Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink

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 begin...

Full description

Saved in:
Bibliographic Details
Published inPloS one Vol. 11; no. 2; p. e0146715
Main Authors Olier, Ivan, Springate, David A, Ashcroft, Darren M, Doran, Tim, Reeves, David, Planner, Claire, Reilly, Siobhan, Kontopantelis, Evangelos
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 26.02.2016
Public Library of Science (PLoS)
Subjects
Algorithms
Clinical Coding
Coding
Commands
Data base management systems
Data bases
Databases, Factual
Diabetes
Electronic Health Records
Electronic medical records
Electronic records
Health care
Health informatics
Health services
Humans
Lists
Medical records
Medical research
Medical services
Medicine and Health Sciences
Mental Disorders
Models, Statistical
Patients
Pay for performance
Population
Practice research
Primary care
Primary Health Care
Quality
Research and Analysis Methods
Researchers
Studies
United Kingdom
United Kingdom
United Kingdom > UK
Online AccessGet full text

Cover

More Information
Summary: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. 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. 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. 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Conceived and designed the experiments: IO DAS DMA TD DR CP SR EK. Performed the experiments: IO. Analyzed the data: IO. Contributed reagents/materials/analysis tools: EK IO DAS. Wrote the paper: IO EK DAS DR DMA TD CP SR. Developed the Stata command: EK. Developed the R command: IO DAS.
Competing Interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare the following: EK and SR were partly supported by NIHR School for Primary Care Research fellowships in primary health care; TD was supported by a NIHR Career Development Fellowship; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. The authors cannot share the Clinical Practice Research Datalink data (which was used for the SMI exemplar) due to licensing restrictions.
Current address: Centre for Health Informatics, Vaughan House, Portsmouth Street, University of Manchester, M13 9GP, Manchester, United Kingdom
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0146715