Using Simulation to Design and Improve an Outpatient Procedure Center

Discrete-event simulation (DES) is an effective tool to for analyzing and improving healthcare processes. In this chapter we discuss the use of simulation to improve patient flow at an outpatient procedure center (OPC) at Mayo Clinic. The OPC addressed is the Pain Clinic, which was faced with high patient volumes in a new, untested facility. Simulation was particularly useful due to the uncertain patient procedure and recovery times. We discuss the simulation process and show how it helped reduce patient waiting time while ensuring the clinic could meet its target patient volumes.

Intensive Care Unit Operational Modeling and Analysis

The outcome of critical illness depends not only on life threatening pathophysiologic disturbances, but also on several complex “system” dimensions: health care providers’ performance, organizational factors, environmental factors, family preferences and the interactions between each component. Systems engineering tools offer a novel approach which can facilitate a “systems understanding” of patient-environment interactions enabling advances in the science of healthcare delivery. Due to the complexity of operations in critical care medicine, certain assumptions are needed in order to understand system behavior. Patient variation and uncertainties underlying these assumptions present a challenge to investigators wishing to model and improve health care delivery processes. In this chapter we present a systems engineering approach to modeling critical care delivery using sepsis resuscitation as an example condition.

Simulation Modeling of Healthcare Delivery

Healthcare has delivered incredible improvements in diagnosis and treatment of diseases but faces challenges to improve the delivery of services. Healthcare is a complex system using expensive and scarce resources. Benchmarking, experience, and lean management techniques currently provide the basis for developing service delivery models and facility planning. Simulation modeling can supplement these methods to enable a better understanding of the complex systems involved. This provides the basis for developing and evaluating options to provide improved healthcare delivery. Simulation modeling enables a better understanding of the processes and the resources used in delivering healthcare services and improving healthcare delivery systems. Options to improve the cost effectiveness can be evaluated without experimenting with patients. This chapter reviews the current challenges and methods including the use of simulation modeling. Analysis of emergency patient flows through a major hospital shows the capability of simulation modeling to enable improvement of the healthcare delivery system. This chapter enables healthcare managers to understand the power simulation modeling brings to the improvement of healthcare delivery.

A New Cost Accounting Model and New Indicators for Hospital Management Based on Personnel Cost

New financial indicators were developed based on personnel costs which were calculated using this new cost accounting system. Indicator 1: The ratio of the marginal profit after personnel cost per personnel cost (RMP). Indicator 2: The ratio of investment (=indirect cost) per personnel cost (RIP). Operation profit per one dollar of personnel cost (OPP) was demonstrated to be the difference between the RMP and RIP. The break-even point (BEP) and break-even ratio (BER) could be determined by combining the indicators. RMP demonstrates not only the medical efficiency, but also the medical productivity in the case of DPC/DRG groups. OPP can be utilized to compare the medical efficiency of each department in either one hospital or multiple hospitals. It also makes it possible to evaluate the management efficiency of multiple hospitals.

Reducing Patient Waiting Time at an Ambulatory Surgical Center

This chapter describes a methodology to reduce patient waiting time in a for-profit ambulatory surgical center. Patients in this facility are scheduled in advance for the various operations, and yet operations start late, last longer than expected creating undesired delays. Although this facility is limited to ambulatory surgery, it provides a large number of different surgeries, which are scheduled using “block” scheduling approach. The methodology presented generates a more accurate schedule by creating better time estimates for the operations and with lower variability. The effect of sequencing the surgeries, such that the ones with lower variability are performed earlier in the day, is also discussed.

Modeling Clinical Engineering Activities to Support Healthcare Technology Management

In the chapter we describe a model to estimate the number of clinical engineers and biomedical equipment technicians (BMET) that will constitute the Clinical Engineering department staff. The model is based on the activities to be simulated, the characteristics of the healthcare facility, and the experience of human resources. Our model is an important tool to be used to start a Clinical Engineering department or to evaluate the performances of an existing one. It was used by managers of Regione Piemonte to start a regional network of Clinical Engineering departments.

Health Information Exchange for Improving the Efficiency and Quality of Healthcare Delivery

In healthcare industry, providers, patients, and all other stakeholders must have the right information at the right time for achieving efficient and cost effective services. Exchange of information between the heterogeneous system entities plays a critical role. Health information exchange (HIE) is not only a process of transmitting data, but also a platform for streamlining operations to improve healthcare delivery in a secure manner. In this chapter, we present a comprehensive view of electronic health record (EHR) systems and HIE by presenting their architecture, benefits, challenges, and other related issues. While providing information on the current state of EHR/HIE applications, we also discuss advanced issues and secondary uses of HIE implementations, and shed some light on the future research in this area by highlighting the challenges and potentials.

Electronic Medical Records (EMR)

For many years the electronic medical record has been the holy grail of hospital system integration. Hundreds of millions of dollars have been spent in attempts to develop effective electronic medical records (EMR) to provide clinical care for patients. The advantages of an EMR are listed as reducing error, streamlining care, and allowing multiple people to provide simultaneous care. Unfortunately, most current EMR implementations are developed without completely understanding the processes that are being automated. In some implementations, there is an effort to first outline the process, and then try to create software that will facilitate the existing process, but this effort is not typically done systematically or with the discipline of an engineer. We will discuss the areas that management systems engineers can facilitate the design and implementation of the EMR, reducing the errors in the current processes and preparing the healthcare system for further improvements.

Reducing Consultation Waiting Time and Overtime in Outpatient Clinic

Outpatient clinics face increasing pressure to handle more appointment requests due to aging and growing population. The increase in workload impacts two critical performance indicators: consultation waiting time and clinic overtime. Consultation waiting time is the physical waiting time a patient spends in the waiting area of the clinic, and clinic overtime is the amount of time the clinic is open beyond its normal opening hours. Long consultation waiting time negatively affects patient safety and satisfaction, while long clinic overtime negatively affects the morale of clinic staff. This chapter analyzes the complexity of an outpatient clinic in a Singapore public hospital, and factors causing long consultation waiting time and clinic overtime. Discrete event simulation and design of experiments are applied to quantify the effects of the factors on consultation waiting time/clinic overtime. Implementation results show significant improvement once those factors are well addressed.

Healthcare Delivery as a Service System

Both during and after the recent reform efforts, healthcare delivery has been identified as the key to transforming the U.S. healthcare system. In light of this background, we borrow from systems engineering and business management to present the concept of service co-production as a new paradigm for healthcare delivery and, using the foresight afforded by this model, to systematically identify the barriers to healthcare delivery functioning as a service system. The service co-production model requires for patient, provider, insurer, administrator, and all the related healthcare individuals to collaborate at all stages – prevention, triage, diagnosis, treatment, and follow-up – of the healthcare delivery system in order to produce optimal health outcomes. Our analysis reveals that the barriers to co-production – the misalignment of financial and legal incentives, limited incorporation of collaborative point of care systems, and poor access to care – also serve as the source of many of the systemic failings of the U.S. healthcare system. The Patient Protection and Affordable Care Act takes steps to reduce these barriers, but leaves work to be done. Future research and policy reform is needed to enable effective and efficient co-production in the twenty-first century. With this review, we assess the state of service co-production in the U.S. healthcare system, and propose solutions for improvement.