Implementation of complex adaptive chronic care: the Patient Journey Record system (PaJR)

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BackgroundThe Patient Journey Record system (PaJR) is an application of a complex adaptive chronic care model in which early detection of adverse changes in patient biopsychosocial trajectories prompts tailored care, constitute the cornerstone of the model.AimsTo evaluate the PaJR system's impact on care and the experiences of older people with chronic illness, who were at risk of repeat admissions over 12months.DesignCommunity-based cohort study – random assignment into intervention and usual care group, with process and outcome evaluation.Study populationAdult and older patients with multiple morbidity, one or more chronic diseases with one or more overnight hospitalizations, and seven or more general practice visits in the past 6months.Complex interventionPaJR lay care guides/advocates call patients and their caregivers. The care guides summarize their semi-structured conversations about health concerns and well-being. Predictive modelling and rules-based algorithms trigger alerts in relation to online call summaries. Alerts are acted upon according to agreed guidelines.AnalysisDescriptive and comparative statistics.OutcomesImpact on unplanned emergency ambulatory care sensitive admissions (ACSC) with an overnight stay; sensitivity of alerts and predictions; rates of care guides-supported activities.FindingsFive part-time lay care guides and a care manager monitored 153 intervention patients for 500 person months with 5050 phone calls. The 153 patients in the intervention group were comparable to the 61 controls. The intervention group reported in 50% of calls that their health limited their social activities; and one-third of calls reported immediate health concerns. Predictive analytics were highly sensitive to risk of hospitalization. ACSC admissions were reduced by 50% compared to controls across the sites.DiscussionThe initial implementation of a complex patient-centred adaptive chronic care model using lay care guides, supported by machine learning, appeared sensitive to risk of hospitalization and capable of stabilizing illness journeys in older patients with multi-morbidity.ConclusionActions based on alerts produced in this study appeared to significantly reduce hospitalizations. This paves the way for further testing of the model.

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