Using the electronic medical records to identify patients with hypertension in primary care: A validation study using EMRALD.

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1 Using the electronic medical records to identify patients with hypertension in primary care: A validation study using EMRALD. Noah Ivers, Bogdan Pylypenko, Karen Tu CAHSPR: May 10, 2011

2 Acknowledgements No Conflicts of Interest This project was supported by team grant from CIHR: The Canadian Cardiovascular Outcomes Research Team NI is supported by a CIHR Fellowship in Clinical Research and a Fellowship from the Department of Family and Community Medicine, University of Toronto KT is supported by a CIHR Fellowship in Primary Care Research and is a Research Scholar with the Department of Family and Community Medicine, University of Toronto

3 EMR: Promise versus Practice

4 Clinical Problem: Hypertension Canadian prevalence: 23% of adults in 2006 Public Health Agency of Canada, using CCDSS data files 7% of office visits to Canadian physicians in 2005 Hemmelgarn et al. Can J Cardiol June; 24(6): % of hypertension is untreated Mohan et al. Can J Cardiol Oct;26(8): Treating hypertension reduces CV outcomes Staessen et al. Lancet 2001;358:

5 Objectives To validate methods for identifying patients with HTN within the EMR Compare EMR-based searches of past medical history fields, BP measurements, billing data, etc. Discuss strengths and weaknesses of different approaches

6 Setting: EMRALD

7 Age Groups (Years) Age Groups (Years) Setting: EMRALD > 100 FPs, > 120,000 patients Ontario Population 2007 EMR Population Distribution % -4.00% -2.00% 0.00% 2.00% 4.00% 6.00% Percent Male Female % -4.00% -2.00% 0.00% 2.00% 4.00% 6.00% Percent Male Female

8 Methods Manual Abstraction 5% random sample from 17 FPs Three trained nurse abstractors Clinical history: physician diagnosis in medical history, visit notes, consultation letters, discharge summaries, diagnostic tests elevated BP and an antihypertensive prescription on the same day

9 Methods Reference Standard Add BP measurements for unrecognized patients CHEP diagnostic criteria, any of the following: Any Diastolic >120mmHg Two consecutive readings of systolic > 140mmHg or diastolic > 90mmHg for patients with diabetes, chronic kidney disease, or CVD. Systolic >180mmHg or diastolic >110mmHg, with previous measurement of systolic >140mmHg or diastolic >90mmHg Three consecutive measurements averaging systolic >160mmHg or diastolic >100mmHg, after a reading of systolic >140mmHg or diastolic >90mmHg Five consecutive visits with an average systolic >140mmHg or diastolic >90mmHg after a reading of systolic >140mmHg or diastolic >90mmHg (including the initial elevated BP reading).

10 Results - Participants 969 Patients: 49 years old (SD 17), 54% female # HTN from manual chart abstraction: 263 Chart abstraction Kappa > 0.80 # HTN from chart abstraction + CHEP: 300 Total prevalence of HTN in random sample = 31% (unrecognized, untreated = 37/969, 3.8%)

11 Results - Validation Sensitivity Specificity PPV NPV Past Medical Hx (CPP) search 43.3% 96.3% 83.9% 79.1%

12 Results - Validation Sensitivity Specificity PPV NPV Past Medical Hx (CPP) search 43.3% 96.3% 83.9% 79.1% BP measurements: Ever >140/ % 74.9% 61.0% 93.1% >140/90 x2 in 1 year 75.0% 95.1% 87.2% 89.5% >140/90 x3 in 3 years 67.3% 98.2% 94.4% 87.0%

13 Results - Validation Sensitivity Specificity PPV NPV Past Medical Hx (CPP) search 43.3% 96.3% 83.9% 79.1% BP measurements: >140/90 ever 87.7% 74.9% 61.0% 93.1% >140/90 x2 in 1 year 75.0% 95.1% 87.2% 89.5% >140/90 x3 in 3 years 67.3% 98.2% 94.4% 87.0% Billing data: Any HTN billing code* 56.1% 94.9% 83.5% 82.3% HTN billing code x2 in 1 year 41.1% 97.8% 89.8% 78.1% HTN billing code x3 in 3 years 33.9% 98.7% 92.2% 76.3% * Billing code = ICD Billing data analysis limited to 883 patients

14 Results - Validation Combination Algorithms: Sensitivity Specificity PPV NPV CPP or > 140/90 ever 91.7% 74.9% 62.1% 95.2% CPP or > 140/90 ever or HTN bill x1 CPP or > 140/90 ever or HTN bill x3 in 3 years 93.2% 67.0% 56.7% 95.5% 92.5% 68.3% 57.6% 95.2% CPP or >140/90 x3 in 3 years 82.0% 98.2% 95.3% 92.4% CPP or >140/90 x3 in 3 years or HTN bill x1 CPP or >140/90 x3 in 3 years or HTN bill x3 in 3 years 87.5% 93.2% 85.7% 94.1% 84.6% 97.0% 92.9%% 93.2% Next step: combining with antihypertensive prescriptions

15 Next Steps: Implications for Quality Martirosyan et al. BMC Health Services Research :137 Identification of HTN in Dutch primary care patients with comorbid diabetes: avg sbp > 140 in past 6 months: 85% sensitivity diagnosis in problem list: 47% sensitivity Search based on diagnostic code identified only 21% of uncontrolled patients, but search based on sbp identified 88%

16 Choosing an algorithm: What s the goal? Want to send a letter to your hypertensive patients? PPV: CPP search or search for multiple billing codes or elevated BP measurements Want to assess hypertension utilization? Sensitivity: CPP search or search for single billing code or elevated BP measurements Need to consider patients who are not recognized / diagnosed / treated

17 Conclusions EMR-based quality assessment requires validation Diagnostic and/or billing fields in EMR charts do not show the whole picture for HTN How patients are identified has implications for quality assessment and quality improvement Best algorithm depends on purpose

18 Thank you Questions/comments? noah.ivers at utoronto.ca

19 Additional info slides

20 Methods Manual Chart Abstraction 5% random sample = 969 charts from first 17 family physicians in EMRALD Convenience sample of physicians, but heterogeneous:» 42% rural» 29% female» 20.5 years in practice (SD 10)» 7.4 years on EMR (SD 7.3)» all but one in group practice

21 Previous Validation Studies Using EMRALD Validation of administrative data algorithms for ischemic heart disease, myocardial infarction Chronic Dis Can. 2010;30(4):141-6, Can J Cardiol. 2010;26(7):e Validation of automated ways to identify patients with diabetes, ischemic heart disease within the EMR J Clin Epidemiol Apr;64(4):431-5, J Prim Care Comm H. 2011; 2(1):49-53.

22 EMRALD: data structure, de-identification STRUCTURED FIXED ENTRY FORMAT Identifying information Numeric data Predefined list Name Lab results Prescriptions Address Blood pressures Phone number Height Postal code Weight Date of birth Healthcard number UNSTRUCTURED FREE TEXT FORMAT Less likely to contain ID info Likely contains ID info Cumulative patient profiles Progress notes Investigations/diagnostic tests Radiology/pathology reports Consultation letters Hospital discharge summaries

Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record

Original Research Identifying Patients With Ischemic Heart Disease in an Electronic Medical Record Journal of Primary Care & Community Health 2(1) 49 53 The Author(s) 2011 Reprints and permission: sagepub.com/journalspermissions.nav