Key result
Machine learning decision support for heart failure readmissions awaits evaluation for clinical and workflow impact.
Why the study?
Optimal discharge timing for heart failure patients involves clinical uncertainty and high readmission rates, prompting investigation into how a machine learning-based clinical decision support tool alters clinicians' decision-making processes.
Does a machine learning-based clinical decision support tool alter decision-making processes and improve decision consistency for healthcare professionals discharging patients with heart failure?
Does a machine learning-based clinical decision support tool alter decision-making processes and improve decision consistency for healthcare professionals discharging patients with heart failure?
This study protocol describes a quasi-experimental crossover trial to assess the impact of an ML-based readmission prediction tool on clinician decision-making in heart failure care.
BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52744.
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Nair et al. (2024) studied Heart Failure (n=12). Machine learning-based clinical decision support (CDS) tool vs. Standard electronic health record data without CDS output was evaluated on Decision consistency, decision quality, and work efficiency. A machine learning-based clinical decision support tool for predicting heart failure readmissions will be evaluated to assess its impact on clinician decision-making and workflow.
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