Kalyan S. Pasupathy

Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force.

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.

Large-scale data envelopment analysis (DEA) implementation: a strategic performance management approach

We present one of the first large-scale implementations of data envelopment analysis (DEA) at the heart of a permanent performance management system in its third year of operation. The system evaluates more than 1000 field unit operations devoted to disaster relief, emergency communications, and life-saving skills training. The following research objectives were accomplished: (a) advanced a conceptual model for measuring performance in the nonprofit sector; (b) adapted a DEA formulation to account for differences in the operational environment of the field units, and included service quality, and effectiveness measures alongside traditional efficiency measures; and (c) created from scratch data collection (service quality and outcome achievement survey instruments) and report generation tools necessary for the deployment of evaluation results to the field in an user-friendly format for managers. While the suitability of DEA for real-life performance measurement is demonstrated, challenges of a DEA implementation are also discussed.

Impact of fatigue on performance in registered nurses: data mining and implications for practice.

&NA; Performance of nurses has a direct effect on the quality and safety of care that is delivered. Fatigue has been identified as a factor that leads to performance decrements in healthcare workers, especially nurses. Determining associations between dimensions of fatigue and performance is imperative to better understanding fatigue in nurses and the potential implications for both patient and provider safety. This article identifies associations between ranges of fatigue levels and significant differences in perceived performance, and analyzes interactions between fatigue dimensions in relation to perceived performance scores. Overall, mental fatigue tended to have higher perceived performance decrements than physical and total fatigue in the highest fatigue ranges. As physical fatigue begins to develop in nurses, physical exertion rather than discomfort is more critical to perceived performance. As acute fatigue levels increase, perceived performance levels continue to decrease, whereas the role of chronic fatigue is relatively constant. Minimizing the development of acute fatigue may help in maintaining higher performance levels. The findings from this study provide valuable information in quantifying the changes in perceived performance with regard to specific fatigue levels, as well as an initial understanding of how the individual dimensions and states of fatigue vary in their association with perceived performance decrements.

Integrating Excel, Access, and Visual Basic to Deploy Performance Measurement and Evaluation at the American Red Cross

The American Red Cross (ARC) is one of the worlds largest nonprofit social service organizations. ARC has a network of approximately 1,000 chapters in the United States and its territories; each is responsible for a specific geographic jurisdiction in which ARC provides an array of social services. ARC is under pressure to demonstrate to the public how it is responsible and accountable for the resources it uses to deliver these services and the outcomes it achieves. Its decision makers are challenged to make accurate decisions to improve resource utilization and service-delivery processes. We designed and developed a system that uses data envelopment analysis to make resource-allocation recommendations that help ARC managers evaluate the performance of chapters at various levels. We used Microsoft Excel for the model formulation and reporting. This paper describes its use, in conjunction with Premium Solver, Visual Basic for Applications, and Microsoft Access, to formulate 4,000 linear-programming models. The system performs the analysis and uses Excel to display the results visually. We also discuss the benefits to ARC, lessons learned, and potential future improvements.

The economics of social computing: Some preliminary findings on healthcare organizations

Abstract Large-scale organizations have used social computing platforms for various purposes. This research focuses on how hospitals utilize these platforms to attract potential customers (which represents the “extensivity” of a social computing platform) and generate interests in specific topics (which represents the “intensivity” of a platform). Specifically, we examine the effects of size of a hospital (or “size”) and the time that the social computing platform has been in existence (or “time”) on extensivity and intensivity. Our findings show that time is a significant variable on both dimensions; whereas size affects intensivity under certain conditions. We discuss the implications of these findings, and set the stage for future research.

Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research

AbstractIn the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic—big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.

Multi-disciplinary communication networks for skin risk assessment in nursing homes with high IT sophistication

BACKGROUND The role of nursing home (NH) information technology (IT) in quality improvement has not been clearly established, and its impacts on communication between care givers and patient outcomes in these settings deserve further attention. OBJECTIVES In this research, we describe a mixed method approach to explore communication strategies used by healthcare providers for resident skin risk in NH with high IT sophistication (ITS). METHODS Sample included NH participating in the statewide survey of ITS. We incorporated rigorous observation of 8- and 12-h shifts, and focus groups to identify how NH IT and a range of synchronous and asynchronous tools are used. Social network analysis tools and qualitative analysis were used to analyze data and identify relationships between ITS dimensions and communication interactions between care providers. RESULTS Two of the nine ITS dimensions (resident care-technological and administrative activities-technological) and total ITS were significantly negatively correlated with number of unique interactions. As more processes in resident care and administrative activities are supported by technology, the lower the number of observed unique interactions. Additionally, four thematic areas emerged from staff focus groups that demonstrate how important IT is to resident care in these facilities including providing resident-centered care, teamwork and collaboration, maintaining safety and quality, and using standardized information resources. CONCLUSION Our findings in this study confirm prior research that as technology support (resident care and administrative activities) and overall ITS increases, observed interactions between staff members decrease. Conversations during staff interviews focused on how technology facilitated resident centered care through enhanced information sharing, greater virtual collaboration between team members, and improved care delivery. These results provide evidence for improving the design and implementation of IT in long term care systems to support communication and associated resident outcomes.

Asset management in healthcare: Evaluation of RFID

Healthcare costs in the United States have continued to rise throughout the last decade and poor medical asset management is a contributing factor. Radio frequency identification (RFID) technology has been found to be one way to potentially alleviate this problem by improving process efficiency and reducing costs. However, return on investment (ROI) in RFID technology and its impact are based on the specifics for each healthcare organization and there is no standard methodology. Therefore, a methodology for ROI was created and a case study in a 600-bed hospital was undertaken to determine the feasibility of RFID implementation and its potential impact on asset management. The variables used in the ROI computational methodology were clinical and biomedical asset searching time, shrinkage rates, utilization rates and RFID implementation costs. Specific mobile assets that would benefit most from RFID technology were then selected within these variables. Under the assumptions from past studies, this work determined that implementing RFID technology within the 600-bed hospital was a financially viable decision with a 10.2-month payback period of the initial investment costs, and an expected 327% ROI within three years. This study highlights important RFID asset management techniques and characteristics for hospitals to consider as they determine their own financial feasibility with regards to RFID implementation. The approach can be used to inform budget planning in institutions for RFID implementation.

Optimal nurse scheduling based on quantitative models of work-related fatigue

Previous nurse scheduling models have mainly focused on managerial constraints to minimize costs. Although some models incorporate nurse preferences and safety guidelines, human factors considerations related to performance of nurses (fatigue) have not been studied extensively. Fatigue has been linked to nursing injuries and medical errors, and shown to be impacted by schedule-related parameters (shift length). Thus, the objective of this article was to develop a nurse scheduling model incorporating quantitative models of fatigue. This model can help a nurse manager to make schedule-related decisions by highlighting trade-offs among many (conflicting) objectives including nurse shift preferences and nurse fatigue levels obtained from two different fatigue models, namely survey-based and circadian function-based fatigue models. The data used in the numerical experiments were obtained from real patient census data and various surveys of nurses working in different hospitals across the United States. Numerical results show that it is possible to obtain Pareto-optimal schedules where the nurse fatigue levels are significantly reduced for a slight decrement in nurse preferences.

A Framework to Evaluate Service Operations: Dynamic Service-Profit Chain

Managers in organizations make operational investment decisions all the time. These investment decisions have an impact on the bottom line. Typically, not all such decisions are evaluated for their impact. The authors propose a dynamic model based on the service-profit chain to evaluate the impact of the investments made in operational attributes, on market penetration of the organization over time. They then operationalize this model to demonstrate applicability by identifying dimensions and observed variables that are measurable.