BACKGROUND: There is substantial hospital-level variation in end-of-life (EOL) treatment intensity. OBJECTIVE: To explore the association between organizational factors and EOL treatment intensity in Pennsylvania (PA) hospitals. RESEARCH DESIGN: Cross-sectional mixed-mode survey of Chief Nursing Officers of PA hospitals linked to hospital-level measures of EOL treatment intensity calculated from PA Health Care Cost Containment Council (PHC4) hospital discharge data. HOSPITALS: One hundred sixty-four hospitals, of which 124 (76%) responded to the survey. MEASURES: : The dependent variable was an index of hospital EOL treatment intensity; the independent variables included administrative data-derived structural and market characteristics and 29 survey-derived hospital or ICU programs, policies, or practices. RESULTS: : In models restricted to independent variables drawn from administrative sources (available for all 164 hospitals), bed size (P < 0.001), proportion of admissions among black patients (P < 0.001), and county-wide hospital market competitiveness (Herfindahl-Hirschman index) (P = 0.001) were independently associated with greater EOL treatment intensity (adjusted R = 0.5136). In models that additionally included hospital programs, policies, and practices (available for 124 hospitals), only an ICU long length of stay review committee (P = 0.03) was independently associated with greater EOL treatment intensity (adjusted R = 0.5357). CONCLUSIONS: Information about hospital and ICU programs, policies, and practices believed relevant to the treatment of patients near the end of life offers little additional explanatory power in understanding hospital-level variation in EOL treatment intensity than administratively-derived variables alone. Future studies should explore the contribution of more difficult to measure social norms in shaping hospital practice patterns.
BACKGROUND: There is substantial hospital-level variation in end-of-life (EOL) treatment intensity. OBJECTIVE: To explore the association between organizational factors and EOL treatment intensity in Pennsylvania (PA) hospitals. RESEARCH DESIGN: Cross-sectional mixed-mode survey of Chief Nursing Officers of PA hospitals linked to hospital-level measures of EOL treatment intensity calculated from PA Health Care Cost Containment Council (PHC4) hospital discharge data. HOSPITALS: One hundred sixty-four hospitals, of which 124 (76%) responded to the survey. MEASURES: : The dependent variable was an index of hospital EOL treatment intensity; the independent variables included administrative data-derived structural and market characteristics and 29 survey-derived hospital or ICU programs, policies, or practices. RESULTS: : In models restricted to independent variables drawn from administrative sources (available for all 164 hospitals), bed size (P < 0.001), proportion of admissions among black patients (P < 0.001), and county-wide hospital market competitiveness (Herfindahl-Hirschman index) (P = 0.001) were independently associated with greater EOL treatment intensity (adjusted R = 0.5136). In models that additionally included hospital programs, policies, and practices (available for 124 hospitals), only an ICU long length of stay review committee (P = 0.03) was independently associated with greater EOL treatment intensity (adjusted R = 0.5357). CONCLUSIONS: Information about hospital and ICU programs, policies, and practices believed relevant to the treatment of patients near the end of life offers little additional explanatory power in understanding hospital-level variation in EOL treatment intensity than administratively-derived variables alone. Future studies should explore the contribution of more difficult to measure social norms in shaping hospital practice patterns.
Authors: L A O'Brien; J A Grisso; G Maislin; K LaPann; K P Krotki; P J Greco; E A Siegert; L K Evans Journal: JAMA Date: 1995-12-13 Impact factor: 56.272
Authors: Derek C Angus; Amber E Barnato; Walter T Linde-Zwirble; Lisa A Weissfeld; R Scott Watson; Tim Rickert; Gordon D Rubenfeld Journal: Crit Care Med Date: 2004-03 Impact factor: 7.598
Authors: R S Pritchard; E S Fisher; J M Teno; S M Sharp; D J Reding; W A Knaus; J E Wennberg; J Lynn Journal: J Am Geriatr Soc Date: 1998-10 Impact factor: 5.562
Authors: Donald R Sullivan; Xinggang Liu; Douglas S Corwin; Avelino C Verceles; Michael T McCurdy; Drew A Pate; Jennifer M Davis; Giora Netzer Journal: Chest Date: 2012-12 Impact factor: 9.410
Authors: Martin P Charns; Mary K Foster; Elaine C Alligood; Justin K Benzer; James F Burgess; Donna Li; Nathalie M McIntosh; Allison Burness; Melissa R Partin; Steven B Clauser Journal: J Natl Cancer Inst Monogr Date: 2012-05
Authors: Rachel Kohn; Vanessa Madden; Jeremy M Kahn; David A Asch; Amber E Barnato; Scott D Halpern; Meeta Prasad Kerlin Journal: Ann Am Thorac Soc Date: 2017-02
Authors: Howard L Saft; Paul S Richman; Andrew R Berman; Richard A Mularski; Paul A Kvale; Daniel E Ray; Paul Selecky; Dee W Ford; Steven M Asch Journal: J Grad Med Educ Date: 2014-03
Authors: Joanna L Hart; Michael O Harhay; Nicole B Gabler; Sarah J Ratcliffe; Caroline M Quill; Scott D Halpern Journal: JAMA Intern Med Date: 2015-06 Impact factor: 21.873
Authors: Vinay Kini; Fenton H McCarthy; Sheeva Rajaei; Andrew J Epstein; Paul A Heidenreich; Peter W Groeneveld Journal: Am Heart J Date: 2015-07-26 Impact factor: 4.749
Authors: Daniel D Matlock; Traci E Yamashita; Sung-Joon Min; Alexander K Smith; Amy S Kelley; Stacy M Fischer Journal: J Am Geriatr Soc Date: 2016-05-16 Impact factor: 5.562