The Summary Quality Index (SQUID):

A summary measure for multiple quality indicators in primary care

Paul J. Nietert, PhDRuth G. Jenkins, MS

Andrea M. Wessell, PharmDSarah T. Corley, MD

Steven M. Ornstein, MD

The Medical University of South Carolina

AcademyHealth, Boston MA June 2005

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

PPRNet Sites

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.

A-TRIP Methods

• We provide practice reports, showing practice’s 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

Example: DM pts with HgbA1C in past 6 months

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

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

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!

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

The SQUID: Algorithm

• Create indicator variables (ei) 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 (mi) 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

The SQUID: Algorithm

• E = The number of measures for which the pt is eligible (denominator) = Σ ei

• M = The number of eligible measures for which the pt has met his/her morbidity-specific target (numerator) = Σ mi

• Create a pt-level SQUID = • Create a practice-level SQUID = average of all

pt-level SQUIDs

M

E

The SQUID: Interpretation

• A patient’s SQUID reflects the percentage of targets met out of the total number of targets for which he/she is eligible.

• A practice’s SQUID reflects the average percentage of targets achieved by their patients.

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%.

Mean Practice-Level SQUID

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Fre

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cy

(N

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f P

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0% 10% 20% 30% 40% 50% 60%

SQUID Mean

Results: SQUID Over Time

27.3%

31.5%

0%

5%

10%

15%

20%

25%

30%

35%

Mea

n P

atie

nt

SQ

UID

Apr03

May03

Jun03

Jul03

Aug03

Sep03

Oct03

Nov03

Dec03

Jan04

Feb04

Mar04

Apr04

May04

Jun04

Jul04

Aug04

Sep04

Oct04

Nov04

Dec04

Jan05

Feb05

Mar05

Apr05

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

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.

Thank you!