title: Using a claims data-based sentinel system to improve compliance with clinical guidelines: results of a randomized prospective study.
abstract: OBJECTIVE
  To demonstrate the potential effect of deploying a sentinel system that scans administrative claims information and clinical data to detect and mitigate errors in care and deviations from best medical practices.
  
  
  METHODS
  Members (n = 39 462; age range, 12-64 years) of a midwestern managed care plan were randomly assigned to an intervention or a control group. The sentinel system was programmed with more than 1000 decision rules that were capable of generating clinical recommendations. Clinical recommendations triggered for subjects in the intervention group were relayed to treating physicians, and those for the control group were deferred to study end.
  
  
  RESULTS
  Nine hundred eight clinical recommendations were issued to the intervention group. Among those in both groups who triggered recommendations, there were 19% fewer hospital admissions in the intervention group compared with the control group (P < .001). Charges among those whose recommendations were communicated were dollar 77.91 per member per month (pmpm) lower and paid claims were dollar 68.08 pmpm lower than among controls compared with the baseline values (P = .003 for both). Paid claims for the entire intervention group (with or without recommendations) were dollar 8.07 pmpm lower than those for the entire control group. In contrast, the intervention cost dollar 1.00 pmpm, suggesting an 8-fold return on investment.
  
  
  CONCLUSIONS
  Ongoing use of a sentinel system to prompt clinically actionable, patient-specific alerts generated from administratively derived clinical data was associated with a reduction in hospitalization, medical costs, and morbidity.
authors.affiliation: Active Health Management, Inc., New York, NY, USA. jjavitt@healthdirections.net
authors.forename: Jonathan C
authors.initials: JC
authors.lastname: Javitt
authors.forename: Gregory
authors.initials: G
authors.lastname: Steinberg
authors.forename: Todd
authors.initials: T
authors.lastname: Locke
authors.forename: James B
authors.initials: JB
authors.lastname: Couch
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Jacques
authors.forename: Iver
authors.initials: I
authors.lastname: Juster
authors.forename: Lonny
authors.initials: L
authors.lastname: Reisman
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D005260:Female; D019983:Guideline Adherence / Q000191:economics / Q000706:statistics & numerical data*; D006302:Health Services Research; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D008329:Managed Care Programs / Q000592:standards*; D008875:Middle Aged; D015146:Midwestern United States; D017063:Outcome Assessment, Health Care; D017410:Practice Guidelines as Topic; D011446:Prospective Studies; D018571:Sentinel Surveillance