Operational Impact of mHealth Adoption in Clinical Practice

Chronic conditions place a high cost burden on the healthcare system and deplete the quality of life for millions of Americans. There is significant medical literature that shows that continuous monitoring of patient health at home with the addition of provider support, improves patient health. Digital innovations such as mHealth technology can be used to provide efficient, effective, and patient centered healthcare. However, implementing mHealth technology can significantly change the composition of clinical staff and patient flow. In this paper, we evaluate the trade-offs of implementing mHealth technology in a clinical practice

Redesigning Chronic Care Delivery Using Mobile Health Technology

Typical management of chronic conditions is through sporadic office visits. But health indicators (such as blood pressure) can fluctuate significantly within a day. The infrequent office visits, however, offer the provider little information about the medical history of the patient between office visits resulting in delayed and sometimes inappropriate interventions. Providing the right product (making appropriate interventions) at the right place (patient's location) at the right time (before the worsening condition leads to a costlier intervention) is the objective of effective supply chain management. Use of mobile health (mHealth) technology in clinical care can help achieve all three objectives. mHealth enables continuous monitoring of measurements resulting in bidirectional information flow between providers and patients, thereby reducing information asymmetry. Our study examines redesigning of chronic care delivery using mHealth. It is important to make sure the redesigned delivery process is both efficient (reduces cost) and effective (improves patient health). In this paper we first present a big picture of the redesigned care delivery process. We then show how this delivery process can improve patient health by analyzing a panel dataset of 1627 patients. We examine the relationship between use of mobile health applications (to remotely upload measurements and receive physician intervention) and quality of care delivery (as measured by blood pressure readings) for hypertensive patients. We observe the blood pressure readings to decrease with frequency of app usage and time since adoption. With the use of mHealth apps increasing in the post COVID-19 era, our analysis indicates an efficient use of physician's time and an increased role for support-staff under the supervision of the physician. The chronic care delivery process can therefore be redesigned with the help of mHealth, improving patient health and reducing cost for both patients and providers

A Server Backup Model with Markovian Arrivals and Phase Type Services

We consider a finite capacity queueing system with one main server who is supported by a backup server. We assume Markovian arrivals, phase type services, and a threshold-type server backup policy with two pre-determined lower and upper thresholds. A request for a backup server is made whenever the buffer size (number of customers in the queue) hits the upper threshold and the backup server is released from the system when the buffer size drops to the lower threshold or fewer at a service completion of the backup server. The request time for the backup server is assumed to be exponentially distributed. For this queuing model we perform the steady state analysis and derive a number of performance measures. We show that the busy periods of the main and backup servers, the waiting times in the queue and in the system, are of phase type. We develop a cost model to obtain the optimal threshold values and study the impact of fixed and variable costs for the backup server on the optimal server backup decisions. We show that the impact of standard deviations of the interarrival and service time distributions on the server backup decisions is quite different for small and large values of the arrival rates. In addition, the pattern of use of the backup server is very different when the arrivals are positively correlated compared to mutually independent arrivals

Optimal Workforce Mix in Service Systems with Two Types of Customers

We consider a service system with two types of customers. In such an environment, the servers can either be specialists (or dedicated) who serve a specific customer type, or generalists (or flexible) who serve either type of customers. Cross-trained workers are more flexible and help reduce system delay, but also contribute to increased service costs and reduced service efficiency. Our objective is to provide insights into the choice of an optimal workforce mix of flexible and dedicated servers. We assume Poisson arrivals and exponential service times, and use matrix-analytic methods to investigate the impact of various system parameters such as the number of servers, server utilization, and server efficiency on the choice of server mix. We develop guidelines for managers that would help them to decide whether they should be either at one of the extremes, i.e., total flexibility or total specialization, or some combination. If it is the latter, we offer an analytical tool to optimize the server mix

Appointment scheduling in surgery pre-admission testing clinics

Pre-admission testing clinics are care units serving outpatients prior to surgical operation and performing procedure-specific tests to prepare them. Patients may need multiple tests, each performed by a specialized operator and delivered in any order. Exam rooms act as renewable resources: rooms are limited, tests are administered to patients inside the rooms, individually, and patients occupy the room until all the required tests are completed. Careful scheduling of patient appointments is essential in clinic management for both the patient and the provider: on the one hand, minimizing patient waiting time improves service quality, on the other hand, minimizing completion time (makespan) improves system efficiency. In this paper, we propose offline policies for the daily scheduling of pre-admission test appointments. As a benchmark, we consider two online scheduling policies widely used in common practice. Each of these offers a different compromise between complexity and resource exploitation. The proposed optimization-based offline booking policy is identified as a new problem in the machine scheduling literature, for which we propose a network-flow model representation. A family of matheuristics based on different variable fixing criteria is provided to circumvent the high computational effort required to solve the mathematical model to optimality on real-size instances. The performance, advantages and disadvantages of each of the online and offline policies are compared in a variety of scenarios based on realistic data. Through this work, decision-makers have a new set of tools they can choose from according to their priorities