JMIR Publications

ABSTRACT

Background:

Care for Heart Failure (HF) patients causes a substantial load on healthcare systems where prominent challenge is the elevated rate of readmissions within a 30-day period 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 a patient 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 like machine learning.

Objective:

This study aims to investigate how a developed clinical decision support tool alters the decision-making processes of healthcare professionals in the specific context of discharging HF patients, and if so, in which ways. Additionally, the aim is to capture the experiences of healthcare practitioners as they engage with the system's outputs to analyze usability aspects and get insights related to future implementation.

Methods:

A quasi-experimental design with randomized crossover assessment will be conducted with healthcare professionals on heart failure patients’ scenarios in a region located in South of Sweden. 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided 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 a machine learning-based clinical decision support model on the risk level for readmission of a concrete patient. 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. The 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. The project was funded by the Knowledge Foundation via the Center for Applied Intelligent Systems Research (CAISR) Health research profile. This study will be carried out in collaboration with Cambio Healthcare Systems.

Results:

An ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). Recruitment process of the clinicians and the patient scenario selection will start in September 2023.

Conclusions:

This study protocol will contribute to the development of future formative evaluation studies to test machine learning models with clinical professionals.