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

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

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

An Intelligent Scheduling of Non-Critical Patients Admission for Emergency Department

The combination of the progressive growth of an aging population, increased life expectancy and a greater number of chronic diseases all contribute significantly to the growing demand for emergency medical care, and thus, causing saturation in Emergency Departments (EDs). This saturation is usually due to the admission of non-urgent patients, who constitute a high percentage of patients in an ED. The Agent-based Model (ABM) is one of the most important tools that helps to study complex systems and explores the emergent behavior of this type of department. Its simulation more accurately reflects the complexity of the operation of real systems. Our proposal is the design of an ABM to schedule the access of these non-critical patients into an ED, which can be useful for the service management dealing with the actual growing demand for emergency care. We suppose that a relocation of these non-critical patients within the expected input pattern, provided initially by historical records, enables a reduction in waiting time for all patients, and therefore, it will lead to an improvement in the quality of service. It would also allow us to avoid long waiting times. This research offers the availability of relevant knowledge for Emergency Department managers in order to help them make decisions to improve the quality of the service, in anticipation of the expected growing demand of the service in the very near future

Mercy Medical Center: Reducing Readmissions Through Clinical Excellence, Palliative Care, and Collaboration

Outlines strategies and practices behind low readmissions rates for heart attack, heart failure, and pneumonia patients, such as investing in advanced practice nurses who help incorporate evidence-based standards into patient care. Lists lessons learned

Modeling emergency departments using discrete event simulation techniques

This paper discusses the application of Discrete Event Simulation (DES) for modeling the operations of an Emer-gency Department (ED). The model was developed to help the ED managers understand the behavior of the system with regards to the hidden causes of excessive waiting times. It served as a tool for assessing the impact of major departmental resources on Key Performance Indicators (KPIs), and was also used as a cost effective method for testing various what-if scenarios for possible system im-provement. The study greatly enhanced managers’ under-standing of the system and how patient flow is influenced by process changes and resource availability. The results of this work also helped managers to either reverse or modify some proposed changes to the system that were previously being considered. The results also show a possible reduc-tion of more than 20% in patients waiting times

The Veterans Health Administration: Implementing Patient-Centered Medical Homes in the Nation's Largest Integrated Delivery System

Describes the implementation of a model that organizes care around an interdisciplinary team of providers who work to identify and remove barriers to access and clinical effectiveness in primary care clinics. Outlines two case studies and lessons learned