title: Implementing Computerized Provider Order Entry in Acute Care Hospitals in the United States Could Generate Substantial Savings to Society.
abstract: BACKGROUND
  Computerized provider order entry (CPOE) with clinical decision support is a basic criterion for hospitals' meaningful use of electronic health record systems. A study was conducted to evaluate from the societal perspective the cost-utility of implementing CPOE in acute care hospitals in the United States.
  
  
  METHODS
  A decision-analytical model compared CPOE with paper ordering among patients admitted to acute care hospitals with >25 beds. Parameters included start-up and maintenance costs, as well as costs for provider time use, medication and laboratory test ordering, and preventable adverse drug events. Probabilistic analyses produced incremental costs, effectiveness, and cost-effectiveness ratios for hospitals in four bed-size categories (25-72, 72-141, 141-267, 267-2,249).
  
  
  RESULTS
  Relative to paper ordering and using typical estimates of implementation costs, CPOE had, on average, >99% probability of yielding savings to society and improving health. Per hospital in each size category, mean life-time savings -in millions-were $11.6 (standard deviation, $9.30), $34.4 ($21.2), $71.8 ($43.8), and $170 ($119) (2012 dollars), respectively, and quality-adjusted life-years (QALYs) gained were 19.9 (16.9), 53.7 (38.7), 109 (79.6), and 249 (205). Incremental effectiveness and costs were less favorable in certain circumstances, such as high implementation costs. Nationwide, anticipated increases in CPOE implementation from 2009 through 2015 could save $133 billion and 201,000 QALYs.
  
  
  CONCLUSIONS
  In addition to improving health, implementing CPOE with clinical decision support could yield substantial long-term savings to society in the United States, although results for individual hospitals are likely to vary.
authors.affiliation: Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at University of California, Los Angeles (UCLA), USA.
authors.forename: Teryl K
authors.initials: TK
authors.lastname: Nuckols
authors.forename: Steven M
authors.initials: SM
authors.lastname: Asch
authors.forename: Vaspaan
authors.initials: V
authors.lastname: Patel
authors.forename: Emmett
authors.initials: E
authors.lastname: Keeler
authors.forename: Laura
authors.initials: L
authors.lastname: Anderson
authors.forename: Melinda B
authors.initials: MB
authors.lastname: Buntin
authors.forename: José J
authors.initials: JJ
authors.lastname: Escarce
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003362:Cost-Benefit Analysis; D020000:Decision Support Systems, Clinical / Q000191:economics*; D004363:Drug Utilization; D064420:Drug-Related Side Effects and Adverse Reactions / Q000191:economics / Q000517:prevention & control; D005260:Female; D006739:Hospital Administration / Q000191:economics*; D006742:Hospital Bed Capacity; D006801:Humans; D008297:Male; D050316:Medical Order Entry Systems / Q000191:economics*; D008508:Medication Errors / Q000191:economics* / Q000517:prevention & control; D008875:Middle Aged; D017059:Models, Econometric; D019057:Quality-Adjusted Life Years; D013997:Time Factors; D014481:United States