Using HL7 FHIR to achieve interoperability in patient health record

Enhancing the Functionalities of Personal Health Record Systems: Empirical Study Based on the HL7 Personal Health Record System Functional Model Release 1

Background

The increasing demand for personal health record (PHR) systems is driven by individuals' desire to actively manage their health care. However, the limited functionality of current PHR systems has affected users' willingness to adopt them, leading to lower-than-expected usage rates. The HL7 (Health Level Seven) PHR System Functional Model (PHR-S FM) was proposed to address this issue, outlining all possible functionalities in PHR systems. Although the PHR-S FM provides a comprehensive theoretical framework, its practical effectiveness and applicability have not been fully explored.

Objective

This study aimed to design and develop a tethered PHR prototype in accordance with the guidelines of the PHR-S FM. It sought to explore the feasibility of applying the PHR-S FM in PHR systems by comparing the prototype with the results of previous research.

Methods

The PHR-S FM profile was defined to meet broad clinical data management requirements based on previous research. We designed and developed a PHR prototype as a web application using the Fast Healthcare Interoperability Resources R4 (FHIR) and Logical Observation Identifiers Names and Codes (LOINC) coding system for interoperability and data consistency. We validated the prototype using the Synthea dataset, which provided realistic synthetic medical records. In addition, we compared the results produced by the prototype with those of previous studies to evaluate the feasibility and implementation of the PHR-S FM framework.

Results

The PHR prototype was developed based on the PHR-S FM profile. We verified its functionality by demonstrating its ability to synchronize data with the FHIR server, effectively managing and displaying various health data types. Validation using the Synthea dataset confirmed the prototype's accuracy, achieving 100% coverage across 1157 data items. A comparison with the findings of previous studies indicated the feasibility of implementing the PHR-S FM and highlighted areas for future research and improvements.

Conclusions

The results of this study offer valuable insights into the potential for practical application and broad adoption of the PHR-S FM in real-world health care settings.

Pediatric Clinical Staff Perspectives on Secure Messaging

BACKGROUND

Secure messaging (SM) is a communication feature within a patient portal that allows patients and clinical staff to exchange health-related information securely and confidentially.

PURPOSE

This study aimed to explore how pediatric clinical staff use SM, identify challenges in its implementation, and suggest quality improvements.

METHODS

A descriptive quantitative study was administered using an online survey in a large health care system. The Task, User, Representation, and Function framework guided the research.

RESULTS

The survey participants were moderately satisfied with the SM. Opportunities to design this system to be more efficient and maximize patient safety were identified.

CONCLUSION

Improving training and workflow can aid in incorporating SM into clinician's daily routines, focusing on enhancing user satisfaction. Future developments aimed at increasing usage and standardizing message content are crucial for encouraging adoption and ensuring patient safety.

The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile

Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.

FHIR Implementation Guide for Stroke: A dual focus on the patient's clinical pathway and value-based healthcare

BACKGROUND

Stroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use.

OBJECTIVE

Develop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles.

METHOD

We represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN).

RESULTS

Our BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures.

CONCLUSION

The FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care.

Conceptualizing Patient as an Organization With the Adoption of Digital Health

The concept of viewing a patient as an organization within the context of digital healthcare is an innovative and evolving concept. Traditionally, the patient-doctor relationship has been centered around the individual patient and their interactions with healthcare providers. However, with the advent of technology and digital healthcare solutions, the dynamics of this relationship are changing. Digital healthcare platforms and technologies enable patients to have more control and active participation in managing their health and healthcare processes. This shift empowers patients to take on a more proactive role, similar to how an organization functions with various stakeholders, goals, and strategies. The prevalence of mobile phones and wearables is regarded as an important factor in the acceptance of digital health.

Objective

This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well.

Results

PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (b > 0.1, t > 1.96, P < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness.

Conclusions

Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.

Emergency Medical Access Control System Based on Public Blockchain

IT has made significant progress in various fields over the past few years, with many industries transitioning from paper-based to electronic media. However, sharing electronic medical records remains a long-term challenge, particularly when patients are in emergency situations, making it difficult to access and control their medical information. Previous studies have proposed permissioned blockchains with limited participants or mechanisms that allow emergency medical information sharing to pre-designated participants. However, permissioned blockchains require prior participation by medical institutions, and limiting sharing entities restricts the number of potential partners. This means that sharing medical information with local emergency doctors becomes impossible if a patient is unconscious and far away from home, such as when traveling abroad. To tackle this challenge, we propose an emergency access control system for a global electronic medical information system that can be shared using a public blockchain, allowing anyone to participate. Our proposed system assumes that the patient wears a pendant with tamper-proof and biometric authentication capabilities. In the event of unconsciousness, emergency doctors can perform biometrics on behalf of the patient, allowing the family doctor to share health records with the emergency doctor through a secure channel that uses the Diffie-Hellman (DH) key exchange protocol. The pendant's biometric authentication function prevents unauthorized use if it is stolen, and we have tested the blockchain's fee for using the public blockchain, demonstrating that the proposed system is practical.

HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review

BACKGROUND

The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows.

OBJECTIVES

This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management.

METHODS

To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used.

RESULTS

The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article.

CONCLUSIONS

HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.

Establishing Medical Intelligence - Leveraging FHIR to Improve Clinical Management: a retrospective cohort and clinical implementation study

BACKGROUND

FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data.

OBJECTIVE

Here, we designed and implemented a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making.

METHODS

A Python package for the utilization of multimodal FHIR data (FHIRPACK) was developed and pioneered in five real-world clinical use cases, i.e., myocardial infarction (MI), stroke, diabetes, sepsis, and prostate cancer (PC). Patients were identified based on ICD-10 codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards.

RESULTS

For 2022, 1,302,988 patient encounters were analyzed. MI: In 72.7% of cases (N=261) medication regimens fulfilled guideline recommendations. Stroke: Out of 1,277 patients, 165 patients received thrombolysis and 108 thrombectomy. Diabetes: In 443,866 serum glucose and 16,180 HbA1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (N=13,887). Among those with dysglycemia, diagnosis was coded in 44.2% (N=6,138) of the patients. Sepsis: In 1,803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (N=773, 28.9%) and piperacillin/tazobactam was the primarily prescribed antibiotic (N=593, 37.2%). PC: Three out of 54 patients who received radical prostatectomy were identified as cases with PSA persistence or biochemical recurrence.

CONCLUSIONS

Leveraging FHIR data through large-scale analytics can enhance healthcare quality and improve patient outcomes across five clinical specialties. We identified i) sepsis patients requiring less broad antibiotic therapy, ii) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, iii) stroke patients with longer than recommended times to intervention, iv) patients with hyperglycemia who could benefit from specialist referral and v) PC patients with early increases in cancer markers.

CLINICALTRIAL

Musculoskeletal Disorder (MSD) Health Data Collection, Personalized Management and Exchange Using Fast Healthcare Interoperability Resources (FHIR)

With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient's health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

Data mining and machine learning in HIV infection risk research: An overview and recommendations

In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the study presents a roadmap based on seven different aspects, utilizing various machine learning techniques for both novice researchers and experienced researchers seeking to comprehend the current state of the art in this area. While traditional regression modeling techniques have been commonly used, researchers are increasingly adopting more advanced fully supervised machine learning and deep learning techniques, which often outperform the traditional methods in predictive performance. Additionally, the study identifies nine new open research issues and outlines possible future research plans to enhance the outcomes of HIV infection risk research. This review is expected to be an insightful guide for researchers, illuminating current practices and suggesting advancements in the field.

A blockchain-based healthcare data marketplace: prototype and demonstration

Objectives

This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.

Materials and methods

We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios.

Results

We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario.

Discussion

Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data.

Conclusion

The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

Transforming Ontology Web Language Elements into Common Terminology Service 2 Terminology Resources

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange

BACKGROUND

Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange.

OBJECTIVE

This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability.

METHODS

Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes).

RESULTS

The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names.

CONCLUSIONS

This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Crossing the digital divide: The workload of manual data entry for integration between mobile health applications and eHealth infrastructure

Background

Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to "integrate" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood.

Methodology

As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel.

Results

We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone.

Conclusion

Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.

Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study

BACKGROUND

Interoperability between health information systems is a fundamental requirement to guarantee the continuity of health care for the population. The Fast Healthcare Interoperability Resource (FHIR) is the standard that enables the design and development of interoperable systems with broad adoption worldwide. However, FHIR training curriculums need an easily administered web-based self-learning platform with modules to create scenarios and questions that the learner answers. This paper proposes a system for teaching FHIR that automatically evaluates the answers, providing the learner with continuous feedback and progress.

OBJECTIVE

We are designing and developing a learning management system for creating, applying, deploying, and automatically assessing FHIR web-based courses.

METHODS

The system requirements for teaching FHIR were collected through interviews with experts involved in academic and professional FHIR activities (universities and health institutions). The interviews were semistructured, recording and documenting each meeting. In addition, we used an ad hoc instrument to register and analyze all the needs to elicit the requirements. Finally, the information obtained was triangulated with the available evidence. This analysis was carried out with Atlas-ti software. For design purposes, the requirements were divided into functional and nonfunctional. The functional requirements were (1) a test and question manager, (2) an application programming interface (API) to orchestrate components, (3) a test evaluator that automatically evaluates the responses, and (4) a client application for students. Security and usability are essential nonfunctional requirements to design functional and secure interfaces. The software development methodology was based on the traditional spiral model. The end users of the proposed system are (1) the system administrator for all technical aspects of the server, (2) the teacher designing the courses, and (3) the students interested in learning FHIR.

RESULTS

The main result described in this work is Huemul, a learning management system for training on FHIR, which includes the following components: (1) Huemul Admin: a web application to create users, tests, and questions and define scores; (2) Huemul API: module for communication between different software components (FHIR server, client, and engine); (3) Huemul Engine: component for answers evaluation to identify differences and validate the content; and (4) Huemul Client: the web application for users to show the test and questions. Huemul was successfully implemented with 416 students associated with the 10 active courses on the platform. In addition, the teachers have created 60 tests and 695 questions. Overall, the 416 students who completed their courses rated Huemul highly.

CONCLUSIONS

Huemul is the first platform that allows the creation of courses, tests, and questions that enable the automatic evaluation and feedback of FHIR operations. Huemul has been implemented in multiple FHIR teaching scenarios for health care professionals. Professionals trained on FHIR with Huemul are leading successful national and international initiatives.

Mapping Injection Order Messages to Health Level 7 Fast Healthcare Interoperability Resources to Collate Infusion Pump Data

BACKGROUND

When administering an infusion to a patient, it is necessary to verify that the infusion pump settings are in accordance with the injection orders provided by the physician. However, the infusion rate entered into the infusion pump by the health care provider cannot be automatically reconciled with the injection order information entered into the electronic medical records (EMRs). This is because of the difficulty in linking the infusion rate entered into the infusion pump by the health care provider with the injection order information entered into the EMRs.

OBJECTIVES

This study investigated a data linkage method for reconciling infusion pump settings with injection orders in the EMRs.

METHODS

We devised and implemented a mechanism to convert injection order information into the Health Level 7 Fast Healthcare Interoperability Resources (FHIR), a new health information exchange standard, and match it with an infusion pump management system in a standard and simple manner using a REpresentational State Transfer (REST) application programming interface (API). The injection order information was extracted from Standardized Structured Medical Record Information Exchange version 2 International Organization for Standardization/technical specification 24289:2021 and was converted to the FHIR format using a commercially supplied FHIR conversion module and our own mapping definition. Data were also sent to the infusion pump management system using the REST Web API.

RESULTS

Information necessary for injection implementation in hospital wards can be transferred to FHIR and linked. The infusion pump management system application screen allowed the confirmation that the two pieces of information matched, and it displayed an error message if they did not.

CONCLUSION

Using FHIR, the data linkage between EMRs and infusion pump management systems can be smoothly implemented. We plan to develop a new mechanism that contributes to medical safety through the actual implementation and verification of this matching system.

Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians

The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.

A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability

Introduction

Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools.

Methods

We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms.

Results

We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK.

Discussion

Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.

Examining supporting and constraining factors of physicians' acceptance of telemedical online consultations: a survey study

As healthcare demands exceed outpatient physicians' capacities, telemedicine holds far-reaching potential for both physicians and patients. It is crucial to holistically analyze physicians' acceptance of telemedical applications, such as online consultations. This study seeks to identify supporting and constraining factors that influence outpatient physicians' acceptance of telemedicine.We develop a model based on the unified theory of acceptance and use of technology (UTAUT). To empirically examine our research model, we conducted a survey among German physicians (n = 127) in 2018-2019. We used the partial least squares (PLS) modeling approach to test our model, including a mediation analysis. The results indicate that performance expectancy (β = .397, P < .001), effort expectancy (β = .134, P = .03), and social influence (β = .337, P < .001) strongly impact the intention to conduct online consultations and explain 55% of its variance. Structural conditions regarding data security comprise a key antecedent, associating with performance expectancy (β = .193, P < .001) and effort expectancy (β = .295, P < .001). Regarding potential barriers to usage intentions, we find that IT anxiety predicts performance (β = -.342, P < .001) and effort expectancy (β = -.364, P < .001), while performance expectancy fully mediates (βdirect = .022, P = .71; βindirect = -.138, P < .001) the direct relationship between IT anxiety and the intention to use telemedical applications.This research provides explanations for physicians' behavioral intention to use online consultations, underlining UTAUT's applicability in healthcare contexts. To boost acceptance, social influences, such as personal connections and networking are vital, as colleagues can serve as multipliers to reach convergence on online consultations among peers. To overcome physicians' IT anxiety, training, demonstrations, knowledge sharing, and management incentives are recommended. Furthermore, regulations and standards to build trust in the compliance of online consultations with data protection guidelines need reinforcement from policymakers and hospital management alike.

Feasibility of Implementation of a Mobile Digital Personal Health Record to Coordinate Care for Children and Youth With Special Health Care Needs in Primary Care: Protocol for a Mixed Methods Study

BACKGROUND

Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation.

OBJECTIVE

This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes.

METHODS

This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study.

RESULTS

Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures.

CONCLUSIONS

Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/46847.

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

Outcomes of End-User Testing of a Care Coordination Mobile App With Families of Children With Special Health Care Needs: Simulation Study

BACKGROUND

Care for children with special health care needs relies on a network of providers who work to address the medical, behavioral, developmental, educational, social, and economic needs of the child and their family. Family-directed, manually created visual depictions of care team composition (ie, care mapping) and detailed note-taking curated by caregivers (eg, care binders) have been shown to enhance care coordination for families of these children, but they are difficult to implement in clinical settings owing to a lack of integration with electronic health records and limited visibility of family-generated insights for care providers. Caremap is an electronic health record-integrated digital personal health record mobile app designed to integrate the benefits of care mapping and care binders. Currently, there is sparse literature describing end-user participation in the co-design of digital health tools. In this paper, we describe a project that evaluated the usability and proof of concept of the Caremap app through end-user simulation.

OBJECTIVE

This study aimed to conduct proof-of-concept testing of the Caremap app to coordinate care for children with special health care needs and explore early end-user engagement in simulation testing. The specific aims included engaging end users in app co-design via app simulation, evaluating the usability of the app using validated measures, and exploring user perspectives on how to make further improvements to the app.

METHODS

Caregivers of children with special health care needs were recruited to participate in a simulation exercise using Caremap to coordinate care for a simulated case of a child with complex medical and behavioral needs. Participants completed a postsimulation questionnaire adapted from 2 validated surveys: the Pediatric Integrated Care Survey (PICS) and the user version of the Mobile Application Rating Scale (uMARS). A key informant interview was also conducted with a liaison to Spanish-speaking families regarding app accessibility for non-English-speaking users.

RESULTS

A Caremap simulation was successfully developed in partnership with families of children with special health care needs. Overall, 38 families recruited from 19 different US states participated in the simulation exercise and completed the survey. The average rating for the survey adapted from the PICS was 4.1 (SD 0.82) out of 5, and the average rating for the adapted uMARS survey was 4 (SD 0.83) out of 5. The highest-rated app feature was the ability to track progress toward short-term, patient- and family-defined care goals.

CONCLUSIONS

Internet-based simulation successfully facilitated end-user engagement and feedback for a digital health care coordination app for families of children with special health care needs. The families who completed simulation with Caremap rated it highly across several domains related to care coordination. The simulation study results elucidated key areas for improvement that translated into actionable next steps in app development.

Challenges of vaccination information system implementation: A systematic literature review

Globally, healthcare services have begun to show interest in switching from paper-based to electronic-based vaccination records through Vaccination Information Systems (VIS). VIS have been implemented in various countries, but the study on the challenges of implementing VIS in these countries is still limited. The challenges of implementing VIS need to be understood to become a subject of discussion and anticipation by other countries that are just starting to implement VIS. We analyzed 32 selected publications from 634 initially retrieved. Fourteen challenges were successfully identified when implementing VIS, including interoperability, data quality, security and privacy, standardization, usability, internet connectivity, infrastructure, workflow, funding, government regulations, awareness, skeptical response, computer literacy, and staff-related challenges. The challenges of interoperability and data quality were found to be the most widely discussed by previous studies. In addition to identifying the challenges, this study includes a series of solutions that can be applied to overcome each challenge.

Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records

As foundation doctors, we have often found ourselves informing patients that a certain aspect of their medical information cannot be immediately found, either because it is on an electronic system we cannot access, or it is in a hospital that is unlinked to our own. Unsurprisingly, this frequently leaves patients flabbergasted and confused. We started to wonder: if patients' data are entered onto an electronic system: where do those data go? If medical data are searched for, where do those data come from? Why are there so many hidden sources of information that clinicians cannot access? In an ever-increasing digital sphere, electronic data will be the future of holistic health and social care planning, impacting every clinician's day-to-day role. From electronic healthcare records to the use of artificial intelligence solutions, this article will serve as an introduction to how data flows in modern healthcare systems.

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

Smart Contract Authentication assisted GraphMap-Based HL7 FHIR architecture for interoperable e-healthcare system

The exponential growth in the global population and significant advancements in healthcare broadened the scope of intervention for e-Healthcare through decentralized data access and information exchange, making complex clinical decisions. e-Healthcare can perform several functionalities, including EHR communication, telemedicine, and complex clinical decision systems (CCDS), but large-scale users still find it challenging to maintain interoperability, stability, and scalability. Accommodating an extensive array of stakeholders, which includes patients, doctors, hospitals, and laboratories, demands interoperability to serve scalable services. FHIR frameworks have played a vital role in e-Healthcare designs. Most of the existing HL7-FHIR frameworks have used REST-API using HTTP-query for CRUD tasks that impose numerous rules and constraints, making the process more complex and time-consuming, violating the quality-of-service (QoS) standards on different levels. This paper develops a novel, robust Smart-Contract Authentication Assisted HL7-FHIR framework toward an interoperable e-Healthcare solution. Unlike classical REST API-based FHIR, our proposed method applies a Graph-mapping concept that transforms each resource variable into an equivalent Graph-Mapped Data Structure (GMS), which is subsequently stored in the NoSQL MongoDB database, reducing computational costs and time to meet QoS demands. The proposed model employs three key components, GMS-driven HL7 FHIR Gateway Model, Smart Contract Authentication and Client Model. The Smart Contract function helped verify and authenticate users to ensure privacy and secure EHR exchange. The assessment of the performance of the proposed model reveals a significant reduction in computational time with optimal resource utilization making it a significant and viable option to better the real-world e-Healthcare mechanisms.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

Integrated Personal Health Record in Indonesia: Design Science Research Study

BACKGROUND

Personal health records (PHRs) are consumer-centric tools designed to facilitate the tracking, management, and sharing of personal health information. PHR research has mainly been conducted in high-income countries rather than in low- and middle-income countries. Moreover, previous studies that proposed PHR design in low- and middle-income countries did not describe integration with other systems, or there was no stakeholder involvement in exploring PHR requirements.

OBJECTIVE

This study developed an integrated PHR architecture and prototype in Indonesia using design science research. We conducted the research in Indonesia, a low- to middle-income country with the largest population in Southeast Asia and a tiered health system.

METHODS

This study followed the design science research guidelines. The requirements were identified through interviews with 37 respondents from health organizations and a questionnaire with 1012 patients. Afterward, the proposed architecture and prototype were evaluated via interviews with 6 IT or eHealth experts.

RESULTS

The architecture design refers to The Open Group Architecture Framework version 9.2 and comprises 5 components: architecture vision, business architecture, application architecture, data architecture, and technology architecture. We developed a high-fidelity prototype for patients and physicians. In the evaluation, improvements were made to add the stakeholders and the required functionality to the PHR and add the necessary information to the functions that were developed in the prototype.

CONCLUSIONS

We used design science to illustrate PHR integration in Indonesia, which involves related stakeholders in requirement gathering and evaluation. We developed architecture and application prototypes based on health systems in Indonesia, which comprise routine health services, including disease treatment and health examinations, as well as promotive and preventive health efforts.

Integration Strategy for e-Intensive Care Unit: A Narrative Review and Implementation Plan

Introduction: The objectives of this article are to review previously established tele-intensive care unit (ICU) services describing their impact at the technical and medical level, and to propose an implementation plan to equip health care facilities in need of telehealth. Materials and Methods: We searched MEDLINE, EMBASE, PubMed, and ISI web of knowledge, using terms related to "e-ICU" and "tele-ICU" from inception to May 2021. Discussion: At the technical level, an increase in private insurance enrollment and routine checkups, as well as a reduction in hospital utilization rates and improvement in health outcomes was seen in the aftermath of the adoption of telehealth insurance mandates. Moreover, e-ICU helped reducing mortality and length of hospital stay of critically ill patients. The main approach to implementation should include features that are widely accepted for quality improvement, including being focused on patient-centered outcomes, having strong executive support, and targeting changes that were known to improve outcomes. HL7 Fast Healthcare Interoperability Resources stands out as one of the best candidates to achieve structural interoperability for patient health records. Conclusions: Adoption of tele-ICU services requires a substantial up-front investment and ongoing cost of maintenance. This could be challenging for hospitals with low budgets. Hence the importance of further investigating more efficient strategies of e-ICU services integration and implementation.

An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications

World population and life expectancy have increased steadily in recent years, raising issues regarding access to medical treatments and related expenses. Through last-generation medical sensors, NFC (Near Field Communication) and radio frequency identification (RFID) technologies can enable healthcare internet of things (H-IoT) systems to improve the quality of care while reducing costs. Moreover, the adoption of point-of-care (PoC) testing, performed whenever care is needed to return prompt feedback to the patient, can generate great synergy with NFC/RFID H-IoT systems. However, medical data are extremely sensitive and require careful management and storage to protect patients from malicious actors, so secure system architectures must be conceived for real scenarios. Existing studies do not analyze the security of raw data from the radiofrequency link to cloud-based sharing. Therefore, two novel cloud-based system architectures for data collected from NFC/RFID medical sensors are proposed in this paper. Privacy during data collection is ensured using a set of classical countermeasures selected based on the scientific literature. Then, data can be shared with the medical team using one of two architectures: in the first one, the medical system manages all data accesses, whereas in the second one, the patient defines the access policies. Comprehensive analysis of the H-IoT system can be useful for fostering research on the security of wearable wireless sensors. Moreover, the proposed architectures can be implemented for deploying and testing NFC/RFID-based healthcare applications, such as, for instance, domestic PoCs.

One Digital Health for more FAIRness

BACKGROUND

 One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

OBJECTIVES

 This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

METHODS

 An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

RESULTS

 A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

CONCLUSIONS

 ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project

Introduction

Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.

Objectives

This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism.

Methods

The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k.

Results

The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected.

Conclusion

The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review

OBJECTIVES

The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research.

MATERIALS AND METHODS

We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities.

RESULTS

Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself.

DISCUSSION

Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities.

CONCLUSION

The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

An Interoperable Electronic Health Record System for Clinical Cardiology

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.

Rebooting the Electronic Health Record

Justifications for the widespread adoption and integration of an electronic health record (EHR) have long leaned on the purported benefits of the technology. However, the performance of the EHR has been underwhelming relative to the promises of immediate access to relevant patient information, clinical decision supports, computerized ordering, and transferable patient data. In this narrative review, we provide an overview of the historical problems and limitations of the EHR, detail the core principles that define agile processes that may overcome the barriers faced by the current EHR, and re-imagine what an integrated, seamless EHR that serves its users and patients might look like. Moving forward, the EHR should be redesigned using a middle-out framework and empowering dual-type champions to maintain the sustainable diffusion of future innovations.

A systematic review of technologies and standards used in the development of rule-based clinical decision support systems

A Clinical Decision Support System (CDSS) is a technology platform that uses medical knowledge with clinical data to provide customised advice for an individual patient's care. CDSSs use rules to encapsulate expert knowledge and rules engines to infer logic by evaluating rules according to a patient's specific information and related medical facts. However, CDSSs are by nature complex with a plethora of different technologies, standards and methods used to implement them and it can be difficult for practitioners to determine an appropriate solution for a specific scenario. This study's main goal is to provide a better understanding of different technical aspects of a CDSS, identify gaps in CDSS development and ultimately provide some guidelines to assist their translation into practice. We focus on issues related to knowledge representation including use of clinical ontologies, interoperability with EHRs, technology standards, CDSS architecture and mobile/cloud access.

This study performs a systematic literature review of rule-based CDSSs that discuss the underlying technologies used and have evaluated clinical outcomes. From a search that yielded an initial set of 1731 papers, only 15 included an evaluation of clinical outcomes. This study has found that a large majority of papers did not include any form of evaluation and, for many that did include an evaluation, the methodology was not sufficiently rigorous to provide statistically significant results. From the 15 papers shortlisted, there were no RCT or quasi-experimental studies, only 6 used ontologies to represent domain knowledge, only 2 integrated with an EHR system, only 5 supported mobile use and only 3 used recognised healthcare technology standards (and all these were HL7 standards). Based on these findings, the paper provides some recommendations for future CDSS development.

We Can Define the Domain of Information Online and Thus Globally Uniformly

Any information is (transported as) a selection from an ordered set, which is the “domain” of the information. For example, any piece of digital information is a number sequence that represents such a selection. Its senders and receivers (with software) should know the format and domain of the number sequence in a uniform way worldwide. So far, this is not guaranteed. However, it can be guaranteed after the introduction of the new “Domain Vector” (DV) data structure: “UL plus number sequence”. Thereby “UL” is a “Uniform Locator”, which is an efficient global pointer to the machine-readable online definition of the number sequence. The online definition can be adapted to the application so that the DV represents the application-specific, reproducible features in a precise (one-to-one), comparable, and globally searchable manner. The systematic, nestable online definition of domains of digital information (number sequences) and the globally defined DV data structure have great technical potential and are recommended as a central focus of future computer science.

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study

Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.

Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review

BACKGROUND

The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided.

OBJECTIVE

This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts.

METHODS

We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions.

RESULTS

The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health-related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used.

CONCLUSIONS

We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.

The Power of Patient Engagement with Electronic Health Records as Research Participants (Preprint)

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study

Background

Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support.

Objective

To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service.

Methods

An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges.

Results

Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email.

Conclusions

Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.

Development of a Mobile Application for Smart Clinical Trial Subject Data Collection and Management

Wearable devices and digital health technologies have enabled the exchange of urgent clinical trial information. We developed an application to improve the functioning of decentralized clinical trials and performed a heuristic evaluation to reflect the user demands of existing clinical trial workers. The waterfall model of the software life cycle was used to guide the development. Focus group interviews (N = 7) were conducted to reflect the needs of clinical research professionals, and Wizard of Oz prototyping was performed to ensure high usability and completeness. Unit tests and heuristic evaluation (N = 11) were used. Thematic analysis was performed using the focus group interview data. Based on this analysis, the main menu was designed to include health management, laboratory test results, medications, concomitant medications, adverse reactions, questionnaires, meals, and My Alarm. Through role-playing, the functions and configuration of the prototype were adjusted and enhanced, and a heuristic evaluation was performed. None of the heuristic evaluation items indicated critical usability errors, suggesting that the revised prototype application can be practically applied to clinical trials. The application is expected to increase the efficiency of clinical trial management, and the development process introduced in this study will be helpful for researchers developing similar applications in the future.

Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care

Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades. Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. The creation of neonatal neurocritical care units, or Neuro-NICUs, provides strategies to reduce brain injury using standardized clinical protocols, methodologies, and provider education and training. Bedside neuromonitoring has dramatically improved our ability to provide assessment of newborns at high risk. Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous evaluation of brain function and cerebral oxygenation at the bedside. Extended and combined uses of these techniques, also described as multimodal monitoring, may allow practitioners to better understand the physiology of critically ill neonates. Furthermore, the rapid growth of technology in the Neuro-NICU, along with the increasing use of telemedicine and artificial intelligence with improved data mining techniques and machine learning (ML), has the potential to vastly improve decision-making processes and positively impact outcomes. This article will cover the current applications of neuromonitoring in the Neuro-NICU, recent advances, potential pitfalls, and future perspectives in this field.

G. van Roon J, Xavier M, De Vita S, Tzioufas AG, Fotiadis DI. Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.