Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery

Gerchak, Yigal; Gupta, Diwakar; Henig, Mordechai I.

doi:10.1287/mnsc.42.3.321

Cited by 238 publications

(157 citation statements)

References 20 publications

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“…Kwak and Lee 1997, Green and Meissner 2002, Huang 1995, Green et al 2006 as well as tactical decisions such as the scheduling of procedures (see e.g. Gerchak et al 1996). This stream of research is quite extensive and we refer the readers to Smith-Daniels et al (1988) and Green (2004) for more comprehensive overviews.…”

Section: Literature Reviewmentioning

confidence: 99%

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Terwiesch

2012

M&SOM

208

135

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This paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We find that the length of stay for patients admitted to the ICU is influenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU effective capacity-an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question of whether an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the first time. " By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we find that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby effectively reducing peak bed capacity. Christian TerwieschThe Wharton School, University of Pennsylvania terwiesch@wharton.upenn.eduThis paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We …nd that the length of stay for patients admitted to the ICU is in ‡uenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU e¤ective capacity -an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question if an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the …rst time." By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we …nd that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby e¤ectively reducing peak bed capacity.

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Section: Literature Reviewmentioning

confidence: 99%

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Terwiesch

2012

M&SOM

208

135

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“…This includes choosing occupancy rates (e.g., Smith-Daniels et al 1988, Huang 1995, Green and Nguyen 2001 and making sta¢ ng decisions (e.g., Aiken et al 2002, Kwak and Lee 1997, Green and Meissner 2002. At the tactical level, decisions need to be made with respect to scheduling and sequencing cases (e.g., Gerchak et al 1996) as well as with respect to allocating capacity to various demand types (e.g. Green et al 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

2009

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Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery-two vastly different health-care delivery services-we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery-an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate. Abstract Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery -two vastly di¤erent healthcare delivery services -we show that the processing speed of service workers is in ‡uenced by the system load. We …nd that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by 2 days for cardiothoracic surgery patients, while a 20% increase in the load for patient transporters reduces the transport time by half a minute. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the e¤ect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by 6 hours. Consistent with prior studies in the medical literature, we also …nd that overwork is associated with a reduction in quality of care in cardiothoracic surgery -an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also …nd that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate.We are grateful to the cardiac anesthesiologists and executives at the teaching hospital where this study was conducted. We also thank the Management Science review team as well as Stefanos Zenios, Chris Lee, and Marcelo Olivares for their insightful and constructive comments on an earlier version of this paper. The authors can be reached at dkc@emory.edu and terwiesch@wharton.upenn.edu.

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“…In sequential scheduling on the other hand appointment requests arrive gradually over time and the scheduler has to fit each patient to one of the available slots. See, for example, Gerchak et al (1996), Patrick et al (2008), Liu et al (2010), andFeldman et al (2014). Our investigation here is at a more strategic level and can be considered as a prerequisite to advance and online scheduling: we find the capacity needed for pre-booked non-urgent patients so that a reasonably quick access can be guaranteed.…”

Section: Literature Reviewmentioning

confidence: 95%

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Izady

2015

Operations Research

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Specialty clinics provide specialized care for patients referred by primary care physicians, emergency departments, or other specialists. Urgent patients must often be seen on the referral day, while non-urgent referrals are typically booked an appointment for the future. To deliver a balanced performance, the clinics must know how much 'appointment capacity' is needed for achieving a reasonably quick access for non-urgent patients. To help identify the capacity that leads to the desired performance, we model the dynamics of appointment backlog as novel discrete-time bulk service queues, and develop numerical methods for efficient computation of corresponding performance metrics. Realistic features such as arbitrary referral and clinic appointment cancellation distributions, delay-dependent no-show behaviour and rescheduling of no-shows are explicitly captured in our models. The accuracy of the models in predicting performance as well as their usefulness in appointment capacity planning is demonstrated using real data. We also show the application of our models in capacity planning in clinics where patient panel size, rather than appointment capacity, is the major decision variable. Permanent repository link:

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Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery

Cited by 238 publications

References 20 publications

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

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