Clinical Data Flow in Botswana Clinics: Protocol for a Mixed-Methods Assessment.

BACKGROUND: Botswana has made significant investments in its health care information infrastructure, including vertical programs for child health and nutrition, HIV care, and tuberculosis. However, effectively integrating the more than 18 systems in place for data collection and reporting has proved to be challenging. The Botswana Health Data Collaborative Roadmap Strategy (2020-24) states that "there exists parallel reporting systems and data is not integrated into the mainstream reports at the national level," seconded by the Botswana National eLearning strategy (2020), which states that "there is inadequate information flow at all levels, proliferation of systems, reporting tools are not synthesized; hence too many systems are not communicating." OBJECTIVE: The objectives of this study are to (1) create a visual representation of how data are processed and the inputs and outputs through each health care system level; (2) understand how frontline workers perceive health care data sharing across existing platforms and the impact of data on health care service delivery. METHODS: The setting included a varied range of 30 health care facilities across Botswana, aiming to capture insights from multiple perspectives into data flow and system integration challenges. The study design combined qualitative and quantitative methodologies, informed by the rapid assessment process and the technology assessment model for resource limited settings. The study used a participatory research approach to ensure comprehensive stakeholder engagement from its inception. Survey instruments were designed to capture the intricacies of data processing, sharing, and integration among health care workers. A purposive sampling strategy was used to ensure a wide representation of participants across different health care roles and settings. Data collection used both digital surveys and in-depth interviews. Preliminary themes for analysis include perceptions of the value of health care data and experiences in data collection and sharing. Ethical approvals were comprehensively obtained, reflecting the commitment to uphold research integrity and participant welfare throughout the study. RESULTS: The study recruited almost 44 health care facilities, spanning a variety of health care facilities. Of the 44 recruited facilities, 27 responded to the surveys and participated in the interviews. A total of 75% (112/150) of health care professionals participating came from clinics, 20% (30/150) from hospitals, and 5% (8/150) from health posts and mobile clinics. As of October 10, 2023, the study had collected over 200 quantitative surveys and conducted 90 semistructured interviews. CONCLUSIONS: This study has so far shown enthusiastic engagement from the health care community, underscoring the relevance and necessity of this study's objectives. We believe the methodology, centered around extensive community engagement, is pivotal in capturing a nuanced understanding of the health care data ecosystem. The focus will now shift to the analysis phase of the study, with the aim of developing comprehensive recommendations for improving data flow within Botswana's health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52411.

Comparing public health-related material in print and web page versions of legacy media.

Objectives: The objectives of this study were to create a database of public health content from a sample of legacy media, and to compare the prevalence of public health themes in print and web-based versions over time. Materials and Methods: A database was created from eleven nationally published magazines as a sample of legacy media content. Relevant material was extracted and coded by the title of the article, periodical, print or web edition, month of publication, item type, and 1-3 public health theme codes. Results: Theme codes emerged as the documents were reviewed based on the primary discussion in each piece. A total of 2558 unique documents were extracted from print issues and 6440 from web-based issues. Seventeen public health themes were identified. Individual coded documents were saved with file names identical to the code string, thus creating a searchable database. Discussion: Legacy media are those that existed before the internet and social media. Publishers target readership groups defined by age, gender, race, sexual orientation, and other commonalities. Although legacy media have been identified as trusted sources of health information, they have not been examined as sources of public health communication. Because both print and web-based versions exist as unstructured textual data, these are rarely examined with informatics methods. Conclusion: The process described can serve as a model for application of informatics approaches to similar data and assist development of targeted public health communications. Having a better understanding of what types of health content is distributed through legacy media can help to target health messages to specific demographic and interest groups in ways that are understandable and appealing to them.

Development of a SNOMED CT Mapping Process and Tool at a Data Integration Centre - Lessons Learned.

INTRODUCTION: 16 million German-language free-text laboratory test results are the basis of the daily diagnostic routine of 17 laboratories within the University Hospital Erlangen. As part of the Medical Informatics Initiative, the local data integration centre is responsible for the accessibility of routine care data for medical research. Following the core data set, international interoperability standards such as FHIR and the English-language medical terminology SNOMED CT are used to create harmonised data. To represent each non-numeric laboratory test result within the base module profile ObservationLab, the need for a map and supporting tooling arose. STATE OF THE ART: Due to the requirement of a n:n map and a data safety-compliant local instance, publicly available tools (e.g., SNAP2SNOMED) were insufficient. Concept and Implementation: Therefore, we developed (1) an incremental mapping-validation process with different iteration cycles and (2) a customised mapping tool via Microsoft Access. Time, labour, and cost efficiency played a decisive role. First iterations were used to define requirements (e.g., multiple user access). LESSONS LEARNED: The successful process and tool implementation and the described lessons learned (e.g., cheat sheet) will assist other German hospitals in creating local maps for inter-consortia data exchange and research. In the future, qualitative and quantitative analysis results will be published.

InterAgent on FHIR - Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR.

INTRODUCTION: For an interoperable Intelligent Tutoring System (ITS), we used resources from Fast Healthcare Interoperability Resources (FHIR) and mapped learning content with Unified Medical Language System (UMLS) codes to enhance healthcare education. This study addresses the need to enhance the interoperability and effectiveness of ITS in healthcare education. STATE OF THE ART: The current state of the art in ITS involves advanced personalized learning and adaptability techniques, integrating technologies such as machine learning to personalize the learning experience and to create systems that dynamically respond to individual learner needs. However, existing ITS architectures face challenges related to interoperability and integration with healthcare systems. CONCEPT: Our system maps learning content with UMLS codes, each scored for similarity, ensuring consistency and extensibility. FHIR is used to standardize the exchange of medical information and learning content. IMPLEMENTATION: Implemented as a microservice architecture, the system uses a recommender to request FHIR resources, provide questions, and measure learner progress. LESSONS LEARNED: Using international standards, our ITS ensures reproducibility and extensibility, enhancing interoperability and integration with existing platforms.

Design and Implementation of a Community Driven Clinical Trial Information Management System for Precision Oncology Settings.

INTRODUCTION: Trial recruitment is a crucial factor for precision oncology, potentially improving patient outcomes and generating new scientific evidence. To identify suitable, biomarker-based trials for patients' clinicians need to screen multiple clinical trial registries which lack support for modern trial designs and offer only limited options to filter for in- and exclusion criteria. Several registries provide trial information but are limited regarding factors like timeliness, quality of information and capability for semantic, terminology enhanced searching for aspects like specific inclusion criteria. METHODS: We specified a Fast Healthcare Interoperable Resources (FHIR) Implementation Guide (IG) to represent clinical trials and their meta data. We embedded it into a community driven approach to maintain clinical trial data, which is fed by openly available data sources and later annotated by platform users. A governance model was developed to manage community contributions and responsibilities. RESULTS: We implemented Community Annotated Trial Search (CATS), an interactive platform for clinical trials for the scientific community with an open and interoperable information model. It provides a base to collaboratively annotate clinical trials and serves as a comprehensive information source for community members. Its terminology driven annotations are coined towards precision oncology, but its principles can be transferred to other contexts. CONCLUSION: It is possible to use the FHIR standard and an open-source information model represented in our IG to build an open, interoperable clinical trial register. Advanced features like user suggestions and audit trails of individual resource fields could be represented by extending the FHIR standard. CATS is the first implementation of an open-for-collaboration clinical trial registry with modern oncological trial designs and machine-to-machine communication in mind and its methodology could be extended to other medical fields besides precision oncology. Due to its well-defined interfaces, it has the potential to provide automated patient recruitment decision support for precision oncology trials in digital applications.

Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems - A Technical Case Report.

INTRODUCTION: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis. BACKGROUND: The EAV model is widely used in clinical information systems due to its adaptability, but it often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. OBJECTIVE: The objective of this study is to develop a methodological approach to address the handling of these generic data structures, Methods: We introduce a five-step methodological approach: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. RESULTS: Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion reduced data sparsity and improved the manageability of schema changes without affecting other classes of data. CONCLUSION: The developed approach provides a systematic method for handling complex data relationships and maintaining data integrity in clinical systems using EAV models. This approach facilitates the secondary use of clinical data, enhancing its utility for medical research and practice.

Tracking Changes for Inter-Version Interoperability in Heterogeneous Evolving Medical Terminologies.

INTRODUCTION: Medical terminologies and code systems, which play a vital role in the health domain, are rarely static but undergo changes as knowledge and terminology evolves. This includes addition, deletion and relabeling of terms, and, if terms are organized hierarchically, changing their position. Tracking these changes may become important if one uses multiple versions of the same terminology and interoperability is desired. METHOD: We propose a new method for automatic change tracking between terminology versions. It consists of a declarative import pipeline, which translates source terminologies into a common data model. We then use semantic and lexical change detection algorithms. They produce an ontology-based representation of terminology changes, which can be queried using semantic query languages. RESULTS: The method proves accurate in detecting additions, deletions, relocations and renaming of terms. In cases where inter-version term mapping information is provided by the publisher, we were able to highly enhance the ability to differentiate between simple additions/deletions and refinements/consolidation of terms. CONCLUSION: The method proves effective for semi-automatic change handling if term refinements and consolidation are relevant and for automatic change detection if additional mapping information is available.

System interoperability and data linkage in the era of health information management: A bibliometric analysis.

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.de/), which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation.

Enhancing Clinical Practice: Creating Dynamic Medical Content in Electronic Medical Records.

The integration of Electronic Medical Records (EMRs) revolutionized healthcare but often retained limitations from paper-based structures. This study proposes a framework for developing dynamic medical content specifically adapted to the clinical context including medical specialty and diseases. Tailoring content to this dynamic context offers several benefits, including improved access to relevant information, streamlined workflows, and potentially better patient outcomes. We applied our framework to develop neurosurgical content, focusing on brain tumors. The method involves defining the medical specialty, outlining user journeys, and iteratively developing artifacts like assessment forms, dashboards, and order sets. Standardized terminologies ensure consistency and interoperability. Our results demonstrate a successful development of content meeting user needs and clinical relevance. While initial implementation focused on neurosurgery, exploring scalability and AI integration offers promising avenues for further advancement. Future studies could quantitatively evaluate the impact of this method on user satisfaction and patient outcomes.

Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.

Investigating Decision Factors Affecting Subjective Description Disclosure in Health Information Exchange in Japan.

Despite the perceived advantages of health information exchange (HIE), their utilization has been stagnant, and a detailed survey at the regional level is needed to address this issue. We conducted interviews with operators of HIEs in operation in Japan as a pilot study and found that subjective descriptions by healthcare professionals contained in clinical notes are frequently referenced in HIEs. Currently, however, only a limited number of medical institutions in Japan disclose subjective descriptions. In this study, we aim to clarify the factors that influence whether or not to disclose subjective descriptions through an interview survey. Although the final results are yet to be determined, it is anticipated that the non-disclosing medical institutions will show negative aspects of disclosure, such as privacy concerns and misuse of information. Addressing the concerns raised in this study is important for the future dissemination of HIE.

Converting Entity-Attribute-Value Data Sources to OMOP's CDM: Lessons Learned.

Clinical data repositories often use entity-attribute-value (EAV) data models. To be valuable for secondary use, these health data can be transformed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The present paper describes the lessons learned from such an endeavour based on the concept of registering transformation functions on source data elements. We further provide future work directions for follow-up projects.

A scoping review of rule-based clinical decision support malfunctions.

OBJECTIVE: Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions. MATERIALS AND METHODS: In April 2022, we searched three bibliographic databases (MEDLINE, CINAHL, and Embase) for literature referencing CDS malfunctions. We coded the identified malfunctions according to an existing CDS malfunction taxonomy and added new categories for factors not already captured. We also extracted and summarized information related to the CDS system, such as architecture, data source, and data format. RESULTS: Twenty-eight articles met inclusion criteria, capturing 130 malfunctions. Architectures used included stand-alone systems (eg, web-based calculator), integrated systems (eg, best practices alerts), and service-oriented architectures (eg, distributed systems like SMART or CDS Hooks). No standards-based CDS malfunctions were identified. The "Cause" category of the original taxonomy includes three new types (organizational policy, hardware error, and data source) and two existing causes were expanded to include additional layers. Only 29 malfunctions (22%) described the potential impact of the malfunction on patient care. DISCUSSION: While a substantial amount of research on CDS exists, our review indicates there is a limited focus on CDS malfunctions, with even less attention on malfunctions associated with modern delivery architectures such as SMART and CDS Hooks. CONCLUSION: CDS malfunctions can and do occur across several different care delivery architectures. To account for advances in health information technology, existing taxonomies of CDS malfunctions must be continually updated. This will be especially important for service-oriented architectures, which connect several disparate systems, and are increasing in use.

The Use of COVID-19 Mobile Apps in Connecting Patients with Primary Healthcare in 30 Countries: Eurodata Study.

BACKGROUND: The COVID-19 pandemic has necessitated changes in European healthcare systems, with a significant proportion of COVID-19 cases being managed on an outpatient basis in primary healthcare (PHC). To alleviate the burden on healthcare facilities, many European countries developed contact-tracing apps and symptom checkers to identify potential cases. As the pandemic evolved, the European Union introduced the Digital COVID-19 Certificate for travel, which relies on vaccination, recent recovery, or negative test results. However, the integration between these apps and PHC has not been thoroughly explored in Europe. OBJECTIVE: To describe if governmental COVID-19 apps allowed COVID-19 patients to connect with PHC through their apps in Europe and to examine how the Digital COVID-19 Certificate was obtained. METHODOLOGY: Design and setting: Retrospective descriptive study in PHC in 30 European countries. An ad hoc, semi-structured questionnaire was developed to collect country-specific data on primary healthcare activity during the COVID-19 pandemic and the use of information technology tools to support medical care from 15 March 2020 to 31 August 2021. Key informants belong to the WONCA Europe network (World Organization of Family Doctors). The data were collected from relevant and reliable official sources, such as governmental websites and guidelines. MAIN OUTCOME MEASURES: Patient's first contact with health system, governmental COVID-19 app (name and function), Digital COVID-19 Certification, COVID-19 app connection with PHC. RESULTS: Primary care was the first point of care for suspected COVID-19 patients in 28 countries, and 24 countries developed apps to complement classical medical care. The most frequently developed app was for tracing COVID-19 cases (24 countries), followed by the Digital COVID-19 Certificate app (17 countries). Bulgaria, Italy, Serbia, North Macedonia, and Romania had interoperability between PHC and COVID-19 apps, and Poland and Romania's apps considered social needs. CONCLUSIONS: COVID-19 apps were widely created during the first pandemic year. Contact tracing was the most frequent function found in the registered apps. Connection with PHC was scarcely developed. In future pandemics, connections between health system levels should be guaranteed to develop and implement effective strategies for managing diseases.

Healthcare professionals' experiences of eHealth in palliative care for older people: challenges, compromises and the price of dignity.

PURPOSE: To explore whether and how eHealth solutions support the dignity of healthcare professionals and patients in palliative care contexts. METHOD: This qualitative study used phenomenographic analysis involving four focus group interviews, with healthcare professionals who provide palliative care to older people. RESULTS: Analysis revealed four categories of views on working with eHealth in hierarchical order: Safeguarding the patient by documenting-eHealth is a grain of support, Treated as less worthy by authorities-double standards, Distrust in the eHealth solution-when the "solution" presents a danger; and Patient first-personal contact with patients endows more dignity than eHealth. The ability to have up-to-date patient information was considered crucial when caring for vulnerable, dying patients. eHealth solutions were perceived as essential technological support, but also as unreliable, even dangerous, lacking patient information, with critical information potentially missing or overlooked. This caused distrust in eHealth, introduced unease at work, and challenged healthcare professionals' identities, leading to embodied discomfort and feeling of a lack of dignity. CONCLUSION: The healthcare professionals perceived work with eHealth solutions as challenging their sense of dignity, and therefore affecting their ability to provide dignified care for the patients. However, healthcare professionals managed to provide dignified palliative care by focusing on patient first.

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Current Health Data Standardization Project and Future Directions to Ensure Interoperability in Korea.

OBJECTIVES: The need for interoperability at the national level was highlighted in Korea, leading to a consensus on the importance of establishing national standards that align with international technological standards and reflect contemporary needs. This article aims to share insights into the background of the recent national health data standardization policy, the activities of the Health Data Standardization Taskforce, and the future direction of health data standardization in Korea. METHODS: To ensure health data interoperability, the Health Data Standardization Taskforce was jointly organized by the public and private sectors in December 2022. The taskforce operated three working groups. It reviewed international trends in interoperability standardization, assessed the current status of health data standardization, discussed its vision, mission, and strategies, engaged in short-term standardization activities, and established a governance system for standardization. RESULTS: On September 15, 2023, the notice of "Health Data Terminology and Transmission Standards" in Korea was thoroughly revised to improve the exchange of health information between information systems and ensure interoperability. This notice includes the Korea Core Data for Interoperability (KR CDI) and the Korea Core Data Transmission Standard (HL7 FHIR KR Core), which are outcomes of the taskforce's efforts. Additionally, to reinforce the standardized governance system, the Health-Data Standardization Promotion Committee was established. CONCLUSIONS: Active interest and support from medical informatics experts are needed for the development and widespread adoption of health data standards in Korea.