Evaluating the quality of care for patients with type 2 diabetes using the electronic medical record information in Mexico 1 Epidemiology and Health Services Research Unit at Instituto Mexicano del Seguro Social; 2 Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute; 3 Centre for Health Services and Policy Research, The University of British Columbia, Canada. 3 Centre for Health Services and Policy Research, The University of British Columbia, Canada. Ricardo Pérez-Cuevas 1 Svetlana Doubova 1 Michael Law 3 Aakanksha Pande 2 Magdalena Suárez 1 Dennis Ross-Degnan 2 Anita Wagner 2

OBJECTIVES 1.To develop quality of care indicators (QCI) for type 2 diabetes in the Mexican Institute of Social Security (IMSS) 2.To assess the feasibility of extracting data from IMSS Electronic Health Record to construct the QCI; and 3.To evaluate the quality of care provided to patients with T2DM cared for at IMSS

METHODS Design: The study used a mixed method approach consisting of : a. Development of quality of care indicators for T2DM using the RAND-UCLA method; b.Data extraction and construction of Indicators c. Evaluation of quality of care for T2DM Setting: 4 clinics in Mexico City covering 520,000 people Study Population: Patients with T2DM who received care in 2009.

Electronic health record Affiliates database Essential list of drugs PharmacyPharmacy System for disability leaves Transfer of data to other institutional systems- data warehouses e-prescription Laboratory tests Family medicine health information system

Data extraction Electronic health record Electronic health record – Clinical information, diagnosis, treatment, number of visits, laboratory tests ordered Membership database Membership database – Members information: address, demographics, Prescription Prescription – Drugs prescribed, amount and dosages Laboratory Laboratory – Laboratory results

Integration of data Generation of information Analysis Extraction of routine EHR data to construct pre-defined QCI QCI analytical models ValidationValidation Sources of data Extract, standardize, and load high quality data Integrated data base Difssemination of results Potential institutional benefits EHR Lab Prescr. Affiliation

Population affiliated with the family medicine clinic n Total number of members123,276 Members per family doctor2,241 Members ≥ 20 years old that attended to at least 1 visit to the clinic in ,703 T2D patients 7, % Table 1. Population and characteristics of the family medicine clinic

Characteristics n=7184 % Employment status Housewife Employed Unemployed Retired Missing data Insurance status Subscriber Dependent Characteristics n=7184 % Female gender59.3 Age, years, mean 62.9 Schooling Illiterate Primary school Secondary school High school and college Missing data Marital status Married or Partnership Single or Divorced Widow Missing data Type 2 diabetes patients general characteristics

Medical history n=7184 % Comorbidlity and chronic complications Hypertensive disease 63.3 Hyperlipidemia49.2 Diabetic chronic complications29.9 Peripheral vascular disease8.9 Diabetic nephropathy14.5 Diabetic retinopathy7.0 Peripheral neuropathy5.6 Nutritional status Nutritional status at the end of the year Under weight (<18.5 kg/m 2 ) Normal weight (IMC kg/m 2 ) Overweight (IMC de 25.0 a 29.9 kg/m 2 ) Obesity (IMC ≥30.0 kg/m 2 ) Missing data Medical history n=7184 % Health care characteristics Mean number of visits 4.4 Type of hypoglycemic drugs Metformin Glibenclamide Acarbose Thiazolidinedione Insulin Medical history and use of healthcare services

Indicators I. Process of care n=7184 % A. Timely detection of T2D complications and comorbidity in the last year At least one measurement of HbA1c9.0 Comprehensive foot evaluation51.9 Referral to the ophthalmologist22.2 B. Non-pharmacological treatment in the last year n%n% Nutritional counseling provided by the nutrition service 7, C. Pharmacological treatment in the last three visits Overweight /obese (BMI ≥ 25 kg/m2) patients who received metformin, otherwise contraindicated 5, Patients with hypertension receiving inhibitors of angiotensin converting enzyme or angiotensin-receptor blocker, otherwise contraindicated 4, Table 4. Quality of care indicators

Indicators II. Clinical outcomes N=7,184 % HbA1c <7% or fasting glucose ≤130 mg/dl in the last 3 measurements 4, Total cholesterol levels<200 mg/dl in the last measurement 5, Blood pressure <130/80 mmHg in the last 3 measurements 7, Patients with HbA1c <7%, or fasting glucose ≤130 mg/dl, total cholesterol levels<200 mg/dl and blood pressure <130/80 mmHg in the last 3 measurements Table 4. Quality of care indicators

CONCLUSIONS It is feasible to evaluate QC using the IMSS EHR data. It is necessary to improve both QC and quality of information in the EHR in IMSS. Measuring QC in this way is efficient It is possible to identify the performance of clinics or single providers and guide future interventions aimed at improving QC.