Improving Clinical Access and Continuity Using Physician Panel Redesign

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA.
Journal of General Internal Medicine (Impact Factor: 3.42). 10/2010; 25(10):1109-15. DOI: 10.1007/s11606-010-1417-7
Source: PubMed


Population growth, an aging population and the increasing prevalence of chronic disease are projected to increase demand for primary care services in the United States.
Using systems engineering methods, to re-design physician patient panels targeting optimal access and continuity of care.
We use computer simulation methods to design physician panels and model a practice's appointment system and capacity to provide clinical service. Baseline data were derived from a primary care group practice of 39 physicians with over 20,000 patients at the Mayo Clinic in Rochester, MN, for the years 2004-2006. Panel design specifically took into account panel size and case mix (based on age and gender).
The primary outcome measures were patient waiting time and patient/clinician continuity. Continuity is defined as the inverse of the proportion of times patients are redirected to see a provider other than their primary care physician (PCP).
The optimized panel design decreases waiting time by 44% and increases continuity by 40% over baseline. The new panel design provides shorter waiting time and higher continuity over a wide range of practice panel sizes.
Redesigning primary care physician panels can improve access to and continuity of care for patients.

Download full-text


Available from: James E Stahl, Sep 29, 2015
32 Reads
  • Source
    • "Age and gender is the simplest patient classification in absence of other data, yet is generally effective [Murray et al., , Balasubramanian et al. 2010]. Figure 2 illustrates the distribution of the fraction (or percentage) of total patients requesting appointments in a week for two categories males (48-53 y.o.) and women (73-78 y.o.), based on on historical data "
    [Show abstract] [Hide abstract]
    ABSTRACT: We discuss capacity allocation for primary care practices at three different planning levels: the strategic, the tactical and the operational. The goal in each case is to maximize two important but often conflicting metrics: (1) timely access and (2) patient-physician continuity. Timely access focuses on the ability of a patient to get access to a physician as soon as possible. Patient-physician continuity refers to building a strong relationship between a patient and a specific physician by maximizing patient visits to that physician. Each primary care provider (PCP) has a panel of patients for whose long term holistic care the PCP is responsible. At the highest or strategic level, the design of physician panels, we demonstrate the impact of case-mix, or the type of patients in a physician’s panel, and show how panels can be redesigned effectively. Panel redesign, however, involves changing existing patient-physician relationships. A viable alternative is managing the inherent flexibility of PCPs to see patients of other physicians. At the tactical level, this requires allocating the flexible capacity to two types of appointments: 1) prescheduled appointments which are booked in advance and require continuity; and 2) same-day appointments. Using a 2-stage stochastic optimization model, we show that greedy algorithms find the optimal capacity allocation, and find that a partially flexible practice provides a good compromise between timely-access and continuity. Finally, at the operational level, the implementation of flexibility during a workday has to be made under partial demand information, as patient calls arrive over the course of a day. We discuss the impact of flexibility and suggest heuristics that practices can use in this dynamic case.
  • Source
    • "The productivity for Model I is estimated to be 2 , 380 – 2 , 440 patients , and this is consistent with those reported elsewhere ( Green and Savin 2008 ; Balasubramanian et al . 2010 ; Liu and Ziya unpublished data ) , validating our use of queueing models to estimate productivity . The estimated productivity for models with two providers ( Model II and Model III ) ranges from 2 , 400 to 4 , 600 patients , less than the sum of two solo PCPs ' productivity . These are rea - sonable estimates because NPs have longer c"
    [Show abstract] [Hide abstract]
    ABSTRACT: To develop simple stylized models for evaluating the productivity and cost-efficiencies of different practice models to involve nurse practitioners (NPs) in primary care, and in particular to generate insights on what affects the performance of these models and how. DATA SOURCES AND STUDY DESIGN: The productivity of a practice model is defined as the maximum number of patients that can be accounted for by the model under a given timeliness-to-care requirement; cost-efficiency is measured by the corresponding annual cost per patient in that model. Appropriate queueing analysis is conducted to generate formulas and values for these two performance measures. Model parameters for the analysis are extracted from the previous literature and survey reports. Sensitivity analysis is conducted to investigate the model performance under different scenarios and to verify the robustness of findings. Employing an NP, whose salary is usually lower than a primary care physician, may not be cost-efficient, in particular when the NP's capacity is underutilized. Besides provider service rates, workload allocation among providers is one of the most important determinants for the cost-efficiency of a practice model involving NPs. Capacity pooling among providers could be a helpful strategy to improve efficiency in care delivery. The productivity and cost-efficiency of a practice model depend heavily on how providers organize their work and a variety of other factors related to the practice environment. Queueing theory provides useful tools to take into account these factors in making strategic decisions on staffing and panel size selection for a practice model.
    Health Services Research 11/2011; 47(2):594-613. DOI:10.1111/j.1475-6773.2011.01343.x · 2.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Panel management is a central component of the primary care medical home, but faces numerous challenges in the safety net setting. In the San Francisco Department of Public Health, many of our community-based primary care clinics have difficulty accommodating all patients seeking care. We evaluated patient panel size in our 7 clinics providing cradle-to-grave primary care services to more than 25,000 active patients. We adjusted panel size for age, gender, diagnoses, homelessness, and substance abuse; set related policies; and assessed the effects on our clinics. On the basis of our previous data and targets set by other safety net providers, we established a minimum of 1125 patients per full-time paid primary care provider (ie, full-time equivalent [FTE]) in April 2009. We calculated the target panel size each clinic would have if all their providers reached the minimum panel size goal and compared it with the panel size attained by the clinic. Nine months after establishing panel size policy, providers reached 82% of the aggregate target panel size. Five of the 7 clinics increased their adjusted panel size per FTE in the range of 2% to 23%. Two data-oriented and innovative clinics with some of the highest adjusted panel sizes per FTE largely maintained their panel size. Two clinics that had the lowest adjusted panel size per FTE realized a 23% and 8% respective gain; both clinics reduced barriers to new patient appointments. Two clinics acquired new providers and experienced a concomitant drop in panel size per FTE while the new clinicians expanded their panels. One of these clinics had difficulty managing high no-show rates and creating effective appointment templates. Routine data generation, review of data with administrators and providers, data-driven policies and panel size standards, and interventions to bolster team-based care are important tools for increasing capacity at our primary care clinics.
    Journal of public health management and practice: JPHMP 11/2011; 17(6):506-12. DOI:10.1097/PHH.0b013e318211393c · 1.47 Impact Factor
Show more