title: Discrepancies between explicit and implicit review: physician and nurse assessments of complications and quality.
abstract: OBJECTIVE
  To identify and characterize discrepancies between explicit and implicit medical record review of complications and quality of care.
  
  
  METHODS
  Forty-two acute-care hospitals in California and Connecticut in 1994.
  
  
  METHODS
  In a retrospective chart review of 1,025 Medicare beneficiaries age >65, we compared explicit (nurse) and implicit (physician) reviews of complications and quality in individual cases. To understand discrepancies, we calculated the kappa statistic and examined physicians' comments.
  
  
  METHODS
  With Medicare discharge abstracts, we used the Complications Screening Program to identify and then select a stratified random sample of cases flagged for 1 of 15 surgical complications, 5 medical complications, and unflagged controls. Peer Review Organization nurses and physicians performed chart reviews.
  
  
  RESULTS
  Agreement about complications was fair (kappa = 0.36) among surgical and was moderate (kappa = 0.59) among medical cases. In discordant cases, physicians said that complications were insignificant, attributable to a related diagnosis, or present on admission. Agreement about quality was poor among surgical and medical cases (kappa = 0.00 and 0.13, respectively). In discordant cases, physicians said that quality problems were unavoidable, small lapses in otherwise satisfactory care, present on admission, or resulted in no adverse outcome.
  
  
  CONCLUSIONS
  We identified many discrepancies between explicit and implicit review of complications and quality. Physician reviewers may not consider process problems that are ubiquitous in hospitals to represent substandard quality.
authors.affiliation: Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
authors.forename: Saul N
authors.initials: SN
authors.lastname: Weingart
authors.forename: Roger B
authors.initials: RB
authors.lastname: Davis
authors.forename: R Heather
authors.initials: RH
authors.lastname: Palmer
authors.forename: Michael
authors.initials: M
authors.lastname: Cahalane
authors.forename: Mary Beth
authors.initials: MB
authors.lastname: Hamel
authors.forename: Kenneth
authors.initials: K
authors.lastname: Mukamal
authors.forename: Russell S
authors.initials: RS
authors.lastname: Phillips
authors.forename: Donald T
authors.initials: DT
authors.lastname: Davies
authors.forename: Lisa I
authors.initials: LI
authors.lastname: Iezzoni
mesh_terms: D000368:Aged; D000465:Algorithms; D015982:Bias; D002140:California; D003237:Connecticut; D006302:Health Services Research; D006761:Hospitals / Q000592:standards*; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology; D008485:Medical Audit; D008499:Medical Records; D008505:Medical Staff, Hospital; D006278:Medicare / Q000592:standards*; D009731:Nursing Audit; D009741:Nursing Staff, Hospital; D011183:Postoperative Complications / Q000453:epidemiology; D011366:Professional Review Organizations / Q000592:standards*; D018571:Sentinel Surveillance; D012984:Software