title: Predicting 30- to 120-Day Readmission Risk among Medicare Fee-for-Service Patients Using Nonmedical Workers and Mobile Technology.
abstract: OBJECTIVE
  Hospital readmissions are a large source of wasteful healthcare spending, and current care transition models are too expensive to be sustainable. One way to circumvent cost-prohibitive care transition programs is complement nurse-staffed care transition programs with those staffed by less expensive nonmedical workers. A major barrier to utilizing nonmedical workers is determining the appropriate time to escalate care to a clinician with a wider scope of practice. The objective of this study is to show how mobile technology can use the observations of nonmedical workers to stratify patients on the basis of their hospital readmission risk.
  
  
  METHODS
  An area agency on aging in Massachusetts implemented a quality improvement project with the aim of reducing 30-day hospital readmission rates using a modified care transition intervention supported by mobile predictive analytics technology. Proprietary readmission risk prediction algorithms were used to predict 30-, 60-, 90-, and 120-day readmission risk.
  
  
  RESULTS
  The risk score derived from the nonmedical workers' observations had a significant association with 30-day readmission rate with an odds ratio (OR) of 1.12 (95 percent confidence interval [CI], 1 .09-1.15) compared to an OR of 1.25 (95 percent CI, 1.19-1.32) for the risk score using nurse observations. Risk scores using nurse interpretation of nonmedical workers' observations show that patients in the high-risk category had significantly higher readmission rates than patients in the baseline-risk and mild-risk categories at 30, 60, 90, and 120 days after discharge. Of the 1,064 elevated-risk alerts that were triaged, 1,049 (98.6 percent) involved the nurse care manager, 804 (75.6 percent) involved the patient, 768 (72.2 percent) involved the health coach, 461 (43.3 percent) involved skilled nursing, and 235 (22.1 percent) involved the outpatient physician in the coordination of care in response to the alert.
  
  
  CONCLUSIONS
  The predictive nature of the 30-day readmission risk scores is influenced by both nurse and nonmedical worker input, and both are required to adequately triage the needs of the patient.
  
  
  CONCLUSIONS
  Although this preliminary study is limited by a modest effect size, it demonstrates one approach to using technology to contribute to delivery model innovation that could curb wasteful healthcare spending by tapping into an existing underutilized workforce.
authors.affiliation: Care at Hand in San Francisco, CA.
authors.forename: Andrey
authors.initials: A
authors.lastname: Ostrovsky
authors.affiliation: Elder Services of Merrimack Valley in Lawrence, MA.
authors.forename: Lori
authors.initials: L
authors.lastname: O'Connor
authors.affiliation: University of Massachusetts Lowell School of Nursing in Lowell, MA.
authors.forename: Olivia
authors.initials: O
authors.lastname: Marshall
authors.affiliation: University of Massachusetts Lowell School of Nursing in Lowell, MA.
authors.forename: Amanda
authors.initials: A
authors.lastname: Angelo
authors.affiliation: University of Massachusetts Lowell School of Nursing in Lowell, MA.
authors.forename: Kelsy
authors.initials: K
authors.lastname: Barrett
authors.affiliation: University of Massachusetts Lowell School of Nursing in Lowell, MA.
authors.forename: Emily
authors.initials: E
authors.lastname: Majeski
authors.affiliation: Nestle Corporation in Copenhagen, Denmark.
authors.forename: Maxwell
authors.initials: M
authors.lastname: Handrus
authors.affiliation: Care at Hand in San Francisco, CA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Levy
mesh_terms: D000465:Algorithms; D040421:Cell Phone; D018588:Fee-for-Service Plans; D005260:Female; D006801:Humans; D020407:Internet; D008297:Male; D008404:Massachusetts; D006278:Medicare; D010359:Patient Readmission; D011237:Predictive Value of Tests; D058996:Quality Improvement; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D014481:United States
keywords: care transitions; digital health; hospital readmissions; long-term supports and services; post-acute care; risk prediction