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Panel Sizes for Primary Care PhysiciansOptimize Based on Both Patient and Practice Variables

Learning Objectives
1. Describe the benefits of appropriately sized panels
2. Identify metrics that define the optimal panel size for your practice
3. Apply different methods to determine optimal panel size
4. Adapt and maintain the optimal panel size for your practice

0.5 Credit

AMA STEPS Forward

Published Online: August 30, 2018

Team-Based Learning

Activity Information and Disclosures

Coleen Kivlahan, MD, MSPH^a;

Christine Sinsky, MD, MACP^b

Author Affiliations

^aExecutive Director Primary Care, Professor Family and Community Medicine, University of California San Francisco
^bVice President, Professional Satisfaction, American Medical Association

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How Will This Toolkit Help Me?

Learning Objectives

Describe the benefits of appropriately sized panels
Identify metrics that define the optimal panel size for your practice
Apply different methods to determine optimal panel size
Adapt and maintain the optimal panel size for your practice

Disclaimer: This Toolkit is being made available to the general public and is for informational purposes only. Reimbursement-related information provided by the American Medical Association (“AMA”) and contained herein is for medical coding guidance purposes only. It does not (i) supersede or replace the AMA's Current Procedural Terminology (CPT®) manual (“CPT Manual”) or other coding authority, (ii) constitute clinical advice, (iii) address or dictate payer coverage or reimbursement policy, or (iv) substitute for the professional judgement of the practitioner performing a procedure, who remains responsible for correct coding.

Introduction

Maintaining meaningful relationships between patients and physicians is the foundation of primary care. A patient panel is a group of patients assigned to one specific physician or clinical team. The team is dedicated to the care of those within that panel.

The ability of a physician to build and sustain these meaningful relationships depends on their panel size.

But what is the right panel size for a primary care physician (PCP)? How many patients can a family physician, pediatrician, or internist manage while still providing sufficient same-day access for their patients' acute needs, planned care appointments for chronic care and prevention, and between-visit care and population management? How does a practice manage access for both new and established patients while also ensuring asynchronous access to care, such as after-hours care, email follow-up, and communication through online patient portals? There is not yet an exact science for determining the ideal patient panel size; in the meantime, this toolkit presents current panel size determination and optimization approaches.

Q&A

What are the adverse effects of panels that are not appropriately sized?
Inappropriate panel sizes can cause problems with quality, access, patient satisfaction, and clinician burnout.¹ Specifically, larger panel sizes have been shown to result in poorer diabetes control and decreased cancer screening rates.¹ Over-empaneled physicians may have higher workloads related to patient care and excessive after-work hours spent on electronic health record (EHR)-related tasks that put them at increased risk for burnout.¹
Large panel sizes also contribute to patient access challenges and could result in patients (1) waiting longer to see their physician, (2) seeking care outside of the practice, and (3) seeing a physician/clinician who is not their primary care physician. All of these factors further reduce continuity and quality of care and potentially increase costs.
Conversely, small panel sizes may result from low physician engagement, clinical performance concerns, perception of delivering low-quality care, or poor communication skills. Too small of a panel size can also potentially result in financial instability for the practice.
What are the benefits of establishing appropriately sized patient panels?
Quiz Ref IDAppropriately sized panels will:
1. Prevent underutilization of physician skills and avoid limitations in access to care for patients of over-empaneled practices.
2. Avoid the stresses placed on physicians and care teams in over-empaneled practices.
3. Facilitate equitable compensation within organizations that are moving away from a fee-for-service model of payment.
4. Develop sustainable models of primary care to reduce physician burnout.
Does a smaller than average panel size mean that the physician is slow or not working hard enough?
Not necessarily. A smaller than average panel size may reflect:
- A more comprehensive scope of care (for example, more conditions managed within the practice and fewer conditions referred for outside care)
- Providing greater same-day access for established patients
- A patient population with high acuity or more complex needs
- Serving a rural population where one physician may fulfill the role of other specialists
The amount of support a physician receives from the rest of the care team can also influence the size of the panel and how well it is managed. For example, a physician with fewer support team members, lower skill levels among the support staff, and/or local policy guidelines that limit task delegation has a decreased capacity for patients that the physician can effectively manage, resulting in a smaller panel size. This smaller panel size has nothing to do with how personally efficient or hardworking that physician is.

Five STEPS for Optimizing Your Patient Panel Size

Identify Your Patient Panel
Choose an Initial Method for Determining Optimal Panel Size Based on Patient Variables
Adjust Panel Size Based on Practice and Organizational Variables
Modify Patient Panel Sizes as Needed
Monitor and Maintain

STEP 1 Identify Your Patient Panel

Quiz Ref IDThe first step in panel size optimization is attributing individual patients to a single physician or clinical care team. In some organizations, patients have pre-selected their primary care physicians (PCPs) through the insurance/health plan. In other settings, patients are permitted to change primary care physicians regularly or see multiple primary care physicians.

Use the following guidelines to help you define which patient is part of your patient panel:

Define the look-back period (the duration of the patient's care in the practice)
A look-back period between 18 and 36 months is commonly accepted when assigning patients to a particular physician. A look-back period of 12 months or less runs the risk of missing healthy patients who may only see the physician once a year for preventative purposes, whereas a look-back period greater than 3 years may include patients who are no longer active within the practice.
Note: Three-year rule: A patient is considered “new” if he or she has not had a face-to-face service in the last 3 years from the previous date of service.
Determine the number of qualifying visits
To be assigned to a particular physician, many practices require a 2-visit minimum in the look-back period. This toolkit gives 2 examples of how to determine the number of qualifying visits. In some payment models, patients are assigned to practices or physicians based on their health plan but may not have any visits or contacts in the look-back period. Your attribution model should still consider these patients.
Create specific rules for patients who have seen multiple physicians
The Safety Net Medical Home Initiative model includes a look-back period of 24 to 36 months and a 2-visit requirement for inclusion in the physician's panel. To assign patients to a specific physician's panel, the model uses the “Four-Cut Methodology”²:
- 1st cut: Patients who saw only 1 physician in the past year are assigned to that physician.
- 2nd cut: Patients who saw multiple physicians but saw 1 physician for the majority of services in the past year are assigned to that majority physician.
- 3rd cut: Patients who saw 2 or more physicians for the same number of visits in the past year are assigned to the physician who performed the last physical exam.
- 4th cut: Patients who saw multiple physicians with no majority of visits with a single physician are assigned to the most recent physician seen.

Figure 1 depicts an example of one organization's algorithm to determine attribution.

Figure 1. Patient Attribution Model

Q&A

What is the “Modified Four-Cut Method”?
The University of California, Los Angeles (UCLA) has an attribution approach that uses other information sources to inform the Four Cut Methodology. Their “modified” model includes visit types and physician feedback about whom they believe is the primary responsible physician.¹ The process occurs in 3 steps:
1. Define the overall primary care population base for all practices and all primary care physicians in the UCLA health system. Define the population using one of the following³^,4:
  - Patients with 2 or more PCP in-person ambulatory office visits in the past 36 months (CPT® Evaluation and Management [E&M] codes 99201-99215, 99381-99397, or G0438/G0439)
  or
  - Patients with 1 or more PCP in-person ambulatory office visit with preventive service in the past year (CPT E&M codes 99381-99397 or G0438/G0439)
  and
  - All patients assigned by a health plan, even if they had no visits
  Note: This is a hybrid approach. The default is a model that includes patients with at least 2 visits within a 36-month look-back period, with the goal of capturing patients who may infrequently visit while also excluding patients who only had 1 visit since these patients are unlikely to have a relationship with the practice. This model also assumes that the use of the CPT preventive services code/annual Medicare Wellness code indicates that the physician has a special role with that patient. Urgent care center visits may count for 1 but not both of the inclusion visits.
2. Assign patients from the defined population base to specific, active, primary care physicians
  - If a preventive service (CPT codes 99381-99397 or G0438/G0439) is present in the past 12 months, assign the patient to the physician from the most recent visit.
  - If E&M services (CPT codes 99201-99215) are present, excluding urgent care visits, assign the patient to the physician with the most visits. If there is a tie, assign the patient to the physician seen at the most recent visit.
  - If there are no visits in the look-back period, assign the patient to the health plan-assigned physician.
3. Review and disseminate a monthly analysis of patient attribution to all primary care physicians and administrators
Are there other methodologies that organizations use for attribution?
Yes, other organizations take different approaches. Here is one example:
- Step 1: Patient-declared primary care physician is the first determinant of attribution to a panel.
- Step 2: If the patient has not chosen a primary care physician, an algorithm is used for attribution (Figure 1).
- Step 3: Use a continuity index to evaluate the primary care physician's annual performance and monitor how well the care team manages the panel.
Usual Provider Continuity Index (UPC): The percentage of primary care visits for a specific PCP's panel members with that assigned PCP and not another clinician. When the continuity index is above 75%, the primary care physician will earn 100% of the panel payment, with a linear decrease in payment for values below 75%. This index applies to physicians specializing in pediatrics, family medicine, and internal medicine. Pediatricians with a continuity index of 70% or above will earn 100% of the panel payment, with a similar linear decrease in payment for values below 70%.⁵^,6
I have residents in my practice who are attributed patient panels. How do I account for this?
Include patients of resident trainees in the overall practice population identification; however, the attribution strategy will decide the empanelment process. For example, UCLA uses the EHR primary care physician field to determine PCP empanelment.¹ This method excludes those patients who have a resident primary care physician when they are assigned to a supervising faculty physician for billing purposes.
How often should panels be reassessed?
There are no specific guidelines on how often to measure panel sizes; some organizations track panel sizes monthly, while others assess this annually.

STEP 2 Choose an Initial Method for Determining Optimal Panel Size Based on Patient Variables

While there is no “one size fits all” standard or benchmark panel size for a primary care physician, there are several methods described in the literature to identify an optimal panel size based on patient variables.

The age, gender, medical, and social complexity (eg, income, education, homeownership, insurance status) represented in the patient population affect the work needed for management and predict health care utilization patterns. A population of healthy patients who are socially and financially stable may be more likely to require fewer in-person and virtual visits than a patient population with high and complex care needs.

A primary starting point is to assess patient complexity and stratify by risk. This stratification is known as risk adjustment. Risk adjustment is necessary because physicians care for patients of varying complexity and severity. Morbidity is not distributed randomly in practices, so organizations should apply risk adjustment methods to assemble panels with relatively lower or higher risk. Not adjusting for risk can impact clinical outcomes reporting and reimbursement metrics.

The most common methods for risk-adjusting patient panels based on demographics and diagnosis include age/gender adjustment, the Hierarchical Condition Category (HCC), Charlson Comorbidity Index (CCI) score, the Chronic Illness and Disability Payment System, and Medicaid Rx (MRX).⁷ The HCC uses age, sex, and diagnosis data generated from claims data to predict cost and utilization.

Risk-adjustment models to consider include:

Identifying risk adjustment factors and applying them to all patients using parameters such as age, gender, HCC scores, comorbidity counts, etc. Note, however, that there are no standard weighting or multiplier factors for each parameter.
Acquiring a commercial or proprietary risk adjustment product on the market and applying it to your patients.
Leveraging your EHR's built-in risk adjustment model

Apply the selected risk adjustment model to all physicians' panels and continually update the risk adjustment based on EHR patient-level data.

The most commonly used risk-adjustment factors are age, gender, and types of conditions present for each patient. These factors reflect the clinical components of risk but do not capture non-clinical factors like social, behavioral, and economic risk. They also do not capture primary care physicians' and teams' current workload, including non-visit work (eg, refills, patient portal messages, etc.).

The non-visit (asynchronous) workload is significant regardless of the overall health status of patients.⁸ This workload should be considered in your risk adjustment for panel size and measured as part of the total primary care workload.⁹

More comprehensive risk-adjustment models include:

Age/gender stratification
Selected chronic conditions with high risk for primary care utilization
- Diabetes
- Asthma
- Chronic obstructive pulmonary disease (COPD)
- Arrhythmias
- Congestive heart failure (CHF)
- Pulmonary embolism (PE)
- Major depression
- Unstable angina
- Respiratory arrest
Clinical risk groups (CRGs)
Maternity status
Payer status (commercial/Medicaid/Medicare)
Disability
End-stage renal disease markers

Consider the following questions when evaluating various risk-assessment products or vendors.

Q&A

Are there downsides to using these standard methods?
Yes, there can be downsides. These methods may not consider patients who may have multiple symptoms and frequent primary care visits without a clear, identifiable diagnosis. In addition, these models do not factor in an asynchronous workload, the influence of social determinants of health (SDOH), and resource availability. For example, a patient diagnosed with cancer and undergoing chemotherapy may have many costly office visits, but most of the visits are with sub-specialty physicians and thus would not be included in risk assessment models for primary care patient panel attribution.
What could happen if I don't use a risk-adjustment tool?
Physicians whose patient panel consists of more patients with complex care needs or significant adverse SDOH factors may be expected to provide more care without proper resources. Providing more care with insufficient resources may lead to physician burnout. Alternatively, a physician could decide to exit or limit their practice to maintain quality of care.
What other factors should risk adjustment consider?
Consider demographic factors that may not be represented in claims data. For example, a patient may need a translator for calls and visits, and visits that require an interpreter often take 2 to 3 times longer than anticipated. Apply the selected risk adjustment model to all physicians' panels and continually update those panels based on patient-level EHR data.
Once you completed your practice's patient population risk assessment, you can use one of these 3 methods to estimate the optimal panel size for an individual physician within the practice:
1. Visit-Based Method (also known as the Murray Method²)
  The visit-based method matches office visit capacity (supply) to the panel size (demand). A practice first multiplies the number of workdays per year by the number of visits per day to calculate supply. Then calculate the demand by multiplying the average number of visits per patient per year obtained from utilization data for the practice by the existing panel size.
  For example, take a physician who works in the clinic four days per week, 47 weeks per year, for a total of 188 clinic days per year. That physician sees an average of 20 patients per day, yielding a supply or capacity of 3760 visits per year. If this physician's patients had 2.5 visits per year on average, the suggested panel size would be 1504.
  Formula:
  PCP's available visits per day × PCP's workdays per year ÷ average visits per patient per year = optimally sized panel
  This method is widely used to match the panel size to the number of face-to-face visits per year. However, it does not account for many of the activities occurring outside of the visit, such as email, telephone/virtual visits, and completing EHR work. For this reason, it is ideal to ask the physician to indicate the number of patient visits per day that feels sustainable. This number may vary greatly from physician to physician and is dependent on the:
  - Efficiency of the practice's workflow
  - Degree of task-sharing among team members
  - Number of support staff and their training level(s)
  - Complexity of the patient population's care needs
  - Proportion of virtual care versus in-person care
2. Time-Based Method
  The time-based method is another technique for determining an appropriate panel size that estimates the overall primary care physician work effort required to deliver comprehensive care. This model shifts away from using the capacity for visits to calculate panel size and instead considers the total hours necessary to provide patient care and the effectiveness of the rest of the care team. For example, primary care physicians can manage larger panels by delegating work—such as visit note documentation, pre-visit planning, medication reconciliation, inbox management, and routine immunizations—to other care team members.
  Researchers used theoretical modeling to conclude that a full-time primary care physician may be able to manage approximately 1400 patients per year in a lightly supported model. When that same physician can share tasks with other members of the care team in an advanced team-based model of care, the panel size might increase to 2000 patients.¹⁰
3. Normative Benchmarking
  Normative benchmarking uses existing panel sizes in different health care settings as benchmarks. While many studies have published average panel sizes for primary care practices, there is no single method of panel size determination. Thus, the amount of time physicians spend providing care can vary widely.

STEP 3 Adjust Panel Sized Based on Practice and Organizational Variables

As detailed in STEP 2, risk adjustments of patient variables determine the initial patient panel size. Once the initial panel size is determined, it is important to evaluate practice and organizational variables to adjust the effective panel size. Taking into account these variables allows for greater equity in panel size expectations across different physician practices.¹¹^,12 At present, there is no standard algorithm to mathematically adjust risk based on practice variables.

Figure 2 depicts patient, practice, and physician variables that can influence how a practice arrives at the optimal panel size.

Figure 2. Variables That Influence Panel Size

Some organizations are utilizing their EHR data to compare the balance of face-to-face work and asynchronous work. One study showed that for healthy patients, physicians spend the same amount of face-to-face time as non-face-to-face time; when patients have 3 or more chronic diseases, physicians spent 3 times as much non-face-to-face time (during asynchronous work) as they do face to face with the patients.⁸

It is important to consider the growing burden of asynchronous work on primary care physicians when computing panel size adjustments. To address this, the University of California San Diego (UCSD) Health developed a physician workload index to adjust the raw attributed panel patient counts for individual physicians.¹

The UCSD workload index includes office visits as well as non-clinical physician activities. Examples of non-clinical activities include telephone encounters, refill requests, and electronic messages; the EHR is the data source for each activity for each patient. Each encounter activity type is assigned workload points. The faculty compensation committee was surveyed about the relative workload associated with each activity to assign points. Table 1 shows the points assigned to different activities.

Table 1. UCSD Physician Workload Index Points¹

Each physician's average workload points per patient per year are calculated. The average is then compared to the practice's average to calculate a relative workload index for each physician. Next, multiply the raw attributed patient count by the relative workload index to compute a workload-adjusted panel size. UCSD's model is an example of how to use the asynchronous work involved in caring for a patient panel to adjust the attributed panel size to account for variation in patient complexity (Table 2).

Table 2. Example of Workload-Adjusted Panel Sizes¹

UCSF also developed a panel adjustment method that includes both visit and asynchronous care recorded in the EHR using the primary care workload. The UCSF method uses a statistical model involving machine learning and “big data” analytics to generate a complexity weight for each patient.¹ UCSF implemented this method to generate monthly reports of weighted panel sizes for each PCP and for each practice. Figure 3 displays 3 UCSF primary clinics that serve distinct populations. For example, with only 4% high-workload patients, women's health saw a drop in their adjusted panel size (1485) to a level below its raw panel size (1616). The general medicine practice saw a modest increase from their raw panel size of 1345 to an adjusted panel of 1505.

Figure 3. Primary Care Work Across Specialties¹^,13

Rates of adoption of this model by primary care physicians at UCSF have been high. The report extracted in Table 3 illustrates some of the metrics included in the balanced scorecard for annual bonuses and a tool for monthly adjustment of access at the practice level.

Table 3. UCSF Primary Care Practice Monthly Panel Tool

Q&A

Which physician variables impact a manageable panel size?
Some physicians may be comfortable with a more rapid pace of patient care, while others prefer a slower pace. Some physicians are comfortable sharing elements of care—such as medication reconciliation, visit note documentation, and in-basket management— with team members, while other physicians choose to perform the majority of tasks themselves.
Which practice variables impact a manageable panel size?
A physician who manages patients in the hospital and the clinic, who provides regular same-day access and performs a comprehensive scope of services, will have a smaller panel than a physician whose work is confined to the ambulatory clinic and has a lower referral threshold. A practice with a well-designed and optimally implemented EHR may allow its physicians to manage larger panels safely.
Other variables that may positively impact appropriate panel size include the number of available exam rooms, the availability of documentation assistance, the number and skill level of support team members, the stability of the care team, and the presence of an extended care team (eg, behavioralist, social worker, dietician). Factors that may negatively impact appropriate panel size include highly risk-averse compliance policies affecting team member duties and high rates of burnout in physicians and other health care workers.

STEP 4 Modify Patient Panel Sizes as Needed

Once you have attributed the patient population and identified a targeted, adjusted panel size, there may be imbalanced panels across physicians in your practice. Leverage the practice's data to assess how panel size impacts patient access, experience, and care quality. You may also want to consider the following as proxy indicators of suboptimal panel size:

Burnout scores
Total time spent in the EHR
After-hours documentation (also known as work-after-work), including on weekends and during vacation
Chart closure rates

Finally, physicians have different comfort levels with patient panel size and daily visit volume. Acknowledging and respecting these preferences can pay dramatic dividends to the organization in terms of physician well-being, retention, and willingness to support the organization's larger mission.

Q&A

What if I am an over-empaneled physician?
You may want to develop a plan to increase the amount of team support or reduce your patient panel size.
For example, additional team members, such as nurses, medical assistants, documentation assistants, health coaches, and/or behavioral health specialists can help the physician manage a larger panel. Introducing workflow efficiency and team-based care concepts, such as pre-visit laboratory testing, expanded rooming and discharge protocols, pre-visit planning, team documentation, preauthorization support, and synchronized prescription renewal, will help the care team be more efficient.
To reduce a physician's panel size, temporarily close the physician's practice to new patients. In addition, offer patients who cannot be seen in their requested time frame the choice to establish care with a physician or team member who is not over-empaneled.
What are the downsides to closing a practice to new patients?
If exceptions to a closed panel allow for the physician to see new patients, the panel soon becomes filled with patients requiring even more time and communication. It can be made clear to all that there are no exceptions when a panel is closed, and that new patients should wait in a queue with current patients. It is often difficult for the over-paneled physician to turn down a request from colleagues to take on new patients, so leadership support is helpful.
What can I do to increase my patient panel size if I am under-empaneled?
Strategies to increase panel size include partnering with an over-empaneled physician to assume responsibility for some of the over-empaneled physician's patients and direct scheduling of patients transitioning from the emergency department or hospital to home. Providing same-day access to unassigned patients can also help grow a panel.

STEP 5 Monitor and Maintain

Some organizations choose to periodically monitor panel sizes, particularly if compensation or other resources depend on panel size. Sharing monthly reports can help the organization understand if strategies to manage access and workload are effective. This frequency allows practice leaders to adjust schedules and staffing and detect early signs of burnout or poor physician engagement.¹⁴

Conclusion

As demand for primary care and accountability for population health through empanelment increases, determining the optimal patient panel size and appropriate management of panel size to an optimal target is essential. While the science of panel size optimization is in its infancy, panel size has significant downstream effects on care quality, patient and physician satisfaction, and access.¹⁵ Creating the optimal panel for physicians can contribute to the success of a primary care practice.

Consider patient complexity, practice support networks, and physician preferences for practice scope and pace when figuring out optimal panel size. Practices may take a look beyond panel size to answer the question, “What is the best practice model that results in the best outcomes for the entire US population?”¹⁶ Efficient workflows and advanced models of team-based care can expand panel capacity while also improving physician well-being and reducing burnout.

Further Reading

Journal Articles and Other Publications

Hartley W, Horton F, Cuddeback J, Stempniewicz R, Stempniewicz N. Why panel size matters: operational considerations and risk adjustment. American Medical Group Association (AMGA) Group Practice Journal (GJP). June 2018. Accessed June 4, 2021. https://www.amga.org/amga/media/store/products/670628.pdf
Shekelle PG, Paige NM, Apaydin EA, et al. What is the optimal panel size in primary care? A systematic review. US Department of Veterans Affairs, Veterans Health Administration, Health Services Research & Development Service. August 2019. Accessed June 4, 2021. https://www.hsrd.research.va.gov/publications/esp/panel-size-primary-care.pdf
Weber R, Murray M. The right-sized patient panel: a practical way to make adjustments for acuity and complexity. Fam Pract Manag. 2019;26(6):23-29. https://www.aafp.org/fpm/2019/1100/p23.html
Paige NM, Apaydin EA, Goldhaber-Fiebert JD, et al. What is the optimal primary care panel size?: a systematic review. Ann Intern Med. 2020;172(3):195-201. doi:10.7326/M19-2491
Kamnetz S, Trowbridge E, Lochner J, Koslov S, Pandhi N. A simple framework for weighting panels across primary care disciplines: findings from a large US multidisciplinary group practice. Qual Manag Health Care. 2018;27(4):185-190. doi:10.1097/QMH.0000000000000190

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Article Information

Disclosure Statement: The project described was supported by Funding Opportunity Number CMS-1L1-15-002 from the U.S. Department of Health & Human Services, Centers for Medicare & Medicaid Services. The contents provided are solely the responsibility of the authors and do not necessarily represent the official views of HHS or any of its agencies.

Disclaimer: AMA STEPS Forward™ content is provided for informational purposes only, is believed to be current and accurate at the time of posting, and is not intended as, and should not be construed to be, legal, financial, medical, or consulting advice. Physicians and other users should seek competent legal, financial, medical, and consulting advice. AMA STEPS Forward™ content provides information on commercial products, processes, and services for informational purposes only. The AMA does not endorse or recommend any commercial products, processes, or services and mention of the same in AMA STEPS Forward™ content is not an endorsement or recommendation. The AMA hereby disclaims all express and implied warranties of any kind related to any third-party content or offering. The AMA expressly disclaims all liability for damages of any kind arising out of use, reference to, or reliance on AMA STEPS Forward™ content.

About the AMA Professional Satisfaction and Practice Sustainability Group

The AMA Professional Satisfaction and Practice Sustainability group has been tasked with developing and promoting innovative strategies that create sustainable practices. Leveraging findings from the 2013 AMA/RAND Health study, “Factors affecting physician professional satisfaction and their implications for patient care, health systems and health policy,” and other research sources, the group developed a series of practice transformation strategies. Each has the potential to reduce or eliminate inefficiency in broader office-based physician practices and improve health outcomes, increase operational productivity, and reduce health care costs.

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