Research Foci

Purpose

Recent research suggests that more than two-thirds of people use online reviews to find a new primary care physician (PCP). However, it is unclear what role review content plays when a patient uses online reviews to decide about a new PCP. This paper aims to understand how a review's content, related to competence (communication and technical skills) and benevolence (fidelity and fairness), impacts patients’ trusting intentions to select a PCP. The authors build the model around information diagnosticity, construal level theory, and valence asymmetries and use review helpfulness as a mediator and review valence as a moderator in this process.

Design/methodology/approach

The authors use two experimental studies to test our hypotheses and collect data through prolific.

Findings

The authors find that people have a harder time making inferences about the technical and communication skills of a PCP. Reviews about fidelity are perceived as more helpful and influential in building trust than reviews about fairness. Overall, reviews about the communication skills of a PCP have stronger effects on trusting intentions than other types of reviews. The authors also find that positive reviews are perceived as more helpful for the readers than negative reviews, but negative reviews have a stronger impact on patients' trust intentions than positive ones.

Originality/value

The authors identify how online reviews about a PCP’s competency and benevolence affect patients’ trusting intentions to choose the PCP. The implication of the findings of this study for primary medical practice and physician review websites is discussed.

Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians’ operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center’s attributes and patients’ input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements.

Excessive wait-time is the most common reason patients become dissatisfied and leave the Emergency Department (ED) before being treated, which leads to negative financial and medical consequences. In a field experiment in an urban ED, we study the impact of announcing wait-times on patient satisfaction. Our research demonstrates how a wait-time predictor that is relatively inexpensive to develop and implement can improve significantly patient satisfaction and the hospital's financial performance as the Centers for Medicare and Medicaid in the US tie reimbursements to the patient satisfaction. 

We observe that providing patients with their estimated wait-times improves their self-reported satisfaction by up to 18%.  Using Prospect Theory, we then explain how the announced wait-time acts as a reference point against which the patients compare the actual wait time. We find that the change in satisfaction decreases when the gap between patients announced wait-time and actual wait-time is positive compared to when the wait gap is negative, i.e., patient are loss-averse with respect to their wait. Nonetheless, we find that too much overestimation may have a negative impact on the patient waiting experience.

When customers arrive, service providers often collect information to generate delay forecasts. We study how delay data-collection and forecasting systems can be designed to improve customer satisfaction. We assume that customers may be loss-averse in the sense that an increase in the expected wait causes more distress than the positive response caused by an equivalent decrease and that they may be risk conscious in that an increase in the variance of expected delay reduces utility. Our goal is to find the structure of delay information that optimizes the customers’ experience while waiting. Delay forecasts follow Bayes’ rule, given a prior distribution, the additional information collected for a particular customer, and the passage of time.

We find that when loss aversion dominates, the optimal delay information focuses on the tails of the delay distribution. When risk consciousness is dominant more traditional information about the duration of delay–along a continuum from ‘short’ to ‘long’–is optimal, and this information should be most precise about the longest delays. The optimal information design also affects the timing of delay revelation. When customers are loss averse, it is optimal to avoid changes in expected delay over time, so that waiting times are revealed as customers go into service. When customers are risk conscious, it is optimal to provide information so that they learn the good (or bad) news immediately, when they arrive. 

Motivated by a mortgage application process, we investigate how much the lack of such information impacts the job's waiting times in a two-stage system with two types of jobs. We use stochastic dynamic programming to identify how and when the server can make the optimal decision to work on the type-1 jobs or type-2 jobs without knowing full information about the number of jobs at a downstream stage. We then analyze how this lack of information affects this decision and under what conditions the server can capture the benefit of the full information. We develop heuristic policies for the server to make this decision without knowing full information about the number of jobs at a downstream stage and discuss implementation in practice.

Customer abandonment as a performance measure is of great importance in many service systems, especially in call centers. For a cable company that receives 3,000 calls a day, transforming just 10% of abandoned calls into conversions means up to $15 million in additional revenue per year. However, minimizing the loss of revenue due to the abandonment of impatient customers is rarely studied in the literature. We characterize the structure of a server's optimal scheduling policy that minimizes the total average customer abandonment cost in a multi-class queuing system. We find that the optimal service policy is a static priority policy, which is easy-to-implement in practice. We derive sufficient conditions under which the so-called b mu--rule is optimal. Under the b mu-rule, it is optimal to give priority to the customer type that has higher service rate (mu) and higher abandonment cost (b), i.e., higher b mu index.

Working with a team of physicians, we develop a feedback system that provides ED prescribers at an urban academic medical center with regular feedback on individual rates of opioid prescribing relative to their de-identified peers. This feedback system is associated with a significant immediate reduction (around 30%) in the rate of ED discharge opioid prescribing. 

We develop a decision support system, using a compartmental model of the opioid epidemic and Markov Decision Process (MDP), to find the optimal portfolio of preventive and mitigating policies to maximize population health benefits while minimizing the implementation costs.

In humanitarian and nonprofit operations, distributing aid such as food, shelter, and medical supplies becomes challenging in an online setting, where the future demand is unknown, since allocation decisions are made in real time as uncertainties unfold. Being overly conservative in allocating items at the beginning of the supply chain to save stock for fulfilling demand further down the supply chain increases the likelihood of unallocated items (waste). On the other hand, fully addressing the demand of nodes in the earlier stages of the supply chain may negatively impact the equity of the allocation policy, as downstream nodes may receive significantly fewer items in proportion to their demand. This study proposes a framework for modeling the sequential decisions involved in this online resource allocation problem as a Markov Decision Process (MDP). Given that the size of the state–action space can become very large for this problem, standard dynamic programming methods in the reinforcement learning domain reach their limits, so using Approximate Dynamic Programming (ADP) is a practical solution. In this study, two methods of measuring downstream uncertainty are proposed, and Policy Function Approximation (PFA) is used to develop an optimal allocation policy. Numerical results and the application of the proposed model to the Food Bank of Southern Tier in New York suggest a reasonable balance between maximizing efficiency (minimizing the waste of unallocated items) and ensuring an equitable allocation. 

We review recent studies that develop Continuous Approximation (CA) models for transportation, distribution and logistics problems. Continuous Approximation is an efficient and parsimonious technique for modeling complex logistics problems, which is used as an alternative or a complement to discrete solution approaches.

We study a supply chain of perishable fruits and vegetables using system dynamics simulation and develop a multi-objective model identifying the best import quota policy for Tehran Municipality Management of Fruit And Vegetables Organization.

We develop a practical evaluation tool to assess the efficiency of cultural heritage institutions in Tehran using Data Envelopment Analysis (DEA) approach. Using this tool, we identify the efficient institutions and deficiencies in inefficient institutions are introduced for further revisions.

We review recent studies that develop Continuous Approximation (CA) models for transportation, distribution and logistics problems. Continuous Approximation is an efficient and parsimonious technique for modeling complex logistics problems, which is used as an alternative or a complement to discrete solution approaches.