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Studying the Quality of Primary Care

Objectives. Argue for the importance of quality of care research;Describe an outcomes-based definition of quality;Develop a tentative research agendaDescribe and discuss various outcome measuresDevelop a preliminary proposal for a longitudinal study of quality of care . Importance. Quality repor

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Studying the Quality of Primary Care

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    1. Studying the Quality of Primary Care Jim Mold, M.D., M.P.H. University of Oklahoma Chris van Weel, M.D., Ph.D. University Medical Center Nijmegen, The Netherlands

    2. Objectives Argue for the importance of quality of care research; Describe an outcomes-based definition of quality; Develop a tentative research agenda Describe and discuss various outcome measures Develop a preliminary proposal for a longitudinal study of quality of care

    3. Importance Quality report cards and competition for patients Pay-for-performance Survival of primary care Why not just have NPs and PAs follow clinical practice guidelines?

    4. Quality Indicators CAD DM CHF HTN Preventive Care

    5. Quality Indicators CAD Lipid profile LDL cholesterol level Blood pressure level Anti-platelet therapy Drug to lower LDL cholesterol Beta blocker if prior MI ACE inhibitor therapy

    6. Quality Indicators DM A1c measurement A1c level Lipid measurement LDL cholesterol level Blood pressure level Urine protein testing Foot exam Eye exam

    7. Potential Problems Life prolongation may not be a goal (e.g. patient with terminal illness, dementia) Risk of adverse events may be low (e.g. blindness, ESRD in an older diabetic) All indicators have equal weight though impact on outcomes differ greatly No credit for progress in a particular patient (e.g. BP lowered from 200 to 140) In patients with multiple chronic conditions, prioritization is often necessary People aren’t widgets. Some variability is desirable.

    8. An Alternative Approach High quality care involves doing the things most likely to help patients achieve the health-related goals they have established in consultation with the health care team. Interventions that should be pursued are those that are most likely to increase the probability of the outcomes desired by each patient.

    9. Case Example 65 y.o. Caucasian, non-smoker, with a 5-year history of Type 2 DM. No end-organ damage. BMI 27, BP 140/90, LDL 120, HDL 45, A1c 10%.

    10. Quality Indicators DM A1c measurement A1c level Lipid measurement LDL cholesterol level Blood pressure level Urine protein testing Foot exam Eye exam

    11. Risk Engines DiabForecaster Risk Engine Sheffield University Risk Engine UKPDS Risk Engine** CDC/RTI Risk Engine CORE Risk Engine EAGLE Risk Engine PHD Risk Engine**

    12. Diabetes Personal Health Decisions (PHD) Engine Archimedes program Attempts to model diabetes by including >100 biological variables, symptoms, signs, tests, treatments, and outcomes Uses differential equations and object-oriented programming to model the links between variables Keeps all continuous variables continuous

    13. PHD Validation Subjected to a series of 74 validation exercises involving 18 clinical trials, 10 of which were not used in the construction of the engine Correlation between results of PHD simulations and clinical trials overall was astounding (r=0.99) Correlation between absolute differences in outcomes also amazing (r=0.97)

    14. Life Prolongation 10-yr. Risk of MI 27% -------------------------------------------- Aspirin 81mg/day -11% Lower BP to 120 -7% Lower BP to 130 -4% Lower A1c to 7% -5% Lower A1c to 8.5% -2% Lower LDL to 70 -6% Lower LDL to 90 -4%

    15. CDC Risk Engine Months of life gained from various interventions in an average 55 y.o. type 2 diabetic: LDL (<100 with statin): 8 months BP (<130/80): 6 months ACEI (aside from BP effect): 2 months BG (<7.0 with metformin): 4 months BG (<7 with other agents): < 1month CDC Cost Effectiveness Group. JAMA 2002; 287 (19):2542-2551 Bjorholt, et al. J Intern Med. 2002; 251(6): 508

    16. Quality of Life With no A1c reduction, this patient’s 10-year risks of developing blindness, end-stage renal disease, and amputation are 0.73%, 0%, and 0.15%. Reducing A1c from 10% to 7% would change those risks to 0.67%, 0%, and 0.15%. Symptoms of hyperglycemia could be relieved by lowering A1c to 8%.

    17. CDC Risk Engine End Stage Renal Disease (ESRD) by age at diagnosis of DM: Age at Dx A1c Lifetime Risk_ 55 7.0 0.9% 55 9.0 1.6% 65 7.0 0.3% 65 9.0 0.6%

    18. CDC Risk Engine Blindness from DM Retinopathy by age at diagnosis of DM: Age at Dx A1c Lifetime Risk 55 7.0 0.1% 55 9.0 1.2% 65 7.0 <0.1% 65 9.0 0.5%

    19. Case Example 65 y.o. Caucasian non-smoker with a 5-year history of Type 2 DM. No end-organ damage. BMI 30, BP 140/90, LDL 140, HDL 38, A1c 10%. Assuming that both life prolongation and prevention of disabilities are goals: ASA plus reduction of BP to 130, A1c to 8, LDL to 70 would account for 90% of the benefits that could be obtained from medical interventions. (Urine for protein, eye and foot exams would add almost nothing.)

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