title: ICU Telemedicine and Critical Care Mortality: A National Effectiveness Study.
abstract: BACKGROUND
  Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain.
  
  
  OBJECTIVE
  To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals.
  
  
  METHODS
  We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach.
  
  
  RESULTS
  A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals.
  
  
  CONCLUSIONS
  Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.
authors.affiliation: *Department of Critical Care Medicine, CRISMA Center, University of Pittsburgh School of Medicine †Department of Health Policy & Management, University of Pittsburgh Graduate School of Public Health ‡Division of General Internal Medicine, University of Pittsburgh School of Medicine §Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing ∥Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA.
authors.forename: Jeremy M
authors.initials: JM
authors.lastname: Kahn
authors.forename: Tri Q
authors.initials: TQ
authors.lastname: Le
authors.forename: Amber E
authors.initials: AE
authors.lastname: Barnato
authors.forename: Marilyn
authors.initials: M
authors.lastname: Hravnak
authors.forename: Courtney C
authors.initials: CC
authors.lastname: Kuza
authors.forename: Francis
authors.initials: F
authors.lastname: Pike
authors.forename: Derek C
authors.initials: DC
authors.lastname: Angus
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D015897:Comorbidity; D003953:Diagnosis-Related Groups; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D061847:Hospitals, High-Volume / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D012111:Residence Characteristics; D012189:Retrospective Studies; D017216:Telemedicine / Q000706:statistics & numerical data*; D014481:United States