title: A validated value-based model to improve hospital-wide perioperative outcomes: adaptability to combined medical/surgical inpatient cohorts.
abstract: OBJECTIVE
  Authors hypothesized that building safe hospital systems to improve value-based surgical outcomes is predicated on workflow redesign for dynamic risk stratification, coupled with "real-time" mitigation of risk. We developed a comanagement model for hospitalized surgical cohort, and determined whether this iterative process redesign for surgery will be adaptable to disparate hospital systems and will be beneficial for combined medical/surgical adult inpatients.
  
  
  BACKGROUND
  Concerns about preventable harm in hospitalized patients have generated a plethora of both, process-driven and outcome-based strategies in US Healthcare. Although comparison between hospitals is a common mechanism to drive quality, other innovative approaches are needed for real-time risk mitigation to improve outcomes.
  
  
  METHODS
  Prospective implementation of Surgical Continuum of Care (SCoC) model in hospitals initially for surgery patients; subsequently Continuum of Care (CoC) for medical/surgical population. Redesign of hospital care delivery model: patient cohorting, floor-based team building, and intensivist/hospitalist staffing of progressive care unit (PCU). Work flow redesign for clinical effectiveness: multidisciplinary team rounds, acuity stratified care rounding based on dynamic risk assessment into a novel HAWK (high risk)/DOVE (low risk) patient grouping, intensivist/hospitalist comanagement of surgical patients, and targeted response.
  
  
  METHODS
  Pre- and postintervention with concurrent cohort control design.
  
  
  METHODS
  Academic medical centers for SCoC and integrated health system hospital for CoC.
  
  
  METHODS
  SCoC Pilot Study-Campus A: Preintervention control group 1998-2000, Intervention Group 2001-2004; Campus B: Comparator Control Group 1998-2004. SCoC Validation Study-Campus C: Preintervention Group 2001-2005; Intervention Group 2006-2008. CoC Study-Campus D: Hospital-wide Group 2009. METRICS: Mortality, length of stay (LOS): overall, surgical intensive care unit and PCU, readmission rates, and cost. Case mix index for risk adjustment.
  
  
  RESULTS
  Total >100,000 admissions. There was a significant reduction in overall surgical mortality in both, pilot (P < 0.002) and validation (P < 0.02) SCoC studies and overall hospital mortality in the medical/surgical CoC study (risk-adjusted mortality index progressively declined in CoC study from 1.16 pre-CoC to 0.77 six months post-CoC implementation; significant at 75% confidence level). Case mix index was unchanged during study period in each campus. Nested study in validation cohort of hospital-wide versus surgery alone (observed/expected mortality index) demonstrated significant benefit to SCoC in intervention group. The mortality benefit was primarily derived from risk-stratified rounding and actively managing risk prone population in the PCU. Surgical intensive care unit, PCU, and total hospital patient-days significantly decreased in SCoC pilot study (P < 0.05), reflecting enhanced throughput. LOS reduction benefit persisted in SCoC validation and CoC studies. In addition to decreased LOS, cost savings were in PCU (range, $851,511-2,007,388) and top diagnosis-related groups, for example, $452 K/yr for diagnosis-related group 148.
  
  
  CONCLUSIONS
  SCoC is patient-centered, outcomes-driven, value-based approach for hospital-wide surgical patient safety. The principles of this value paradigm are adaptable to other hospitals as demonstrated in our longitudinal study in 3 hospital systems, and the initial experience of CoC suggests that this model will have benefit beyond surgical hospital cohort.
authors.affiliation: Divisions of Quality/Innovation, Geisinger Health System, Wilkes-Barre, PA 18711, USA. tsravikumar@geisinger.edu
authors.forename: Thanjavur S
authors.initials: TS
authors.lastname: Ravikumar
authors.forename: Cordelia
authors.initials: C
authors.lastname: Sharma
authors.forename: Corrado
authors.initials: C
authors.lastname: Marini
authors.forename: Glenn D
authors.initials: GD
authors.lastname: Steele
authors.forename: Garry
authors.initials: G
authors.lastname: Ritter
authors.forename: Rafael
authors.initials: R
authors.lastname: Barrera
authors.forename: Mimi
authors.initials: M
authors.lastname: Kim
authors.forename: Steven M
authors.initials: SM
authors.lastname: Safyer
authors.forename: Kathy
authors.initials: K
authors.lastname: Vandervoort
authors.forename: Marcella
authors.initials: M
authors.lastname: De Geronimo
authors.forename: Lindsay
authors.initials: L
authors.lastname: Baker
authors.forename: Peter
authors.initials: P
authors.lastname: Levi
authors.forename: Steven
authors.initials: S
authors.lastname: Pierdon
authors.forename: Meg
authors.initials: M
authors.lastname: Horgan
authors.forename: Kenric
authors.initials: K
authors.lastname: Maynor
authors.forename: Gerald
authors.initials: G
authors.lastname: Maloney
authors.forename: Mark
authors.initials: M
authors.lastname: Wojtowicz
authors.forename: Karen
authors.initials: K
authors.lastname: Nelson
mesh_terms: D000328:Adult; D016009:Chi-Square Distribution; D003266:Continuity of Patient Care / Q000592:standards*; D003953:Diagnosis-Related Groups; D005240:Feasibility Studies; D013502:General Surgery / Q000592:standards*; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D016014:Linear Models; D008137:Longitudinal Studies; D019300:Medical Errors / Q000517:prevention & control*; D017146:Models, Organizational; D010043:Outcome and Process Assessment, Health Care; D010348:Patient Care Team / Q000592:standards; D010359:Patient Readmission / Q000706:statistics & numerical data; D010865:Pilot Projects; D011446:Prospective Studies; D011785:Quality Assurance, Health Care; D012189:Retrospective Studies; D018570:Risk Assessment; D017751:Safety Management / Q000592:standards*; D018709:Statistics, Nonparametric