Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability

Background

Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025.

Objective

To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development?

Methods

Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research.

Results

Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43%), increasing the quality of care (n=4, 29%), and enhancing clinical data use and reuse for varied purposes (n=4, 29%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57%). Enhancing health data quality through standardization (n=5, 36%) and developing EHR-integrated tools based on interoperable data (n=1, 7%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers).

Conclusions

When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes.

Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data

Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurrences of some data that are in some way unusual and do not fit the general patterns. It is considered one of the major problems of big data. Data trust method (DTM) is a technique used to identify and replace anomaly or untrustworthy data using the interpolation method. This article discusses the DTM used for univariate time series (UTS) forecasting algorithms for big data, which is considered the preprocessing approach by using a neural network (NN) model. In this work, DTM is the combination of statistical-based untrustworthy data detection method and statistical-based untrustworthy data replacement method, and it is used to improve the forecast quality of UTS. In this study, an enhanced NN model has been proposed for big data that incorporates DTMs with the NN-based UTS forecasting model. The coefficient variance root mean squared error is utilized as the main characteristic indicator in the proposed work to choose the best UTS data for model development. The results show the effectiveness of the proposed method as it can improve the prediction process by determining and replacing the untrustworthy big data.

Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review

BACKGROUND

Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited.

OBJECTIVE

The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation.

METHODS

The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis.

RESULTS

Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity.

CONCLUSIONS

The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing.

TRIAL REGISTRATION

PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50339.

A Conference (Missingness in Action) to Address Missingness in Data and AI in Health Care: Qualitative Thematic Analysis

BACKGROUND

Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings.

OBJECTIVE

This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings.

METHODS

A digital conference was held in September 2022, attracting 861 registered participants, with 164 (19%) attending the live event. The conference featured presentations and panel discussions by experts in AI, machine learning, and health care. Transcripts of the event were analyzed using the stepwise framework of Braun and Clark to identify key themes related to missingness in health care data.

RESULTS

Three principal themes-data quality and bias, human input in model development, and trust and privacy-emerged from the analysis. Topics included the accuracy of predictive models, lack of inclusion of underrepresented communities, partnership with physicians and other populations, challenges with sensitive health care data, and fostering trust with patients and the health care community.

CONCLUSIONS

Addressing the challenges of data quality, human input, and trust is vital when devising and using machine learning algorithms in health care. Recommendations include expanding data collection efforts to reduce gaps and biases, involving medical professionals in the development and implementation of AI models, and developing clear ethical guidelines to safeguard patient privacy. Further research and ongoing discussions are needed to ensure these conclusions remain relevant as health care and AI continue to evolve.

Quality registry improves the data of chronic ulcers: Validation of Tampere Wound Registry

Quality registries are potential tools for improving health care documentation, but the quality and completeness of each registry should be ensured. This study aimed to evaluate the completion rate (completeness) and accuracy of data, first contact-to-registration time (timeliness), and case coverage of the Tampere Wound Registry (TWR) to assess whether it can be reliably used in clinical practice and for research purposes. Data from all 923 patients registered in the TWR between 5 June 2018 and 31 December 2020 were included in the analysis of data completeness, while data accuracy, timeliness and case coverage were analysed in those registered during the year 2020. In all analyses values over 80% were considered good and values over 90% excellent. The study showed that the overall completeness of the TWR was 81% and overall accuracy was 93%. Timeliness achieved 86% within the first 24 h, and case coverage was found to be 91%. When completion of seven selected variables was compared between TWR and patient medical records, the TWR was found to be more complete in five out of seven variables. In conclusion, the TWR proved to be a reliable tool for health care documentation and an even more reliable data source than patient medical records.

How to produce sound predictions of incidence at a district level using either health care or mortality data in the absence of a national registry: the example of cancer in France

BACKGROUND

In many countries, epidemiological surveillance of chronic diseases is monitored by local registries (LR) which do not necessarily cover the whole national territory. This gap has fostered interest in using non-registry databases (e.g., health care or mortality databases) available for the whole territory as proxies for incidence at the local level. However, direct counts from these databases do not provide reliable incidence measures. Accordingly, specific methods are needed to correct proxies and assess their epidemiological usefulness.

METHODS

This study's objective was to implement a three-stage turnkey methodology using national non-registry data to predict incidence in geographical areas without an LR as follows: constructing a calibration model to make predictions including accurate prediction intervals; accuracy assessment of predictions and rationale for the criteria to assess which predictions were epidemiologically useful; mapping after spatial smoothing of the latter predictions. The methodology was applied to a real-world setting, whereby we aimed to predict cancer incidence, by gender, at the district level in France over the 2007-15 period for 24 different cancer sites, using several health care indicators and mortality. In the present paper, the spatial smoothing performed on predicted incidence of epidemiological interest is illustrated for two examples.

RESULTS

Predicted incidence of epidemiological interest was possible for 27/34 solid site-gender combinations and for only 2/8 haematological malignancies-gender combinations. Mapping of smoothed predicted incidence provided a clear picture of the main contrasts in incidence between districts.

CONCLUSIONS

The methodology implemented provides a comprehensive framework to produce valuable predictions of incidence at a district level, using proxy measures and existing LR.

The role of medical data in efficient patient care delivery: a review

Background

Implementing accurate data management systems ensure safe and efficient transfer of confidential health care data. However, health care professionals overlooked their important tasks of medical data processing. Hence, using high-quality electronic health record (EHR) applications in health care is important to minimize medical errors. Therefore, this review tries to indicate the roles of EHR in advancing quality health care service provisions.

Methods

The keywords identified were EHR, EMR, medical data processing, medical data retention, medical data destruction, health care, and patient care, and a few related terms with different combinations. PubMed (National Library of Medicine), Google Scholar, and Google search engine were used to search for articles from those databases. Searching was done using boolean words “AND”, “OR”, and “NOT” using all [All fields] and [MeSH Terms] searching strategies.

Results

Articles were screened using the title, checked by their abstract, and the remaining related full-text materials were included or excluded by two individuals deciding its eligibility. Finally, 73 materials issued from 2013–2018 were used for qualitatively synthesizing and reconciling the idea to produce this review article.

Conclusion

Poor medical data processing systems are the key reasons for medical errors. Employing standardized data management systems reduce errors and associated sufferings. Therefore, using electronic tools in the health care institution ensures safe and efficient data management. Therefore, it is important to establish appropriate medical data management systems for efficient health care delivery.

We've Only Just Begun - Insights from a 25-Year Journey to Accelerate Health Care Transformation Through Delivery System Research

Even though it is well known that quality, safety, and patient-centeredness of health care can be improved, leveraging the organizational apparatus of a care delivery environment to render improvement in a consistent and comprehensive manner has proven difficult. The Health Care Systems Research Network (HCSRN), which began as the HMO Research Network, emerged from a desire to improve health and study problems in health care in a systematic and collaborative way, spurring the delivery of true evidence-informed medicine. The HCSRN has honed network-wide data resources, a collaborative culture, and shared infrastructure, enabling multicenter health care research that is often more difficult for researchers working in less integrated settings and across organizational boundaries. The HCSRN’s 25-year track record confers both an opportunity and obligation to share what we have learned through our research. Considering the quarter-century since the HCSRN was established, we describe three evolving areas—health data, new health care models, and diversified research teams that must be thoughtfully harnessed to realize a transformed health care ecosystem that generates and learns with research.

Age- and sex-specific first health care use for migraine in 2016 in children and adolescents from prospectively collected health insurance data in Germany

BACKGROUND

Migraine in children and adolescents is associated with significant disability and a high risk of persistence into adulthood.

OBJECTIVE

Data on migraine incidence in children and adolescents are few and relatively coarse. To tailor interventions starting shortly after disease onset, detailed information on age- and sex-specific incidence of migraine in children and adolescents is needed.

METHODS

We used health care data prospectively collected by the BARMER statutory health insurance, representing ∼11% of the German population. The incidence of migraine diagnoses (International Classification of Diseases (ICD)-10 code G43) in the year 2016 was assessed in subjects aged 0-19 years, who had been continuously insured with the BARMER between 2005 and 2016 or during their entire lifespan.

RESULTS

Data from ∼1.2 million children and adolescents were available. The incidence of migraine diagnoses steadily increased with age, reaching ∼1% per year around the age of 10 for both sexes, and 3.49% per year in females and 1.72% per year in males at the age of 19. Incidences in males and females were similar up to the age of 13 and higher in females from there on. The proportion of incident migraine diagnoses specified as "definite" versus "probable" migraine increased with age.

CONCLUSION

The present study provides a representative estimation of the first documented health care use for migraine in children and adolescents in Germany within 1-year age bins, separately for boys and girls. These data will be helpful for tailoring early healthcare interventions to reduce disability and prevent migraine chronification.

Internet information on urology

Keeping up with all the current information in the field of urology is not as difficult anymore, with the many sources available on the World Wide Web. But which sites will give you the most and best data? Where can you go for continuing medical education? For sources of patient education information? For a list of upcoming meetings? Check below for recommendations on general medical, urology-specific, and reference resources.