Operations Research in Healthcare: a survey

Rais, Abdur; Viana, Ana

doi:10.1111/j.1475-3995.2010.00767.x

Cited by 278 publications

(131 citation statements)

References 207 publications

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…Although there is a vast literature on this type of problems (for reviews see Rahman and Smith, 1999;Daskin and Dean, 2005), the health-care facility location is still a very active research area (Rais and Viana, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Matching patient and physician preferences in designing a primary care facility network

Güneş

Yaman

Çekyay

et al. 2014

Journal of the Operational Research Society

View full text Add to dashboard Cite

This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Matching patient and physician preferences in designing a primary care facility network

Güneş

Yaman

Çekyay

et al. 2014

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…[15] Reduction in excess capacity has been the focus of hospital optimization problems designed to improve efficiency. [20] Hospital size was logged for this analysis.…”

Section: Empirical Modelmentioning

confidence: 99%

The financial impact of hospital closures on surrounding hospitals

Hodgson

Roback

Hartman

et al. 2015

JHA

View full text Add to dashboard Cite

Objective: To test whether hospital closures hurt or help surrounding hospitals financially. Do hospital closures improve market efficiency or do they merely shift the least profitable patients to hospitals that can better cross-subsidize them? Methods: Using California hospital data from 2000 to 2011, the analysis employed random-effect and fixed-effect models to test for a change in operating margin before and after a series of 2004, 2007 and 2009 hospital closures (the highest volume years for closures). The main independent variable was each hospital's predicted percent increase in patient volume due to absorption from closing hospitals. We used 5-digit zip code and DRG patient flow data to predict the number of patients each open hospital would absorb from nearby hospital closures. Results: Hospitals experiencing the biggest increase in patient volume due to nearby hospital closings saw a drop in operating margin following those closures. This drop could not be explained by changes in payer mix or reimbursement type for those patients. Conclusions: Our results suggest that hospital closures are shifting high cost patients to open hospitals, not necessarily improving efficiency in the market.

show abstract

“…For this matter, the paper resorts to tasking information from an electronic management system, and results contribute to the quantitative study of care frag-490 mentation, scheduling and team management in healthcare (cf. [17][18][19]). …”

mentioning

confidence: 99%

Out of hours workload management: Bayesian inference for decision support in secondary care

Perez

Brown

Pinchin

et al. 2016

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures.Methods and Material : We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data.Results: Main results support claims relating to (i) the importance of datadriven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation.Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Response to reviewersGeneral Comments: We are grateful to all reviewers for taking the time to review our paper and make a number of helpful comments that we believe have significantly improved the quality of the manuscript. All comments have been, according to their nature, merged, described and addressed here: 5Comment 1: The major concern shared by reviewers relates to a lack practical relevance of the results presented; for instance, managerial conclusions are not well exploited, the paper lacks a managerial perspective of the output from professionals involved in the study, or results do not extrapolate to different settings neither is it clear why they support the decision making. 10Response to Comment 1: The authors agree that the previous version of the manuscript offered a mostly descriptive study of the data and lacked relevant interpretations and conclusions drawn from results. In addition, we believe we were not successful in clearly conveying the aims and scope of our work; yet, we have made the efforts to undertake the alterations necessary in order to correct 15 these problems.Nationwide hospital settings are indeed subject to disparate workload pressures; these may drastically vary within distant geographical regions and in relation to specialities covered by each facility. The focus of our research team and this work relates to support systems for effective management that can 20 target local facilities and working groups. This is indeed a great problem, in that policy alterations and intelligent design of support systems (for instan...

show abstract

Operations Research in Healthcare: a survey

Cited by 278 publications

References 207 publications

Matching patient and physician preferences in designing a primary care facility network

Matching patient and physician preferences in designing a primary care facility network

The financial impact of hospital closures on surrounding hospitals

Out of hours workload management: Bayesian inference for decision support in secondary care

Contact Info

Product

Resources

About