Primary care physician panels vary markedly in work effort needed. Age/sex adjustment is sufficient to account for this for children, but not for adults.
Published Online: July 12, 2012
Sukyung Chung, PhD; Laura J. Eaton, MD, MPH; and Harold S. Luft, PhD
Objectives: To determine if patient clinical conditions need to be considered when assessing primary care physician (PCP) workload in the context of standardizing panel sizes.
Study Design: Work resource value units (wRVUs) were used to standardize PCP panel workload. Standardized panels were created using (1) age and sex– and (2) clinical condition–based risk indicators. Billing data were used for all patients, regardless of insurance, for PCPs in a group practice (n = 190). Weighting methods were assessed
for subgroups based on PCP specialty (family medicine, internal medicine, and pediatrics) and patient age (adults vs children) and for different levels of aggregation (patient vs PCP).
Methods: Groupwide weights based on wRVUs of all primary care services delivered during the year were applied to individual patients and then aggregated to PCP panels. For age/sex weighting, only patient age and sex were taken into account. For condition-based weighting, 1275 disease categories, based on a combination of episode treatment groups (ETGs) and age and/or sex, were used.
Results: As expected, at the patient level, condition-based weights were far more discriminative than age/sex. At the PCP level, this discrimination was less important; panel weights varied 1.9- (age/sex–based) to 2.6-fold (condition-based) across PCPs. Correlations between the 2 weighting methods were high (r = 0.93) for child panels and moderate (r = 0.71) for adult panels (all P <.001).
Conclusions: The heterogeneity of PCP panels should be considered when assessing PCP workload for panel management. Panel variability in workload is well captured by age/sex–based weights for children, but for adults condition-based adjustment may be necessary.
(Am J Manag Care. 2012;18(7):e262-e268)
There is a wide variation in patient demographic and clinical characteristics across primary care physician (PCP) panels, even within a single group practice organization. For panel-based productivity or performance assessment, standardization of PCP panels requires adjustment for such differences.
Child and adult panels should be assessed separately.
For child panels, age/sex–based adjustment is probably sufficient.
For adult panels, further adjustment reflecting patient clinical conditions is warranted.
In the setting studied, the adult and child panels of family practitioners differed from those seen in the panels of general internists and pediatricians, respectively.
Panel size standardization should account for unusual, but predictable, coding patterns.
Efforts to slow the healthcare expenditure growth include a potential shift from the current standard fee-for-service (FFS) or productivity-based model to compensating primary care physicians (PCPs) for the appropriate management of a patient panel.1-3 Many group practices and managed care organizations are exploring such panel-based payment models.4 The transition to panel-based compensation must address each physician’s concern that his or her panel is “sicker than average” and thus requires more physician time. In spite of many flaws with respect to its incentives, FFS-based payment roughly reflects physician work effort and thus is perceived by physicians as fair in this regard. An important first step in panel-based payment is determining the method, or even the need, to account for variations in case mix among physician panels. To facilitate acceptance of change, the new methods should be simple and transparent.
Historically, correcting for patient case mix has been performed using various risk-adjustment techniques. The vast risk-adjustment literature has typically focused on predicting individuals’ costs, including hospitalization and other very expensive services. Age and sex alone perform poorly in predicting individual healthcare costs,5,6 and sophisticated risk-adjustment measures, using indicators of clinical conditions and/or prior healthcare costs, are far superior.7,8 Physician-level risk adjustment focuses on profiling physicians based on all costs incurred by their patients. These studies found that after including clinical condition indicators, adjustment for condition severity did not change provider ranking9 and methods of dealing with cost outliers and attributing costs to a provider did not substantially influence provider ranking.10 This literature, however, does not address what is necessary when standardizing panels for the PCP’s anticipated work effort. Panel standardization for PCP payment may be a simpler challenge because we need only focus on the (1) primary care work effort and (2) work effort aggregated to the provider panel level. This led us to ask if age/ sex-based weights are good enough for such panel size standardization.
In our study, we seek a simple and robust approach to standardize PCP panel size that is easy to implement and understandable by clinicians. Age and sex categorization meets that requirement, but may be insufficiently accurate. We therefore consider standardized workloads for a panel using 2 weighting methods: (1) finely defined condition-based weights, and (2) simple age/sex–based weights. Poorly constructed conditionbased weights might be no better than age- and sex-based weights, so we compared the 2 for both individual patients and PCP panels to see if the expected higher discrimination of condition- based weights for individuals matters as much for panels. To determine whether panel weights would need to be recalculated each year as panel composition changes, we then examined the stability of adjusted panel sizes over time to see if values change much from year to year.
We used data from 2007 to 2008 on services provided by PCPs in the Palo Alto Division of Palo Alto Medical Foundation (PAMF/PAD), California. We focused on 2008 data except for the cross-year correlations. The primary care departments were located in 7 clinics of varying sizes (number of PCPs ranged from 4 to 72). Most PAMF patients were insured and represented a multitude of plans (preferred provider organization [PPO] 57%, health maintenance organization [HMO] 26%, Medicare 10%, other or no insurance 7%). The average age of all patients with a PCP was 35.4 years, and 57.5% were female. Among adults (>18 years) (n = 281,842, 70.7%), the average age was 47.6 years and 60.1% were female; among children (<18 years), the average age was 6.1 years and 49.9% were female.
Definition of PCP Panels
Panels were created based on each patient’s identified PCP, as determined at registration and updated whenever a patient formally changes PCPs. On average, 87% of primary care visits were made to the patient’s own PCP. Patients who used any primary care service during the year were included in the panel estimates. We used 2 panel definitions: (a) overall panel, and (b) separate adult (aged >18 years) and child (aged <18 years) panels. After excluding PCP panels of fewer than 100 patients (n = 7), 183 panels (76 family medicine, 62 general internal medicine, and 45 pediatric) were included in the analyses for overall panels. For adult/child-specific panel analyses, 141 adult panels (77 family medicine, 62 internal medicine, and 2 pediatrics) and 110 children panels (65 family medicine and 45 pediatrics) were used, after excluding panels with less than 50 adults or children. Panel size varied widely across PCPs. “Trimmed” overall panels ranged from 203 to 2220 (average 959; standard deviation [SD] = 344) and adult/child–specific panels ranged from 50 to 2126 (average 693; SD = 456).
Scope of Service Use
Our focus was the standardized primary care service workload for each panel, assuming each patient received the average work resource value units (wRVUs) for “similar patients” across all PAMF/PAD PCPs. (Our key question is whether “similar patients” should be characterized by age/sex or by clinical conditions.) This essentially matched the current compensation model at PAMF in which salaries are proportional to wRVUs. Primary care wRVUs were predominantly for evaluation and management services (ie, consultation during office visits). Excluded are material or capital (ie, non-wRVU) costs associated with procedures, imaging, tests, immunizations and injections delivered in the primary care offices, or other services ordered by a PCP but delivered by non-PCP clinicians. A uniform Medicare resource-based relative value scale schedule (RBRVS) was used to eliminate differences in payer source across patients.
For each age (0-90 years, in 1-year increments, top-coded at 90) and sex, the average annual PCP wRVUs per patient in the category was calculated. The resulting age/sex-based weights were then applied to individual patients in each category and further aggregated to the panel level. The distribution of wRVUs differed noticeably by sex after adolescence, with higher PCP-generated wRVUs for females (Figure 1), although care delivered by obstetrics and gynecology specialists was excluded.
The procedure to compute condition-based weights was similar, but with many more categories. Adjustment based solely on diagnosis codes may over-reward clinicians who code more conditions. To reduce this effect, we used Symmetry’s Episode Treatment Groups (ETG version 7.5) to aggregate codes to clinical conditions and attribute wRVUs. The Symmetry ETG system creates episodes of illness, ranging from simple, short-term acute problems to long-term chronic illnesses. The ETG grouping methodology uses data on each service provided and the associated diagnosis. Based on the diagnoses, it assigns each service to 1 of a set of mutually exclusive and collectively exhaustive episodes of care regardless of provider, treatment location, or duration. The Symmetry disease classifi cation methodology is a widely used and validated system for building clinically homogeneous episodes. This is important because: (1) physicians can relate to the illness groupings, allowing for meaningful communication regarding treatment; and (2) clinical homogeneity within an ETG provides the basis for substantive comparison and detailed drill-down analysis.11 Several other systems in the market produce comparable results.12,13
The following example illustrates the approach for a patient with diabetes who visits a PCP for a fall and laceration. During that visit an x-ray is ordered, the laceration sutured, a glycated hemoglobin (A1C) test ordered to check on the patient’s diabetes, and a flu shot given. As the primary diagnosis recorded for the visit is trauma, the evaluation and management component of the visit would be grouped into a trauma ETG. The suturing procedure would also be attributed to that episode. The x-ray cost and its interpretation by a radiologist would also be attributed to the trauma ETG, but in this analysis non-PCP costs are ignored. Likewise, although the A1C test will be assigned to a diabetes ETG, its costs are ignored because the test is provided by the laboratory rather than the PCP. The patient will probably have other visits that capture PCP offi ce visit services associated with diabetes. To examine potential misclassifi cation errors (ie, the episode cost for diabetes in this example does not capture the PCP’s effort in managing the patient’s diabetes), we repeated the entire analysis using episodes based just on the primary diagnosis in the billing data when defining condition-based weights, and found no noticeable difference in patient- and panel-level weights.
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