Get access

Assessment of Chronic Illness Care (ACIC): A Practical Tool to Measure Quality Improvement


  • Amy E. Bonomi,

  • Edward H. Wagner,

  • Russell E. Glasgow,

  • Michael VonKorff

This paper was supported by grant no. 0347984 from The Robert Wood Johnson Foundation to Group Health Cooperative of Puget Sound. The authors gratefully acknowledge Nirmala Sandhu, M.P.H., for her assistance with data analysis; Jane Altemose for her input on tables presented in this manuscript; and Brian Austin, Connie Davis, M.N., Lou Grothaus, M.S., Mike Hindmarsh, M.A., George Jackson, Ph.D. (candidate, University of North Carolina, Charlotte), Paula Lozano, M.D., M.P.H., and Judith Schaefer, M.P.H., for their input on the content of the Assessment of Chronic Illness Care.

Address correspondence to Amy E. Bonomi, M.P.H., MacColl Institute for Healthcare Innovation, Center for Health Studies, Group Health Cooperative of Puget Sound, 1730 Minor Avenue, Suite 1290, Seattle, WA 98101-1448. MS. Bonomi is a Ph.D. candidate in health services at the University of Washington. Edward H. Wagner, M.D., Russell E. Glasgow, and Michael VonKorff, Sc.D., are also with the MacColl Institute. Dr. Glasgow is also with the AMC Cancer Research Center, Denver, CO.


Objective. To describe initial testing of the Assessment of Chronic Illness Care (ACIC), a practical quality-improvement tool to help organizations evaluate the strengths and weaknesses of their delivery of care for chronic illness in six areas: community linkages, self-management support, decision support, delivery system design, information systems, and organization of care.

Data Sources. (1) Pre-post, self-report ACIC data from organizational teams enrolled in 13-month quality-improvement collaboratives focused on care for chronic illness; (2) independent faculty ratings of team progress at the end of collaborative.

Study design. Teams completed the ACIC at the beginning and end of the collaborative using a consensus format that produced average ratings of their system's approach to delivering care for the targeted chronic condition. Average ACIC subscale scores (ranging from 0 to 11, with 11 representing optimal care) for teams across all four collaboratives were obtained to indicate how teams rated their care for chronic illness before beginning improvement work. Paired t-tests were used to evaluate the sensitivity of the ACIC to detect system improvements for teams in two (of four) collaboratives focused on care for diabetes and congestive heart failure (CHF). Pearson correlations between the ACIC subscale scores and a faculty rating of team performance were also obtained.

Results. Average baseline scores across all teams enrolled at the beginning of the collaboratives ranged from 4.36 (information systems) to 6.42 (organization of care), indicating basic to good care for chronic illness. All six ACIC subscale scores were responsive to system improvements diabetes and CHF teams made over the course of the collaboratives. The most substantial improvements were seen in decision support, delivery system design, and information systems. CHF teams had particularly high scores in self-management support at the completion of the collaborative. Pearson correlations between the ACIC subscales and the faculty rating ranged from .28 to .52.

Conclusion. These results and feedback from teams suggest that the ACIC is responsive to health care quality-improvement efforts and may be a useful tool to guide quality improvement in chronic illness care and to track progress over time.