Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

The Potential Trajectory of Carbapenem-Resistant Enterobacteriaceae, an Emerging Threat to Health-Care Facilities, and the Impact of the Centers for Disease Control and Prevention Toolkit.

Carbapenem-resistant Enterobacteriaceae (CRE), a group of pathogens resistant to most antibiotics and associated with high mortality, are a rising emerging public health threat. Current approaches to infection control and prevention have not been adequate to prevent spread. An important but unproven approach is to have hospitals in a region coordinate surveillance and infection control measures. Using our Regional Healthcare Ecosystem Analyst (RHEA) simulation model and detailed Orange County, California, patient-level data on adult inpatient hospital and nursing home admissions (2011-2012), we simulated the spread of CRE throughout Orange County health-care facilities under 3 scenarios: no specific control measures, facility-level infection control efforts (uncoordinated control measures), and a coordinated regional effort. Aggressive uncoordinated and coordinated approaches were highly similar, averting 2,976 and 2,789 CRE transmission events, respectively (72.2% and 77.0% of transmission events), by year 5. With moderate control measures, coordinated regional control resulted in 21.3% more averted cases (n = 408) than did uncoordinated control at year 5. Our model suggests that without increased infection control approaches, CRE would become endemic in nearly all Orange County health-care facilities within 10 years. While implementing the interventions in the Centers for Disease Control and Prevention's CRE toolkit would not completely stop the spread of CRE, it would cut its spread substantially, by half

Efficacy and Safety of Pediatric Critical Care Physician Telemedicine Involvement in Rapid Response Team and Code Response in a Satellite Facility

OBJECTIVES: Satellite inpatient facilities of larger children's hospitals often do not have on-site intensivist support. In-house rapid response teams and code teams may be difficult to operationalize in such facilities. We developed a system using telemedicine to provide pediatric intensivist involvement in rapid response team and code teams at the satellite facility of our children's hospital. Herein, we compare this model with our in-person model at our main campus. DESIGN: Cross-sectional. SETTING: A tertiary pediatric center and its satellite facility. PATIENTS: Patients admitted to the satellite facility. INTERVENTIONS: Implementation of a rapid response team and code team model at a satellite facility using telemedicine to provide intensivist support. MEASUREMENTS AND MAIN RESULTS: We evaluated the success of the telemedicine model through three a priori outcomes: 1) reliability: involvement of intensivist on telemedicine rapid response teams and codes, 2) efficiency: time from rapid response team and code call until intensivist response, and 3) outcomes: disposition of telemedicine rapid response team or code calls. We compared each metric from our telemedicine model with our established main campus model. MAIN RESULTS: Critical care was involved in satellite campus rapid response team activations reliably (94.6% of the time). The process was efficient (median response time 7 min; mean 8.44 min) and effective (54.5 % patients transferred to PICU, similar to the 45-55% monthly rate at main campus). For code activations, the critical care telemedicine response rate was 100% (6/6), with a fast response time (median 1.5 min). We found no additional risk to patients, with no patients transferred from the satellite campus requiring a rapid escalation of care defined as initiation of vasoactive support, greater than 60 mL/kg in fluid resuscitation, or endotracheal intubation. CONCLUSIONS: Telemedicine can provide reliable, timely, and effective critical care involvement in rapid response team and Code Teams at satellite facilities

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Evaluating the capacity of clinical pathways through discrete-event simulation.

Organizing a medical facility efficiently is hard due to the numerous patient trajectories and their use of joint and scarce resources. Moreover, these trajectories tend to be complex and characterized by uncertain medical processes. In this paper, we will structure patient trajectories using clinical pathways and aggregate them in a discrete-event simulation model. This model enables the health manager to evaluate and improve important performance indicators, both for the patient and the hospital, by conducting a detailed sensitivity analysis. Two case studies, performed at large hospitals in Antwerp and Leuven (Belgium), will be introduced and briefly discussed in order to illustrate the generic nature of the model.Capacity management; Case studies; Discrete-event simulation; Health care operations; Processes; Structure; Simulation; Model; Performance; Indicators; Sensitivity; Studies; Hospitals; Belgium; Order;

Integral resource capacity planning for inpatient care services based on hourly bed census predictions

The design and operations of inpatient care facilities are typically largely historically shaped. A better match with the changing environment is often possible, and even inevitable due to the pressure on hospital budgets. Effectively organizing inpatient care requires simultaneous consideration of several interrelated planning issues. Also, coordination with upstream departments like the operating theater and the emergency department is much-needed. We present a generic analytical approach to predict bed census on nursing wards by hour, as a function of the Master Surgical Schedule (MSS) and arrival patterns of emergency patients. Along these predictions, insight is gained on the impact of strategic (i.e., case mix, care unit size, care unit partitioning), tactical (i.e., allocation of operating room time, misplacement rules), and operational decisions (i.e., time of admission/discharge). The method is used in the Academic Medical Center Amsterdam as a decision support tool in a complete redesign of the inpatient care operations

Quality Improvement on the Long-term Care Ventilator Unit: Interventions to Increase Patient Safety and Prevent Patient Harm

BACKGROUND: Tracheostomy patients are susceptible to life-threatening emergencies when their airways are compromised. Epidemiologic data suggests that 3.2% to 30% of tracheostomy patents have a complication. The long-term care ventilator unit (LTCVU) is a 25-bed unit in a nursing home. It has noted that 40% of patients have a complication. A group of hospitals demonstrated a 90% reduction in complications through five interventions. METHODS: The Johns Hopkins Nursing Evidence-Based Practice model was utilized to take the Global Tracheostomy Collaborative interventions and apply them to the LTCVU with the aim of reducing the number of airway complications on the unit by 50%. INTERVENTIONS: Five interventions were implemented for this quality improvement project: Bedside multidisciplinary team rounds, nursing in-services, continued protocolization of care, tracking complication rates and active prevention measures. Pre- and post-education surveys were distributed to nurses. Pre-education surveys averaged a 49% score, while the post-education average was 98%. RESULTS: Complications per patient per day were tracked pre- and post-intervention and a control chart compared pre- and post-intervention rates. Pre-implementation there were 0.00655 complications per patient per day over 22-weeks. Post-implementation there were 0.01012 complications per patient per day over 6-weeks. CONCLUSIONS: While complication rates seem to have increased following implementation, there are many reasons that an increase may have been noted. During implementation, census increased while staffing did not. Additionally, the project was implemented during the winter season, when dry air often causes increased mucous plugging. Finally, the post-implementation period has only covered six weeks. Perhaps with extended monitoring, rates would decrease

How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study.

BackgroundPatients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources.MethodsWe used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600-2200) and during the night (2200-0600) that resulted in acceptable wait times.ResultsSimulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8-12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.ConclusionsMonte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures

Screening strategies in surveillance and control of methicillin-resistant Staphylococcus aureus (MRSA)

With reports of hospital-acquired methicillin-resistant Staphylococcus aureus (MRSA) continuing to increase and therapeutic options decrease, infection control methods are of increasing importance. Here we investigate the relationship between surveillance and infection control. Surveillance plays two roles with respect to control: it allows detection of infected/colonized individuals necessary for their removal from the general population, and it allows quantification of control success. We develop a stochastic model of MRSA transmission dynamics exploring the effects of two screening strategies in an epidemic setting: random and on admission. We consider both hospital and community populations and include control and surveillance in a single framework. Random screening was more efficient at hospital surveillance and allowed nosocomial control, which also prevented epidemic behaviour in the community. Therefore, random screening was the more effective control strategy for both the hospital and community populations in this setting. Surveillance strategies have significant impact on both ascertainment of infection prevalence and its control