Abstract
Background: Nursing home residents often exhibit both polypathology and a dependency that require medical care. However, in medical deserts, access to care is difficult, sometimes associated with hospitalisation or disruption of the care pathway. Clinical decision support systems with Artificial Intelligence algorithms have been validated in various clinical fields but none yet takes the holistic approach required when caring for nursing home residents.
Aim: We explored the feasability of integrating a clinical decision support systems with Artificial Intelligence algorithm tool into nursing home care. We sought to improve holistic gerontological care.
Methods: We included nursing home residents with medical events requiring the attendance of a general practitioner. Nurses and residents completed interviews using the clinical decision support systems with Artificial Intelligence algorithm tool incorporated into a tablet. Next, reports were sent to remote physicians. We compared the diagnostic severity of the medical event and the aetiological diagnostic hypotheses suggested by the tool and the remote physician. We also evaluated user acceptability.
Results: Eighteen medical events were reported. The clinical decision support systems with Artificial Intelligence algorithm tool was unable to provide reports on four occasions because details were lacking, but diagnostic severity was always assessed. Sixteen missed diagnoses specific to the elderly were identified. The concordances between the on-site and remote physician diagnostic severity levels and aetiological hypotheses were 66.7% and 71.4% respectively. Fourteen users (residents and professionals) of the tool completed the acceptability questionnaire. Nurses and physicians found that the tool was convenient, useful, and simple, but also rather time-consuming because of poor between-software interoperability. Some remote physicians did not trust their diagnoses because medical histories were not available to them. Residents reported that evaluations using the tool and remote physicians were acceptable.
Conclusion: This Intel@Med-Faisa study identified how the clinical decision support systems with Artificial Intelligence algorithm tool can be better adapted to reflect the characteristics of nursing home residents and the needs of different users. The next step is proof-of-concept evaluation.
Clinicaltrials.gov number: NCT04242043