Evaluating the quality of care for patients with type 2 diabetes

using the electronic medical record information in Mexico

1Epidemiology 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; 3Centre for Health Services and Policy Research, The University of British Columbia, Canada.

Ricardo Pérez-Cuevas1 Svetlana Doubova1 Michael Law3 Aakanksha Pande2 Magdalena Suárez1 Dennis Ross-Degnan2 Anita Wagner2

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 Indicatorsc. Evaluation of quality of care for T2DM

Setting: 4 clinics in Mexico City covering 520,000 peopleStudy Population: Patients with T2DM who received

care in 2009.

Electronic health record

Affiliates database

Essential list of drugs

Pharmacy

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– Clinical information, diagnosis, treatment, number

of visits, laboratory tests ordered• Membership database– Members information: address, demographics,

• Prescription– Drugs prescribed, amount and dosages

• Laboratory– Laboratory results

Membership EHR data prescriptions Laboratory results

2009

Integration of data Generation of informationAnalysis

Extraction of routine EHR data to construct pre-defined QCI

QCI analytical models

Valid

ation

Sources of data

Extract, standardize, and load high quality

data

Integrated data base

Difssemination of results

Decision makers

Clinicians

Researchers

Potential institutional benefits

EHR

Lab

Prescr.

Affiliation

Population affiliated with the family medicine clinic

n

Total number of members 123,276Members per family doctor 2,241Members ≥ 20 years old that attended to at least 1 visit to the clinic in 2009 45,703

T2D patients7,18415.7%

Table 1. Population and characteristics of the family medicine clinic

Characteristicsn=7184

%

Employment statusHousewifeEmployedUnemployedRetiredMissing data

32.719.30.1

11.936.0

Insurance statusSubscriberDependent

24.076.0

Characteristics n=7184%

Female gender 59.3Age, years, mean 62.9

SchoolingIlliteratePrimary schoolSecondary schoolHigh school and collegeMissing data

16.124.311.922.80.9

Marital statusMarried or PartnershipSingle or Divorced WidowMissing data

48.311.914.225.6

Type 2 diabetes patients general characteristics

Medical history n=7184%

Comorbidlity and chronic complicationsHypertensive disease 63.3Hyperlipidemia 49.2Diabetic chronic complications 29.9• Peripheral vascular disease 8.9• Diabetic nephropathy 14.5• Diabetic retinopathy 7.0• Peripheral neuropathy 5.6 Nutritional statusNutritional status at the end of the year Under weight (<18.5 kg/m2) Normal weight (IMC18.5- 24.9 kg/m2) Overweight (IMC de 25.0 a 29.9 kg/m2) Obesity (IMC ≥30.0 kg/m2) Missing data

0.316.435.833.913.6

Medical history n=7184%

Health care characteristicsMean number of visits 4.4Type of hypoglycemic drugs MetforminGlibenclamideAcarboseThiazolidinedioneInsulin

57.251.83.30.0

14.1

Medical history and use of healthcare services

IndicatorsI. Process of care n=7184

%A. Timely detection of T2D complications and comorbidity in the last year• At least one measurement of HbA1c 9.0• Comprehensive foot evaluation 51.9• Referral to the ophthalmologist 22.2

B. Non-pharmacological treatment in the last year n%

• Nutritional counseling provided by the nutrition service 7,1841.8

C. Pharmacological treatment in the last three visits

Overweight /obese (BMI ≥ 25 kg/m2) patients who received metformin, otherwise contraindicated

5,06657.2

Patients with hypertension receiving inhibitors of angiotensin converting enzyme or angiotensin-receptor blocker, otherwise contraindicated

4,54546.0

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

4,64423.0

Total cholesterol levels<200 mg/dl in the last measurement 5,09752.2

Blood pressure <130/80 mmHg in the last 3 measurements 7,08812.3

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

42721.8

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 efficientIt is possible to identify the performance of clinics or single providers and guide future interventions aimed at improving QC.