Team structure and adverse events in home health care

Med Care. 2007 Jun;45(6):553-61. doi: 10.1097/MLR.0b013e31803bb49c.

Abstract

Objective: To identify relationships between variations in team structure and risk-adjusted adverse events across 86 teams in a large US home health care organization.

Methods: Patient episode data were collected for two 6-month periods, January-June 2002 (N = 54,732 episodes) and January-June 2003 (N = 51,560 episodes). An adverse event was defined as having 1 or more events defined by the Centers for Medicare and Medicaid Services for home health care episodes. Events were risk adjusted using 2 alternative approaches-a Z-score and a Fixed Effects (FE)-score, for each team in each period. These scores (1 for each team in each period) were then regressed against objective measures of team structure.

Results: The regressions based on the FE-score as the measure of quality performed better than the traditional Z-score. Based on these regressions we find that volume (number of episodes) (P = 0.03), number of weekend visits (P = 0.02), and workload distribution (P = 0.02) were negatively associated with the occurrence of adverse events, whereas higher weekend admissions (P = 0.01) were positively associated with adverse events.

Conclusions: Our analysis identifies a number of key team-level organizational variables that influence adverse events in home health care services. We also have demonstrated that the FE-score is a more accurate measure of team quality, as opposed to the Z-score, given that it focuses only on "team attributable" adverse events by isolating and excluding random variation from the quality score.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Female
  • Home Care Services / organization & administration*
  • Home Care Services / standards
  • Humans
  • Male
  • Models, Organizational
  • Multivariate Analysis
  • Patient Care Team / organization & administration*
  • Quality of Health Care*
  • Regression Analysis
  • Risk Adjustment
  • Risk Management / organization & administration*
  • United States