{"title":"A simulation-based framework to schedule surgeries in an eye hospital","authors":"H. Ewen, L. Mönch","doi":"10.1080/19488300.2014.965395","DOIUrl":"https://doi.org/10.1080/19488300.2014.965395","url":null,"abstract":"In this article, we propose heuristics to schedule surgeries in a German Eye hospital on a daily basis. We are interested in reducing the waiting time of the patients and in increasing the utilization of the operating rooms (OR). The sum of the overtime of the staff and the time where the ORs are not utilized for surgeries are used as a surrogate measure for OR utilization. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) that uses a random key representation for the schedules is proposed to solve heuristically this scheduling problem. The NSGA-II approach is hybridized with a local search approach. Discrete-event simulation is used to assess the fitness of the chromosomes within each generation of the NSGA-II approach taking into account the expected availability of surgeons, anesthesia doctors, nurses, stochastic surgery durations, and preferences of the patients with respect to the point of time of the surgery. The simulation model is described in detail and used to study the impact of stochastic arrival patterns of the patients and stochastic surgery durations on the performance of the executed schedules. Rescheduling strategies are investigated in different situations. We present the results of computational experiments using data from an actual eye hospital. These results clearly demonstrate that schedules obtained by dispatching rules and manually by the medical staff are outperformed by the proposed NSGA-II approach. Finally, a successful real-world implementation of the scheduling heuristic is briefly discussed.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"191 - 208"},"PeriodicalIF":0.0,"publicationDate":"2014-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.965395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-objective homecare worker scheduling: A fuzzy simulated evolution algorithm approach","authors":"M. Mutingi, C. Mbohwa","doi":"10.1080/19488300.2014.966213","DOIUrl":"https://doi.org/10.1080/19488300.2014.966213","url":null,"abstract":"As the need for improved homecare services continues to rise in every society, homecare service providers need to develop efficient staff scheduling methods. Homecare services are aimed at providing medical, paramedical, and social aid to patients at their own homes, leading to reduced hospitalization and healthcare operations costs in the medium to long term. However, the home healthcare worker scheduling problem is a complex one since it combines the hard vehicle routing and the staff assignment problems. As such, the purpose of this research is to present a fuzzy simulated evolution algorithm, based on fuzzy evaluation techniques, to address the homecare worker scheduling problem in a home care environment. The main objective is to decide on (i) which patients to assign to each worker, and (ii) the best route or trip for each care giver to execute the healthcare tasks so as to satisfy the patient's preferences in the time window during which the care giver should visit and offer the requested healthcare service. Computational results on illustrative experiments show that the proposed algorithm is a promising decision support tool.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"209 - 216"},"PeriodicalIF":0.0,"publicationDate":"2014-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.966213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Process capability estimation for leukocyte filtering process in blood service: A comparison study","authors":"Z. Hosseinifard, B. Abbasi, S. T. A. Niaki","doi":"10.1080/19488300.2014.965393","DOIUrl":"https://doi.org/10.1080/19488300.2014.965393","url":null,"abstract":"This article considers a comparison study between different non-normal process capability estimation methods and utilizing them in the leukocyte filtering process in blood service sectors. Since the amount of leukocyte in a unit of the blood is a critical issue in the blood transfusion process and patient safety, estimating and monitoring the capability of the leukocyte filtering process to meet the target window is very important for blood service sectors. However, observed data from the leukocyte filtering process show that the leukocyte levels after filtering demonstrate a right skewed distribution and applying conventional methods with a normality assumption fails to provide trustful results. Hence, we first conduct a simulation study to compare different methods in estimating the process capability index of non-normal processes and then we apply these techniques to obtain the process capability of the leukocyte filtering process. The study reveals that the Box-Cox transformation method provides reliable estimation of the process capability of the leukocyte filtering process.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"167 - 177"},"PeriodicalIF":0.0,"publicationDate":"2014-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.965393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A brief history of health systems engineering - its early years through 1989: An industrial engineering perspective","authors":"B. T. Ross, B. Bidanda","doi":"10.1080/19488300.2014.966214","DOIUrl":"https://doi.org/10.1080/19488300.2014.966214","url":null,"abstract":"In the very early 1900s, there were a few traces of industrial engineering thinking being applied to clinical and administrative activities in hospitals. These discrete efforts did not materialize for several decades until, a little more than fifty years ago, when formal use of industrial engineering in hospitals first surfaced. The profession appeared as a force and coalesced in the 1950s and 1960s. This paper traces the evolution of Industrial Engineering in healthcare from its beginning through the present with an emphasis on the period ending 1989. It discusses the hospital roots of the profession that began with emulating the use of IE in manufacturing (hospital management engineering) and how it has come to embrace various dimensions of healthcare as Health Systems Engineering (HSE). The paper's objective is to provide the background of HSE which includes discussions of the environmental, governmental, and healthcare industry factors that were and remain the impetus for growth of the profession; the various ways by which a healthcare organization can avail itself of securing Health Systems Engineering capabilities; the past and current ways by which practitioners enter the HSE field; the professional organizations to provide easy networking, career development, professional recognition opportunities, and advocacy for the profession; etc.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"217 - 229"},"PeriodicalIF":0.0,"publicationDate":"2014-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.966214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60566109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Characterizing the value of predictive analytics in facilitating hospital patient flow","authors":"J. Peck, J. Benneyan, D. Nightingale, S. Gaehde","doi":"10.1080/19488300.2014.930765","DOIUrl":"https://doi.org/10.1080/19488300.2014.930765","url":null,"abstract":"We apply discrete event simulation to characterize the patient flow affects of using admission predictions and current state information, generated in an Emergency Department (ED), to influence the prioritization of inpatient unit (IU) physicians between treating and discharging IU patients. Shared information includes crowding levels and total expected bed need (based on the sum of individual patients’ imperfect admission predictions and perfect admission predictions). It is found that sharing prediction and crowding information to influence inpatient staff priorities, using specific information sensitivity schedules, can result in statistically significant (p ≪ 0.05) reductions in boarding time (between 11.69% and 18.38% compared to baseline performance). The range of improvement is dependent on varying simulated hospital configurations.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"135 - 143"},"PeriodicalIF":0.0,"publicationDate":"2014-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.930765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Generating and evaluating simulation scenarios to improve emergency department operations","authors":"Maya Kaner, Tamar Gadrich, S. Dror, Y. Marmor","doi":"10.1080/19488300.2014.938281","DOIUrl":"https://doi.org/10.1080/19488300.2014.938281","url":null,"abstract":"Overcrowding and long patient length of stay, staff shortage, arrival volume increases, and budget constraints are problems hampering ED operations (Sinreich and Marmor, 2005; Maull et al., 2009; NHS, 2010). This paper suggests a framework for schematic generation and evaluation of simulation scenarios to improve ED processes in real-life environments. We illustrate the application of our methodology in a specific ED. We contribute to the area of ED computer simulation by suggesting a methodology that offers the following advantages: (1) Simulation scenarios can be schematically formulated rather than based on trial-and-error experiments. (2) Scenario development can be integrated in the different stages of simulation model development to support designers and management in understanding ED problems, improvement goals, data that should be collected and operational changes that should be applied.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"156 - 166"},"PeriodicalIF":0.0,"publicationDate":"2014-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.938281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Justin B. Rousek, K. Pasupathy, D. Gannon, S. Hallbeck
{"title":"Asset management in healthcare: Evaluation of RFID","authors":"Justin B. Rousek, K. Pasupathy, D. Gannon, S. Hallbeck","doi":"10.1080/19488300.2014.938207","DOIUrl":"https://doi.org/10.1080/19488300.2014.938207","url":null,"abstract":"Healthcare costs in the United States have continued to rise throughout the last decade and poor medical asset management is a contributing factor. Radio frequency identification (RFID) technology has been found to be one way to potentially alleviate this problem by improving process efficiency and reducing costs. However, return on investment (ROI) in RFID technology and its impact are based on the specifics for each healthcare organization and there is no standard methodology. Therefore, a methodology for ROI was created and a case study in a 600-bed hospital was undertaken to determine the feasibility of RFID implementation and its potential impact on asset management. The variables used in the ROI computational methodology were clinical and biomedical asset searching time, shrinkage rates, utilization rates and RFID implementation costs. Specific mobile assets that would benefit most from RFID technology were then selected within these variables. Under the assumptions from past studies, this work determined that implementing RFID technology within the 600-bed hospital was a financially viable decision with a 10.2-month payback period of the initial investment costs, and an expected 327% ROI within three years. This study highlights important RFID asset management techniques and characteristics for hospitals to consider as they determine their own financial feasibility with regards to RFID implementation. The approach can be used to inform budget planning in institutions for RFID implementation.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"144 - 155"},"PeriodicalIF":0.0,"publicationDate":"2014-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.938207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Morrice, D. Wang, J. Bard, Luci K. Leykum, S. Noorily, Poornachand Veerapaneni
{"title":"A patient-centered surgical home to improve outpatient surgical processes of care and outcomes","authors":"D. Morrice, D. Wang, J. Bard, Luci K. Leykum, S. Noorily, Poornachand Veerapaneni","doi":"10.1080/19488300.2014.922142","DOIUrl":"https://doi.org/10.1080/19488300.2014.922142","url":null,"abstract":"Preparing patients for surgery is critical for achieving the best possible surgical outcomes. To do this effectively, care must be coordinated across several types of specialists, and potentially across multiple settings. In this paper, we develop a Patient-Centered Surgical Home (PCSH) for outpatient surgery based on the concept of the Perioperative Surgical Home proposed by the American Society of Anesthesiologists. A key feature of the PCSH is to have an anesthesiology preoperative assessment clinic (APC) serve as system coordinator and information integrator. Based on a study of outpatient surgery at the University of Texas Health Science Center at San Antonio and its primary teaching hospital using statistical analysis and simulation, we demonstrate how this can be accomplished. We show that for the PCSH to succeed, APC must see the right patients with the right information by overcoming improper triaging of patients and patient information deficiencies. Our analysis shows that with the proper screening tool and modifications to the way triage is handled, it is possible to increase the number of patients that the APC sees each day with a modest increase in resources. Much of the potential benefits rest on the cooperation of the referring clinics as well as closing the gap between the current level of patient information and what is needed for optimizing medical decisions. Estimated cost savings are over one million dollars annually with a PCSH. Since APC-like clinics are common, our findings have great potential for widespread implementation of similar PCSH models with commensurate benefits.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"119 - 134"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.922142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Challenges and opportunities in the analysis of risk in healthcare","authors":"Laila Cure, J. Zayas-Castro, P. Fabri","doi":"10.1080/19488300.2014.911786","DOIUrl":"https://doi.org/10.1080/19488300.2014.911786","url":null,"abstract":"Since 1999, the estimates of annual preventable deaths in U.S. hospitals suggest that healthcare services add some risk to the patient's clinical condition. Such risks are often associated with harm resulting from errors in medication, diagnosis, and clinical procedures, among others. Preventing harm to patients demands the timely identification of risks to support the selection and implementation of effective strategies. While identifying and assessing risks in healthcare are particularly challenging due to the ambiguity and uncertainty that characterize such systems, most of the risk analysis methods currently used or proposed in healthcare have been developed for systems where unsafe conditions and risk metrics are well-defined. Therefore, their actual use in healthcare systems is inconsistent. The objective of this paper is to provide systems engineers, and researchers from related fields, with an overview of the current state of healthcare risk analysis and to highlight research contributions needed to support the proactive identification and assessment of risks in healthcare systems.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"104 - 88"},"PeriodicalIF":0.0,"publicationDate":"2014-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.911786","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The role of hospital selection in ambulance logistics","authors":"Seokcheon Lee","doi":"10.1080/19488300.2014.914608","DOIUrl":"https://doi.org/10.1080/19488300.2014.914608","url":null,"abstract":"Response time in emergency medical service is the time taken to reach patient after an emergency call is received, and it directly affects the welfare and safety of patients. An ambulance dispatched to a call serves patient on site and, if it is necessary to transfer to an emergency department (ED), a decision has to be made in selecting an appropriate hospital. The hospital selection is closely associated with the response time especially by influencing the availability of ambulances. The hospital selection problem is getting increasingly important because of the crowding problems EDs are struggling with. In this research, the impact of various hospital selection policies (including the diversion policy popularly used in practice) on response time is systematically investigated and a novel hospital selection policy—3C policy—is proposed that integrates three decision principles of closeness, congestion, and centrality. The experimental results demonstrate that the proposed policy significantly and robustly outperforms other policies, e.g. reducing response time by up to 90% over diversion policy. The 3C policy emphasizes the critical role that networking and information technology will play in measuring and communicating information associated with the policy.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"105 - 117"},"PeriodicalIF":0.0,"publicationDate":"2014-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.914608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60564698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}