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

Modelling the feedback effects of reconfiguring health services

The shift in the balance of health care, bringing services ‘closer to home’, is a well-established trend, which has been motivated by the desire to improve the provision of services. However, these efforts may be undermined by the improvements in access stimulating demand. Existing analyses of this trend have been limited to isolated parts of the system with calls to control demand with stricter clinical guidelines or to meet demand with capacity increases. By failing to appreciate the underlying feedback mechanisms, these interventions may only have a limited effect. We demonstrate the contribution offered by system dynamics modelling by presenting a study of two cases of the shift in cardiac catheterization services in the UK. We hypothesize the effects of the shifts in services and produce model output that is not inconsistent with real world data. Our model encompasses several mechanisms by which demand is stimulated. We use the model to clarify the roles for stricter clinical guidelines and capacity increases, and to demonstrate the potential benefits of changing the goals that drive activity

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

Simulation analysis of the consequences of shifting the balance of health care: a system dynamics approach

Objectives: The shift in the balance of health care, bringing services 'closer to home', is a well-established trend. This study sought to provide insight into the consequences of this trend, in particular the stimulation of demand, by exploring the underlying feedback structure. Methods: We constructed a simulation model using the system dynamics method, which is specifically designed for the analysis of feedback structure. The model was calibrated to two cases of the shift in cardiac catheterization services in the UK. Data sources included archival data, observations and interviews with senior health care professionals. Key model outputs were the basic trends displayed by waiting lists, average waiting times, cumulative patient referrals, cumulative patient activity and cumulative overall costs. Results: Demand was stimulated in both cases via several different mechanisms. We revealed the roles for clinical guidelines and capacity changes, and the typical responses to imbalances between supply and demand. Our analysis also demonstrated the potential benefits of changing the goals that drive activity by seeking a waiting list goal rather than a waiting time goal. Conclusions: Appreciating the wider consequences of shifting the balance of care is essential if services are to be improved overall. The underlying feedback mechanisms of both intended and unintended effects need to be understood. Using a systemic approach, more effective policies may be designed through coordinated programmes rather than isolated initiatives, which may have only a limited impact

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

WAITING TIME AND PATIENTS’ SATISFACTION

In line with Vision 2021, the UAE’s National Agenda has six pillars: providing world-class healthcare is one of them. It is hence not surprising that the UAE healthcare industry is allocating substantial weight to the element of quality. Patient-centered care is internationally becoming part of the quality domain. Patient-centered quality may be defined as “providing the care that the patient needs in the manner the patient desires at the time the patient desires”. This requires substantially more attention to learning about patients’ preferences. One of the main dimensions of patient-centered quality is timely access to care, which includes shorter waiting times and efficient use of physicians’ time. Long waiting time is a globally challenging phenomenon that most healthcare systems face; it is the main topic of this thesis. The thesis consists of two main studies. The first empirical study was conducted by interviewing a sample of 552 patients to assess their satisfaction with their waiting experience in UAE hospitals. The collected data allowed us to test several hypotheses that were formulated based on an extensive literature study to better understand the relationship between waiting time and certain variables. In the second study, a simulation model for a typical clinic was built from real data obtained from a public hospital in Abu Dhabi emirate, considering two types of patients’ arrival; by appointment and walk-in, to test the effect of delayed arrivals and number of resources on the waiting time. The objective of the simulation study was to determine effective strategies for reducing the patients’ waiting time. The results of both studies are presented and discussed, with some recommendations, managerial implications, and conclusions

Understanding the UK hospital supply chain in an era of patient choice

Author Posting © Westburn Publishers Ltd, 2011. This is a post-peer-review, pre-copy-edit version of an article which has been published in its definitive form in the Journal of Marketing Management, and has been posted by permission of Westburn Publishers Ltd for personal use, not for redistribution. The article was published in Journal of Marketing Management, 27(3-4), 401 - 423, doi:10.1080/0267257X.2011.547084 http://dx.doi.org/10.1080/0267257X.2011.547084The purpose of this paper is to investigate the UK hospital supply chain in light of recent government policy reform where patients will have, inter alia, greater choice of hospital for elective surgery. Subsequently, the hospital system should become far more competitive with supply chains having to react to these changes as patient demand becomes less predictable. Using a qualitative case study methodology, hospital managers are interviewed on a range of issues. Views on the development of the hospital supply chain in different phases are derived, and are used to develop a map of the current hospital chain. The findings show hospital managers anticipating some significant changes to the hospital supply chain and its workings as Patient Choice expands. The research also maps the various aspects of the hospital supply chain as it moves through different operational phases and highlights underlying challenges and complexities. The hospital supply chain, as discussed and mapped in this research, is original work given there are no examples in the literature that provide holistic representations of hospital activity. At the end, specific recommendations are provided that will be of interest to service to managers, researchers, and policymakers

Simulation-based metrics analysis of an outpatient center.

With more and more attention surrounding healthcare, Industrial Engineers have championed the task to help hospitals and outpatient centers operate as efficiently as possible. Simulation is often used to analyze hospital performance measures. The University of Louisville Health Care Outpatient Center is a relatively new building occupying 169,000 ft2 and opened in October of 2008. The clinic is experiencing uneven workloads, over scheduling of Medical Assistants, and highly variable patient waiting times. The Arena Simulation package has been used to develop a model of the outpatient center\u27s current state. Using the data from July 2009 the model has been validated and verified. The model uses actual patient arrival data and discrete probability distributions to describe the current patient processing scheme for 41 doctors who regularly operate out of the outpatient center. Utilization rates for doctors were generally very high, but great variability was also present. Room utilization rates were lower than the author expected, confirming that the clinic could potentially house more doctors. Medical Assistants (MA) and doctors had almost equal numbers of patients waiting for them so it can be suggested that when deciding staffing levels MAs and doctors should be added at a one to one ratio. Patient waiting times were also highly variable based on which doctor was being visited and it was suggested that the current doctors review their scheduled patient visit times to make sure they are scheduling for an accurate time. Further research can be done to increase usability and add animation to the model

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

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