Assessing appointment systems’ operational performance with policy targets

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

Evaluation of Patient Satisfaction With the New Web-Based Medical Appointment Systems "Mawid" at Primary Health Care Level in Southwest Saudi Arabia: A Cross-Sectional Study

Background Patient satisfaction has become an influential corner in the health services process. Web-based appointment scheduling has been expanded for its benefits and has become a popular research topic. This study's objectives were to assess patients' satisfaction and perception with the new Web-Based Medical Appointment System "Mawid" program and determine the associated factors at the Primary Health Care Centers level in Jazan Southwest Saudi Arabia. Methods An observational cross-sectional survey was implemented among 424 adults aged 18 years and above, attending a randomly selected 12 primary health care centers in the Jizan region, Southwest Saudi Arabia. The study instrument included socio-demographic background information, perception, and level of satisfaction with the new appointment system. Responses were analyzed using the SPSS program by applying descriptive and inferential statistical techniques. Results The overall level of satisfaction was very high at 94.3% with 95% C.I. (91.7-96.1). A large proportion of study participants were highly satisfied with the new Web-Based Medical appointment System "Mawid" as nine satisfaction items scored a level of satisfaction of 90% and above. Regarding the perception, 89.1% of the participants agreed that the appointment booking system regulates the number of patients, while 87.7% of participants considered that the appointment system reduces clinic crowding. More than half of respondents (61.8%) agreed that the community culture might limit the scheduling system's use. Univariate and multivariate logistic regression analysis suggested that male patients were more likely to have a higher level of satisfaction as compared with female (COR= 2.95, 95% C.I.:1.15-7.60, p = 0.025) and (AOR= 3.12, 95% C.I:1.14-8.52, p = 0.026), respectively. Conclusions In conclusion, this study revealed a high level of satisfaction among study the participants with the new Web-Based Medical Appointment System "Mawid." The system effectively improved patients' satisfaction with registration and reduced waiting times. Patients' satisfaction can be assessed regularly and used systematically as a quality and benchmarking instrument in primary health care.

Designing and scheduling a multi-disciplinary integrated practice unit for patient-centred care

This paper presents the design and analysis of a newly proposed form of care delivery called an integrated practice unit (IPU) in which a multi-disciplinary team of providers and staff work together to cover the full care cycle for a given condition. In an IPU, the different providers circulate among the patients, according to the need for their expertise, while patients remain in a single location once they check-in. From the patient's perspective, the benefits of such an arrangement should be self-evident. For payers and providers there will also be benefits as the fee-for-service market gives way to structured payments for each episode of care. Before setting up an IPU, it is necessary to gain an understanding of how available resources will limit patient flow and system performance. Treating resources such as providers, imaging equipment, and rooms parametrically, the primary goal of our work is to determine the number of patients that can be seen per day in an IPU while trying to constrain overtime, length of stay, and waiting time to best practice targets. Discrete-event simulation serves as our analytic tool. While we are involved in the design of a comprehensive suite of musculoskeletal IPUs, we illustrate our approach with an extensive computational study of one: a Lower Extremity Joint Pain IPU. Using the simulation methodology, we are not only able to determine the number of patients that can be scheduled for an in-clinic visit each day, but also the daily number of follow-up patients that can be served virtually through telemedicine with no additional resources and minimal impact on IPU performance. These results assisted the Department of Surgery at the Dell Medical School at The University of Texas in the optimal design of its first IPU, which opened in the fall of 2017.

A decision support system for real-time scheduling of multiple patient classes in outpatient services

We propose a methodology to provide real-time assistance for outpatient scheduling involving multiple patient types. Schedulers are shown how each prospective placement in the appointment book would impact a day's operational performance for patients and providers. Rooted in prior literature and analytical findings, the information provided to schedulers about vacant slots is based on the probabilities that the calling patient, the already-existing appointments, and the session-end time will be unduly delayed. The information is updated in real-time before and after every new booking; calculations are driven by each patient type's historical consultation times and no-show data, and implemented via a simulation tool based on the underlying analytical methodology. Our findings lead to practical guidelines for dynamically constructing templates that provide allowances for different consultation durations, service time variability, no-show rates, and provider-driven performance targets for patient waiting and provider overtime. Extensions to healthcare batch scheduling applications such as radiology, surgery, or chemotherapy-where patient mixes may be known in advance-are suggested as future research opportunities since avoiding session overtime and procedures' completion time delays involve similar considerations.

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

PURPOSE

Approximately 90% of concussions are transient, with symptoms resolving within 10-14 days. However, a minority of patients remain symptomatic several months post-injury, a condition known as post-concussion syndrome (PCS). The treatment of these patients can be challenging. The goal of our study was to assess the utility and cost-effectiveness of neurologic imaging two or more weeks post-injury in a cohort of youth with PCS.

METHODS

We conducted a retrospective study of 52 pediatric patients with persistent post-concussion symptoms after 3 months. We collected demographics and neuroimaging results obtained greater than 2 weeks post-concussion. Neuroimaging ordered in the first 2 weeks post-concussion was excluded, except to determine the rate of re-imaging. Descriptive statistics and corresponding cost data were collected.

RESULTS

Of 52 patients with PCS, 23/52 (44%) had neuroimaging at least 2 weeks after the initial injury, for a total of 32 diagnostic studies. In summary, 1/19 MRIs (5.3%), 1/8 CTs (13%), and 0/5 x-rays (0%) yielded significant positive findings, none of which altered clinical management. Chronic phase neuroimaging estimated costs from these 52 pediatric patients totaled $129,025. We estimate the cost to identify a single positive finding was $21,000 for head CT and $104,500 for brain MRI.

CONCLUSIONS

In this cohort of pediatric PCS patients, brain imaging in the chronic phase (defined as more than 2 weeks after concussion) was pursued in almost half the study sample, had low diagnostic yield, and had poor cost-effectiveness. Based on these results, outpatient management of pediatric patients with long-term post-concussive symptoms should rarely include repeat neuroimaging beyond the acute phase.

Improving patient flow at a family health clinic

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.