Murray J. Côté

Patient flow and resource utilization in an outpatient clinic

Abstract This paper describes the results of a study taken at an outpatient clinic to describe and quantify issues related to patient flow and resource utilization for an individual physician during the delivery of primary health care. A discrete-event simulation model is constructed of the physician’s practice to examine the relationship between examining room capacity and patient flow across four clinic-based perfomance measures. A simulation experiment is presented with results identifying several important and generalizable aspects of outpatient clinic operations. In particular, increased resource utilization does not necessarily imply longer waiting lines nor longer patient flow times.

Five Constants of Information Technology Adoption in Healthcare

The healthcare industry has developed a dependence on information technology (IT) for maintaining and improving both clinical and business operations. Whether IT is used for office automation or for reducing medical errors, there are five constants that routinely influence the successful integration of IT in healthcare. These constants are the proper use and maintenance of the IT budget, the role of supportive leadership, the use of project management, the process of implementation, and the significance of end user involvement. These constants challenge healthcare organizations to efficiently and effectively use their financial and human resources when adopting new IT. These constants also shape how the healthcare industry approaches the adoption and utilization of new IT. A collective understanding of these constants and their interrelationships will enable healthcare organizations to better integrate new IT and achieve organizational goals of developing a solid technological infrastructure to truly enhance the delivery of quality healthcare.

A stochastic model for a visit to the doctor's office

This paper presents a stochastic model of an individual patients experience during a visit to a doctors office. The stochastic model is based on tracking the visit of patients at a local family practice clinic. A rigorous, iterative procedure for model development allows the stochastic model to be constructed, evaluated, and validated to establish consistency with both the theoretical stochastic assumptions and the clinics actual operating environment. This model extends the use of stochastic models in health care in two important respects. First, the stochastic model represents an application of semi-Markov processes in outpatient health care settings. Second, through the use of the infinitesimal generator associated with the transition probability matrices governing patient flow, numerical predictions for first passage times are easily obtained.

Nonlinear programming for multiperiod capacity planning in a manufacturing system

Abstract In this paper, we present nonlinear programming methods for capacity planning in a manufacturing system that consists of a set of machines or work stations producing multiple products. We model the facility as an open network of queues where capacity at each work station in the system may be changed in each of a finite number of time periods. To determine the timing and size of capacity changes, we present two nonlinear programming models and methods for solving the resulting problems. One model involves minimizing total capacity costs such that plant congestion is controlled via upper limits on work-in-process. The other model involves minimizing a weighted sum of product lead times subject to budget constraints on capacity costs. We present solution methods for continuous and discrete capacity options and convex and nonconvex (e.g., economies of scale) capacity cost functions. We use branch and bound and outer approximation techniques to determine globally optimal solutions to the nonconvex problems. Computational testing of the algorithms is reported.

Scheduling arrivals to a queue

Abstract We obtain an optimal spacing for arrivals to a single server queue with exponential service time. This is a continuation of work by Jansson [2], Mercer [3] and Pegden and Rosenshine [1]. Pegden and Rosenshine determined spacings minimizing the sum of the expected costs of customer waiting and server availability. We also determine the optimal solution for equally spaced arrivals. For a large number of customers to be scheduled, n, we show the equally spaced model is a limiting case of the model of Pegden and Rosenshine. This provides a simple large n approximation which avoids considerable computation.

An Erlang-based stochastic model for patient flow

In this paper, an Erlang-based stochastic model for patient flow is presented. The model is proposed as an effective tool to obtain analytic results for the stochastic process of patient flow in a health care environment. Through the use of two previously published examples, it is shown that the stochastic model can be developed quickly, is not computationally intensive and provides a good representation of the empirical patient flow.

Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas

This paper addresses the drone-aided delivery and pickup planning of medication and test kits for patients with chronic diseases who are required to visit clinics for routine health examinations and/or refill medicine in rural areas. For routine healthcare services, the work proposes two models: the first model is to find the optimal number of drone center locations using the set covering approach, and the second model is the multi-depot vehicle routing problem with pickup and delivery requests minimizing the operating cost of drones in which drones deliver medicine to patients and pick up exam kits on the way back such as blood and urine samples. In order to improve computational performance of the proposed models, a preprocessing algorithm, a Partition method, and a Lagrangian Relaxation (LR) method are developed as solution approaches. A cost-benefit analysis method is developed as a tool to analyze the benefits of drone-aided healthcare service. The work is tested on a numerical example to show its applicability.

The identification of high-cost patients.

The authors examine whether retrospective claims data are useful to distinguish future high-cost cases among the uninsured. They rely on internal claims and accounting data for the calendar years from 1999 to 2001 from a representative safety net facility to describe the distribution of costs and any characteristics that distinguish high-cost patients from other uninsured patients. They conclude that administrative data combined with in-depth survey information could be a useful approach for identifying cases for intensive case management.

Forecasting emergency department arrivals: a tutorial for emergency department directors.

Abstract This article is a tutorial for emergency department (ED) medical directors needing to anticipate ED arrivals in support of strategic, tactical, and operational planning and activities. The authors demonstrate our regression-based forecasting models based on data obtained from a large teaching hospitals ED. The versatility of the regression analysis is shown to readily accommodate a variety of forecasting situations. Trend regression analysis using annual ED arrival data shows the long-term growth. The monthly and daily variation in ED arrivals is captured using zero/one variables while Fourier regression effectively describes the wavelike patterns observed in hourly ED arrivals. In our study hospital, these forecasting methods uncovered: long-term growth in demand of about 1,000 additional arrivals per year; February was generally the slowest month of the year while July was the busiest month of the year; there were about 20 fewer arrivals on Fridays (the slowest day) than Sundays (the busiest); and arrivals typically peaked at about 10 per hour in the afternoons from 1 p.m. to 6 p.m., approximately. Because similar data are routinely collected by most hospitals and regression analysis software is widely available, the forecasting models described here can serve as an important tool to support a wide range of ED resource planning activities.

Costs Associated with Data Collection and Reporting for Diabetes Quality Improvement in Primary Care Practices: A Report from SNOCAP-USA

Purpose: Information about the costs and experiences of collecting and reporting quality measure data are vital for practices deciding whether to adopt new quality improvement initiatives or monitor existing initiatives. Methods: Six primary care practices from Colorados Improving Performance in Practice program participated. We conducted structured key informant interviews with Improving Performance in Practice coaches and practice managers, clinicians, and staff and directly observed practices. Results: Practices had 3 to 7 clinicians and 75 to 300 patients with diabetes, half had electronic health records, and half were members of an independent practice association. The estimated per-practice cost of implementation for the data collection and reporting for the diabetes quality improvement program was approximately