A recent review by Shah et al. [72] highlights the role of EHR in Public Health Surveillance (PHS), which focuses on monitoring and controlling diseases that can impact large populations, such as epidemics and pandemics. EHRs enhance PHS by collecting and analyzing data on diseases and their risk factors, enabling more effective disease control and prevention. The authors note that EHRs, with their rich data, provide valuable insights for public health agencies, improving community health monitoring and automating surveillance processes. This proactive approach helps prevent outbreaks by identifying high-risk cases early, rather than just reacting to them. More generally, EHR may help monitor trends in chronic health conditions such as diabetes and obesity. A better understanding of what health conditions have greater prevalence may help public health practitioners focus their efforts on these specific conditions.
EHR can also help monitor trends in communicable diseases such as influenza, hepatitis, tuberculosis, and sexually transmitted diseases. Being able to identify which infectious diseases may be spreading could help public health officials mitigate their spread. Publicly available datasets focused on public health pandemics or epidemics have been crucial for analyzing emergencies, assessing their impact, and guiding response strategies. During the COVID-19 pandemic, for instance, datasets such as the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University [73], the WHO COVID-19 Dashboard [74], and the CDC COVID Data Tracker [75] provided crucial real-time information on cases, deaths, recoveries, and vaccinations globally. These resources played a pivotal role in tracking the virus’s spread, informing public health interventions, and supporting epidemiological research. By making these data accessible to governments, researchers, and the public, these datasets enabled a coordinated global response, allowing for timely decision-making and the development of strategies that mitigated the pandemic’s impact. The comprehensiveness of these datasets was key to understanding the pandemic’s dynamics, which ultimately contributed to more effective and rapid recovery efforts in many regions.
Consequently, EHR standardization has the potential to improve the delivery of healthcare services, reduce the incidence of diseases, and reduce the spread of communicable diseases, which would improve population health.

6. Need for Standards in Dental Health Informatics

The need for dental EHRs is growing to enhance the quality of dental care by improving accuracy and expediting clinical documentation, such as X-rays, to facilitate inter-professional collaboration [32,76]. Dental EHRs are not a simple transposition of paper records. They need to be interoperable with other components of health information systems to allow data capture, facilitate data storage, and support data management for usage in research, education, and public health policies [38]. However, back in the early 2000s, no consensus-standardized nomenclature was available for oral diagnoses, dental treatment, or outcomes [32]. Governments, health centers, and software developers did not have a collective strategy, which negatively impacted the progress of this field [38]. Additionally, the International Classification of Diseases (ICD-9), the most popular coding system during that time, has limited coding to adequately make dental diagnoses [3]. Researchers have attempted to solve this standardization issue by proposing various coding systems such as Oral Health and Disease Ontology, Ontology for Dental Research, EZCode, and Systematized Nomenclature of Dentistry (SNODENT) [3,4,77]. These proposed systems were confusing when they were first introduced. Some of them have been redefined several times or merged with other coding systems to meet the needs of dentistry [3,38].
Many existing health record systems prioritize clinical tasks and overlook oral health needs, including conditions of the teeth, periodontal charts, radiographs, and dental terminology [3]. The implementation of universal treatment plans faces various challenges, such as communication difficulties and discrepancies in clinical documentation between medical and dental professionals [78]. The usage of non-standardized clinical dental coding is a concern not only for academic settings but also for private practitioners [79]. Challenges in obtaining standardized data may lead to a failure to adopt standardized clinical coding systems among private dental practitioners [38]. Capturing standardized data more effectively is critical to improving workflow integration and data usability [3]. The use of EHR data may help reduce the cost of clinical research because shared dental data warehouses can surpass many oral health registries and data repositories in volume [78].
Moreover, there is also a need for other types of data capture that can help reduce basic errors and provide real-time feedback to ensure the collection of accurate and complete information [38]. Dental EHRs are also useful for clinical decision support systems that can provide real-time patient-centered recommendations in clinical settings as well as in education to monitor students’ activities for didactic training and clinical practice [4,38]. Levitin et al. [80] discuss the issue of incompleteness in electronic dental records and attempt to identify specific details associated with this problem. Their findings reveal that the issue of completeness is prevalent in dental records, similar to regular electronic records. In a related study, Simon et al. [81] emphasize the importance of interoperability in electronic dental records and provide a comprehensive analysis demonstrating the interconnectedness of dental records with allergies and general health records. This observation underscores the significant link between dental and vision records with regular health records. For instance, Vu et al. [82] exemplify this interconnectedness by linking dental records to the occurrence of type 2 diabetes.

7. Discussion

In this study, we comprehensively discuss the current EHR standards across various countries and systems. This perspective emphasizes the significance of uniform standards, encourages collaboration toward shared objectives, and sheds light on the existing gaps that hinder effective healthcare delivery and interoperability. Standardized EHRs can minimize errors stemming from miscommunication or data discrepancies, ultimately enhancing patient outcomes and enforcing privacy and safety. Table 2 summarizes the key observations leading to the need for standards with EHRs based on the discussions presented in this article. There is a need to establish globally consistent standards through cross-disciplinary collaboration among various organizations to ensure that emerging innovations meet regulatory requirements and promote the global adoption of standardized systems.
Although this study provides valuable insights into the impact of non-standardization in health and dental records on the healthcare ecosystem, it is important to acknowledge its limitations. The study is based on the existing literature, which may not encompass all perspectives or the latest developments in EHR standardization. The absence of publicly available case studies on EHR standards and their implications limits this study to a discussion of the existing literature rather than empirical research. Consequently, it can be challenging to assess the negative outcomes and missed opportunities resulting from the lack of standardization. This reliance on the existing literature may restrict the depth of insights into the practical implications of standardization. Furthermore, the rapid advancement of healthcare technology may cause some of the insights and recommendations to quickly become outdated. This study may not fully capture emerging trends and innovations affecting EHR standardization.
Future research could explore the role of emerging technologies, such as artificial intelligence (AI) and blockchain, in enhancing the standardization and interoperability of EHRs. It is crucial to establish international regulatory standards for the ethical use of AI in analyzing and enhancing EHRs to ensure transparency in AI-powered clinical decision support systems. Moreover, conducting an empirical evidence-based study using a longitudinal case study dataset that tracks the long-term effects of standardization on healthcare delivery, costs, and patient outcomes could further advance the implementation of these standards in real-time clinical settings. Gaining insights into the trade-offs between standardization and flexibility can lead to the development of well-balanced approaches to standardization.

8. Conclusions

The widespread adoption of EHRs has resulted in significant diversity in their implementation across different countries, healthcare systems, and individual facilities. In this article, we analyzed the prevailing standards for health and dental records, focusing on aspects such as communication, privacy, security, and interoperability. Additionally, we explored the role of standardization in supporting learning health systems. Some of the key contributions added to the discussion of standards are as follows: (a) the idea of implementing learning healthcare systems, (b) the discussion of global interoperability based on standards with EHRs and dental records, (c) the suggestion for improvement in the analysis and prediction of public health data using data standards, and (d) the discussion of the negative consequences of the lack of standards.

Author Contributions

All authors contributed to the study conception and design. Conceptualization, investigation, material preparation, analysis and interpretation, and writing—original draft preparation were performed by V.P.G. Conceptualization, investigation, analysis and interpretation, and writing—review and editing were performed by G.V., V.M. and C.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data analyzed in this review can be found in the individual articles cited. The DOI of the individual articles can be found in the “References” section.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Evans, R. Electronic Health Records: Then, Now, and in the Future. Yearb. Med. Inform. 2016, 25, S48–S61. [Google Scholar] [CrossRef] [PubMed]
  2. Gurupur, V. Understanding An Electronic Health Record System and Its Applicable Data Quality Measures. IEEE Stand. 2024, 1–14. Available online: https://ieeexplore.ieee.org/servlet/opac?punumber=10482889 (accessed on 2 August 2024).
  3. Acharya, A.; Shimpi, N.; Mahnke, A.; Mathias, R.; Ye, Z. Medical care providers’ perspectives on dental information needs in electronic health records. J. Am. Dent. Assoc. 2017, 148, 328–337. [Google Scholar] [CrossRef] [PubMed]
  4. Weiskopf, N.G.; Weng, C. Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. J. Am. Med. Inform. Assoc. 2013, 20, 144–151. [Google Scholar] [CrossRef]
  5. Lewis, A.E.; Weiskopf, N.; Abrams, Z.B.; Foraker, R.; Lai, A.M.; Payne, P.R.O.; Gupta, A. Electronic health record data quality assessment and tools: A systematic review. J. Am. Med Inform. Assoc. JAMIA 2023, 30, 1730–1740. [Google Scholar] [CrossRef] [PubMed]
  6. Liu, C.; Zowghi, D.; Talaei-Khoei, A.; Daniel, J. Achieving data completeness in electronic medical records: A conceptual model and hypotheses development. In Proceedings of the 51st Hawaii International Conference on System Sciences, Hilton Waikoloa Village, HI, USA, 3–6 January 2018; pp. 2824–2833. [Google Scholar]
  7. Young, M.; Smith, M. Standards and Evaluation of Healthcare Quality, Safety, and Person-Centered Care; NCBI Bookshelf; A Service of the National Library of Medicine; National Institutes of Health: Bethesda, MD, USA, 2022. [Google Scholar]
  8. Schnipper, J.; Fitall, E.; Hall, K.; Gale, B. Approach to Improve Patient Safety: Communication. 2021. Available online: https://psnet.ahrq.gov/perspective/approach-improving-patient-safety-communication (accessed on 10 June 2024).
  9. Hess, L.M.; Das, S.; Asaithambi, R.; Delbecq, E.; Molleda Castro, C.; Molchen, W.; Lemke, D. Impact of EHR on Realism, Skills, and Workload in Sepsis Simulation. Clin. Simul. Nurs. 2024, 93, 101560. [Google Scholar] [CrossRef]
  10. Burde, H. The HITech act—An overview. Virtual Mentor 2011, 13, 172–175. [Google Scholar] [CrossRef]
  11. Pool, J.; Akhlaghpour, S.; Fatehi, F.; Burton-Jones, A. A systematic analysis of failures in protecting personal health data: A scoping review. Int. J. Inf. Manag. 2024, 74, 102719. [Google Scholar] [CrossRef]
  12. Shojaei, P.; Vlahu-Gjorgievska, E.; Chow, Y.W. Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review. Computers 2024, 13, 41. [Google Scholar] [CrossRef]
  13. Health Insurance Portability and Accountability Act of 1996. Public Law 104-191. United States Statut. Large. 1996, 110, 1936–2103. [Google Scholar]
  14. Gostin, L.O. National health information privacy: Regulations under the health insurance portability and accountability act. JAMA 2001, 285, 3015–3021. [Google Scholar] [CrossRef] [PubMed]
  15. Kuykendall, S.; Figueroa, L. Health Insurance Portability And Accountability Act (HIPAA); CDC: Atlanta, GA, USA, 2018; Volume 1, pp. 295–297. [Google Scholar]
  16. Hoofnagle, C.J.; Sloot, B.v.d.; Borgesius, F.Z. The European Union general data protection regulation: What it is and what it means. Inf. Commun. Technol. Law 2019, 28, 65–98. [Google Scholar] [CrossRef]
  17. Thorburn, J. The Personal Information Protection and Electronic Documents Act and the protection of personal health information. Health Law Can. 2001, 22, 52–56. [Google Scholar] [PubMed]
  18. International Organization for Standardization (ISO). ISO/IEC 27001:2022(en). 2013. Available online: https://www.iso.org/obp/ui/#iso:std:iso-iec:27001:ed-3:v1:en (accessed on 2 August 2024).
  19. Bender, D.; Sartipi, K. HL7 FHIR: An agile and RESTful approach to healthcare information exchange. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, 20–22 June 2013; pp. 326–331. [Google Scholar] [CrossRef]
  20. Vorisek, C.N.; Lehne, M.; Klopfenstein, S.A.I.; Mayer, P.J.; Bartschke, A.; Haese, T.; Thun, S. Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review. JMIR Med. Inform. 2022, 10, 35724. [Google Scholar] [CrossRef] [PubMed]
  21. Baumann, L.A.; Baker, J.; Elshaug, A.G. The impact of electronic health record systems on clinical documentation times: A systematic review. Health Policy 2018, 122, 827–836. [Google Scholar] [CrossRef]
  22. Gurupur, V.P. Can the theories of information and communication channels be used to explain the complexities associated with transformation of data into information, and information to knowledge? J. Integr. Des. Process Sci. 2023, 27, 59–69. [Google Scholar] [CrossRef]
  23. Garde, S.; Hovenga, E.; Buck, J.; Knaup, P. Expressing clinical data sets with openEHR archetypes: A solid basis for ubiquitous computing. Int. J. Med. Inform. 2007, 76, S334–S341. [Google Scholar] [CrossRef]
  24. Cardoso de Moraes, J.L.; de Souza, W.L.; Pires, L.F.; do Prado, A.F. A methodology based on openEHR archetypes and software agents for developing e-health applications reusing legacy systems. Comput. Methods Programs Biomed. 2016, 134, 267–287. [Google Scholar] [CrossRef]
  25. Bodenreider, O.; Cornet, R.; Vreeman, D.J. Recent Developments in Clinical Terminologies—SNOMED CT, LOINC, and RxNorm. Yearb. Med. Inform. 2018, 27, 129–139. [Google Scholar] [CrossRef]
  26. Gliklich, R.E.; Leavy, M.B. Data Standards, 3rd ed.; Agency for Healthcare Research and Quality (AHRQ): Rockville, MD, USA, 2019; Chapter 3. [Google Scholar]
  27. Chinnasamy, P.; Albakri, A.; Khan, M.; Raja, A.A.; Kiran, A.; Babu, J.C. Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System. Appl. Sci. 2023, 13, 3970. [Google Scholar] [CrossRef]
  28. Richesson, R.L. Learning health systems, embedded research, and data standards - Recommendations for healthcare system leaders. JAMIA Open 2020, 3, 488–491. [Google Scholar] [CrossRef] [PubMed]
  29. Schulz, S.; Stegwee, R.; Chronaki, C. Standards in Healthcare Data. In Fundamentals of Clinical Data Science; Springer: Cham, Switzerland, 2018; pp. 19–36. [Google Scholar] [CrossRef]
  30. Commission, E. Recommendation on a European Electronic Health Record exchange format (C(2019)800). 2019. Available online: https://digital-strategy.ec.europa.eu/en/library/recommendation-european-electronic-health-record-exchange-format (accessed on 3 October 2024).
  31. Bonacina, S.; Koch, S.; Meneses, I.; Chronaki, C. Can the European EHR Exchange Format Support Shared Decision Making and Citizen-Driven Health Science? IOS Press: Amsterdam, The Netherlands, 2021; pp. 1056–1060. [Google Scholar] [CrossRef]
  32. Atkinson, J.C.; Zeller, G.G.; Shah, C. Electronic patient records for dental school clinics: More than paperless systems. J. Dent. Educ. 2002, 66, 634–642. [Google Scholar] [CrossRef] [PubMed]
  33. Slee, V. The International Classification of Diseases: Ninth revision (ICD-9). Ann. Intern. Med. 1978, 88, 424–426. [Google Scholar] [CrossRef] [PubMed]
  34. Torres-Urquidy, M.H.; Schleyer, T. Evaluation of the Systematized Nomenclature of Dentistry using case reports: Preliminary results. AMIA Symp. AMIA Symp. 2006, 2006, 1124. [Google Scholar]
  35. Kalenderian, E.; Ramoni, R.L.; White, J.M.; Schoonheim-Klein, M.E.; Stark, P.C.; Kimmes, N.S.; Zeller, G.G.; Willis, G.P.; Walji, M.F. The development of a dental diagnostic terminology. J. Dent. Educ. 2011, 75, 68–76. [Google Scholar] [CrossRef] [PubMed]
  36. White, J.M.; Kalenderian, E.; Stark, P.C.; Ramoni, R.L.; Vaderhobli, R.; Walji, M.F. Evaluating a dental diagnostic terminology in an electronic health record. J. Dent. Educ. 2011, 75, 605–615. [Google Scholar] [CrossRef]
  37. Schleyer, T.K.; Ruttenberg, A.; Duncan, W.; Haendel, M.; Torniai, C.; Acharya, A.; Song, M.; Thyvalikakath, T.P.; Liu, K.; Hernandez, P. An ontology-based method for secondary use of electronic dental record data. AMIA Summits Transl. Sci. Proc. 2013, 2013, 234. [Google Scholar]
  38. Benoit, B.; Frédéric, B.; Jean-Charles, D. Current state of dental informatics in the field of health information systems: A scoping review. BMC Oral Health 2022, 22, 131. [Google Scholar] [CrossRef]
  39. Agency for Healthcare Research and Quality (AHRQ). What Is a Learning Health System? 2024. Available online: https://www.ahrq.gov/learning-health-systems/index.html (accessed on 22 July 2024).
  40. Easterling, D.; Perry, A.C.; Woodside, R.; Patel, T.; Gesell, S.B. Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learn. Health Syst. 2022, 6, e10287. [Google Scholar] [CrossRef]
  41. Menear, M.; Blanchette, M.A.; Demers-Payette, O.; Roy, D. A framework for value-creating learning health systems. Health Res. Policy Syst. 2019, 17, 79. [Google Scholar] [CrossRef]
  42. Iyawa, G.E.; Herselman, M.; Botha, A. Digital Health Innovation Ecosystems: From Systematic Literature Review to Conceptual Framework. Procedia Comput. Sci. 2016, 100, 244–252. [Google Scholar] [CrossRef]
  43. Stephanie, L.; Sharma, R.S. Digital health eco-systems: An epochal review of practice-oriented research. Int. J. Inf. Manag. 2020, 53, 102032. [Google Scholar] [CrossRef]
  44. Gurupur, V.P.; Abedin, P.; Hooshmand, S.; Shelleh, M. Analyzing the Data Completeness of Patients’ Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records. Appl. Sci. 2022, 12, 746. [Google Scholar] [CrossRef]
  45. Nasir, A.; Gurupur, V.; Liu, X. A new paradigm to analyze data completeness of patient data. Appl. Clin. Inform. 2016, 7, 745–764. [Google Scholar] [CrossRef] [PubMed]
  46. Gurupur, V.P.; Shelleh, M. Machine Learning Analysis for Data Incompleteness (MADI): Analyzing the Data Completeness of Patient Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records. IEEE Access 2021, 9, 95994–96001. [Google Scholar] [CrossRef]
  47. Gurupur, V.P. Key observations in terms of management of electronic health records from a mHealth perspective. mHealth 2022, 8, 18. [Google Scholar] [CrossRef]
  48. Bowman, S. Impact of electronic health record systems on information integrity: Quality and safety implications. Perspect. Health Inf. Manag. 2013, 10, 1c. [Google Scholar]
  49. Moeenian, M.; Ghazinoory, S.; Yaghmaie, P. Analysing the performance of a health innovation ecosystem in the COVID-19 crisis: Complexity and chaos theory perspective. Health Res. Policy Syst. 2024, 22, 59. [Google Scholar] [CrossRef]
  50. Mamlin, B.W.; Tierney, W.M. The Promise of Information and Communication Technology in Healthcare: Extracting Value from the Chaos. Am. J. Med Sci. 2016, 351, 59–68. [Google Scholar] [CrossRef]
  51. Tsou, A.Y.; Lehmann, C.U.; Michel, J.; Solomon, R.; Possanza, L.; Gandhi, T. Safe practices for copy and paste in the EHR: Systematic review, recommendations, and novel model for Health IT collaboration. Appl. Clin. Inform. 2017, 8, 12–34. [Google Scholar] [CrossRef]
  52. Tsai, C.H.; Eghdam, A.; Davoody, N.; Wright, G.; Flowerday, S.; Koch, S. Effects of electronic health record implementation and barriers to adoption and use: A scoping review and qualitative analysis of the content. Life 2020, 10, 327. [Google Scholar] [CrossRef] [PubMed]
  53. Ozonze, O.; Scott, P.J.; Hopgood, A.A. Automating Electronic Health Record Data Quality Assessment. J. Med. Syst. 2023, 47, 23. [Google Scholar] [CrossRef] [PubMed]
  54. Johnson, A.E.W.; Pollard, T.J.; Shen, L.; Lehman, L.H.; Feng, M.; Ghassemi, M.; Moody, B.E.; Szolovits, P.; Mark, R.G.; Saeed, M. MIMIC-III, a freely accessible critical care database. Sci. Data 2016, 3, 160035. [Google Scholar] [CrossRef] [PubMed]
  55. Pollard, T.J.; Johnson, A.E.W.; Raffa, J.D.; Celi, L.A.; Mark, R.G.; Badawi, O. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data 2018, 5, 180178. [Google Scholar] [CrossRef]
  56. Wang, X.; Yu, K.; Silk, H.; Zhang, X.; Liu, J.W.; Lu, H.; Ma, H.C.; Xu, C.; Zhang, C.; Han, C.; et al. ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 2097–2106. [Google Scholar]
  57. Ait Nasser, A.; Akhloufi, M.A. A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography. Diagnostics 2023, 13, 159. [Google Scholar] [CrossRef]
  58. Setio, A.A.A.; Traverso, A.; Bel, T.D.; Berens, M.S.; Bogaard, C.V.; Cerello, P.; Chen, H.; Dou, Q.; Fantacci, M.E.; Geurts, B.; et al. Validation, comparison, and combination of algorithms for automatic detection of lung nodules in computed tomography scans. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 3413–3422. [Google Scholar]
  59. Jalloul, R.; Chethan, H.; Alkhatib, R. A Review of Machine Learning Techniques for the Classification and Detection of Breast Cancer from Medical Images. Diagnostics 2023, 13, 2460. [Google Scholar] [CrossRef]
  60. Huang, S.Y.; Hsu, W.L.; Hsu, R.J.; Liu, D.W. Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey. Diagnostics 2022, 12, 2765. [Google Scholar] [CrossRef]
  61. Bernard, O.; Puybareau, P.; Markl, M.; Delingette, H.; Michel, S.; Renaud, P.; Leung, M.; Cai, W. The Automated Cardiac Diagnosis Challenge (ACDC): An overview of the challenge and the results. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Quebec City, QC, Canada, 11–13 September 2017; Springer: Cham, Switzerland, 2017; pp. 1–9. [Google Scholar]
  62. Menze, B.H.; Jakab, A.; Bauer, S.; Kalpathy-Cramer, J.; Farahani, K.; Kirby, J.; Burren, Y.; Porz, N.; Slotboom, J.; Wiest, R.; et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 2015, 34, 1993–2024. [Google Scholar] [CrossRef]
  63. Subudhi, A.; Dash, P.; Mohapatra, M.; Tan, R.S.; Acharya, U.R.; Sabut, S. Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review. Diagnostics 2022, 12, 2535. [Google Scholar] [CrossRef]
  64. Sudlow, C.; Gallagher, M.D.; Allen, N.; Beral, V.; Burton, P.; Danesh, J.; Dunn, J.; Elliott, P.; Green, J.; Khalid, S.; et al. UK Biobank: An Open Access Resource for Large-Scale Brain Imaging and Genetic Studies. Nature 2018, 562, 201–210. [Google Scholar]
  65. Xu, Z.; Li, W.; Dong, X.; Chen, Y.; Zhang, D.; Wang, J.; Zhou, L.; He, G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin. Chim. Acta 2024, 559, 119686. [Google Scholar] [CrossRef] [PubMed]
  66. Hoseini, S.H.; Enayati, P.; Nazari, M.; Babakhanzadeh, E.; Rastgoo, M.; Sohrabi, N.B. Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective. J. Gastrointest. Cancer 2024, 55, 49–510. [Google Scholar] [CrossRef] [PubMed]
  67. Xie, H.; Jia, Y.; Liu, S. Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges. Interdiscip. Med. 2024, 2, 411. [Google Scholar] [CrossRef]
  68. Carini, C.; Seyhan, A.A. Tribulations and future opportunities for artificial intelligence in precision medicine. J. Transl. Med. 2024, 22. [Google Scholar] [CrossRef]
  69. Hufstedler, H.; Roell, Y.; Peña, A.; Krishnan, A.; Green, I.; Barbosa-Silva, A.; Kremer, A.; Blacketer, C.; Fortier, I.; Howard, K.; et al. Navigating data standards in public health: A brief report from a data-standards meeting. J. Glob. Health 2024, 14. [Google Scholar] [CrossRef]
  70. Facile, R.; Muhlbradt, E.E.; Gong, M.; Li, Q.; Popat, V.; Pétavy, F.; Cornet, R.; Ruan, Y.; Koide, D.; Saito, T.I.; et al. Use of Clinical Data Interchange Standards Consortium (CDISC) Standards for Real-world Data: Expert Perspectives from a Qualitative Delphi Survey. JMIR Med. Inform. 2022, 10, e30363. [Google Scholar] [CrossRef]
  71. Dagliati, A.; Malovini, A.; Tibollo, V.; Bellazzi, R. Health informatics and EHR to support clinical research in the COVID-19 pandemic: An overview. Briefings Bioinform. 2021, 22, 812–822. [Google Scholar] [CrossRef]
  72. Shah, S.M.; Khan, R.A. Secondary use of electronic health record: Opportunities and challenges. IEEE Access 2020, 8, 136947–136965. [Google Scholar] [CrossRef]
  73. Dong, E.; Du, H.; Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020, 20, 533–534. [Google Scholar] [CrossRef]
  74. World Health Organization (WHO). WHO Coronavirus (COVID-19) Dashboard. 2020. Available online: https://data.who.int/dashboards/covid19/cases (accessed on 12 July 2024).
  75. Centers for Disease Control and Prevention (CDC). CDC COVID Data Tracker. 2020. Available online: https://covid.cdc.gov/covid-data-tracker (accessed on 15 July 2024).
  76. Alanazi, A.; Alghamdi, G.; Aldosari, B. Informational Needs for Dental-Oriented Electronic Health Records from Dentists’ Perspectives. Healthcare 2023, 11, 266. [Google Scholar] [CrossRef]
  77. Smith, B.; Goldberg, L.J.; Ruttenberg, A. Ontology and the future of dental research informatics. J. Am. Dent. Assoc. 2010, 141, 1173–1175. [Google Scholar] [CrossRef]
  78. Chauhan, Z.; Samarah, M.; Unertl, K.M.; Jones, M.W. Adoption of Electronic Dental Records: Examining the Influence of Practice Characteristics on Adoption in One State. Appl. Clin. Inform. 2018, 9, 635–645. [Google Scholar] [CrossRef] [PubMed]
  79. Lam, R.; Kruger, E.; Tennant, M. How a modified approach to dental coding can benefit personal and professional development with improved clinical outcomes. J. Evid.-Based Dent. Pract. 2014, 14, 174–182. [Google Scholar] [CrossRef] [PubMed]
  80. Levitin, S.A.; Grbic, J.T.; Finkelstein, J. Completeness of electronic dental records in a student clinic: Retrospective analysis. JMIR Med. Inform. 2019, 7, e13008. [Google Scholar] [CrossRef] [PubMed]
  81. Simon, L.; Obadan-Udoh, E.; Yansane, A.I.; Gharpure, A.; Licht, S.; Calvo, J.; Deschner, J.; Damanaki, A.; Hackenberg, B.; Walji, M.; et al. Improving Oral-Systemic Healthcare through the Interoperability of Electronic Medical and Dental Records: An Exploratory Study. Appl. Clin. Inform. 2019, 10, 367–376. [Google Scholar] [CrossRef]
  82. Vu, G.T.; Shakib, S.; King, C.; Gurupur, V.; Little, B.B. Association between uncontrolled diabetes and periodontal disease in US adults: NHANES 2009–2014. Sci. Rep. 2023, 13, 16694. [Google Scholar] [CrossRef]
Bdcc 08 00168 g001
Figure 1. A schematic representation of a standard EHR.
Figure 1. A schematic representation of a standard EHR.
Bdcc 08 00168 g001
Bdcc 08 00168 g002
Figure 2. A schematic representation of common dimensions of data quality in EHRs [4,5].
Figure 2. A schematic representation of common dimensions of data quality in EHRs [4,5].
Bdcc 08 00168 g002
Bdcc 08 00168 g003
Figure 3. A conceptual model for ensuring data completeness in EHRs [6].
Figure 3. A conceptual model for ensuring data completeness in EHRs [6].
Bdcc 08 00168 g003
Bdcc 08 00168 g004
Figure 4. A schematic representation of features of a successful learning healthcare system.
Figure 4. A schematic representation of features of a successful learning healthcare system.
Bdcc 08 00168 g004
Bdcc 08 00168 g005
Figure 5. Percentage of missing values in the eICU research database [55].
Figure 5. Percentage of missing values in the eICU research database [55].
Bdcc 08 00168 g005
Table 1. Key healthcare data standards.
Table 1. Key healthcare data standards.
OrganizationStandard NamePurposeURL
ADASNODENTStandards for electronic dental records.https://www.ada.org/resources/practice/dental-standards/snodent (last accessed on 5 August 2024).
Federative Committee on Anatomical Terminology (FCAT)IFAA terminologiesStandardize anatomical terms internationally.https://fipat.library.dal.ca/ (last accessed on 5 August 2024).
Health Level Seven International (HL7)v2Messaging standard for exchanging clinical information.https://www.hl7.org (last accessed on 5 August 2024).
v3Standard for the exchange of clinical and administrative data.
CDA Level 1–3Clinical document architecture for encoding and exchanging clinical documents.
FHIRFast healthcare interoperability resources; modern standard for exchanging healthcare information.
Integrating the Healthcare Enterprise (IHE)Resources and tools to integrate systemsStandardize communication among healthcare IT systems.https://www.ihe.net/ (last accessed on 6 August 2024).
International Organization for Standardization (ISO)3630Standards for endodontic instruments and materials.https://www.iso.org (last accessed on 12 August 2024).
3950Designation system for teeth and areas of the oral cavity.
13606, 18308Standards for a stable and rigorous EHR information architecture.
14155Standards for the design, conduct, recording, and reporting of clinical investigations.
21090:2011Specifies the data types and format standards.
TS22220:2011Identification of care recipients.
23940 (ContSys)Healthcare processes for ensuring continuity of care.
27799Provides guidance for health organizations to protect the confidentiality, integrity and availability.
IDMPStandards designed to create framework of structured, coded data that uniquely identify all key aspects of medicinal products.
21549Standardizes the structure and content of patient health card data.
OBO FoundryOral Health and Disease OntologyProvides structured framework for representing knowledge about oral health and disease.https://obofoundry.org/ontology/ohd.html (last accessed on 13 August 2024).
openEHR foundationopenEHRDevelops open specifications for creating interoperable EHR systems.https://www.openehr.org/ (last accessed on 13 August 2024).
SNOMED InternationalSNOMED CTStandardized codes for medical concepts.https://www.snomed.org/ (last accessed on 13 August 2024).
World Health Organization (WHO)ATCProvides a structured framework for the systematic identification and classification of medicines.https://www.who.int/ (last accessed on 14 August 2024).
ICDStandards for classifying and coding diseases and health conditions.
ICD-DAProvides a standardized system for classifying and coding dental and oral health conditions.
ICFProvides a comprehensive framework for describing and measuring health and disability.
ICHIStandards for describing and recording healthcare procedures and treatments.
INNStandardized names for pharmaceutical substances and active ingredients.
World Organization of Family Doctors (WONCA)ICPCProvides a classification system to categorize and code the various aspects of primary care.https://www.globalfamilydoctor.com/ (last accessed on 14 August 2024).
Table 2. Key observations leading to the need for standards with EHRs.
Table 2. Key observations leading to the need for standards with EHRs.
FactorKey Observations
CommunicationThe lack of standards can lead to problems in communication, leading to catastrophic consequences.
Learning System DevelopmentStandards are much needed for an EHR system to learn and provide recommendations.
Public Health AnalysisPublic health analysis needs standards for accuracy and validation.
Avoiding ChaosInteroperability and quality assessment standards are needed to avoid disorganization.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gurupur, V.P.; Vu, G.; Mayya, V.; King, C. The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review. Big Data Cogn. Comput. 2024, 8, 168. https://doi.org/10.3390/bdcc8120168

AMA Style

Gurupur VP, Vu G, Mayya V, King C. The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review. Big Data and Cognitive Computing. 2024; 8(12):168. https://doi.org/10.3390/bdcc8120168

Chicago/Turabian Style

Gurupur, Varadraj P., Giang Vu, Veena Mayya, and Christian King. 2024. "The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review" Big Data and Cognitive Computing 8, no. 12: 168. https://doi.org/10.3390/bdcc8120168

APA Style

Gurupur, V. P., Vu, G., Mayya, V., & King, C. (2024). The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review. Big Data and Cognitive Computing, 8(12), 168. https://doi.org/10.3390/bdcc8120168

Article Metrics

Back to TopTop