On Patient Flow in Hospitals: A Data-Based Queueing-Science Perspective

Published Online:https://doi.org/10.1287/14-SSY153

References

  • Aksin, O. Z., Karaesmen, F. and Ormeci, E. L. (2007). A Review of Workforce Cross-Training in Call Centers from an Operations Management Perspective. In Workforce Cross Training Handbook (D. Nembhard, ed.), CRC Press.Google Scholar
  • Allon, G., Bassamboo, A. and Gurvich, I. (2011). “We Will Be Right with You”: Managing Customer Expectations with Vague Promises and Cheap Talk. Operations Research 59 1382–1394. MR2872007LinkGoogle Scholar
  • Armony, M. (2005). Dynamic Routing in Large-Scale Service Systems with Heterogeneous Servers. Queueing Systems 51 287–329. MR2189596Google Scholar
  • Armony, M., Chan, C. W. and Zhu, B. (2013). Critical Care in Hospitals: When to Introduce a Step Down Unit? Working paper, Columbia University.Google Scholar
  • Armony, M. and Ward, A. (2010). Fair Dynamic Routing in Large-Scale Heterogeneous-Server Systems. Operations Research 58 624–637. MR2680568LinkGoogle Scholar
  • Armony, M., Israelit, S., Mandelbaum, A., Marmor, Y. N., Tseytlin, Y. and Yom-Tov, G. B. (2015). On Patient Flow in Hospitals: A Data-Based Queueing-Science Perspective. An Extended Version (EV). Working paper, http://ie.technion.ac.il/serveng/References/Patient%20flow%20main.pdf.Google Scholar
  • Atar, R., Mandelbaum, A. and Zviran, A. (2012). Control of Fork-Join Networks in Heavy Traffic. Allerton Conference.Google Scholar
  • Atar, R. and Shwartz, A. (2008). Efficient Routing in Heavy Traffic under Partial Sampling of Service Times. Mathematics of Operations Research 33 899–909. MR2464649LinkGoogle Scholar
  • Balasubramanian, H., Muriel, A. and Wang, L. (2012). The Impact of Flexibility and Capacity Allocation on the Performance of Primary Care Practices. Flexible Services and Manufacturing Journal 24 422–447.Google Scholar
  • Balasubramanian, H., Banerjee, R., Denton, B., Naessens, J., Wood, D. and Stahl, J. (2010). Improving Clinical Access and Continuity Using Physician Panel RSSedesign. Journal of General Internal Medicine 25 1109–1115.Google Scholar
  • Barak-Corren, Y., Israelit, S. and Reis, B. Y. (2013). Progressive Prediction of Hospitalization in The Emergency Department: Uncovering Hidden Patterns to Improve Patient Flow. Working paper.Google Scholar
  • Baron, O., Berman, O., Krass, D. and Wang, J. (2014). Using Strategic Idleness to Improve Customer Service Experience in Service Networks. Operations Research 62 123–140. MR3188591LinkGoogle Scholar
  • Batt, R. J. and Terwiesch, C. (2014). Doctors Under Load: An Empirical Study of State Dependent Service Times in Emergency Care. Working paper.Google Scholar
  • Bekker, R. and de Bruin, A. M. (2010). Time-Dependent Analysis for Refused Admissions in Clinical Wards. Annals of Operations Research 178 45–65. MR2659096Google Scholar
  • Bernstein, S. L., Verghese, V., Leung, W., Lunney, A. T. and Perez, I. (2003). Development and Validation of a New Index to Measure Emergency Department Crowding. Academic Emergency Medicine 10 938–942.Google Scholar
  • Bertsimas, D. and Mourtzinou, G. (1997). Transient Laws of Non-stationary Queueing Systems and Their Applications. Queueing Systems 25 115–155. MR1458588Google Scholar
  • Brandeau, M. L., Sainfort, F. and Pierskalla, W. P., eds. (2004). Operations Research and Health Care: A Handbook of Methods and Applications. Kluwer Academic Publishers, London.Google Scholar
  • Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S. and Zhao, L. (2005). Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective. Journal of the American Statistical Association 100 36–50. MR2166068Google Scholar
  • Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodal Inference: A Practical Information-Theoretic Approach, 2nd Edition. Springer. MR1919620Google Scholar
  • Canadian-Triage Admission of Paitents to Over-Capacity Inpatient Beds. Appendix A, http://www.calgaryhealthregion.ca/policy/docs/1451/Admission_over-capacity_AppendixA.pdf.Google Scholar
  • Chalfin, D. B., Trzeciak, S., Likourezos, A., Baumann, B. M. and Dellinger, R. P. (2007). Impact of Delayed Transfer of Critically Ill Patients from the Emergency Department to the Intensive Care Unit. Critical Care Medicine 35 1477–1483.Google Scholar
  • Chan, C., Farias, V. and Escobar, G. (2014). The Impact of Delays on Service Times in the Intensive Care Unit. Working paper.Google Scholar
  • Chan, C., Yom-Tov, G. B. and Escobar, G. (2014). When to Use Speedup: An Examination of Service Systems with Returns. Operations Research 62 462–482. MR3209183LinkGoogle Scholar
  • Chao, X., Miyazawa, M. and Pinedo, M. (1999). Queueing Networks: Customers, Signals and Product Form Solutions. Wiley.Google Scholar
  • Chen, C., Jia, Z. and Varaiya, P. (2001). Causes and Cures of Highway Congestion. Control Systems, IEEE 21 26–33.Google Scholar
  • Chen, H. and Yao, D. D. (2001). Fundamentals of Queuing Networks: Performance, Asymptotics, and Optimization. Springer. MR1835969Google Scholar
  • Cooper, A. B., Litvak, E., Long, M. C. and McManus, M. L. (2001). Emergency Department Diversion: Causes and Solutions. Academic Emergency Medicine 8 1108–1110.Google Scholar
  • de Bruin, A. M., van Rossum, A. C., Visser, M. C. and Koole, G. M. (2007). Modeling the Emergency Cardiac In-Patient Flow: An Application of Queuing Theory. Health Care Management Science 10 125–137.Google Scholar
  • de Bruin, A. M., Bekker, R., van Zanten, L. and Koole, G. M. (2009). Dimensioning Hospital Wards Using the Erlang Loss Model. Annals of Operations Research 178 23–43. MR2659095Google Scholar
  • Denton, B. T., ed. (2013). Handbook of Healthcare Operations Management: Methods and Applications. Springer.Google Scholar
  • Dong, J. and Whitt, W. (2014). On Fitted Birth-and-Death Queue Models. Working paper, Columbia University.Google Scholar
  • Dong, J., Yom-Tov, E. and Yom-Tov, G. B. (2014). Hospital Network Synchronization Through Waiting Time Announcements. Working paper.Google Scholar
  • Earnest, A., Chen, M. and Seow, E. (2006). Exploring if Day and Time of Admission is Associated with Average Length of Stay Among Inpatients from a Tertiary Hospital in Singapore: An Analytic Study Based on Routine Admission Data. BMC Health Services Research 6 6.Google Scholar
  • Elkin, K. and Rozenberg, N. (2007). Patients Flow from the Emergency Department to the Internal Wards. IE&M project, Technion (In Hebrew).Google Scholar
  • Feldman, Z., Mandelbaum, A., Massey, W. A. and Whitt, W. (2008). Staffing of Time-Varying Queues to Achieve Time-Stable Performance. Management Science 54 324–338.LinkGoogle Scholar
  • Froehle, C. M. and Magazine, M. J. (2013). Improving Scheduling and Flow in Complex Outpatient Clinics. In Handbook of Healthcare Operations Management: Methods and Applications (B. T. Denton, ed.) 9, 229–307. Springer.Google Scholar
  • Gans, N., Koole, G. and Mandelbaum, A. (2003). Telephone Call Centers: Tutorial, Review and Research Prospects. Manufactoring, Services and Operations Management 5 79–141.AbstractGoogle Scholar
  • Gerla, M. and Kleinrock, L. (1980). Flow Control: A Comparative Survey. IEEE Transactions on Communcations 28 553–574.Google Scholar
  • Gorman, A. and Colliver, V. (2014). The Latest In Medical Convenience: ER Appointments. Chronicle for Kaiser Health News. http://kaiserhealthnews.org/news/the-latest-in-medical-convenience-er-appointments/.Google Scholar
  • Green, L. (2004). Capacity Planning and Management in Hospitals. In Operations Research and Health Care: A Handbook of Methods and Applications (M. L. Brandeau, F. Sainfort and W. P. Pierskalla, eds.) 14–41. Kluwer Academic Publishers, London.Google Scholar
  • Green, L. V. (2008). Using Operations Research to Reduce Delays for Healthcare. In Tutorials in Operations Research (Z.-L. Chen and S. Raghavan, eds.) 1–16. INFORMS.LinkGoogle Scholar
  • Green, L. V., Kolesar, P. J. and Whitt, W. (2007). Coping with Time-Varying Demand When Setting Staffing Requirements for a Service System. Production and Operations Management 16 13–39.Google Scholar
  • Green, L. and Yankovic, N. (2011). Identifying Good Nursing Levels: A Queuing Approach. Operations Research 59 942–955. MR2844415Google Scholar
  • Green, L., Soares, J., Giglio, J. F. and Green, R. A. (2006). Using Queuing Theory to Increase the Effectiveness of Emergency Department Provider Staffing. Academic Emergency Medicine 13 61–68.Google Scholar
  • Gurvich, I. and Perry, O. (2012). Overflow Networks: Approximations and Implications to Call-Center Outsourcing. Operations Research 60 996–1009. MR2979436LinkGoogle Scholar
  • Gurvich, I. and Whitt, W. (2010). Service-Level Differentiation in Many-Server Service Systems via Queue-Ratio Routing. Operations Research 58 316–328. MR2674799LinkGoogle Scholar
  • Hagtvedt, R., Ferguson, M., Griffin, P., Jones, G. T. and Keskinocak, P. (2009). Cooperative Strategies To Reduce Ambulance Diversion. Proceedings of the 2009 Winter Simulation Conference 266 1085–1090.Google Scholar
  • Hall, R. W., ed. (2012). Handbook of Healthcare System Scheduling. Springer.Google Scholar
  • Hall, R. W., ed. (2013). Patient Flow: Reducing Delay in Healthcare Delivery. Springer. 2nd edition.Google Scholar
  • Hall, R., Belson, D., Murali, P. and Dessouky, M. (2006). Modeling Patient Flows Through the Healthcare System. In Patient Flow: Reducing Delay in Healthcare Delivery (R. W. Hall, ed.) 1, 1–45. Springer.Google Scholar
  • Hoot, N. R., Zhou, C., Jones, I. and Aronsky, D. (2007). Measuring and Forecasting Emergency Department Crowding in Real Time. Annals of Emergency Medicine 49 747–755.Google Scholar
  • Huang, J. (2013). Patient Flow Management in Emergency Departments. PhD thesis, National University of Singapore (NUS).Google Scholar
  • Huang, J., Carmeli, B. and Mandelbaum, A. (2015). Control of Patient Flow in Emergency Departments: Multiclass Queues with Feedback and Deadlines. Forthcoming in Operations Research.Google Scholar
  • Hwang, U., McCarthy, M. L., Aronsky, D., Asplin, B., Crane, P. W., Craven, C. K., Epstein, S. K., Fee, C., Handel, D. A., Pines, J. M., Rathlev, N. K., Schafermeyer, R. W., Zwemer, F. L. and Bernstein, S. L. (2011). Measures of Crowding in the Emergency Department: A Systematic Review. Academic Emergency Medicine 18 527–538.Google Scholar
  • Ibrahim, R. and Whitt, W. (2011). Wait-Time Predictors for Customer Service Systems with Time-Varying Demand and Capacity. Operations Research 59 1106–1118. MR2864327LinkGoogle Scholar
  • IHI (2011). Patient First: Efficient Patient Flow Management Impact on the ED. Institute for Healthcare Improvement. http://www.ihi.org/knowledge/Pages/ImprovementStories/PatientFirstEfficientPatientFlowManagementED.aspx.Google Scholar
  • Janssen, A. J. E. M., van Leeuwaarden, J. S. H. and Zwart, B. (2011). Refining Square-Root Safety Staffing by Expanding Erlang C. Operations Research 56 1512–1522. MR2872017Google Scholar
  • JCAHO (2004). JCAHO Requirement: New Leadership Standard on Managing Patient Flow for Hospitals. Joint Commission Perspectives 24 13–14.Google Scholar
  • Jennings, O. B. and de Véricourt, F. (2008). Dimensioning Large-Scale Membership Services. Operations Research 56 173–187. MR2402225LinkGoogle Scholar
  • Jennings, O. B. and de Véricourt, F. (2011). Nurse Staffing in Medical Units: A Queueing Perspective. Operations Research 59 1320–1331. MR2872002LinkGoogle Scholar
  • Jouini, O., Dallery, Y. and Aksin, O. Z. (2009). Queueing Models for Full-Flexible Multi-class Call Centers with Real-Time Anticipated Delays. International Journal of Production Economics 120 389–399.Google Scholar
  • Kaplan, R. S. and Porter, M. E. (2011). How to Solve the Cost Crisis in Health Care. Harvard Business Review 89 46–64.Google Scholar
  • Kc, D. and Terwiesch, C. (2009). Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations. Management Science 55 1486–1498.LinkGoogle Scholar
  • Kelly, F. P. (1979). Markov Processes and Reversibility. Wiley.Google Scholar
  • Kim, S. H. and Whitt, W. (2014). Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes? M&SOM 16 464–480.Google Scholar
  • Koçağa, Y. L., Armony, M. and Ward, A. R. (2015). Staffing Call Centers with Uncertain Arrival Rates and Co-sourcing. Production and Operations Management n/a–n/a.Google Scholar
  • Leite, S. C. and Fragoso, M. D. (2013). Diffusion Approximation for Signaling Stochastic Networks. Stochastic Processes and their Applications 123 2957–2982. MR3062432Google Scholar
  • Long, E. F. and Mathews, K. M. (2012). “Patients Without Patience”: A Priority Queuing Simulation Model of the Intensive Care Unit. Working paper.Google Scholar
  • Maa, J. (2011). The Waits that Matter. The New England Journal of Medicine 364 2279–2281.Google Scholar
  • Maman, S. (2009). Uncertainty in the Demand for Service: The Case of Call Centers and Emergency Departments. Master’s thesis, Technion—Israel Institute of Technology.Google Scholar
  • Maman, S., Zeltyn, S. and Mandelbaum, A. (2011). Uncertainty in the Demand for Service: The Case of Call Centers and Emergency Departments. Working paper.Google Scholar
  • Mandelbaum, A., Momcilovic, P. and Tseytlin, Y. (2012). On Fair Routing from Emergency Departments to Hospital Wards: QED Queues with Heterogeneous Servers. Management Science 58 1273–1291.LinkGoogle Scholar
  • Mandelbaum, A. and Stolyar, S. (2004). Scheduling Flexible Servers with Convex Delay Costs: Heavy-Traffic Optimality of the Generalized cμ-Rule. Operations Research 52 836–855. MR2104141LinkGoogle Scholar
  • Mandelbaum, A., Trofimov, V., Gavako, I. and Nadjhahrov, E. (2013). HomeHospital (Rambam): Readmission Analysis. http://seeserver.iem.technion.ac.il/databases/Docs/HomeHospital_visits_return.pdf.Google Scholar
  • Marmor, Y. N. (2003). Developing a Simulation Tool for Analyzing Emergency Department Performance. Master’s thesis, Technion—Israel Institute of Technology.Google Scholar
  • Marmor, Y. N. (2010). Emergency-Departments Simulation in Support of Service-Engineering: Staffing, Design, and Real-Time Tracking. PhD thesis, Technion—Israel Institute of Technology.Google Scholar
  • Marmor, Y. N., Golany, B., Israelit, S. and Mandelbaum, A. (2012). Designing Patient Flow in Emergency Departments. IIE Transactions on Healthcare Systems Engineering 2 233–247.Google Scholar
  • Marmor, Y. N., Rohleder, T., Cook, D., Huschka, T. and Thompson, J. (2013). Recovery Bed Planning in Cardiovascular Surgery: A Simulation Case Study. Health Care Management Science 16 314–327.Google Scholar
  • McHugh, M., Van Dyke, K., McClelland, M. and Moss, D. (2011). Improving Patient Flow and Reducing Emergency Department Crowding. Agency for Healthcare Research and Quality. http://www.ahrq.gov/research/findings/final-reports/ptflow/index.html.Google Scholar
  • Nadjhahrov, E., Trofimov, V., Gavako, I. and Mandelbaum, A. (2013). HomeHospital (Rambam): EDA via SEEStat 3.0 to Reproduce “On Patients Flow in Hospitals”. http://ie.technion.ac.il/Labs/Serveng/files/HHD/reproducing_flow%_paper.pdf.Google Scholar
  • Nestler, S. (2011). Reproducible (Operations) Research: A Primer on Reproducible Research and Why the O.R. Community Should Care About it. ORMS Today 38.Google Scholar
  • Nguyen, V. (1994). The Trouble with Diversity: Fork-Join Networks with Heterogeneous Customer Population. The Annals of Applied Probability 1–25. MR1258171Google Scholar
  • Plambeck, E., Bayati, M., Ang, E., Kwasnick, S. and Aratow, M. (2015). Accurate ED Wait Time Prediction. Working paper, Stanford.Google Scholar
  • Plonski, O., Efrat, D., Dorban, A., David, N., Gologorsky, M., Zaied, I., Mandelbaum, A. and Rafaeli, A. (2013). Fairness in Patient Routing: Maternity Ward in Rambam Hospital. Technical report.Google Scholar
  • Ramakrishnan, M., Sier, D. and Taylor, P. G. (2005). A Two-Time-Scale Model for Hospital Patient Flow. IMA Journal of Management Mathematics 16 197–215. MR2204891Google Scholar
  • Rambam Rambam Health Care Campus, Haifa, Israel. http://www.rambam.org.il/Home+Page/.Google Scholar
  • RambamData Rambam Hospital Data Repositories. Technion SEELab, http://seeserver.iem.technion.ac.il/databases/HomeHospital/.Google Scholar
  • Saghafian, S., Austin, G. and Traub, S. J. (2014). Operations Research Contributions to Emergency Department Patient Flow Optimization: Review and Research Prospects. Working paper.Google Scholar
  • SEELab SEE Lab, Technion—Israel Institute of Technology. http://ie.technion.ac.il/Labs/Serveng/.Google Scholar
  • SEEServer Server of the Center for Service Enterprise Engineering. http://seeserver.iem.technion.ac.il/see-terminal/.Google Scholar
  • SEEStat SEEStat Documentation, Technion—Israel Institute of Technology. http://ie.technion.ac.il/Labs/Serveng/.Google Scholar
  • Senderovich, A., Weidlich, M., Gal, A. and Mandelbaum, A. (2015). Queue Mining for Delay Prediction in Multi-class Service Processes. Information Systems n/a–n/a.Google Scholar
  • Shi, P., Dai, J. G., Ding, D., Ang, J., Chou, M., Jin, X. and Sim, J. (2013). Patient Flow from Emergency Department to Inpatient Wards: Empirical Observations from a Singaporean Hospital. Working paper.Google Scholar
  • Shi, P., Chou, M. C., Dai, J. G., Ding, D. and Sim, J. (2014). Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time. Management Science 24 13–14.Google Scholar
  • Song, H., Tucker, A. L. and Murrell, K. L. (2015). The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay. Forthcoming in Management Science.Google Scholar
  • Stolyar, S. (2005). Optimal Routing in Output-Queued Flexible Server Systems. Probability in the Engineering and Informational Sciences 19 141–189. MR2127332Google Scholar
  • Sullivan, S. E. and Baghat, R. S. (1992). Organizational Stress, Job Satisfaction, and Job Performance: Where Do We Go from Here? Journal of Management 18 353–375.Google Scholar
  • Sun, J. (2006). The Statistical Analysis of Interval-Censored Failure Time Data. Springer. MR2287318Google Scholar
  • HCA North Texas Hospitals are Moving at the Speed of Life—Real Time Delays Announcement Web-page of Emergency Departments in North Texas, USA. FASTERTX.COM, http://hcanorthtexas.com/.Google Scholar
  • Tezcan, T. (2008). Optimal Control of Distributed Parallel Server Systems Under the Halfin and Whitt Regime. Math of Operations Research 33 51–90. MR2393541LinkGoogle Scholar
  • Thompson, S., Nunez, M., Garfinkel, R. and Dean, M. D. (2009). Efficient Short-Term Allocation and Reallocation of Patients to Floors of a Hospital During Demand Surges. Operations Research 57 261–273.LinkGoogle Scholar
  • Thorin, O. (1977). On the Infinite Divisibility of the Lognormal Distribution. Scandinavian Actuarial Journal 1977 121–148. MR0552135Google Scholar
  • Tseytlin, Y. (2009). Queueing Systems with Heterogeneous Servers: On Fair Routing of Patients in Emergency Departments. Master’s thesis, Technion—Israel Institute of Technology.Google Scholar
  • Tseytlin, Y. and Zviran, A. (2008). Simulation of Patients Routing from an Emergency Department to Internal Wards in Rambam Hospital. OR Graduate project, IE&M, Technion.Google Scholar
  • Tukey, J. W. (1977). Exploratory Data Analysis. Addison Wesley.Google Scholar
  • Medicare USA (2013). Hospital Compare: 30-Day Death and Readmission Measures Data. http://www.medicare.gov/HospitalCompare/Data/RCD/30-day-measures.aspx.Google Scholar
  • Ward, A. and Armony, M. (2013). Blind Fair Routing in Large-Scale Service Systems with Heterogeneous Customers and Servers. Operations Research 61 228–243. MR3042753LinkGoogle Scholar
  • Whitt, W. (2012). Fitting Birth-and-Death Queueing Models to Data. Statistics and Probability Letters 82 998–1004. MR2910048Google Scholar
  • Yom-Tov, G. B. (2010). Queues in Hospitals: Queueing Networks with ReEntering Customers in the QED Regime. PhD thesis, Technion—Israel Institute of Technology.Google Scholar
  • Yom-Tov, G. B. and Mandelbaum, A. (2014). Erlang-R: A Time-Varying Queue with Reentrant Customers, in Support of Healthcare Staffing. M&SOM 16 283–299.LinkGoogle Scholar
  • Zacharias, C. and Armony, M. (2013). Joint Panel Sizing and Appointment Scheduling in Outpatient Care. Working paper, NYU.Google Scholar
  • Zaied, I. (2011). The Offered Load in Fork-Join Networks: Calculations and Applications to Service Engineering of Emergency Department. Master’s thesis, Technion—Israel Institute of Technology.Google Scholar
  • Zeltyn, S., Marmor, Y. N., Mandelbaum, A., Carmeli, B., Greenshpan, O., Mesika, Y., Wasserkrug, S., Vortman, P., Schwartz, D., Moskovitch, K., Tzafrir, S., Basis, F., Shtub, A. and Lauterman, T. (2011). Simulation-Based Models of Emergency Departments: Real-Time Control, Operations Planning and Scenario Analysis. Transactions on Modeling and Computer Simulation (TOMACS) 21.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.