The study was conducted in the Central District primary care clinics of CHS. CHS is the largest health maintenance organization in Israel, covering more than 50% of Israel’s population, with over 70% of the elderly patients (65 years and above) (8
). The average income of persons insured by CHS is lower than of those insured by other large health funds. CHS has a nationwide framework consisting of 8 districts; the Central district population is representative of the CHS’s overall population according to its socio-demographic characteristics (unpublished data). Every person insured by CHS is allocated to a PCP, either a family physician or a pediatrician. Patients only visit the PCP to whom they are allocated (except when their physician is on vacation, when patients are out of town, or in cases of emergency). For each visit to a different PCP, a special administrative certificate of approval is needed and the peer physician is instructed to give only “first aid.”
The present study covered 161 (60.5%) of 266 PCPs employed in the Central District, who worked in the same clinic throughout the study period from January 2003 or before and throughout 2004-2005. Pediatricians, specialists in internal medicine and in geriatrics (due to their small number as primary care physicians), group practices (where physicians do not have individual patient lists), PCPs not working exclusively with CHS (due to inconsistent infrastructure in their clinics), and PCPs treating fewer than 300 patients (due to the expected small number of patients in the denominator of the QIs) were excluded.
PCPs’ background data: age, sex, board certification in family medicine, managerial position held in clinic (medical manager or not), years in practice, and clinic location (urban or rural), were derived from the employment and administrative database of the district. The age-adjusted number of patients allocated to each PCP was taken from the health maintenance organization registry. Of the 266 PCPs in CHS central district, 161 qualified for the study.
The visit to a PCP in CHS is free of charge. Since we did not know the time spent with each patient or the number of PCP visits, we used the age-adjusted number of patients as a marker of the PCP's workload. The age-adjusted number of patients allocated to each PCP was calculated according to the number of allocated patients, age distribution, and the capitation formula of the National Insurance Institute of Israel, which gives a different weight for each age group according to its utilization of health services (9
The QIs for each PCP were measured at the end of 2003 and the end of 2005, based on the CHS computerized database for QIs. All QIs that were measured by CHS at both time periods were included in the study. The QIs were classified into 3 main sub groups: follow-up of the chronic patient, control of the chronic patient, and preventive care.
Follow up of the chronic patients included:
1. Diabetic eye examination – the percentage of diabetic patients who had an eye test at an eye clinic at least once in the previous year.
2. HbA1c measurement in the diabetic patient – the percentage of diabetes patients with an HbA1c measurement at least once in the previous year. This definition is based on the minimum frequency for testing.
3. Urine microalbumin measurement in the diabetic patient – the percentage of diabetic patients who had a urine microalbumin or albumin test at least once in the previous year.
4. Low density lipoprotein (LDL) measurement in the diabetic patient – the percentage of diabetic patients who had at least one LDL measurement in the previous year.
Control of chronic disease:
5. Target HbA1c measurement in the diabetic patient – the percentage of diabetic patients with HbA1c lower than 7% on the most recent test in the measurement year.
6. Target LDL measurement in the diabetic patient – the percentage of diabetes patients with LDL cholesterol levels in the minimal range of below 100 mg/dL on the most recent test in the measurement year.
7. Admissions of chronic obstructive pulmonary disease patients – the percentage of patients with chronic obstructive pulmonary disease admitted in the last year to internal medicine, acute geriatric, and intensive care wards.
8. Admissions of congestive heart failure patients – the percentage of patients with congestive heart failure admitted in the last year to internal medicine, acute geriatric, and intensive care wards.
9. Repeat admissions of congestive heart failure patients – the percentage of patients with congestive heart failure admitted in the last year more than once to internal medicine, acute geriatric, and intensive care wards.
10. Mammography – the percentage of women aged 52-74 who had at least 1 mammography screening in the course of the past 2 years.
11. Influenza immunizations – the percentage of high risk patients immunized for influenza (7
Calculation of performance
The scales of performance in each QI were different (). The performance of each physician on each QI was ranked and then divided into quartiles. According to the quartile, the physicians' performance was ranked as 1 for those with QI performance score in the first quartile, 2 for the second, 3 for the third, and 4 for the fourth quartile.
Comparison of primary care physicians (n=161) performance indicators in 2003 and 2005*
Total score was the total of new quartile ranks for all QIs together. Diabetes score was the sum of quartile ranks for QI 1-6, diabetes patient control score was the sum of quartile ranks for QI 5 and 6, admissions score was the sum of quartile ranks for QI 7-9, and preventive medicine score was the sum of quartile ranks for QI 10 and 11.
The data were analyzed using SPSS for Windows, version 13.0 (SPSS Inc., Chicago, IL, USA). The continuous PCPs' background variables had normal distribution (age, years in practice, number of patients on list) (). The paired t-test was used for testing the change of PCPs’ QI scores between 2003 and 2005.
Demographic and professional characteristics of primary care physicians
The QIs, total score, diabetes scores, admission score, and preventive medicine score had normal distributions. The association between the PCPs’ background data and their QI scores was examined using a multivariate linear regression model. The model was generated to examine the effect of independent background and clinic variables on QI scores. Variables that were found to be insignificant, such as work experience and age, were not included in the final regression model. There was no co-linearity between the other variables in the model. QI scores were treated as continuous variables. A P level of 0.05 was considered significant.