A prospective observational concordance study to evaluate computational model-driven clinical practice guidelines for Type 2 diabetes mellitus.

BACKGROUND: Clinical Practice Guidelines (CPGs) provide healthcare professionals with performance and decision-making support during the treatment of patients. Sometimes, however, they are poorly implemented. The IDE4ICDS platform was developed and validated with CPGs for type 2 diabetes mellitus (T2DM). OBJECTIVE: The main objective of this paper is to present the results of the clinical validation of the IDE4ICDS platform in a real clinical environment at two health clinics in the Andalusian Public Health System (SSPA) in the southern Spanish region of Andalusia. METHODS: National and international knowledge sources on T2DM were selected and reviewed and used to define a diabetes CPG model on the IDE4ICDS platform. Once the diabetes CPG was configured and deployed, it was validated. A total of 506 patients were identified as meeting the inclusion criteria, of whom 130 could be recruited and 89 attended the appointment. RESULTS: A concordance analysis was performed with the kappa value. Overall agreement between the recommendations provided by the system and those recorded in each patient's EHR was good (0.61 - 0.80) with a total kappa index of 0.701, leading to the conclusion that the system provided appropriate recommendations for each patient and was therefore well-functioning. CONCLUSIONS: A series of possible improvements were identified based on the limitations for the recovery of variables related to the quality of these recolected variables, the detection of duplicate recommendations based on different input variables for the same patient, and clinical usability, such as the capacity to generate reports based on the recommendations generated. Nevertheless, the project resulted in the IDE4ICDS platform: a Clinical Decision Support System (CDSS) capable of providing appropriate recommendations for improving the management and quality of patient care and optimizing health outcomes. The result of this validation is a safe and effective pathway for developing and adopting digital transformation at the regional scale of the use of biomedical knowledge in real healthcare.

FAIR principles to improve the impact on health research management outcomes.

Background: The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes. Objective: This paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution. Methods: To analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed. Results: In the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used. Conclusions: Adopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques.

Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

[This corrects the article DOI: 10.2196/27990.].

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

BACKGROUND: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE: The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS: A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS: This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS: An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study.

BACKGROUND: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. OBJECTIVE: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). METHODS: The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. RESULTS: Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. CONCLUSIONS: Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

Desiderata for a New Generation of EHRs for Inclusive Healthcare.

OBJECTIVES: This research addresses several factors relevant to inequity in healthcare that may be susceptible to being addressed in a new generation of electronic health records (EHRs). METHODS: Through a scoping review of the literature, inequities related to ethnicity, gender, and socioeconomic aspects in healthcare in general and, more specifically in EHRs, have been considered. Papers have been identified between 2011 and 2022 in three categories: EHR, gender inequalities, and ethnicity inequalities. RESULTS: Twenty-two recommendations have been identified within the scope of the three categories indicated above. These exposed requirements focus on two spheres: (1) technical sphere, mainly focused on the characteristics and tools that the EHR should develop from taking into account the studied inequalities; and (2) clinical sphere, which mainly affects patients, health professionals, and health providers. CONCLUSIONS: Ethnic and gender inequalities are essential factors to take into account when diagnosing, monitoring, preventing, and treating a patient. These factors give us the keys to discovering recommendations for a new generation of EHRs to help mitigate these needs.