Improving Patient Access and Patient-Clinician Continuity through Panel Redesign (Massachusetts)

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Summary:

Primary care practices need to balance the timeliness of care delivery with continuity of care so that appointment schedules allow patients to see their primary physician whenever possible. Timeliness and continuity are intrinsically tied to the makeup of a provider's patient population, or "physician-patient panel." Using patient appointment data, physician panel sizes, and case mix from primary care databases, the project team investigated how group practices can dynamically manage physician panels to improve timeliness of access and continuity. The team developed a quantitative decision support system to help clinicians, practice managers, and health systems answer the following questions:

  • How should patient panel composition be altered over time to best match patient demand with physician supply?
  • How should practices best match patient and physician preferences, while simultaneously considering the influence of panel size and case mix on patient access?
  • How many additional new patients can be empanelled without adversely affecting the goals of timely access and continuity?

In developing the system, the team constructed a general modeling framework for managing physician panels in a group practice and utilized systems engineering to model the system over time. The model incorporated specific features, such as patient and physician preferences, changes in scheduling regimens, group visits, and changes in the supply and demand dynamics of a practice. The models indicated optimized physician-patient panels increased physician capacity and may create an opportunity to mitigate physician shortages.

Improving Patient Access and Patient-Clinician Continuity Through Panel Redesign - 2012

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-268: Small Research Grant to Improve Health Care Quality Through Health Information Technology (IT) (R03)
  • Grant Number: 
    R03 HS 018795
  • Project Period: 
    February 2010 – February 2012
  • AHRQ Funding Amount: 
    $100,000
  • PDF Version: 
    (PDF, 327.42 KB)

Summary: Primary care practices in the United States must balance the timeliness of care delivery with continuity. Continuity of care includes balancing the lead time for appointments with the goal of having patients see their own primary physician whenever possible. Timeliness and continuity are intrinsically tied to the makeup of the patient population—the “physician-patient panel”—that a physician oversees. In addition to these priorities, teaching hospitals must take into account the learning requirements of their medical residents. In order to prepare for future practice, residents should be exposed to the widest possible range of clinical experiences.

Using patient appointment data, physician-patient panel sizes, and physician case mix, Dr. Balasubramanian and his team investigated how group practices can manage physician and resident-patient panels to improve timeliness of access and continuity. They developed a quantitative decision-support system to help clinicians, practice managers, and health systems answer the following questions:

  • How should physician-patient panel composition be altered over time to best match patient demand with physician supply?
  • How should practices best match patient and physician preferences, while simultaneously considering the influence of panel size and case mix on patient access?
  • How many additional new patients can be empanelled without adversely affecting the goals of timely access and continuity?

The project team constructed a general modeling framework for managing physician and resident-patient panels in a group practice and utilized systems engineering methods (optimization and discrete event simulation) to model the system over time. By incorporating specific features such as patient and physician preferences, changes in scheduling regimens, and changes in the supply and demand dynamics of a practice, the project team extended the framework’s applicability to various primary care settings.

Specific Aims:

  • Develop a modeling framework that can translate generally to various primary care settings. (Achieved)
  • Extend the model’s ability to dynamically generate optimal panels and incorporate changes in physician availability and patient demand over time. (Achieved)
  • Develop and disseminate the first two aims in a Web-based decision support tool for clinicians, practice managers, and health care systems. (Achieved) 

2012 Activities: Retrospective data from primary care clinics were used to complete the development of computer simulation models to optimize physician-patient panels and support operational and capacity planning for clinics. Due to delays initiating the project, Dr. Balasubramanian used a 1-year no-cost extension. This project was completed in February 2012.

Impact and Findings: Two primary care clinics were involved in the study: the Primary Care Internal Medicine Practice at the Mayo Clinic in Rochester, MN, and a primary care clinic at the Massachusetts General Hospital (MGH). Retrospective data from the Mayo clinic was used to determine important attributes in the distribution of patient visits among providers in order to develop the model. At MGH, the focus was on applying the developed model and evaluating the impact of reassignments to optimize panels.

The analysis of the Mayo Clinic practice used data from 2004 to 2006 on 20,000 patients and 39 physicians to characterize the practice and predict the probability of a patient appointment request. Patient-level data included the number and type of chronic conditions afflicting each patient, as well as the number of visits for each patient during the 3-year period. Dr. Balasubramanian and his team developed a simulation model to explore the impact of moving patients between panels as a way to improve timely access and continuity of care. The model showed a 40 percent improvement in timely access and continuity of care when older patients with many chronic conditions were shifted from an overburdened physician to a physician with available capacity.

The analysis of the MGH practice used data from 258 residents and approximately 17,000 patients for the 21-month period between July 1, 2008 and April 30, 2010. Dr. Balasubramanian used data from 14 residents and three preceptors to examine the number of diagnoses per resident panel versus the number of patient visits. This analysis showed a wide variation in number of diagnoses per resident panel and that patients with more diagnoses have more clinic visits. The patient reassignment model was applied within and across preceptor panels. Reassigning patients within a preceptor panel reduced the imbalance and maintained continuity between preceptor and patients. Reassigning patients across preceptor panels further reduced the imbalance; however, it also decreased continuity of care. By applying the model within and across preceptor panels, the difference between number of diagnoses and number of visits was reduced. Not-yet optimized panels were compared with optimized panels at current and with a 10 percent increase in physician demand. The optimally designed panels with the 10 percent increase in demand offered more capacity than the not-yet optimized panels without the increased demand. The models indicated that optimized physician-patient panels increase physician capacity and may create an opportunity to mitigate physician shortages. Dr. Balasubramanian noted that while reassignment of patients across preceptors would have serious ramifications for continuity of care, the model may be applied to assign new patients to physicians and to assign patients to new residents.

In a time where more practices are implementing the patient-centered medical home, this research provides a framework for dynamic management of physician panels in a primary care group practice to improve access and continuity. The process of patient panel redesign can be achieved by practices over many years, based on natural attrition rates and new patients joining the practice. The findings from this study may serve as an assessment tool for practices to characterize and benchmark their clinic population on an ongoing basis and ultimately increase access and continuity of care.

Target Population: General

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Improving Patient Access and Patient-Clinician Continuity Through Panel Redesign - 2011

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-268: Small Research Grant to Improve Health Care Quality Through Health Information Technology (IT) (R03)
  • Grant Number: 
    R03 HS 018795
  • Project Period: 
    February 2010 - February 2012
  • AHRQ Funding Amount: 
    $100,000
  • PDF Version: 
    (PDF, 172.34 KB)

Summary: Primary care practices in the United States must balance the timeliness of care delivery with its continuity. Continuity of care includes balancing the lead time for appointments, with the goal of having patients see their own primary physician whenever possible. Timeliness and continuity are intrinsically tied to the makeup of the patient population-the "physician-patient panel"-that a physician oversees. In addition to these priorities, teaching hospitals must take into account the learning requirements of its medical residents. In order to prepare for future practice, residents should be exposed to the widest possible range of clinical experiences.

Using patient appointment data, physician-patient panel sizes, and physician case mix, Dr. Balasubramanian and his team are investigating how group practices can manage physician and resident-patient panels to improve timeliness of access and continuity. They are developing a quantitative decision support system to help clinicians, practice managers, and health systems answer the following questions:

  1. How should physician-patient panel composition be altered over time to best match patient demand with physician supply?
  2. How should practices best match patient and physician preferences, while simultaneously considering the influence of panel size and case mix on patient access?
  3. How many additional new patients can be empanelled without adversely affecting the goals of timely access and continuity?

In developing the system, the project team constructed a general modeling framework for managing physician and resident-patient panels in a group practice and utilized systems engineering methods (optimization and discrete event simulation) to model the system over time. By incorporating specific features such as patient and physician preferences, changes in scheduling regimens, and changes in the supply and demand dynamics of a practice, the project team will extend the framework's applicability to various primary care settings. The models will be disseminated through a Web-based decision tool.

Specific Aims:

  • Develop a modeling framework that can translate generally to various primary care settings. (Ongoing)
  • Extend the model's ability to dynamically generate optimal panels and incorporate changes in physician availability and patient demand over time. (Ongoing)
  • Develop and disseminate the first two aims in a Web-based decision support tool for clinicians, practice managers, and health care systems. (Ongoing)

2011 Activities: Retrospective data from primary care clinics were used to develop computer simulation models to optimize physician-patient panels. Visit rate, patient co-morbidities, case mix, physician preferences, and physician capacity were assessed as model inputs. Particular emphasis was placed on the use of physician teams to manage urgent care appointments and maximize continuity of care. In the context of medical resident education, where a heterogeneous physician-patient panel offers greater learning opportunities, Dr. Balasubramanian developed a measure of imbalance of resident panels to determine the mix of diagnoses in each resident's panel. Next, a patient reassignment model was developed and applied to the data to attempt to correct the imbalance in resident panels. Finally, the impact of the patient reassignment model was assessed. The patient reassignment model will be turned into a Web-based decision support tool for use by other practices.

Due to delays initiating the project, Dr. Balasubramanian is using a 1 year no-cost extension. As last self-reported in the AHRQ Research Reporting System, project progress and activities are mostly on track and the project budget is roughly on target.

Preliminary Impact and Findings: Encounter data from a primary care clinic at the Massachusetts General Hospital were characterized for a 21-month period, July 1, 2008 to April 30, 2010. The data indicated that the practice consisted of 258 residents and approximately 17,000 patients. Using data from 14 residents and three preceptors, Dr. Balasubramanian examined the number of diagnoses per resident panel versus the number of patient visits. This analysis showed a wide variation in number of diagnoses per resident panel and that patients with more diagnoses have more clinic visits. The patient reassignment model was applied both within and across preceptor panels. Reassigning patients within a preceptor panel reduced the imbalance and maintained continuity between the preceptor and patients. Reassigning patients across preceptor panels further reduced the imbalance; however, it also decreased continuity of care. By applying the model within and across preceptor panels, the difference between number of diagnoses and number of visits was reduced. Not-yet-optimized panels were compared with optimized panels at current physician demand and with a 10 percent increase in physician demand. The optimally-designed panels with the 10 percent increase in demand offered more capacity than the not-yet-optimized panels without the increased demand. The models indicated optimized physician-patient panels increase physician capacity and may create an opportunity to mitigate physician shortages.

Dr. Balasubramanian noted that while reassignment of patients across preceptors would have serious ramifications for continuity of care, the model may be applied to assign new patients to physicians as well assign patients to new residents.

Target Population: General

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Improving Patient Access and Patient-Clinician Continuity Through Panel Redesign - 2010

Summary Highlights

  • Principal Investigator: 
  • Funding Mechanism: 
    PAR: HS08-268: Small Research Grant to Improve Health Care Quality Through Health Information Technology (IT) (R03)
  • Grant Number: 
    R03 HS 018795
  • Project Period: 
    February 2010 – February 2012
  • AHRQ Funding Amount: 
    $100,000
  • PDF Version: 
    (PDF, 315.15 KB)


Target Population: Not Applicable

Summary: Primary care practices in the United States must balance the timeliness of care delivery with its continuity, i.e., balance the lead time for appointments with the goal of having patients see their own primary physician whenever possible. Timeliness and continuity are intrinsically tied to the makeup of the patient population—the “physician-patient panel”—that a physician oversees.

Using patient appointment data, physician-patient panel sizes, and physician case mix, Dr. Balasubramanian and his team will investigate how group practices can dynamically manage physician-patient panels to improve timeliness of access and continuity. They will develop a quantitative decision support system to help clinicians, practice managers, and health systems answer the following questions:

  1. How should patient-patient panel composition be altered over time to best match patient demand with physician supply?
  2. How should practices best match patient and physician preferences, while simultaneously considering the influence of panel size and case mix on patient access?
  3. How many additional new patients can be empanelled without adversely affecting the goals of timely access and continuity?

In developing the system, Dr. Balasubramanian will construct a general modeling framework for managing physician-patient panels in a group practice and will utilize systems engineering methods (optimization and discrete event simulation) to model the system over time. By incorporating specific features, such as patient and physician preferences, changes in scheduling regimens, group visits, and changes in the supply and demand dynamics of a practice, the project team will extend the framework’s applicability to various primary care settings. The models will be disseminated through a Web-based decision tool.

Specific Aims:
  • Develop a modeling framework that can translate generally to various primary care settings. (Ongoing)
  • Extend the model’s ability to dynamically generate optimal panels and incorporate changes in physician availability and patient demand over time. (Ongoing)
  • Develop and disseminate the first two aims in a Web-based decision support tool for clinicians, practice managers, and health care systems. (Upcoming)

2010 Activities: Retrospective data from two clinics have been used to develop computer simulation models to optimize physician-patient panels. Visit rate, patient co-morbidities, case mix, physician preferences, and physician capacity were assessed as model inputs. Particular focus was paid to the use of physician teams to manage urgent care appointments and maximize continuity of care. Another modeling approach considered reallocation of patients with the least co-morbidities from physicians with high patient burden to those with lower patient burden, under the assumption that this is less disruptive to patient care and patient-provider relationships. In the context of medical resident education, where a heterogeneous physician-patient panel offers greater learning opportunities, Dr. Balasubramanian is developing measures of physician-patient-panel diversity. These measures, including the mean number of co-morbidities by panel and proportion of disease classes represented in the panel, will serve as additional model inputs. Ultimately this modeling framework will be used to guide the design of the Web-based decision support tool.

Grantee's Most Recent Self-Reported Quarterly Status (as of December 2010): The project is meeting most of its milestones on time. Due to delays initiating the project at the start of the grant period, the budget is somewhat underspent.

Preliminary Impact and Findings: The team compared not yet optimized panels with optimized panels at current physician demand and with a 10 percent increase in physician demand. The models indicate optimized physician-patient panels increase physician capacity and may create an opportunity to mitigate physician shortages. The optimally designed panels with the 10 percent increase in demand offered more capacity than the not yet optimized panels without the increased demand.

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Improving Patient Access and Patient-Clinician Continuity through Panel Redesign - Final Report

Citation:
Balasubramanian H. Improving Patient Access and Patient-Clinician Continuity through Panel Redesign - Final Report. (Prepared by the University of Massachusetts, Amherst, MA under Grant No. R03 HS018795). Rockville, MD: Agency for Healthcare Research and Quality, 2012. (PDF, 353.27 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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