Optimally redesigning panels has the potential to reduce wait times and maximize continuity. Our system matches physician capacity with historical demand from each category of patients better than the other strategies considered. Physicians who have less spare capacity in their schedules are given proportionally fewer patients from more appointment intensive categories and vice versa. The capacity-based design also performs quite well relative to the baseline for the same reason: physician capacities under this method are better matched with appointment demand than in the base case.
The optimized panels do this by increasing the effective capacity of primary care practices: as demand for appointments is better matched to capacity, many patients, who would otherwise wait to see their own provider, no longer need to wait. In addition, fewer follow-up appointments need to be made. Both these factors increase the number of available slots in future periods, with the result that more patients can be empanelled in the practice. Our model also produces similar improvements under an alternate patient classification system, suggesting that it is robust.
Computer-based models have been used in the past to study patient waiting time in outpatient settings. Factors considered include visit times26–28
, caseload of new or old patients29
, number of appointments a day30
, number of preceptors (in a teaching setting)31
, or number of physicians or staff32
. These efforts are primarily target patient flow and reducing wait time in the clinic on the day of the appointment. Our model considers a clinic’s appointment system in relation to a physician’s panel size and case mix rather than patient flow and wait time in a clinic on a particular workday. Wait time or timeliness in our model is the time from when the patient calls to when the appointment is secured; hence, the duration of the appointment on the day the patient sees the physician does not play a role.
Implications for Practice
Optimal panel designs obtained using our method would best be used, at least initially, as benchmarks or targets for real-world practices. It is not expected or desired that any real world clinic would necessarily reallocate patients abruptly, rather a more appropriate strategy would be to reallocate when the opportunities arise. For example, resident clinics have an opportunity to reallocate one third of their patients each year. Many primary clinic panels are dynamic, and patients enter and leave them all the time as people age, are diagnosed with new conditions, move out of area and many other reasons. A useful by-product of this constant state of flux is that it affords continuous opportunities to make incremental changes to patient panels without disrupting the visit patterns of patients who already have strong ties to their PCP, for example, leveraging patients who have yet to decide on a PCP, new patients and the turnover of existing patients. Patient surveys could be used to determine preferences and inclination towards change. In some cases, to minimize disruption, reassignment may simply be to another physician, whom the patient has seen almost as often as her own PCP, or to a physician within the same care team (if the care team consists of multiple physicians). At the very least, our model could provide pointers about how physicians in practice would benefit from enhanced care team support.
In making these recommended changes, the goal is to make steady improvements in timeliness and continuity wherever possible and continuously benchmarking against optimality. We envisage our model as a decision support system with a clinician-friendly interface that the office staff can use to test new panel allocations. This would enable immediate, structured feedback on the implications of changes on continuity and timeliness and therefore promote more informed choices. The model would be consulted on a weekly or monthly basis as panel adjustments are made.
Our study was conducted at an academic medical center with a substantial part time work force. In this setting, because of research and education commitments, physician schedules change from week to week, affecting physician availability and hence timeliness and continuity. As a result our model had to tackle variation in physician supply up front. The model, however, also remains relevant for practices with a full-time work force where rather than physician supply the main drivers of supply-demand mismatch are panel size and case mix.
With appropriate modifications, our approach can be adapted to different scales. Specifically, it is applicable to the workings of a care team, to within a practice group, for a formal network of physicians affiliated with an HMO, PPO or hospital, to an informal network of physicians working within a shared geographic catchment area, for example the state of Massachusetts after the 2006 insurance reform. At the level of a care team, patient assignment among physicians, nurse practitioners and registered nurses needs to be carefully considered, while at the level of the network the appointment burden for different physicians needs to be balanced.
Our study has important limitations. We do not consider individual patient and clinician preferences, which may play a role in how panels are formed. We also do not adjust for clinician practice style, which may impact the number of follow-up appointments. We do not account for operational adjustments that may occur on a daily basis. Physicians, for example, flex their immediate capacity by spending more or less time depending on whether the immediate demand is high or low; they may also use care teams. We do not account for cancellations and no-shows—both of which are important components of the regular running of an office practice.