Health Care Management Science最新文献

In recent years, companies that operate pharmacy store chains have adopted centralized and automated fulfillment systems, which are called Central Fill Pharmacy Systems (CFPS). The Robotic Dispensing System (RDS) plays a crucial role by automatically storing, counting, and dispensing various medication pills to enable CFPS to fulfill high-volume prescriptions safely and efficiently. Although the RDS is highly automated by robots and software, medication pills in the RDS should still be replenished by operators in a timely manner to prevent the shortage of medication pills that causes huge delays in prescription fulfillment. Because the complex dynamics of the CFPS and manned operations are closely associated with the RDS replenishment process, there is a need for systematic approaches to developing a proper replenishment control policy. This study proposes an improved priority-based replenishment policy, which is able to generate a real-time replenishment sequence for the RDS. In particular, the policy is based on a novel criticality function calculating the refilling urgency for a canister and corresponding dispenser, which takes the inventory level and consumption rates of medication pills into account. A 3D discrete-event simulation is developed to emulate the RDS operations in the CFPS to evaluate the proposed policy based on various measurements numerically. The numerical experiment shows that the proposed priority-based replenishment policy can be easily implemented to enhance the RDS replenishment process by preventing over 90% of machine inventory shortages and saving nearly 80% product fulfillment delays.

The Chilean public health system serves 74% of the country's population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients' historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

Cook et al. (Oper Res 61(3):666-676, 2013) propose a DEA-based model for the performance evaluation of non-homogeneous decision making units (DMUs) based on constant returns to scale (CRS), extended by Li et al. (Health Care Manag Sci 22(2):215-228, 2019) to variable returns to scale (VRS). This paper locates these models into more general DDF models to deal with nonhomogeneous DMUs and applies these to Hong Kong hospitals. The production process of each hospital is divided into subunits which have the same inputs and outputs and hospital performance is measured using the subunits. The paper provides CRS and VRS versions of DDF models and compares them with Cook et al. (Oper Res 61(3):666-676, 2013) and Li et al. (Health Care Manag Sci 22(2):215-228, 2019). A kernel-based method is used to estimate the distributions as well as a DEA-based efficiency analysis adapted by Simar and Zelenyuk to test the distributions. Both DDF CRS and VRS versions produce results similar to Cook et al. (Oper Res 61(3):666-676, 2013) and Li et al. (Health Care Manag Sci 22(2):215-228, 2019) respectively. However, the statistical tests find differences for the different technologies assumed as would be expected. For hospital managers, the more generalised DDF models expand their range of options in terms of directional improvements and priorities as well as dealing with non-homogeneity.

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a "no free lunch" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

Standard Data Envelopment Analysis (DEA) models consider continuous-valued and known input and output statuses for measures. This paper proposes an extended Slacks-Based Measure (SBM) DEA model to accommodate flexible (a measure that can play the role of input and output) and integer measures simultaneously. A flexible measure's most appropriate role (designation) is determined by maximizing the technical efficiency of each unit. The main advantage of the proposed model is that all inputs, outputs, and flexible measures can be expressed in integer values without inflation of efficiency scores since they are directly calculated by modifying input and output inefficiencies. Furthermore, we illustrate and examine the application of the proposed models with 28 university hospitals in Germany. We investigate the differences and common properties of the proposed models with the literature to shed light on both teaching and general inefficiencies. Results of inefficiency decomposition indicate that "Third-party funding income" that university hospitals receive from the research-granting agencies dominates the other inefficiencies sources. The study of the efficiency scores is then followed up with a second-stage regression analysis based on efficiency scores and environmental factors. The result of the regression analysis confirms the conclusion derived from the inefficiency decomposition analysis.

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer Programming and Constraint Programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses' biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

Optimal patient appointment grid scheduling improves medical center performance and reduces pressure from excess demand. Appointment scheduling efficiency depends on resource management, and staff are a key resource. Personnel scheduling takes into account union rules, skills, contract types, training, leave, illness, etc. When combined with appointment scheduling constraints, the complexity of the problem increases. In this paper, we study the combination of the patient appointment grid and technologist scheduling. We present a well-detailed framework outlining our approach. We develop two versions of a mixed-integer programming model: integrated and sequential. In the first version, we elaborate the appointment grid and the technologist schedules simultaneously, while in the second version we generate them sequentially. We evaluate the proposed approach using real data from the MRI department of the Centre hospitalier de l'Université de Montréal (CHUM) radiology center. We study different scenarios by testing several technologist rules and planning construction methods. Obtained solutions are compared to the current CHUM scheduling approach.