Background: Interorganizational collaboration management theory contends that cooperation between distinct but related organizations can yield innovation and competitive advantage to the participating organization. Yet, it is unclear if a multi-institutional collaborative can improve quality outcomes across communities.
Methods: We developed a large regional collaborative network of 15 hospitals and 24 emergency medical service agencies surrounding Dallas, Texas, and collected patient-level data on treatment times for acute myocardial infarctions. Using a pre-/posttest research design, we applied median tests of differences to explore outcome changes between groups and over the 6-year period, using data extracted from participating hospital electronic health records.
Results: We analyzed temporal trends and changes in treatment times for 2302 patients with ST-elevation myocardial infarction between the pre- and posttest groups. We found a statistically significant 19-minute median reduction in the key outcome metric (total ischemic time, the time difference between the patient's first reported symptoms and the definitive opening of the artery). This represents a 10.8% community-wide improvement over time.
Conclusions: Interorganizational collaboration focused on quality improvement can impact population health across a community. This study provides a basis for broader understanding and participation by health care organizations in multi-institutional community change efforts.
School of Biomedical Informatics, The University of Texas Health Science Center, Houston (Drs Langabeer and Champagne-Langabeer); Metropolitan State University of Denver, Denver, Colorado (Dr Helton); American Heart Association, Denver, Colorado (Ms Segrest); Texas A&M University School of Public Health, College Station (Dr Kash); Department of Healthcare Leadership & Management, Medical University of South Carolina, Charleston (Dr DelliFraine); and The University of Texas Southwestern Medical Center, Dallas (Dr Fowler).
Correspondence: James R. Langabeer II, PhD, School of Biomedical Informatics, The University of Texas Health Science Center, 7000 Fannin St, Ste 600, Houston, TX 77030 (James.R.Langabeer@uth.tmc.edu).
The authors declare no conflicts of interest.