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

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

Automated Diagnosis of Clinic Workflows

Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization

Designing an Appointment Management System for the Mother and Child Health Department of the Klinik Kesihatan Changlun

Information and Communication Technology has been changing the way things have been carried out. Traditionally many work required people to visit the location where the work has been carried out. ICT has been making these services available at their fingertips by hosting these applications online. Klinik Kesihatan Changlun is a public general clinic in the state of Kedah. The appointment management which is one of the most important services of a clinic is presently carried out manually here. Both patients and the staff have to face a lot of problems due to the inefficiency of the manual system. If the system can be automated and made available on the internet, it will solve a lot of problems currently faced by them. This project proposes to design an appointment management system for the Mother and Child Health Department of the Klinik Kesihatan Changlun. The project has been proposed to follow the formal research methodology proposed by Kuchler and Vaishnavi due to its suitability for small to medium sized development projects. Finally it has been proposed conduct a usability test on the prototype developed for ease of use and user friendliness with the aid of the questionnaire

Re-engineering the outpatient process flow of a multi-speciality hospital

Manufacturing concepts such as Just-in-Time, Lean and Six-Sigma, Japanese 5S, Materials Requirement Planning, Scheduling and Capacity Management have been applied in the Healthcare industries in the West for the last decade and has yielded positive results. In this study, these concepts and philosophies have been applied to an Indian Multi-speciality Hospital to improve its OPD process flow and increase patient satisfaction. The Outpatients Department (OPD) is usually the most crowded sector in a hospital. The frequent problems encountered include the waiting period for consultation, an unpredictable number of Walk-in patients, insufficient and operationally deficient OPD reception staff and unattended appointment patients. This study aims at, identifying methods to standardise OPD operations management. It has made the process more efficient through optimum resource utilisation. This will increase patient satisfaction by meeting and exceeding their expectations while maintaining quality of care. This research was conducted by mapping the process flow and using the data that was collected through an observational, cross-sectional, non-interventional study. Though there were a comprehensive set of recommendations at the end of the study, only a few could be implemented due to the introduction of a new Hospital Information System (HIS) software putting the implementation plan on hold

Scott & White Healthcare: Opening Up and Embracing Change to Improve Performance

Offers a case study of a multispeciality system with the attributes of an ideal healthcare delivery system as defined by the Fund. Describes a culture of continuous improvement, collaboration and peer accountability, and a comprehensive approach to care

Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Operational decision making for medical clinics through the use of simulation and multi-attribute utility theory.

Currently, health care is a large industry that concerns everyone. Outpatient health care is an important part of the American health care system and is one of the strongest growth areas in the health care system. Many people pay attention to how to keep basic health care available to as many people as possible. A large health care system is usually evaluated by many performance measures. For example, the managers of a medical clinic are concerned about increasing staff utilization; both managers and patients are concerned about patient waiting time. In this dissertation, we study decision making for clinics in determining operational policies to achieve multiple goals (e.g. increasing staff utilization，reducing patient waiting time, reducing overtime). Multi-attribute utility function and discrete even simulation are used for the study. The proposed decision making framework using simulation is applied to two case studies, i.e., two clinics associated with University of Louisville in Louisville, Kentucky. In the first case, we constructed of a long period simulation model for a multi-resource medical clinic. We investigated changes to the interarrival times for each type of patient, assigned patients to see different staff in different visits (e.g., visit #2, visit #5) and assigned medical resources accordingly. Two performance measures were considered: waiting time for patients, and utilization of clinic staff. The second case involved the construction of a one-morning simulation model for an ambulatory internal medicine clinic. Although all the resident doctors perform the same task, their service times are different due to their varying levels of experience. We investigated the assignment of examination rooms based on residents’ varying service times. For this model, we also investigated the effect of changing the interarrival times for patients. Four performance measures were considered: waiting time for patients, overtime for the clinic staff, utilization of examination rooms and utilization of clinic staff. We developed a ranking and selection procedure to compare the various policies, each based on a multiple attribute performance. This procedure combines the use of multi-attribute utility functions with statistical ranking and selection in order to choose the best results from a set of possible outputs using an indifferent-zone approach. We applied this procedure to the outputs from “Healthy for Life” clinic and “AIM” clinic simulation models in selecting alternative operational policies. Lastly, we performed sensitivity analyses with respect to the weights of the attributes in the multi-attribute utility function. The results will help decision makers to understand the effects of various factors in the system. The clinic managers can choose a best scheduling method based on the highest expected utility value with different levels of weight on each attribute. The contribution of this dissertation is two-fold. First, we developed a long term simulation model for a multi-resource clinic consisting of providers with diverse areas of expertise and thus vastly different no-show rate and service times. Particularly, we modeled the details on assigning patients to providers when they come to the clinic in their different visits. The other contribution was the development of a special ranking and selection procedure for comparing performances on multiple attributes for alternative policies in the outpatient healthcare modeling area. This procedure combined a multiple attribute utility function with statistical ranking and selection in determining the best result from a set of possible outputs using the indifferent-zone approach

A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled walk-in patient visits. The simulation model captures the complex characteristics of the Orlando Veteran\u27s Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint