TY - JOUR
T1 - Critical Care Health Informatics Collaborative (CCHIC): data, tools and methods for reproducible research: a multi-centre UK intensive care database
AU - Harris, Steve
AU - Shib, Sinan
AU - Brealey, David
AU - MacCallum, Niall S.
AU - Denaxas, Spiros
AU - Perez-Suarez, David
AU - Ercole, Ari
AU - Watkinson, Peter
AU - Jones, Andrew
AU - Ashworth, Simon
AU - Beale, Richard
AU - Young, Duncan
AU - Brett, Stephen
AU - Singer, Mervyn
PY - 2018/1/31
Y1 - 2018/1/31
N2 - Objective To build and curate a linkable multi-centre database of high resolution longitudinal electronic health records (EHR) from adult Intensive Care Units (ICU). To develop a set of open-source tools to make these data ‘research ready’ while protecting patient’s privacy with a particular focus on anonymisation. Materials and Methods We developed a scalable EHR processing pipeline for extracting, linking, normalising and curating and anonymising EHR data. Patient and public involvement was sought from the outset, and approval to hold these data was granted by the NHS Health Research Authority’s Confidentiality Advisory Group (CAG). The data are held in a certified Data Safe Haven. We followed sustainable software development principles throughout, and defined and populated a common data model that links to other clinical areas. Results Longitudinal EHR data were loaded into the CCHIC database from eleven adult ICUs at 5 UK teaching hospitals. From January 2014 to January 2017, this amounted to 21,930 and admissions (18,074 unique patients). Typical admissions have 70 data-items pertaining to admission and discharge, and a median of 1030 (IQR 481 to 2335) time-varying measures. Training datasets were made available through virtual machine images emulating the data processing environment. An open source R package, cleanEHR, was developed and released that transforms the data into a square table readily analysable by most statistical packages. A simple language agnostic configuration file will allow the user to select and clean variables, and impute missing data. An audit trail makes clear the provenance of the data at all times. Discussion Making health care data available for research is problematic. CCHIC is a unique multi-centre longitudinal and linkable resource that prioritises patient privacy through the highest standards of data security, but also provides tools to clean, organise, and anonymise the data. We believe the development of such tools are essential if we are to meet the twin requirements of respecting patient privacy and working for patient benefit. Conclusion The CCHIC database is now in use by health care researchers from academia and industry. The ‘research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.
AB - Objective To build and curate a linkable multi-centre database of high resolution longitudinal electronic health records (EHR) from adult Intensive Care Units (ICU). To develop a set of open-source tools to make these data ‘research ready’ while protecting patient’s privacy with a particular focus on anonymisation. Materials and Methods We developed a scalable EHR processing pipeline for extracting, linking, normalising and curating and anonymising EHR data. Patient and public involvement was sought from the outset, and approval to hold these data was granted by the NHS Health Research Authority’s Confidentiality Advisory Group (CAG). The data are held in a certified Data Safe Haven. We followed sustainable software development principles throughout, and defined and populated a common data model that links to other clinical areas. Results Longitudinal EHR data were loaded into the CCHIC database from eleven adult ICUs at 5 UK teaching hospitals. From January 2014 to January 2017, this amounted to 21,930 and admissions (18,074 unique patients). Typical admissions have 70 data-items pertaining to admission and discharge, and a median of 1030 (IQR 481 to 2335) time-varying measures. Training datasets were made available through virtual machine images emulating the data processing environment. An open source R package, cleanEHR, was developed and released that transforms the data into a square table readily analysable by most statistical packages. A simple language agnostic configuration file will allow the user to select and clean variables, and impute missing data. An audit trail makes clear the provenance of the data at all times. Discussion Making health care data available for research is problematic. CCHIC is a unique multi-centre longitudinal and linkable resource that prioritises patient privacy through the highest standards of data security, but also provides tools to clean, organise, and anonymise the data. We believe the development of such tools are essential if we are to meet the twin requirements of respecting patient privacy and working for patient benefit. Conclusion The CCHIC database is now in use by health care researchers from academia and industry. The ‘research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.
KW - electronic health records
KW - database
KW - clinical decision support
KW - critical care
KW - reproducibility
U2 - 10.1016/j.ijmedinf.2018.01.006
DO - 10.1016/j.ijmedinf.2018.01.006
M3 - Article
SN - 1386-5056
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
ER -
