How much variation in clinical activity is there between general practitioners? A multi-level analysis of decision-making in primary care

Davis, Peter; Gribben, Barry; Lay‐Yee, Roy; Scott, Alastair

doi:10.1258/135581902320432723

Cited by 69 publications

(59 citation statements)

References 19 publications

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“…A number of studies adopted a multilevel approach as a statistical solution, assuming the frequency of services as a dependent variable (encounters, referrals, or prescriptions). In one study the variance unexplained by the models at the practice level was between 12.9% and 27.2% according to the type of service [41]. Two more studies demonstrated a much lower residual unexplained variance at the practice level, comparable to the one found in the present study (0.1% for prescription number [42] and 3.6% for referrals [43]).…”

Section: Resultssupporting

confidence: 66%

Risk-Adjusted Models of Costs Referable to General Practitioners Based on Administrative Databases in the Friuli Venezia Giulia Region in Northern Italy

Piergentili¹,

Simon²,

Paccagnella³

et al. 2015

Arch Community Med Public Health

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Objective: To develop risk adjustment models for cost evaluation in primary health care in Italy based on administrative databases. Setting:The 2007 administrative databases from the National Health Service of the Friuli Venezia Giulia Region were the data source. Data referred to the general population and included information on the use of health services (inpatient, outpatient, medication, home care) as well as on the major chronic health problems. Data included persons who, for their health condition, must not pay the contribution usually required for using health services (ticket exemption).Design: Multilevel (multivariate) statistical analysis, where the tariff of services or the price of drugs (both summed up and separated) were the dependent variables, and the health conditions and other variables related to the citizens were the predictive variables. Results:The analysis included 1,067,239 citizens registered with a General Practitioner (GP) and 1,129 GPs. The number of people with at least one ticket exemption was 461,532. A number of predictive models were developed, which considered the sum of all tariffs and prices, tariffs and prices of inpatient and outpatient services, and medications. The models had very robust results. The models explained a considerable share of the variation using the (R 2 1 ) parameter: the proportional reduction of error for predicting the level-1 dependent variable with respect to the model without any predictors. The R 2 1 was 44.6% for the sum of tariffs and expenditures, 30.9% for hospital tariffs, 27.1% for inpatient services and 49.3% for medications. The intra class correlation coefficient (ICC), which measures the proportion of residual variability due to the second level of analysis (GP), shows that, controlling for the Casemix, the amount of the residual variability driven by the GPs is very low or even negligible: 0.89% for total individual health care tariffs, <0.1% for inpatient services, 1.49% for outpatients services, and 2.0% for drug prescriptions. Discussion:The health status information provided by the ticket exemption database proved to be valid beyond the Researchers expectations, despite known data quality problems. The large study sample size may have played a role in these results. Primary health care risk adjustment can be performed using administrative databases instead of GP clinical records, making it much easier and less costly. The overall variation explained by the models was in line with the findings in the literature. The residual variation attributable to GPs was unexpectedly low. In the inpatient setting, this may be partly due to a systemic effort to reduce inappropriate hospitalisation. In the other settings this result came as a surprise, and may lead to a reconsideration of GP behaviour variation. Outcome and output measures in PHC without proper risk adjustment may lead to inaccurate findings.PHC in order to evaluate quality of care and cost containment on a systemic scale. In fact, hospital care can be viewed as part of the c...

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Section: Resultssupporting

confidence: 66%

Risk-Adjusted Models of Costs Referable to General Practitioners Based on Administrative Databases in the Friuli Venezia Giulia Region in Northern Italy

Piergentili¹,

Simon²,

Paccagnella³

et al. 2015

Arch Community Med Public Health

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“…Female physicians showed better blood pressure registrations, as did physicians with Ͻ10 years of work experience. Previous studies (11,15) have also shown a positive relation between female physicians and quality-of-care aspects. Regarding the choice of treatment, we could not identify specific general practitioner characteristics that were related to better treatment, but the deviance test showed that there remained significant differences between general practitioners after controlling for the effect of the included physician, practice, and patient characteristics.…”

Section: Factors Influencing Blood Pressure Carementioning

confidence: 67%

“…Assessing the relative influence of specific physician and practice characteristics in relation to patient factors is important for the development of targeted interventions to improve the quality of care (15). Our study showed that several patient-related factors are associated with the quality of hypertension management.…”

Section: Factors Influencing Blood Pressure Carementioning

confidence: 76%

“…To determine which aspects interventions should target for change, the influence of physician-related factors on the quality of care must be assessed in relation to the impact of patient-related factors (15). In a multilevel approach, one can account for possible confounding effects of patient characteristics or case-mix differences on the physician or practice level.…”

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confidence: 99%

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Physician, Organizational, and Patient Factors Associated With Suboptimal Blood Pressure Management in Type 2 Diabetic Patients in Primary Care

et al. 2004

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OBJECTIVE -To assess the quality of hypertension care in patients with type 2 diabetes in general practice and identify physician, organizational, and patient factors associated with suboptimal care.RESEARCH DESIGN AND METHODS -Data from 895 randomly selected diabetic patients were extracted from the electronic medical records of 95 general practitioners. Physician and organizational characteristics were collected with a questionnaire. We conducted a multilevel analysis to identify associations with blood pressure registration, hypertension treatment, and achievement of target blood pressure levels.RESULTS -For 652 patients (73%), a blood pressure measurement was recorded in the last year. Of these patients, 132 (20%) reached a target level of 135/85 mmHg. In total, 595 patients were classified as having hypertension, of whom 192 received no treatment (32%), 193 received an ACE inhibitor (32%), and 210 received other antihypertensives. Patients visiting a diabetes facility, referred to a specialist, with a female general practitioner, or with a general practitioner with Յ10 years work experience had better recordings of their blood pressure. Suboptimal treatment was higher in older patients and smoking patients. Treatment was better in patients with coronary comorbidity, hyperlipidemia, or those referred to a specialist. Not achieving the blood pressure target was related to older age of the patients.CONCLUSIONS -Hypertension management of type 2 diabetic patients in primary care is suboptimal. Characteristics of general practitioners as well as additional care provided by a diabetes facility or a specialist are associated with better processes of care, but blood pressure outcomes are not as clearly related to these factors. Diabetes Care 27:123-128, 2004T reatment guidelines uphold stringent target levels for blood pressure control in patients with diabetes because this significantly reduces the risk of developing cardiovascular and microvascular diseases. This has not resulted in adequate blood pressure control (1). Suboptimal management in both diagnosis and treatment of hypertension in type 2 diabetic patients has repeatedly been shown (1-6).The quality of diabetes management can be influenced by physician, organizational, and patient factors (7). Variation in quality of care has been found between physicians as well as patients (5,6,8 -11). Factors of influence are the age and work experience of the physician, size of the practice, presence of a recall system, and proportion of patients attending hospital clinics (5,9,11). Patient characteristics influencing the quality of diabetes and blood pressure management include age, sex, ethnicity, and presence of comorbidities, such as coronary artery disease (6,10,(12)(13)(14).To determine which aspects interventions should target for change, the influence of physician-related factors on the quality of care must be assessed in relation to the impact of patient-related factors (15). In a multilevel approach, one can account for possible confounding effects of patie...

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“…Other factors can, however, contribute to these variations, such as characteristics of the patients, socioeconomic aspects, cultural or leadership issues, among others 28,29 . In this study, for instance, vaginal delivery is significantly protects from the use of inhaled oxygen,; the same goes for births in the Northern and Northeastern regions.…”

Section: Discussionmentioning

confidence: 99%

Práticas de atenção hospitalar ao recém-nascido saudável no Brasil

et al. 2014

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How much variation in clinical activity is there between general practitioners? A multi-level analysis of decision-making in primary care

Cited by 69 publications

References 19 publications

Risk-Adjusted Models of Costs Referable to General Practitioners Based on Administrative Databases in the Friuli Venezia Giulia Region in Northern Italy

Risk-Adjusted Models of Costs Referable to General Practitioners Based on Administrative Databases in the Friuli Venezia Giulia Region in Northern Italy

Physician, Organizational, and Patient Factors Associated With Suboptimal Blood Pressure Management in Type 2 Diabetic Patients in Primary Care

Práticas de atenção hospitalar ao recém-nascido saudável no Brasil

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