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Modelling conditions and health care processes in electronic health records: an application to severe mental illness with the clinical practice research datalink

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  • Ivan Olier
  • David A. Springate
  • Darren M. Ashcroft
  • Tim Doran
  • David Reeves
  • Claire Planner
  • Siobhan Theresa Reilly
  • Evangelos Kontopantelis
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Article number0146715
<mark>Journal publication date</mark>26/02/2016
<mark>Journal</mark>PLoS ONE
Issue number2
Volume11
Number of pages13
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Background

The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example.

Methods

We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients.

Results

We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework.

Conclusion

We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.