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Design and Evaluation of a Mobile-Based Decision Support System to Enhance Lung Transplant Candidate Assessment and Management: Knowledge Translation Integrated With Clinical Workflow Publisher Pubmed



Abtahi H1 ; Shahmoradi L2, 3 ; Amini S4 ; Gholamzadeh M2, 5
Authors
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Authors Affiliations
  1. 1. Pulmonary and Critical Care Medicine Department, Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Halal Research Center of IRI, FDA, Tehran, Iran
  4. 4. Clinical Pharmacy Department, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Ph.D. in Medical Informatics, Health Information Management and Medical informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Source: BMC Medical Informatics and Decision Making Published:2023


Abstract

Background: 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. Method: 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. Results: 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). Conclusion: 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. © 2023, The Author(s).
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3. Analysis of the Most Influential Factors Affecting Outcomes of Lung Transplant Recipients: Amultivariate Prediction Model Based on Unos Data, BMJ Open (2025)
4. Machine Learning-Based Techniques to Improve Lung Transplantation Outcomes and Complications: A Systematic Review, BMC Medical Research Methodology (2022)
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