Diabetes Care Quality and Patient Panel Characteristics
Diabetes Care Quality and Patient Panel Characteristics
Introduction: Health care reimbursement is increasingly based on quality. Little is known about how clinic-level patient characteristics affect quality, particularly in community health centers (CHCs).
Methods: Using data from electronic health records for 4019 diabetic patients from 23 primary care CHCs in the OCHIN practice-based research network, we calculated correlations between a clinic's patient panel characteristics and rates of delivery of diabetes preventive services in 2007. Using regression models, we estimated the proportion of variability in clinics' preventive services rates associated with the variability in the clinics' patient panel characteristics. We also explored whether clinics' performance rates were affected by how patient panel denominators were defined.
Results: Clinic rates of hemoglobin testing, influenza immunizations, and lipid screening were positively associated with the percentage of patients with continuous health insurance coverage and negatively associated with the percentage of uninsured patients. Microalbumin screening rates were positively associated with the percentage of racial minorities in a clinic's panel. Associations remained consistent with different panel denominators.
Conclusions: Clinic variability in delivery rates of preventive services correlates with differences in clinics' patient panel characteristics, particularly the percentage of patients with continuous insurance coverage. Quality scores that do not account for these differences could create disincentives to clinics providing diabetes care for vulnerable patients.
Health care service reimbursements to providers are increasingly based on value; for example, "pay for performance" is a payment mechanism proposed to incentivize the consistent delivery of high-quality services. The premise underlying most such programs is to reward health care providers for delivering high-quality care and to provide regular feedback on adherence to performance standards.
The metrics currently used to measure quality of care rarely account for patient characteristics that might affect the quality of clinics' performance. This is concerning because a growing literature shows an association between the characteristics of clinics' and providers' patient panels and the quality of care provided to these panels. Much of the focus of this literature has been on the relationship between quality of care and the characteristics of patients' comorbidities and disease severity, rather than their sociodemographic factors (e.g., race/ethnicity, income, insurance coverage status), despite the known relationship between such characteristics and quality of care at the individual patient level. Furthermore, little is known about which clinic-level patient panel characteristics are most strongly associated with variation in clinics' rates of delivery of primary care services–information that may be especially pertinent for community health centers (CHCs) and others providing care to underserved populations (e.g., the uninsured and racial/ethnic minorities). While CHCs provide health care comparable in quality to that provided by private practices, quality of care and patient demographics may vary between individual CHCs.
Practice-based research networks (PBRNs), which comprise multiple clinics, provide a unique opportunity to further our understanding of which patient panel characteristics are most associated with a clinic's performance profile. This is especially true if the clinics within a PBRN network share a common electronic health record (EHR). Linked EHR data also make it possible to examine the extent to which a clinic's quality measurements are affected when only patients seen primarily at that clinic are included in its "panel" denominator compared to when all patients seen at the clinic are in the denominator. This question will become increasingly important as methods for measuring quality shift from manual chart reviews to the assessment of EHR data, which will make it possible to determine which patients are being seen at multiple primary care clinics versus those using only one clinic.
We hypothesized that, within our study CHCs, performance variation would be correlated with differences in the characteristics of the clinics' patient populations and that a significant proportion of the clinic-level variability in rates of delivery of preventive services could be explained by the clinic-level summaries of their patients' sociodemographic characteristics. To test this hypothesis, we examined variability in rates of delivery of diabetes preventive services among the CHC primary care clinics that are members of the OCHIN PBRN and share a linked EHR. We assessed the degree to which certain clinic-level patient panel characteristics (e.g., the percentages of patients in various income and insurance coverage status categories) were correlated with clinic performance. Our objectives were to (1) describe differences in patient panels and rates of delivery of recommended diabetes care among 23 CHC primary care clinics in the OCHIN network and (2) assess associations between clinic-level patient characteristics and variability in clinic rates of providing recommended diabetes preventive care services. Last, we sought to (3) assess the effect of using different methods to quantify the patient panel denominators by adjusting panel denominators to assign a patient to only one clinic (the clinic which the patient visited most often) or to all clinics used by that patient.
Abstract and Introduction
Abstract
Introduction: Health care reimbursement is increasingly based on quality. Little is known about how clinic-level patient characteristics affect quality, particularly in community health centers (CHCs).
Methods: Using data from electronic health records for 4019 diabetic patients from 23 primary care CHCs in the OCHIN practice-based research network, we calculated correlations between a clinic's patient panel characteristics and rates of delivery of diabetes preventive services in 2007. Using regression models, we estimated the proportion of variability in clinics' preventive services rates associated with the variability in the clinics' patient panel characteristics. We also explored whether clinics' performance rates were affected by how patient panel denominators were defined.
Results: Clinic rates of hemoglobin testing, influenza immunizations, and lipid screening were positively associated with the percentage of patients with continuous health insurance coverage and negatively associated with the percentage of uninsured patients. Microalbumin screening rates were positively associated with the percentage of racial minorities in a clinic's panel. Associations remained consistent with different panel denominators.
Conclusions: Clinic variability in delivery rates of preventive services correlates with differences in clinics' patient panel characteristics, particularly the percentage of patients with continuous insurance coverage. Quality scores that do not account for these differences could create disincentives to clinics providing diabetes care for vulnerable patients.
Introduction
Health care service reimbursements to providers are increasingly based on value; for example, "pay for performance" is a payment mechanism proposed to incentivize the consistent delivery of high-quality services. The premise underlying most such programs is to reward health care providers for delivering high-quality care and to provide regular feedback on adherence to performance standards.
The metrics currently used to measure quality of care rarely account for patient characteristics that might affect the quality of clinics' performance. This is concerning because a growing literature shows an association between the characteristics of clinics' and providers' patient panels and the quality of care provided to these panels. Much of the focus of this literature has been on the relationship between quality of care and the characteristics of patients' comorbidities and disease severity, rather than their sociodemographic factors (e.g., race/ethnicity, income, insurance coverage status), despite the known relationship between such characteristics and quality of care at the individual patient level. Furthermore, little is known about which clinic-level patient panel characteristics are most strongly associated with variation in clinics' rates of delivery of primary care services–information that may be especially pertinent for community health centers (CHCs) and others providing care to underserved populations (e.g., the uninsured and racial/ethnic minorities). While CHCs provide health care comparable in quality to that provided by private practices, quality of care and patient demographics may vary between individual CHCs.
Practice-based research networks (PBRNs), which comprise multiple clinics, provide a unique opportunity to further our understanding of which patient panel characteristics are most associated with a clinic's performance profile. This is especially true if the clinics within a PBRN network share a common electronic health record (EHR). Linked EHR data also make it possible to examine the extent to which a clinic's quality measurements are affected when only patients seen primarily at that clinic are included in its "panel" denominator compared to when all patients seen at the clinic are in the denominator. This question will become increasingly important as methods for measuring quality shift from manual chart reviews to the assessment of EHR data, which will make it possible to determine which patients are being seen at multiple primary care clinics versus those using only one clinic.
We hypothesized that, within our study CHCs, performance variation would be correlated with differences in the characteristics of the clinics' patient populations and that a significant proportion of the clinic-level variability in rates of delivery of preventive services could be explained by the clinic-level summaries of their patients' sociodemographic characteristics. To test this hypothesis, we examined variability in rates of delivery of diabetes preventive services among the CHC primary care clinics that are members of the OCHIN PBRN and share a linked EHR. We assessed the degree to which certain clinic-level patient panel characteristics (e.g., the percentages of patients in various income and insurance coverage status categories) were correlated with clinic performance. Our objectives were to (1) describe differences in patient panels and rates of delivery of recommended diabetes care among 23 CHC primary care clinics in the OCHIN network and (2) assess associations between clinic-level patient characteristics and variability in clinic rates of providing recommended diabetes preventive care services. Last, we sought to (3) assess the effect of using different methods to quantify the patient panel denominators by adjusting panel denominators to assign a patient to only one clinic (the clinic which the patient visited most often) or to all clinics used by that patient.
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