NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Abstract

Many healthcare organizations are now making good use of e-health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments and their human input, albeit shaped by computer systems, is compromised by many human factors. This data is potentially valuable to health economists and outcomes researchers but is sufficiently large and complex enough to be considered part of the new frontier of "big data". This paper describes emerging methods that draw together data mining, process modeling, activity based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital's EHR with 3 million secondary and tertiary care patients. We created a multi-disciplinary team of health economists, clinical specialists, data and computer scientists and developed a dynamic simulation tool called NETIMIS (www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for "what-if" analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multi-disciplinary team work to help them iteratively and collaboratively "deep dive" into big data. Key Points for Decision Makers Big data can be defined as a data at a scale where research become uncomfortable -the point where existing methods are unable to unlock the value in the data.  Electronic health records (EHR) are a type of big data that will be valuable to future health economists and outcomes researchers but the data is messy, it is the human product of large numbers of individuals working in complex environments. Solutions have yet to emerge.  We found that the use of a dynamic simulation tool provided an effective focal point for multidisciplinary investigations into the messy world of EHR big data and the development of valuable insights.2

The Use of Big Data in Healthcare: Lessons for Developing Countries From Uzbekistan

“…This research used secure links to the Leeds General Infirmary Information System with data from 3 million secondary and tertiary care patients. A dynamic modeling tool called NETIMIS (Network Tools for Intervention Modeling with Intelligent Simulation) was developed to visualize both human-developed and derived AI that could be used for what-if analysis by stakeholders to determine cost, and to develop and evaluate medical interventions [28]. An additional study on the use of dynamic simulation models of BD in healthcare has described the mutually beneficial synergy between BD and these models.…”

Process mining in oncology: A literature review

“…Data limitations were identified in ten papers [20], [22], [33], [36], [37], [44], [48], [57], [60] and were mostly related to limited access to the data, data quality problems, attributes not available in the data being extracted, or the dataset available in inappropriate level of details. Technique limitations were identified in 13 papers [3], [21], [28], [31], [35], [38]- [40], [48], [50], [52], [63], [64] specifically if the study undertaken was done by implementing plug-ins/ functionalities available in tools. Team limitations were identified in two papers [11], [23] because the authors realize that they need medical domain experts to be included in the research team.…”

Using a Multi-Level Process Comparison for Process Change Analysis in Cancer Pathways

“…Our previous literature review found that process mining has been used in oncology and shows promising results to support process analytics [ 8 ]. In earlier work with the Leeds Cancer Centre data [ 9 , 10 ], we had assumed that there were no changes to the process during the time period. We were aware that the period was long, that the organisation, system, and people had evolved and changed over time, but we were not aware of specific process changes before we commenced the study.…”