Mapping Local Codes to Read Codes

Wilfred Bonney; James Galloway; Christopher Hall; Mikhail Ghattas; Leandro Tramma; Thomas Nind; Louise Donnelly; Emily Jefferson; Alexander Doney

doi:10.3233/978-1-61499-742-9-29

Mapping Local Codes to Read Codes

Wilfred Bonney, James Galloway, Christopher Hall, Mikhail Ghattas, Leandro Tramma, Thomas Nind, Louise Donnelly, Emily Jefferson, Alexander Doney

Health and Clinical Services

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

3 Citations (Scopus)

356 Downloads (Pure)

Abstract

Background & Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.

Methods: Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.

Results: The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.

Conclusion: The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

Original language	English
Title of host publication	Building Capacity for Health Informatics in the Future
Editors	Francis Lau, John Bartle-Clar, Gerry Bliss, Elizabeth Borycki, Karen Courtney, Alex Kuo
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	29-36
Number of pages	8
ISBN (Electronic)	9781614997429
ISBN (Print)	9781614997412
DOIs	https://doi.org/10.3233/978-1-61499-742-9-29
Publication status	Published - 2017

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	234

Keywords

Clinical Datasets
Read Codes
Data Mapping
Health Data Standards

Access to Document

10.3233/978-1-61499-742-9-29Licence: CC BY-NC

Final Published Version
© 2017 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Final published version, 162 KBLicence: CC BY-NC

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

Cite this

Bonney, W., Galloway, J., Hall, C., Ghattas, M., Tramma, L., Nind, T., Donnelly, L., Jefferson, E., & Doney, A. (2017). Mapping Local Codes to Read Codes. In F. Lau, J. Bartle-Clar, G. Bliss, E. Borycki, K. Courtney, & A. Kuo (Eds.), Building Capacity for Health Informatics in the Future (pp. 29-36). (Studies in Health Technology and Informatics; Vol. 234). IOS Press. https://doi.org/10.3233/978-1-61499-742-9-29

@inbook{38dd39f2a95a4887a314d4544d397729,

title = "Mapping Local Codes to Read Codes",

abstract = "Background & Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.Methods: Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.Results: The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.Conclusion: The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.",

keywords = "Clinical Datasets , Read Codes, Data Mapping, Health Data Standards",

author = "Wilfred Bonney and James Galloway and Christopher Hall and Mikhail Ghattas and Leandro Tramma and Thomas Nind and Louise Donnelly and Emily Jefferson and Alexander Doney",

note = "This work was supported by the Medical Research Council (MRC) grant number MR/M501633/1 and the Wellcome Trust grant number WT086113 through the Scottish Health Informatics Programme (SHIP). SHIP is a collaboration between the Universities of Aberdeen, Dundee, Edinburgh, Glasgow and St Andrews and the Information Services Division of NHS Scotland. The authors acknowledge the support from the UK Health Informatics Research Network and the Farr Institute of Health Informatics Research. The authors also acknowledge the support of Dundee University Medical School.",

year = "2017",

doi = "10.3233/978-1-61499-742-9-29",

language = "English",

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publisher = "IOS Press",

pages = "29--36",

editor = "Lau, {Francis } and Bartle-Clar, {John } and Bliss, {Gerry } and Borycki, {Elizabeth } and Courtney, {Karen } and Kuo, {Alex }",

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Bonney, W, Galloway, J, Hall, C, Ghattas, M, Tramma, L, Nind, T, Donnelly, L , Jefferson, E & Doney, A 2017, Mapping Local Codes to Read Codes. in F Lau, J Bartle-Clar, G Bliss, E Borycki, K Courtney & A Kuo (eds), Building Capacity for Health Informatics in the Future. Studies in Health Technology and Informatics, vol. 234, IOS Press, Amsterdam, pp. 29-36. https://doi.org/10.3233/978-1-61499-742-9-29

Mapping Local Codes to Read Codes. / Bonney, Wilfred; Galloway, James; Hall, Christopher et al.
Building Capacity for Health Informatics in the Future. ed. / Francis Lau; John Bartle-Clar; Gerry Bliss; Elizabeth Borycki; Karen Courtney; Alex Kuo. Amsterdam: IOS Press, 2017. p. 29-36 (Studies in Health Technology and Informatics; Vol. 234).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

TY - CHAP

T1 - Mapping Local Codes to Read Codes

AU - Bonney, Wilfred

AU - Galloway, James

AU - Hall, Christopher

AU - Ghattas, Mikhail

AU - Tramma, Leandro

AU - Nind, Thomas

AU - Donnelly, Louise

AU - Jefferson, Emily

AU - Doney, Alexander

N1 - This work was supported by the Medical Research Council (MRC) grant number MR/M501633/1 and the Wellcome Trust grant number WT086113 through the Scottish Health Informatics Programme (SHIP). SHIP is a collaboration between the Universities of Aberdeen, Dundee, Edinburgh, Glasgow and St Andrews and the Information Services Division of NHS Scotland. The authors acknowledge the support from the UK Health Informatics Research Network and the Farr Institute of Health Informatics Research. The authors also acknowledge the support of Dundee University Medical School.

PY - 2017

Y1 - 2017

N2 - Background & Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.Methods: Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.Results: The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.Conclusion: The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

AB - Background & Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.Methods: Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.Results: The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.Conclusion: The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

KW - Clinical Datasets

KW - Read Codes

KW - Data Mapping

KW - Health Data Standards

U2 - 10.3233/978-1-61499-742-9-29

DO - 10.3233/978-1-61499-742-9-29

M3 - Chapter (peer-reviewed)

C2 - 28186011

SN - 9781614997412

T3 - Studies in Health Technology and Informatics

SP - 29

EP - 36

BT - Building Capacity for Health Informatics in the Future

A2 - Lau, Francis

A2 - Bartle-Clar, John

A2 - Bliss, Gerry

A2 - Borycki, Elizabeth

A2 - Courtney, Karen

A2 - Kuo, Alex

PB - IOS Press

CY - Amsterdam

ER -

Mapping Local Codes to Read Codes

Abstract

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