Semantic Tagging of Medical Narratives with Top Level Concepts from SNOMED CT Healthcare Data Standard

Hina, Saman; Atwell, Eric; Johnson, Owen

doi:10.20533/ijicr.2042.4655.2012.0025

Cited by 1 publication

(1 citation statement)

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“…In the UK, there has been mandatory use of clinical coding standards for several decades which makes UK EHR data a particularly rich source for data mining [26]. An alternative approach is to use Natural Language Processing (NLP) [27][28][29] of the unstructured notes and this has been particularly important in countries such as the USA where coding standards have not been widely adopted.…”

Section: Context: Big Data and Electronic Health Recordsmentioning

confidence: 99%

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

2016

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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

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Section: Context: Big Data and Electronic Health Recordsmentioning

confidence: 99%