A Capacity Allocation Planning Model for Integrated Care and Access Management

Deglise‐Hawkinson, Jivan; Helm, Jonathan E.; Huschka, Todd R.; Kaufman, David L.; Oyen, Mark P. Van

doi:10.1111/poms.12941

Cited by 30 publications

(17 citation statements)

References 32 publications

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“…Some of these studies consider inpatients and emergency patients in addition to outpatients, where the arrival of inpatients and emergency patients is modeled as random events that interrupt the system (Erdogan et al, 2015; Erdogan & Denton, 2013; Patrick & Puterman, 2007). Deglise‐Hawkinson et al (2018) provide a capacity allocation plan to minimize the indirect waiting time of higher priority patients across an integrated network of care services. On the other hand, scheduling of outpatients in the presence of emergency and inpatient arrivals is studied via appointment scheduling, but not capacity planning, in diagnostic clinics by Green et al (2006), Sickinger and Kolisch (2009), and Bhattacharjee and Ray (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Kiani

Ekşioğlu

Isik

et al. 2021

Naval Research Logistics

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Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can worsen medical outcomes and increase patient no‐shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy that does not require significant investments for diagnostic clinics. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two‐stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage. In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two‐stage stochastic model, we develop a simple, non‐anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy‐to‐implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Kiani

Ekşioğlu

Isik

et al. 2021

Naval Research Logistics

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“…Some of these studies consider inpatients and emergency patients in addition to outpatients, where the arrival of inpatients and emergency patients are modeled as random events that interrupt the system (Patrick and Puterman 2007, Erdogan and Denton 2013, Erdogan et al 2015. Deglise-Hawkinson et al (2018) provide a capacity allocation plan to minimize the indirect waiting time of higher priority patients across an integrated network of care services. On the other hand, scheduling of outpatients in the presence of emergency and inpatient arrivals is studied via appointment scheduling, but not capacity planning, in diagnostic clinics by Green et al (2006), Sickinger and Kolisch (2009), and Bhattacharjee and Ray (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Appointment Postponement in Scheduling Patients at a Diagnostic Clinic

et al. 2020

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Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can cause medical issues and lead to increases in patient no-shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy for such clinics that does not require significant investments. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two-stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage.In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two-stage stochastic model, we develop a simple, non-anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy-to-implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times. ) classify waiting time of patients into two categories. They define direct waiting time as the time the patient waits in the healthcare facility on the day of appointment and indirect waiting time as the time between the day the patient requests an appointment and the appointment day. Unfortunately, long indirect waiting times are common in practice. For instance, Kesling and Nissenbaum (2014) reported that 84% of patients in Veterans Affairs (VA) hospitals wait more than 14 days to see a physician. In addition to the medical issues that long indirect waiting times cause, they can also lead to increases in patient no-shows (Green et al. 2006) which have significant effect on annual revenues (Moore et al. 2001). Thus, healthcare managers face the challenge of improving their appointment systems to decrease waiting times and no-shows without incurring major capital costs. Diagnostic clinics are among the healthcare facilities that generally suffer from long indirect waiting times (McCarthy et al. 2000). One such clinic is the Radiology Department at PrismaHealth, our collaborator on this study. The clinic provides service to outpatients, inpatients, and emergency patients. The requests for appointments are handled on a first-come-first-served (FCFS) basis. The emergency patients are the highest priority group, followed by inpatients and then outpatients. The outpatients are further categorized into a number of priority classes based on co-morbidities and chronic conditions. The emergency patients are seen as soon as they arrive if there is capacity or immediately referred to another clinic. The inpatients are either given a next day appointment during regular hours or seen during overt...

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“…Our model, at least in its current state then, does not apply to adaptive trials. Some of the ideas of our model though have recently been extended to another application area -patient access for a network of outpatient services, in Deglise-Favre-Hawkinson (2015) and Deglise-Hawkingson et al (2018). In the latter, the workload of a patient who is granted access to the network induces a downstream workload that is stochastic because an initial visit may, probabilistically, result in subsequent visits (perhaps in another department) depending on the results of diagnostic tests, etc.…”

Section: Literature Surveymentioning

confidence: 99%

Access planning and resource coordination for clinical research operations

et al. 2019

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This research creates an operations engineering and management methodology to optimize a complex operational planning and coordination challenge faced by sites that perform clinical research trials. The time-sensitive and resource-specific treatment sequences for each of the many trial protocols conducted at a site make it very difficult to capture the dynamics of this unusually complex system. Existing approaches for site planning and participant scheduling exhibit both excessively long and highly variable Time to First Available Visit (TFAV) waiting times and high staff overtime costs. We have created a new method, termed CApacity Planning Tool And INformatics (CAPTAIN) that provides decision support to identify the most valuable set of research trials to conduct within available resources and a plan for how to book their participants. Constraints include (i) the staff overtime costs, and/or (ii) the TFAV by trial. To estimate the site's metrics via a Mixed Integer Program, CAPTAIN combines a participant trajectory forecasting with an efficient visit booking reservation plan to allocate the date for the first visit of every participant's treatment sequence. It also plans a daily nursing staff schedule that is optimized together with the booking reservation plan to optimize each nurse's shift assignments in consideration of participants' requirements/needs.

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A Capacity Allocation Planning Model for Integrated Care and Access Management

Cited by 30 publications

References 32 publications

Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Evaluating Appointment Postponement in Scheduling Patients at a Diagnostic Clinic

Access planning and resource coordination for clinical research operations

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