Managing Advance Admission Requests for Obstetric Care
Pages 1224 - 1239
Abstract
This paper is devoted to the management of advance admission requests for obstetric care. Pregnant women in China select one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy. Schedulers must make the admission decision instantly based on the availability of the most critical resource, that is, hospital beds for postnatal care. The random delay between admission requests and postnatal care has created a distinct advance admission control problem. To address this issue, we propose a basic model that assumes a unit bed requirement for one day. Each admission generates a unit of revenue and each unit of overcapacity use incurs an overcapacity cost. With the objective of maximizing the expected net revenue, we establish an optimal policy for unlimited requests, that is, an expected arrival time quota (EATQ) policy that accepts a fixed quota of advance admission requests with the same expected date of confinement. We then propose an extended model for general capacity requirements. Using the Poisson approximation, we establish the optimality of the EATQ policy, which is shown to be solvable by a simple linear programming model. We compare the numerical results from the different policies and conduct a sensitivity analysis. The EATQ policy is demonstrated to be the best option in all test instances and notably outperforms the current admission rules used in hospitals, which usually accept admission requests according to some empirical monthly quota of the expected delivery month. The Poisson approximation is shown to be effective for determining the optimal EATQ policy for both stationary and nonstationary arrivals.
Summary of Contribution: First, this paper investigates the advance admission control problem for obstetric care. Pregnant women in China choose one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy but the most critical resource is hospitalization beds needed for postnatal care. The random delay between admission request and postnatal care makes the problem unique and challenging to solve. It belongs to the scope of computing and operations research. Second, this paper formulates a dynamic programming model, analyzes the structural properties of the optimal control policy, and finally proposes a mathematical programming model to determine the optimal quota. Numerical experiments show the validity of the proposed approach. It covers the research contents of theories on dynamic stochastic control, mathematic programming model, and experiments. Moreover, this paper is motivated by the practical problem (advance admission control) in obstetric units of Shanghai. Using these optimality properties, solution approaches, and numerical results, this paper provides guidance on how to manage advance obstetric admission requests.
References
[1]
Barz C, Rajaram K (2015) Elective patient admission and scheduling under multiple resource constraints. Production Oper. Management 24(12):1907–1930.
[2]
Bekker R, Koeleman PM (2011) Scheduling admissions and reducing variability in bed demand. Healthcare Management Sci. 14(3):237–249.
[3]
Deglise‐Hawkinson J, Helm JE, Huschka T, Kaufman DL, Van Oyen MP (2018) A capacity allocation planning model for integrated care and access management. Production Oper. Management 27(12):2270–2290.
[4]
Geng N, Xie X (2016) Optimal dynamic outpatient scheduling for a diagnostic facility with two waiting time targets. IEEE Trans. Automated Control 61(12):3725–3739.
[5]
Gocgun Y, Puterman ML (2014) Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking. Healthcare Management Sci. 17(1):60–76.
[6]
Helm JE, Van Oyen MP (2014) Design and optimization methods for elective hospital admissions. Oper. Res. 62(6):1265–1282.
[7]
Helm JE, AhmadBeygi S, Van Oyen MP (2011) Design and analysis of hospital admission control for operational effectiveness. Production Oper. Management 20(3):359–374.
[8]
Kazemian P, Sir MY, Van Oyen MP, Lovely JK, Larson DW, Pasupathy KS (2017) Coordinating clinic and surgery appointments to meet access service levels for elective surgery. J. Biomedical Inform. 66:105–115.
[9]
Kim SC, Horowitz I (2002) Scheduling hospital services: The efficacy of elective-surgery quotas. J. Management Sci. 30(5):335–346.
[10]
Kolesar P (1970) A Markovian model for hospital admission scheduling. Management Sci. 16(6):B-384–B-396.
[11]
Kolker A (2009) Process modeling of ICU patient flow: Effect of daily load leveling of elective surgeries on ICU diversion. J. Medical Systems 33(1):27–40.
[12]
Lang TA, Hodge M, Olson V, Romano PS, Kravitz RL (2004) Nurse–patient ratios: A systematic review on the effects of nurse staffing on patient, nurse employee, and hospital outcomes. J. Nursing Admin. 34(7):326–337.
[13]
Liu Y, Shi P, Helm JE, Van Oyen MP, Ying L, Huschka T (2020) Coordinated care: Capacity allocation to improve itinerary completion in queueing networks Preprint, submitted May 19, https://doi.org/10.2139/ssrn.3667095.
[14]
Marshall AW, Olkin I (1979) Inequalities: Theory of Majorization and its Applications (Academic Press, New York).
[15]
Meng F, Qi J, Zhang M, Ang J, Chu S, Sim M (2015) A robust optimization model for managing elective admission in a public hospital. Oper. Res. 63(6):1452–1467.
[16]
Nunes LGN, de Carvalho SV, Rodrigues RDCM (2009) Markov decision process applied to the control of hospital elective admissions. Artificial Intelligence Medicine 47(2):159–171.
[17]
Patrick J, Puterman ML, Queyranne M (2008) Dynamic multipriority patient scheduling for a diagnostic resource. Oper. Res. 56(6):1507–1525.
[18]
Pehlivan C, Augusto V, Xie X (2013) Admission control in a pure loss healthcare network: MDP and DES approach. Proc. Winter Simulation Conf. (IEEE, Washington, DC), 54–65.
[19]
Puterman ML (2014) Markov Decision Processes: Discrete Stochastic Dynamic Programming (John Wiley & Sons, New York).
[20]
Rahman HA, Shamsudin AS (2015) The impact of patient to nurse ratio on quality of care and patient safety in the medical and surgical wards in Malaysian private hospitals: A cross-sectional study. Asian Soc. Sci. 11(9):326.
[21]
Sauré A, Patrick J, Tyldesley S, Puterman ML (2012) Dynamic multi-appointment patient scheduling for radiation therapy. Eur. J. Oper. Res. 223(2):573–584.
[22]
Wang D, Muthuraman K, Morrice D (2019) Coordinated patient appointment scheduling for a multistation healthcare network. Oper. Res. 67(3):599–618.
[23]
Wang D, Morrice DJ, Muthuraman K, Bard JF, Leykum LK, Noorily SH (2018) Coordinated scheduling for a multi‐server network in outpatient pre‐operative care. Production Oper. Management 27(3):458–479.
[24]
Wu B, Huang H, Gu X (2018) The correlation analysis between the number of people filed and the number of hospitalized patients of the obstetrics department. Data Resources Management Utilization 6:29–31 (in Chinese).
[25]
Yang M, Fry MJ, Scurlock C (2015) The ICU will see you now: Efficient–equitable admission control policies for a surgical ICU with batch arrivals. IIE Trans. 47(6):586–599.
[26]
Yi J, Geng N (2018) A discrete event simulation based approach for admission control of pregnant women in obstetrics units. Oper. Res. Management Sci. 27(3):9–16 (in Chinese).
Index Terms
- Managing Advance Admission Requests for Obstetric Care
Index terms have been assigned to the content through auto-classification.
Recommendations
Improving Health Care Access Using Geographic Information Systems: SISMater-GIS: A Study of Referencing for Childbirth in a University Maternity in Belo Horizonte, Brazil
CBMS '14: Proceedings of the 2014 IEEE 27th International Symposium on Computer-Based Medical SystemsAim: To assess spatial distribution patterns of residence addresses of postpartum women, according to risk pregnancy and obstetric outcome. Method: Descriptive geographic-spatial research. This study analysed a cohort of 1792 women living in Belo ...
Comments
Information & Contributors
Information
Published In
Copyright © 2021, INFORMS.
Publisher
INFORMS
Linthicum, MD, United States
Publication History
Published: 01 March 2022
Accepted: 16 April 2021
Received: 18 June 2019
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in