Using standards to make sense of data

Transcription

1 The Role(s) of Standards in Information Integration or Using standards to make sense of data Ann Wrightson 1 ann.wrightson@ccubesolutions.com Research Associate, College of Medicine University of Swansea

2 Context Data-centric clinical research Gets data where it can (it s surprisingly difficult) Gets what it can out of the data available Foundational/curation work on data quality and semantics Clinical questions answered on the data Strong framework of privacy &c Outside my scope today Acknowledgement: SAIL (technology team) and HIRU (data researchers) in the College of Medicine, University of Swansea For references see papers listed on 2

3 What s the problem? Data-centric research draws on a wide range of data sources Intrinsically secondary, glad to get data as-is, generally little or no influence on data design Some of the most fruitful research uses data linkage to work across different datasets which requires comparability & reproducible methods Identifying & cross-matching who & where the data is about Foundation of SAIL, an established area of work 3 Copyright Ccube Solutions 2014

4 What s the problem? Data-centric research draws on a wide range of data sources Intrinsically secondary, glad to get data as-is, generally little or no influence on data design Some of the most fruitful research uses data linkage to work across different datasets which This is the problem I m requires comparability & reproducible methods discussing today Identifying & cross-matching who & where the data is about Foundation of SAIL, an established area of work Classifying and codifying what the data is saying 4 Copyright Ccube Solutions 2014

5 Quick tour of SAIL 5

6 Addressing the problem Techniques that work now Three directions of ongoing research Natural language processing Terminology Information models 6

7 Techniques that work now Making the most of what we know about the data Extensive metadata for each dataset, including detailed information on each item of data collected Standardizing metadata for data reuse within a research community e.g. the Data Documentation Initiative v2 standard is widely used in social science research, & is becoming more widely adopted in clinical data research 7

8 Techniques that work now Developing standardized criteria, queries & filters that implement stable concepts Simple examples (the ONS has many) Age range, gender, income band More complex examples Standardized social/health deprivation indices for geographical areas The Early Development Index indicator of childhood well-being 8

9 Techniques that work now Using codes that are present in the data Readv2 codes in GP data ICD-n codes in Hospital Episode data Nature of clinical care and record-keeping means codes aren t a free lunch Coding habits of GPs in particular are a study in themselves Administrative influences (policy, incentives) can create spurious signals in longitudinal data 9 Concepts such as Cases of asthma and Incidence of Heart disease can be implemented as medium-complex SQL queries on Readv2 codes Copyright Ccube Solutions 2014

10 Research: Natural Language Processing NLP research group Anonymizing & redacting unstructured narrative for research use Involves reliable identification of words and phrases that are potentially identifying, or have other privacy & data protection aspects such as 3rd party information. Work on content concepts to follow. One of the industry partners is Clinithink Ltd who specialize in interpreting narrative into SNOMED CT expressions. Tie-in with practical issues in EDRMS Classifying unstructured documents to enrich indexing & search (eg find the anaesthesia forms in a scanned record) Improving free-text searching for a clinical purpose 10 Copyright Ccube Solutions 2014

11 Research: Terminology Exploiting SNOMED CT As a structured reference model of clinical concepts To map and harmonize content expressed in a range of terminologies Early days; interest likely to pick up as & when clinical data (such as GP data) with SNOMED CT content, starts to appear in SAIL. Converting to SNOMED CT is a high step up with a number of issues and drawbacks, not least the established skillset of research data analysts 11 Copyright Ccube Solutions 2014

12 Research: Information models Emerging work across the Farr Institute network of data research centres in the UK Building on work in UCL (European projects using openehr, EN 13606) and Swansea (composite indicators, Read codes) Currently seeking funding Overall idea is to use concepts expressed as archetypes as a common language for describing concepts analysed from data Previous in-principle work will be applied and hardened into a more robust methodology in the context of specific clinical research agendas 12

13 Any questions? Thank you for your attention 13