Guidelines for scheduling in primary care under different patient types and stochastic nurse and provider service times

Oh, Hyun-Ju; Muriel, Ana; Balasubramanian, Hari; Atkinson, Katherine; Ptaszkiewicz, Thomas

doi:10.1080/19488300.2013.858379

Cited by 31 publications

(31 citation statements)

References 32 publications

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“…A similar plot for provider assessment time suggests that it is relatively uniform over the day averaging 16.84 min with a standard deviation of 8.68. These statistics are almost exactly the same as those reported by Oh et al [47] who observed an average of 16.7 min with a standard deviation of 8.7 for a group practice. For a preanesthesia evaluation clinic with three providers, Dexter et al [48] found that the fastest practitioner was typically 1.23 times faster than the second fastest practitioner and 1.61 times faster than the slowest of three practitioners.…”

Section: Experimental Design and Analysissupporting

confidence: 87%

Improving patient flow at a family health clinic

Bard

Shu

Morrice

et al. 2014

Health Care Manag Sci

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This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three-year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time-stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance.The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no-show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system's resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers' average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out-of-order arrivals.

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Section: Experimental Design and Analysissupporting

confidence: 87%

Improving patient flow at a family health clinic

Bard

Shu

Morrice

et al. 2014

Health Care Manag Sci

View full text Add to dashboard Cite

show abstract

“…Although service duration variability has an impact on the performance of health care clinics [see e.g. 30], in our case study data on the amount of service duration variability was not known. Furthermore, as our model would explode when adding all sources of variability, we chose to incorporate uncertainty in patient routing over uncertainty in service duration, as the impact of a patient not visiting a provider is higher than the impact of a patient having a shorter visit with a provider.…”

Section: Conclusion and Discussionmentioning

confidence: 95%

“…Multiple authors consider the planning of flow-shop type multi-disciplinary systems, for example in oncology [23,40] and primary care practices [30]. Liang et al [23] analyze the impact of scheduling methods on the oncology clinic performance, where patients visit an oncologist and a nurse for chemotherapy treatment.…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

“…However, the number of patients per patient type, and thus the patient routing, are known in advance. Oh et al [30] sequence patient appointments using a stochastic integer programming model with the sample average approximation method. They included the effects of uncertainty in service time, but fixed the patient routing requirements.…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

“…Both local search methods [42,44] and other meta-heuristics [33] are applied to develop patient schedules, as well as approximate stochastic approaches [30] and simple planning rules [22,41].…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Leeftink

Vliegen

Hans

2017

Health Care Manag Sci

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Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multidisciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

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Identifying the timing and duration of collecting patient priorities in an orthopedic clinic

Jurewicz

Hobbs

Neyens

et al. 2018

Hum Ftrs & Erg Mfg Svc

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The patient‐centered care model supports patients actively engaging in their care. Typically this involves collecting patient‐reported outcomes and priorities that may not be part of the routine practice. There is a research gap related to whether there is time available within the routine orthopedic medical practice to collect patient‐centered data for immediate use without delaying the overall clinical processes or influencing patient clinical experiences. A time study was conducted to quantify the current patient processes at an orthopedic clinic, and a statistical simulation model was used to evaluate potential changes in patients’ clinical process if a patient‐centered survey were to be implemented. The proportion of simulated patients who would experience delays due to the survey, the durations of their delays, and the average duration of the patient process delay were recorded at each step in the clinical process. The results are presented in terms of the relationship between the time devoted to collecting data and the impact on the patient experience for various survey durations at various process points. This study demonstrates an approach to evaluate how introducing a patient priorities survey (for collecting both patient‐reported outcomes and priorities), which could be used in the same patient visit, might impact the clinical process and introduce process delays.

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Guidelines for scheduling in primary care under different patient types and stochastic nurse and provider service times

Cited by 31 publications

References 32 publications

Improving patient flow at a family health clinic

Improving patient flow at a family health clinic

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Identifying the timing and duration of collecting patient priorities in an orthopedic clinic

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