Using a summary measure for multiple quality indicators in primary care: the Summary QUality InDex (SQUID)
1 Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC (USA)
2 Department of Pharmacy and Clinical Sciences, South Carolina College of Pharmacy, Medical University of South Carolina campus, Charleston, SC (USA)
3 Department of Family Medicine, Medical University of South Carolina, Charleston, SC (USA)
4 Department of Family Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA (USA)
5 College of Nursing and Clinical Services, Medical University of South Carolina, Charleston, SC (USA)
Implementation Science 2007, 2:11 doi:10.1186/1748-5908-2-11Published: 2 April 2007
Assessing the quality of primary care is becoming a priority in national healthcare agendas. Audit and feedback on healthcare quality performance indicators can help improve the quality of care provided. In some instances, fewer numbers of more comprehensive indicators may be preferable. This paper describes the use of the Summary Quality Index (SQUID) in tracking quality of care among patients and primary care practices that use an electronic medical record (EMR). All practices are part of the Practice Partner Research Network, representing over 100 ambulatory care practices throughout the United States.
The SQUID is comprised of 36 process and outcome measures, all of which are obtained from the EMR. This paper describes algorithms for the SQUID calculations, various statistical properties, and use of the SQUID within the context of a multi-practice quality improvement (QI) project.
At any given time point, the patient-level SQUID reflects the proportion of recommended care received, while the practice-level SQUID reflects the average proportion of recommended care received by that practice's patients. Using quarterly reports, practice- and patient-level SQUIDs are provided routinely to practices within the network. The SQUID is responsive, exhibiting highly significant (p < 0.0001) increases during a major QI initiative, and its internal consistency is excellent (Cronbach's alpha = 0.93). Feedback from physicians has been extremely positive, providing a high degree of face validity.
The SQUID algorithm is feasible and straightforward, and provides a useful QI tool. Its statistical properties and clear interpretation make it appealing to providers, health plans, and researchers.