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Improving Hypertension Quality Measurement Using Electronic Health Records

Improving Hypertension Quality Measurement Using Electronic Health Records. S Persell, AN Kho, JA Thompson, DW Baker Feinberg School of Medicine Northwestern University Chicago, Illinois Supported by award 1 K08 HS015647-01 from the Agency for Healthcare Research and Quality.

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Improving Hypertension Quality Measurement Using Electronic Health Records

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  1. Improving Hypertension Quality Measurement Using Electronic Health Records S Persell, AN Kho, JA Thompson, DW Baker Feinberg School of Medicine Northwestern University Chicago, Illinois Supported by award 1 K08 HS015647-01 from the Agency for Healthcare Research and Quality

  2. Problems with Current Quality Measures • Simple intermediate outcome measures (e.g., blood pressure at last visit <140/90) may not reliably indicate who is truly receiving poor care • A pt with controlled blood pressure runs out of meds and comes to clinic with BP 150/100 • A pt with coronary disease had an LDL cholesterol of 220 mg/dl, which decreased to 110 on a maximal dose of a statin but did not reach the goal of LDL < 100

  3. Adverse Consequences • These limitations problematic as incentives based on performance measures increase • When used for internal quality improvement, measurement errors such as these may cause physicians to reject measure validity

  4. …and the Solution? • Develop quality measures that more accurately capture what would be defined as poor care • i.e. higher specificity of failures • Electronic health records can help facilitate implementation of more complicated measures

  5. Study Aims • To develop and apply increasingly more sophisticated measures of hypertension quality utilizing data available within an EHR • To compare the results of measured quality using simple outcome measures and more sophisticated measures

  6. Methods • Design: retrospective observational cohort study • Setting: urban Internal Medicine practice with a commercial EHR (Epic) • Eligibility • Hypertensive adults with 3 or more clinic visits between 7/05 and 12/06

  7. Baseline Quality Measure • Baseline: • Patients with hypertension recorded on their problem list, past medical history, or encounter diagnosis codes • Blood pressure at last visit <140/90 • Blood pressure <130/80 if comorbid diabetes

  8. Quality Measure 2:Relax Cutoff • Include last BP ≤ goal as satisfying measure ≤ 140/90 ≤ 130/80 if diabetes

  9. Quality Measure 3:Incorporate Average BP • If either the last or mean of last three BPs are at goal, the patient is considered to satisfy the measure

  10. Quality Measure 4:Account for Aggressive Management • Include patients prescribed 3 or more different antihypertensive drug classes including a diuretic as satisfying the measure • Beta blocker, calcium channel blocker, ACE or ARB, peripheral alpha blocker, centrally acting anti-adrenergic drug, or direct vasodilator • AND diuretic

  11. Quality Measure 5Account for Low Diastolic Blood Pressure, A Safety Concern • Studies suggest that for pts with coronary artery disease and diabetes, lowering the diastolic BP below 70 mmHg may be harmful • Therefore, if patients with uncontrolled systolic blood pressure had diastolic pressure < 70 mmHg, they were consider to satisfy measure

  12. Quality Measure 6:Include Patients with Undiagnosed Hypertension • Include in denominator patients with a mean blood pressure ≥140/90 mmHg or ≥130/80 mmHg if the patient has comorbid diabetes even if they do not have hypertension recorded as a diagnosis

  13. Study Population

  14. Variation Across Measures (no DM) ≤ 140/90 or 3 drugs w/ diuretic or low DBP: 84% ≤ 140/90 or 3 drugs with diuretic:83% Include undiagnosed hypertension: 81% Last or mean ≤ 140/90:76% Last BP ≤ 140/90 67% Last BP < 140/90 58%

  15. Variation Across Measures (DM) ≤ 130/80 or 3 drugs w/ diuretic or low DBP: 76% ≤ 130/80 or 3 drugs with diuretic: 73% Include undiagnosed hypertension: 73% Last or mean ≤ 130/80: 47% Last BP ≤ 130/80 39% Last BP < 130/80 30%

  16. Results of Standard vs. Advanced Hypertension Quality Measures } } Δ 23% Δ 43%

  17. Limitations • We used hypothetical quality measures to demonstrate concept • Single site; generalizability not known • Would be difficult, but not impossible, to apply measures at sites without an EHR • Data within EHRs may be incomplete • Still may miss important exceptions • Home blood pressure controlled

  18. Conclusions • Small changes in measure criteria produce large changes in measured quality • Many patients who did not satisfy the simple measure were receiving aggressive care • More sophisticated measures may better align external measurement with internal quality improvement

  19. Implications • More sophisticated measures may: • Improve detection of true quality problems that need attention by MDs and other staff • Remove incentives to stop caring for patients with resistant hypertension • Remove incentives to unsafely or unnecessarily over treat some patients

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