title: Implementation of complex adaptive chronic care: the Patient Journey Record system (PaJR).
abstract: BACKGROUND
  The 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.
  
  
  OBJECTIVE
  To 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 12 months.
  
  
  METHODS
  Community-based cohort study - random assignment into intervention and usual care group, with process and outcome evaluation.
  
  
  METHODS
  Adult 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 6 months. COMPLEX INTERVENTION: PaJR 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.
  
  
  METHODS
  Descriptive and comparative statistics.
  
  
  RESULTS
  Impact on unplanned emergency ambulatory care sensitive admissions (ACSC) with an overnight stay; sensitivity of alerts and predictions; rates of care guides-supported activities.
  
  
  RESULTS
  Five 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.
  
  
  CONCLUSIONS
  The 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.
  
  
  CONCLUSIONS
  Actions based on alerts produced in this study appeared to significantly reduce hospitalizations. This paves the way for further testing of the model.
authors: Northern Ontario School of Medicine, Sudbury, Ontario, Canada. carmelmarymartin@gmail.com
authors: Carmel M
authors: CM
authors: Martin
authors: Carl
authors: C
authors: Vogel
authors: Deirdre
authors: D
authors: Grady
authors: Atieh
authors: A
authors: Zarabzadeh
authors: Lucy
authors: L
authors: Hederman
authors: John
authors: J
authors: Kellett
authors: Kevin
authors: K
authors: Smith
authors: Brendan
authors: B
authors: O' Shea
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D002908:Chronic Disease / Q000628:therapy*; D002985:Clinical Protocols; D015331:Cohort Studies; D019468:Disease Management; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D004777:Environment; D005260:Female; D006293:Health Promotion; D006296:Health Services / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D012944:Social Support