Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

• Published in: BMC medical informatics and decision making 2023-08, Vol.23 (1), p.145-145, Article 145
• Main Authors: Abtahi, Hamidreza, Shahmoradi, Leila, Amini, Shahideh, Gholamzadeh, Marsa
• Format: Article
• Language: English
• Subjects: Analysis; Candidates; Clinical decision making; Clinical decision support system; Clinical protocols; Collaboration; Decision making; Decision support systems; Decision Support Systems, Clinical; Evaluation; Expert system; Health aspects; Health care; Health informatics; Humans; Information flow; Iterative methods; Knowledge management; Knowledge transfer; Language; Literature reviews; Lung diseases; Lung Transplantation; Lung transplants; Lungs; M-health; Medical records; Medical referrals; Ontology; Patient care planning; Physicians; Questionnaires; Semantic web; Software; Software development; Translational Science, Biomedical; Transplantation; Web Ontology Language-OWL; Workflow
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02249-6

Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.