Assessing appointment systems’ operational performance with policy targets

Abstract

We propose a paradigm shift in how the performance of outpatient clinic appointment schedules is evaluated in practice and academia. Our research addresses the traditional dilemma between patients' wait times and providers' idle time and overtime, but with operational performance metrics that assess their respective probabilities of exceeding established thresholds, instead of optimizing a presumed cost function. Using a stochastic model, we introduce a new way of analyzing appointment schedules that is absent from the literature but appealing to practitioners. We take into account the variable nature of patient consultation times, known differences in the duration of diverse consults, and patients' propensity to miss their appointments. Analysis shows that traditional scheduling systems have serious shortcomings in terms of providing consistent service levels, and we conclude that the managerial decision space so far investigated in the appointment scheduling literature is not adequate for exercising operational control over appointment system performance.

Post-concussion syndrome (PCS) in a youth population: defining the diagnostic value and cost-utility of brain imaging

“…For bivariate analyses, significance was determined at a level of α=0.05. Estimated costs of each imaging modality were obtained from New Choice Health, a consumer-oriented resource widely cited in peer-reviewed literature which reports the average billed cost to insurers of a given procedure at a specific institution [19][20][21][22][23][24][25]. We determined the cost of a single positive radiographic finding as the total cost of each modality divided by the number of clinically relevant positive findings.…”

Improving patient flow at a family health clinic

“…The vast majority of previous studies of clinics similar to the FHC (e.g., see [6,33,39,44]) assume that patients are prompt, which is rarely the case here. The published research that exists on policies related to the order in which waiting patients are called is mostly limited to dealing with different patient classes that have different arrival processes and impose different costs on the system (e.g., see [44,49,51,52]).…”

“…In a more recent study, Millhiser et al [33] also addressed patient waiting time, provider idle time and overtime, but with operational performance metrics that assess their respective probabilities of exceeding established thresholds instead of optimizing a presumed cost function. Using a stochastic model, they introduced a new way of analyzing appointment schedules that takes into account the variable nature of patient consultation times, known differences in the duration of diverse consults, and patients' propensity to miss their appointments.…”

“…In our study, we investigate six of the most popular approaches as well as the rule now in use at the FHC. Table 3 lists the six additional rules, which are defined as follows (see Millhiser et al [33] for more detail).…”

A patient-centered surgical home to improve outpatient surgical processes of care and outcomes

“…As in the first phase, results for all scenarios are based on 600 simulated days with 25 patients per day, and clinic staff report for duty at 7:00 am, unless stated otherwise. Table 6 contains the current patient schedule used by APC along with the patients' schedules generated by several policies considered by Cayirli et al (2006) and Millhiser et al (2012) including an individual-block/fixed-interval (IBFI) rule, a variant of Bailey's Rule (Bailey 1952) with four patients starting at the beginning of the day (one more than the number of providers) (4BEG), a two-block/fixed interval (2BFI) rule, and an individual-block/variable interval rule that results in "dome-shaped" appointment intervals (DOME). Table 7 contains the simulation results for different scheduling policies on scenario (I L I M , P M P H ).…”