Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs

References

• Arifoğlu K, Ren H, Tezcan T (2021) Hospital readmissions reduction program does not provide the right incentives: Issues and remedies. Management Sci. 67(4):2191–2210.Link, Google Scholar
• Armony M, Israelit S, Mandelbaum A, Marmor YN, Tseytlin Y, Yom-Tov GB (2015) On patient flow in hospitals: A data-based queueing-science perspective. Stoch. Systems 5(1):146–194.Link, Google Scholar
• Ata B, Peng X (2020) An optimal callback policy for general arrival processes: A pathwise analysis. Oper. Res. 68(2):327–347.Abstract, Google Scholar
• Avram F, Bertsimas D, Ricard M (1995) Optimization of multiclass queueing networks: A linear control approach. Stochastic Networks, Proc. IMA (Springer, New York), 199–234.Google Scholar
• Badaracco N, Burns M, Dague L (2021) The effects of Medicaid coverage on post-incarceration employment and recidivism. Health Services Res. 56(52):24–25.Crossref, Google Scholar
• Barjesteh N, Abouee-Mehrizi H (2021) Multiclass state-dependent service systems with returns. Naval Res. Logist. 68(5):631–662.Crossref, Google Scholar
• Bayati M, Braverman M, Gillam M, Mack KM, Ruiz G, Smith MS, Horvitz E (2014) Data-driven decisions for reducing readmissions for heart failure: General methodology and case study. PLoS One 9(10):e109264.Crossref, Google Scholar
• Bhulai S, Brooms A, Spieksma F (2014) On structural properties of the value function for an unbounded jump Markov process with an application to a processor sharing retrial queue. Queueing Systems 76:425–446.Crossref, Google Scholar
• Bravo F, Rudin C, Shaposhnik Y, Yuan Y (2023) Interpretable prediction rules for congestion risk in intensive care units. Stochastic Systems 14(2):111–130.Google Scholar
• Chan CW, Huang M, Sarhangian V (2021) Dynamic server assignment in multiclass queues with shifts, with applications to nurse staffing in emergency departments. Oper. Res. 69(6):1936–1959.Link, Google Scholar
• Chan CW, Yom-Tov G, Escobar G (2014) When to use speedup: An examination of service systems with returns. Oper. Res. 62(2):462–482.Link, Google Scholar
• Chen J, Dong J, Shi P (2023) Optimal routing under demand surges: The value of future arrival rates. Oper. Res., ePub ahead of print September 4, https://doi.org/10.1287/opre.2022.0282.Link, Google Scholar
• Chen X, Shi P, Pu S (2022) Data-pooling reinforcement learning for personalized healthcare intervention. Preprint, submitted November 16, https://arxiv.org/abs/2211.08998.Google Scholar
• Down DG, Koole G, Lewis ME (2011) Dynamic control of a single-server system with abandonments. Queueing Systems 67(1):63–90.Crossref, Google Scholar
• Fieller EC (1932) The distribution of the index in a normal bivariate population. Biometrika 24(3/4):428–440.Crossref, Google Scholar
• Filippov AF (1998) Differential Equations with Discontinuous Righthand Sides (Kluwer Academic Publishers, Dordrecht, Netherlands).Google Scholar
• Green LV, Kolesar PJ, Whitt W (2007) Coping with time-varying demand when setting staffing requirements for a service system. Production Oper. Management 16(1):13–39.Crossref, Google Scholar
• Haviv M, Puterman ML (1998) Bias optimality in controlled queueing systems. J. Appl. Probab. 35(1):136–150.Crossref, Google Scholar
• Helm JE, Alaeddini A, Stauffer JM, Bretthauer KM, Skolarus TA (2016) Reducing hospital readmissions by integrating empirical prediction with resource optimization. Production Oper. Management 25(2):233–257.Crossref, Google Scholar
• Hu Y, Chan CW, Dong J (2021) Optimal scheduling of proactive service with customer deterioration and improvement. Management Sci. 68(4):2533–2578.Link, Google Scholar
• Ingolfsson A, Almehdawe E, Pedram A, Tran M (2020) Comparison of fluid approximations for service systems with state-dependent service rates and return probabilities. Eur. J. Oper. Res. 283(2):562–575.Crossref, Google Scholar
• Kirk DE (1998) Optimal Control Theory: An Introduction (Dover Publications, Inc., New York).Google Scholar
• Larrañaga M, Ayesta U, Verloop IM (2013) Dynamic fluid-based scheduling in a multi-class abandonment queue. Performance Evaluation 70(10):841–858.Crossref, Google Scholar
• Larrañaga M, Ayesta U, Verloop IM (2015) Asymptotically optimal index policies for an abandonment queue with convex holding cost. Queueing Systems 81(2):99–169.Crossref, Google Scholar
• Leppin AL, Gionfriddo MR, Kessler M, Brito JP, Mair FS, Gallacher K, Wang Z, et al. (2014) Preventing 30-day hospital readmissions: A systematic review and meta-analysis of randomized trials. JAMA Internal Medicine 174(7):1095–1107.Crossref, Google Scholar
• Lewis ME, Puterman ML (2002) Bias optimality. Feinberg E, Schwartz A, eds. Handbook of Markov Decision Processes (Kluwer, Boston), 89–111.Crossref, Google Scholar
• Liu X, Hu M, Helm JE, Lavieri MS, Skolarus TA (2018) Missed opportunities in preventing hospital readmissions: Redesigning post-discharge checkup policies. Production Oper. Management 27(12):2226–2250.Crossref, Google Scholar
• Maglaras C (2000) Discrete-review policies for scheduling stochastic networks: Trajectory tracking and fluid-scale asymptotic optimality. Ann. Appl. Probab. 10(3):897–929.Crossref, Google Scholar
• Maglaras C (2006) Revenue management for a multiclass single-server queue via a fluid model analysis. Oper. Res. 54(5):914–932.Link, Google Scholar
• Mandelbaum A, Massey WA, Reiman MI (1998) Strong approximations for Markovian service networks. Queueing Systems 30(1):149–201.Crossref, Google Scholar
• Morkisch N, Upegui-Arango LD, Cardona MI, van den Heuvel D, Rimmele M, Sieber CC, Freiberger E (2020) Components of the transitional care model (TCM) to reduce readmission in geriatric patients: A systematic review. BMC Geriatrics 20(1):1–18.Crossref, Google Scholar
• Nuckols TK, Keeler E, Morton S, Anderson L, Doyle BJ, Pevnick J, Booth M, Shanman R, Arifkhanova A, Shekelle P (2017) Economic evaluation of quality improvement interventions designed to prevent hospital readmission: A systematic review and meta-analysis. JAMA Internal Medicine 177(7):975–985.Crossref, Google Scholar
• Pang G, Talreja R, Whitt W (2007) Martingale proofs of many-server heavy-traffic limits for Markovian queues. Probab. Surveys 4:193–267.Google Scholar
• Perry O, Whitt W (2009) Responding to unexpected overloads in large-scale service systems. Management Sci. 55(8):1353–1367.Link, Google Scholar
• Perry O, Whitt W (2011a) A fluid approximation for service systems responding to unexpected overloads. Oper. Res. 59(5):1159–1170.Link, Google Scholar
• Perry O, Whitt W (2011b) An ode for an overloaded x model involving a stochastic averaging principle. Stoch. Systems 1(1):59–108.Link, Google Scholar
• Perry O, Whitt W (2013) A fluid limit for an overloaded x model via a stochastic averaging principle. Math. Oper. Res. 38(2):294–349.Link, Google Scholar
• Perry O, Whitt W (2016) Chattering and congestion collapse in an overload switching control. Stochastic Systems 6(1):132–210.Link, Google Scholar
• Ravn-Nielsen LV, Duckert M-L, Lund ML, Henriksen JP, Nielsen ML, Eriksen CS, Buck TC, Pottegård A, Hansen MR, Hallas J (2018) Effect of an in-hospital multifaceted clinical pharmacist intervention on the risk of readmission: A randomized clinical trial. JAMA Internal Medicine 178(3):375–382.Crossref, Google Scholar
• Shi P, Helm JE, Deglise-Hawkinson J, Pan J (2021) Timing it right: Balancing inpatient congestion vs. readmission risk at discharge. Oper. Res. 69(6):1842–1865.Link, Google Scholar
• Stauffer B, Fullerton C, Fleming N, Ogola G, Herrin J, Stafford P, Ballard D (2011) Effectiveness and cost of a transitional care program for heart failure: A prospective study with concurrent controls. Arch. Internal Medicine 171(14):1238–1243.Crossref, Google Scholar
• Todorov E (2006) Optimal control theory. Doya K, Ishii S, Pouget A, Rao RPN, eds. Bayesian Brain: Probabilistic Approaches to Neural Coding (MIT Press, Cambridge, MA), 269–298.Crossref, Google Scholar
• Usta M, Wein LM (2015) Assessing risk-based policies for pretrial release and split sentencing in Los Angeles county jails. PLoS One 10(12):e0144967.Crossref, Google Scholar
• Véricourt FD, Jennings OB (2011) Nurse staffing in medical units: A queueing perspective. Oper. Res. 59(6):1320–1331.Link, Google Scholar
• Wallace D (2015) Do neighborhood organizational resources impact recidivism? Sociol. Inquiry 85(2):285–308.Crossref, Google Scholar
• Yom-Tov GB, Chan CW (2021) Balancing admission control, speedup, and waiting in service systems. Queueing Systems 97(1):163–219.Crossref, Google Scholar
• Yom-Tov GB, Mandelbaum A (2014) Erlang-R: A time-varying queue with reentrant customers, in support of healthcare staffing. Manufacturing Service Oper. Management 16(2):283–299.Link, Google Scholar
• Zayas-Cabán G, Xie J, Green LV, Lewis ME (2016) Dynamic control of a tandem system with abandonments. Queueing Systems 84(3):279–293.Crossref, Google Scholar
• Zhan D, Ward AR (2014) Threshold routing to trade off waiting and call resolution in call centers. Manufacturing Service Oper. Management 16(2):220–237.Link, Google Scholar
• Zhang DJ, Gurvich I, Van Mieghem JA, Park E, Young RS, Williams MV (2016) Hospital readmissions reduction program: An economic and operational analysis. Management Sci. 62(11):3351–3371.Link, Google Scholar
• Zychlinski N (2023) Applications of fluid models in service operations management. Queueing Systems 103(1–2):161–185.Crossref, Google Scholar