Article

Improving Clinical Access and Continuity through 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

ABSTRACT 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.

Full-text

Available from: James E Stahl, May 29, 2015
0 Followers
 · 
186 Views
  • Source
    [Show description] [Hide description]
    DESCRIPTION: Hospitals are complex systems with essential societal benefits and huge mounting costs. These costs are exacerbated by inefficiencies in hospital processes, which are often manifested by congestion and long delays in patient care. Thus, a queueing-network view of patient flow in hospitals is natural for studying and improving its performance. The goal of our research is to explore patient flow data through the lens of a queueing scientist. The means is exploratory data analysis (EDA) in a large Israeli hospital, which reveals important features that are not readily explainable by existing models. Questions raised by our EDA include: Can a simple (parsimonious) queueing model usefully capture the complex operational reality of the Emergency Department (ED)? What time scales and operational regimes are relevant for modeling patient length of stay in the Inter- nal Wards (IWs)? How do protocols of patient transfer between the ED and the IWs influence patient delay, workload division and fair- ness? EDA also underscores the importance of an integrative view of hospital units by, for example, relating ED bottlenecks to IW physi- cian protocols. The significance of such questions and our related findings raise the need for novel queueing models and theory, which we present here as research opportunities. Hospital data, and specifically patient flow data at the level of the individual patient, is increasingly collected but is typically confiden- tial and/or proprietary. We have been fortunate to partner with a hospital that allowed us to open up its data for everyone to access. This enables reproducibility of our findings, through a user-friendly platform that is accessible via the Technion SEELab.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this chapter, we discuss capacity allocation for primary care practices at three different levels of the capacity planning hierarchy. The goal in each case is to maximize two important but often conflicting metrics for any primary care practice are: (1) Timely Access and (2) Patient-physician Continuity. Timely access focuses on the ability of a patient to get access to a physician (or provider, in general) as soon as possible. Patient-physician continuity refers to building a strong or permanent relationship between a patient and a specific physician by maximizing patient visits to that physician. At the highest level, the design of physician panels, we demonstrate the impact of case-mix, or the type of patients in a physician's panel, on the ability to provide timely access and continuity. Case mix can be considered using age and gender as predictors, or, when patient clinical data is available, using comorbidity counts. Using case-mix a practice can create overflow profiles for the physicians in the practice as a function of daily capacity and determine which physicians are overburdened. This in turn can point to opportunities for redesigning panels so that patients can see their own PCP as much as possible and redirections to unfamiliar physicians are minimized. Panel redesign, however, involves changing existing patient-physician relationships. A viable alternative to redesign is managing the inherent flexibility of primary care physicians to see patients of other physicians. We study this flexibility at the aggregate (or tactical) as well as the dynamic (or operational) levels. The management of a flexible practice in the aggregate requires allocating capacity to two types of appointments: 1) prescheduled appointments which are booked in advance and require continuity with the patient's PCP; and 2) same-day or open access appointments which have to be fulfilled during the course of the day. We propose a framework, commonly observed in practice, in which the short notice open access appointments can be flexibly shared between physicians while mandating continuity for the prescheduled appointments. We show that greedy algorithms find the optimal capacity allocation under no flexibility (i.e. patients can only see their own physician) and under full flexibility (patients can see any physician in the practice). Using a two-stage stochastic integer programming model, we demonstrate the impact of flexibility on the ability to provide timely access to patients, measured by the number of patients seen a given workday. Specifically, we find that a partially flexible practice which restricts the number of physicians a patient sees to two but creates a closed chain between panels and physicians (a 2-chain) performs almost as well as the fully flexible practice with regard to timely access, without severely compromising continuity. The impact of flexibility increases as the number of physicians in the practice increases and as the demand loads between physicians are asymmetric or uneven. Our results also show that practices can heuristically determine their capacity allocation for prescheduled appointments depending on their flexibility configuration and overall system workload. Finally, the implementation of flexibility at the level of a workday has to be made under partial demand information, since calls arrive dynamically over the course of a day. We outline a decision framework to evaluate the impact of flexibility in this dynamic case and discuss heuristics that practices can use to balance timely access and continuity.
  • [Show abstract] [Hide abstract]
    ABSTRACT: To receive adequate training experience, resident panels in teaching clinics must have a sufficiently diverse patient case-mix. However, case-mix can differ from one resident panel to another, resulting in inconsistent training. Encounter data from primary care residency clinics at Massachusetts General Hospital from July 2008 to May 2010 (64 residents and ~3800 patients) were used to characterize patients by gender, age, major disease category (both acute and chronic, e.g., Cardio Acute, Cardio Chronic, etc., for a total of 44 disease categories), and number of disease categories. Imbalance across resident panels was characterized by the standard deviation for disease category, patient panel size, and annual visit frequency. To balance case-mix in resident panels, patient reassignment algorithms were proposed. First, patients were sorted by complexity; then patients were allocated sequentially to the panel with the least overall complexity. Patient reassignment across resident panels was considered under 3 scenarios: 1) within preceptor, 2) within a group of preceptors, and 3) across the entire practice annually. were compared with case-mix (pre-July 2012) and post-July 2012. Results. All 3 reassignment algorithms produced significant reductions in standard deviation of either number of disease categories or diagnoses across residents when compared with baseline (pre-July 2012) and actual July 2012 reassignment. Reassignment across the clinic and group provided the best and second best scenarios, respectively, although both came at the cost of initially reduced patient-preceptor continuity. Systematically reallocating patient panels in teaching clinics potentially can improve the consistency and breadth of the educational experience. The method in principle can be extended to any target of health care system reform where there is patient or clinician turnover.
    Medical Decision Making 03/2014; 34(4). DOI:10.1177/0272989X14524304 · 2.27 Impact Factor