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Quality Care Opportunities: Refining Physician Performance Measurement in Ambulatory Care
Kimberly M. Lovett, MD; and Bryan A. Liang, MD, PhD, JD
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Quality Care Opportunities: Refining Physician Performance Measurement in Ambulatory Care

Kimberly M. Lovett, MD; and Bryan A. Liang, MD, PhD, JD
The quality care opportunities model can lead to increased utility and validity of current measurement tools and more accurate assessment of physician performance.
Accurately measuring the quality of care that ambulatory care physicians provide is an important endeavor. Current measurement instruments, while offering useful information about care systems, remain suboptimal for the measurement of individual physician performance. We offer the quality care opportunities model of ambulatory care physician performance measurement, which may address issues with current instruments while also offering useful information about efficiency and productivity for individual physicians and delivery systems from a patient-centered perspective.

(Am J Manag Care. 2012;18(6):e212-e216)
This quality care opportunities model is important in the context of currently flawed systems of measurement to:

  •  Improve data on physician performance that might eventually be reported publicly.

  • Further involve physicians in the discussion on how to improve models of measurement to more accurately reflect practice realities.
The quality of US healthcare has been called into question.1 Reform efforts have prioritized quality improvement aimed at improving care delivery systems and outcomes.2 Accordingly, there is a push to quantify the care quality delivered by systems and individual physicians.

Quality measures, being published at rapid pace, are aimed at assessing physician performance. Most of these measures, while useful in performance measurement of large physician groups, pose serious limitations in quantifying individual physician performance.3,4 Instead of using isolated diagnosis-specific outcome measures (OMs), process measures (PMs), and composite measures (CMs), we propose that evaluating the sum of opportunities available to each physician for evidence-based patient care and further assessing the fraction addressed over time can increase validity of physician performance measurement. This approach may also offer insights into systems quality.


Studies of physician performance measurement to date have mainly focused on measuring discrete aspects of care applied to groups of diagnosis- related patients.3-12 Current measures include OMs, PMs, and CMs. Outcome measures evaluate physicians based on targeted evidence-based clinical outcomes within diagnosis-related patient groups (eg, percentage of diabetic patients with glycated hemoglobin [A1C] <7%). Process measures assess physicians on whether evidence-based care processes were used within diagnosis-related patient groups (eg, percentage of time a physician ordered A1C tests for diabetic patients at minimum intervals). Individual OMs have been criticized for failing to account for factors outside of physician control (eg, patient preferences, nonadherence).3,4 In addition, while PMs do capture actions directly under provider control, critics indicate that PMs are often too disconnected from clinical outcomes to be individually useful.3,4 Furthermore, most physicians do not have large enough diagnosis-related patient populations to generate a statistically significant result for individual outcome or PMs.3,4

Composite measures are an attempted improvement of the low statistical significance relating to individual OMs and PMs; these CMs combine multiple OMs and PMs into a single measurement (eg, diabetic CM = [A1C <7%] + [low-density lipoprotein cholesterol <100 mg/dL] + [aspirin use] + [nonsmoking status]). Traditionally, CMs, like individual OMs and PMs, are applied across diagnosis- related patient groups cared for by a given physician.4-9,13-15 For example, a diabetes care composite measure such as that proposed by Kaplan et al9 would be applied across all diabetic patients cared for by a single physician in an attempt to illustrate the quality of diabetes care provided by that particular physician. Because more events are measured within this diagnosis-specific CM compared with individual diagnosis-specific OMs or PMs, there is a greater likelihood that a greater number of physicians will have the minimum number of quality events required to generate a statistically significant outcome.3-15

A growing number of CMs have been proposed in the literature, each attempting to measure the quality of care provided by physicians for specific diagnosis-related patient subsets.4,8,13-15 For example, diagnosis-specific CMs used in the Quality Oncology Practice Initiative involve breast cancer care–related measures, lymphoma care–related measures, and so forth.13 Several studies also demonstrate the use of CMs including “preventive care” and “chronic care” CMs.14 These types of CMs are related to types of care rather than specific diagnoses and are typically applied to patients in the same manner as diagnosis-specific CMs. Despite the improved statistical validity of these CMs compared with more granular diagnosis-specific OMs and PMs, several issues limit the clinical utility of these diagnosis-specific (or care type–specific) CMs when used as sole indicators of physician performance.

At the outset, questions about the interpretability of these CMs have limited their use.5 In other words, it is difficult to understand the source of the relative deficit for physicians with lower scores. Further, it is difficult to justify the idea that physicians with otherwise similar composite scores (eg, diabetes care composite scores, preventive care composite scores) could theoretically see major differences in overall patient care quality depending on the source of the deficit in their individual

scores. Overall, scores tend to be less likely to offer meaningful insights for areas of actionable improvement when used as sole indicators of physician performance.5

Other issues that limit traditional CM usefulness are problems common to the use of diagnosis-specific OMs, PMs, and CMs as applied only to diagnosis-related patient groups cared for by the physician being measured. Measuring physician performance by applying diagnosis-specific measures generates perverse incentives for physicians to eliminate complex patients or focus on measured care at the expense of unmeasured care.11 Further, there are various methods of applying physician responsibility to individual patients that lead to widely variant performance scores.4,12 Additionally, these measures fail to address physician productivity and efficiency. Finally, adequately accounting for physician panel complexity has proved difficult when working with diagnosis-specific measures as applied to diagnosis-related patient groups.6


We propose a measurement tool, quality care opportunities (QCOs), that could enhance the utility of diagnosis-specific OMs, PMs, and CMs. It would also potentially address issues associated with those diagnosis-specific measures when applied only to diagnosis-related patient groups for individual physicians.

Over a day’s duration, many opportunities to provide evidence-based care for patients confront physicians. These opportunities include chief complaint management, chronic condition management, and screening/prevention services. Each patient presents with a unique set of needs based on demographics, risk factors, social factors, chronic medical problems, and chief complaint. Each of these needs is reflected by specific outcome or PMs (eg, patient-specific testing, medications, vaccinations, outcomes measures).

To refine performance measurement, the sum of relevant OMs/PMs would first be assigned to each physician’s scope of practice. Each opportunity to provide care (reflected through all relevant OMs/PMs) represents a QCO. Total QCOs for each physician are based on specialty-relevant OMs/PMs applicable to patients seen in the office over a defined time period. Physician performance would be assessed by measuring the fraction of all QCOs met for each patient seen, ie, quality efficiency (QE).

QCO = [Total OMs] + [Total PMs]

QE = ([OMs met] + [PMs met])/[QCO]

Hence, QCO and QE measures change with each patient and at each point of measurement. That is, each patient presents a unique set of applicable OMs/PMs. For example, a 55-year-old woman with uncontrolled diabetes who has not had healthcare in 5 years will carry a set of QCOs different from those of a 68-year-old male smoker with good care continuity. This QCO-QE system captures those unique intervention opportunities at each point of service.

The QCO concept applied across same-specialty physicians can provide substantive productivity and efficiency insights, related both to absolute numbers of care opportunities confronting physicians and to the proportion of QCOs being met by physicians over equal time intervals. For example, over 1 week, assume that physician A meets 500 of 750 QCOs for his patients, while physician B meets 350 of 400 QCOs for hers. In this example, physician A has an arguably more complex patient load presenting with 750 potential QCOs versus physician B’s 400 QCOs. Further, though physician B has been more efficient (physician B’s QE is 0.875 versus physician A’s QE of 0.667), physician A has been more productive (500 QCOs met over the same period of time that physician B has met 350 QCOs).

Using QCO in combination with QE can help define a benchmark balance between efficiency and productivity for same-specialty physicians. Traditionally, physician productivity has been a measure of patients seen per period of time, where same-specialty physicians are expected to see relatively equal numbers of patients over equal periods of time.16 However, for example, assume physicians A and B are both meeting 350 QCOs per week but physician A is confronted with 500 QCOs and physician B is confronted with 1000 QCOs due to patient panel complexity differences. Although both physicians are equally productive in the quality context and by number of patients seen per hour, patients cared for by physician A are arguably receiving better care than patients care  or by physician B because a higher percentage of their needs are being met. Thus, reducing the volume of patients being seen by physician B might be justifiable.

Furthermore, while the QCO-QE model represents an improvement in physician performance measurement, traditional OMs/PMs can be extracted to make relative statements about strengths and weaknesses of individual physicians. This type of information, while subject to aforementioned flaws, remains meaningful for internal quality control and directing continuing medical education efforts. Likewise, extracting OMs/PMs for provider groups can offer the statistically significant information currently measured for health system care quality delivered to diagnosis-related patient populations (eg, diabetic patients).

Finally, QCO and QE measurement utility may be expanded by quantifying the percentage of QCOs met for each patient cared for by the health system and comparing this score with the physician QE. This allows for evaluation of relative nonphysician and physician contributions to system performance. For example, if a targeted fraction of patients cared for by the system have not had minimum QCO percentages met but high physician productivity/efficiency is evident, then greater nonphysician-based concerns may be contributing to poor system performance.

The QCO-QE approach is flexible, reflecting patient changes (eg, QCOs arising from a formerly controlled diabetic patient now presenting with A1C >7%), as well as changes in practice systems (eg, assigning care of diabetic patients to a new, integrated team). Hence, QCO-QE measurement can assess and compare physician action with patient-centered needs, shifts in patient needs, and systems issues related to ongoing opportunities to provide patient-centered quality care.


Beyond shifting the focus toward patient-centered care, the QCO-QE approach may address concerns with current performance measures as related to measuring individual physicians. First, QCO-QE measurement eliminates isolated dependence of performance evaluation on OMs, over which physicians feel they have little control. Given that several processes of care are required to accomplish a given clinical outcome, PMs will be more numerous than OMs in QCO-QE measurements. Consequently, by absolute numbers, actionable PMs will carry heavier weight than uncontrolled OMs. However, because improving clinical outcomes is an important goal in healthcare delivery, OMs will justifiably contribute to the final score.

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