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The Summary Quality Index (SQUID): A summary measure for multiple quality indicators in primary care Paul J. Nietert, PhD Ruth G. Jenkins, MS Andrea M.

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Presentation on theme: "The Summary Quality Index (SQUID): A summary measure for multiple quality indicators in primary care Paul J. Nietert, PhD Ruth G. Jenkins, MS Andrea M."— Presentation transcript:

1 The Summary Quality Index (SQUID): A summary measure for multiple quality indicators in primary care Paul J. Nietert, PhD Ruth G. Jenkins, MS Andrea M. Wessell, PharmD Sarah T. Corley, MD Steven M. Ornstein, MD The Medical University of South Carolina AcademyHealth, Boston MA June 2005

2 Accelerating Translation of Research Into Practice (A-TRIP) AHRQ-funded demonstration project to improve preventive services 92 ambulatory care practices around the U.S. All practices use an electronic medical record. All are part of Practice Partner Research Network (PPRNet). Quarterly data extracts from each practice

3 PPRNet Sites

4 Background Quality indicators are helpful tools for translating research into clinical practice. Providing feedback to clinicians on these indicators and assisting them with process change through on-site visits by our research staff have been shown to help them make system changes that improve the quality of the care they provide.

5 A-TRIP Methods We provide practice reports, showing practices performance on 78 unique quality indicators from 8 clinical domains: –Hypertension –CHD and Stroke –Cancer –Immunizations – Inappropriate Rx in Elderly – MH/SA – Respiratory/Infectious Disease – Nutrition and Obesity

6 Example: DM pts with HgbA1C in past 6 months

7 The Challenge Analyzing improvements in 78 quality indicators presents challenges: –Many measures are correlated with each other LDL < 100 (CHD pts) LDL < 100 (DM pts) –Some lab measures have different targets based upon morbidity BP < 130/80 (DM pts) BP < 140/90 (HTN pts) –Some pts not eligible for some measures

8 Methods Goal: Design a method for summarizing the 78 quality measures that: –Is clinically relevant and interpretable –Is statistically sound –Allows for the evaluation of QI efforts over time

9 Possible Solutions For each pt, add all 78 indicator variables together –Bad idea! (Many pts not eligible for certain measures) Use principal component analysis techniques for analysis –Bad idea! (Too complicated to explain) Use the Summary Quality Index –Good idea!

10 The SQUID: Algorithm Define processes and outcomes of interest, regardless of target –BP Monitoring –LDL Monitoring –HgbA1C Monitoring – BP Control – LDL Control – HgbA1C Control 78 indicators reduced to 32 processes & 5 outcomes

11 The SQUID: Algorithm Create indicator variables (e i ) that reflect whether pt is eligible for each process and outcome measure –PAP Test (Women > 18 yrs old) –FOBT (Men & Women > 50 yrs old) Create indicator variables (m i ) that reflect whether pt has met the target for a process or outcome, given his/her demographics and/or morbidity –If pt has DM, then BP must be < 130/80 –If pt has HTN, BP must be < 140/90

12 The SQUID: Algorithm E = The number of measures for which the pt is eligible (denominator) = Σ e i M = The number of eligible measures for which the pt has met his/her morbidity-specific target (numerator) = Σ m i Create a pt-level SQUID = Create a practice-level SQUID = average of all pt-level SQUIDs M E

13 The SQUID: Interpretation A patients SQUID reflects the percentage of targets met out of the total number of targets for which he/she is eligible. A practices SQUID reflects the average percentage of targets achieved by their patients.

14 Results: SQUIDs as of 4/1/05 Across the 92 physician practices, adult pts were eligible, on average, for 9.7 out of a total of 37 processes and outcomes. On average, pts met 3.7 of their eligible targets. Across all pts, the average SQUID was 31.5%.

15 Mean Practice-Level SQUID

16 Results: SQUID Over Time

17 Notes SQUIDs can be used in patient-level or practice-level analyses. Because it is a continuous measure, and because of its relative normality, certain linear models may be appropriate. Examples: –Mixed effects regression models –Generalized estimating equations models

18 Conclusions The SQUID is an effective tool to aid in the evaluation of multiple quality indicators. –Nice statistical properties –Clinically meaningful In future research studies and quality improvement efforts that include multiple quality indicators, the SQUID should be considered as a measure of overall quality.

19 Thank you!


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