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From the same journal

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

Research output: Contribution to journalArticle

Standard

Modelling Conditions and Health Care Processes in Electronic Health Records : An Application to Severe Mental Illness with the Clinical Practice Research Datalink. / Olier, Ivan; Springate, David A; Ashcroft, Darren M; Doran, Timothy; Reeves, David; Planner, Claire; Reilly, Siobhan; Kontopantelis, Evangelos.

In: PLoS ONE, Vol. 11, No. 2, e0146715, 26.02.2016.

Research output: Contribution to journalArticle

Harvard

Olier, I, Springate, DA, Ashcroft, DM, Doran, T, Reeves, D, Planner, C, Reilly, S & Kontopantelis, E 2016, 'Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink', PLoS ONE, vol. 11, no. 2, e0146715. https://doi.org/10.1371/journal.pone.0146715

APA

Olier, I., Springate, D. A., Ashcroft, D. M., Doran, T., Reeves, D., Planner, C., ... Kontopantelis, E. (2016). Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink. PLoS ONE, 11(2), [e0146715]. https://doi.org/10.1371/journal.pone.0146715

Vancouver

Olier I, Springate DA, Ashcroft DM, Doran T, Reeves D, Planner C et al. Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink. PLoS ONE. 2016 Feb 26;11(2). e0146715. https://doi.org/10.1371/journal.pone.0146715

Author

Olier, Ivan ; Springate, David A ; Ashcroft, Darren M ; Doran, Timothy ; Reeves, David ; Planner, Claire ; Reilly, Siobhan ; Kontopantelis, Evangelos. / Modelling Conditions and Health Care Processes in Electronic Health Records : An Application to Severe Mental Illness with the Clinical Practice Research Datalink. In: PLoS ONE. 2016 ; Vol. 11, No. 2.

Bibtex - Download

@article{eeb48fcbeeed4557b80a8714f1c5d7e0,
title = "Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink",
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.",
author = "Ivan Olier and Springate, {David A} and Ashcroft, {Darren M} and Timothy Doran and David Reeves and Claire Planner and Siobhan Reilly and Evangelos Kontopantelis",
note = "{\circledC} 2016, The authors.",
year = "2016",
month = "2",
day = "26",
doi = "10.1371/journal.pone.0146715",
language = "English",
volume = "11",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Modelling Conditions and Health Care Processes in Electronic Health Records

T2 - PLoS ONE

AU - Olier, Ivan

AU - Springate, David A

AU - Ashcroft, Darren M

AU - Doran, Timothy

AU - Reeves, David

AU - Planner, Claire

AU - Reilly, Siobhan

AU - Kontopantelis, Evangelos

N1 - © 2016, The authors.

PY - 2016/2/26

Y1 - 2016/2/26

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0146715

DO - 10.1371/journal.pone.0146715

M3 - Article

VL - 11

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 2

M1 - e0146715

ER -