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.
CPT © Copyright 2021 American Medical Association. All rights reserved. AMA and CPT are registered trademarks of the American Medical Association.
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.
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.
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
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.
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.
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.
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).
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.
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.
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.
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
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.
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