Electronic medical record integration with a database for adult congenital heart disease

Early experience and progress in automating multicenter data collection

Craig Broberg, Julie Mitchell, Silven Rehel, Andrew Grant, Ann Gianola, Peter Beninato, Christiane Winter, Amy Verstappen, Anne Marie Valente, Joseph Weiss, Ali Zaidi, Michael G. Earing, Stephen Cook, Curt Daniels, Gary Webb, Paul Khairy, Ariane Marelli, Michelle Z. Gurvitz, David Sahn

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background The adoption of electronic health records (EHR) has created an opportunity for multicenter data collection, yet the feasibility and reliability of this methodology is unknown. The aim of this study was to integrate EHR data into a homogeneous central repository specifically addressing the field of adult congenital heart disease (ACHD). Methods Target data variables were proposed and prioritized by consensus of investigators at five target ACHD programs. Database analysts determined which variables were available within their institutions' EHR and stratified their accessibility, and results were compared between centers. Data for patients seen in a single calendar year were extracted to a uniform database and subsequently consolidated. Results From 415 proposed target variables, only 28 were available in discrete formats at all centers. For variables of highest priority, 16/28 (57%) were available at all four sites, but only 11% for those of high priority. Integration was neither simple nor straightforward. Coding schemes in use for congenital heart diagnoses varied and would require additional user input for accurate mapping. There was considerable variability in procedure reporting formats and medication schemes, often with center-specific modifications. Despite the challenges, the final acquisition included limited data on 2161 patients, and allowed for population analysis of race/ethnicity, defect complexity, and body morphometrics. Conclusion Large-scale multicenter automated data acquisition from EHRs is feasible yet challenging. Obstacles stem from variability in data formats, coding schemes, and adoption of non-standard lists within each EHR. The success of large-scale multicenter ACHD research will require institution-specific data integration efforts.

Original languageEnglish (US)
Pages (from-to)178-182
Number of pages5
JournalInternational Journal of Cardiology
Volume196
DOIs
StatePublished - Jul 23 2015

Fingerprint

Electronic Health Records
Heart Diseases
Databases
Research Personnel
Research
Population

Keywords

  • Congenital heart disease
  • Electronic health records
  • Healthcare information systems
  • Multicenter data collection

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Electronic medical record integration with a database for adult congenital heart disease : Early experience and progress in automating multicenter data collection. / Broberg, Craig; Mitchell, Julie; Rehel, Silven; Grant, Andrew; Gianola, Ann; Beninato, Peter; Winter, Christiane; Verstappen, Amy; Valente, Anne Marie; Weiss, Joseph; Zaidi, Ali; Earing, Michael G.; Cook, Stephen; Daniels, Curt; Webb, Gary; Khairy, Paul; Marelli, Ariane; Gurvitz, Michelle Z.; Sahn, David.

In: International Journal of Cardiology, Vol. 196, 23.07.2015, p. 178-182.

Research output: Contribution to journalArticle

Broberg, C, Mitchell, J, Rehel, S, Grant, A, Gianola, A, Beninato, P, Winter, C, Verstappen, A, Valente, AM, Weiss, J, Zaidi, A, Earing, MG, Cook, S, Daniels, C, Webb, G, Khairy, P, Marelli, A, Gurvitz, MZ & Sahn, D 2015, 'Electronic medical record integration with a database for adult congenital heart disease: Early experience and progress in automating multicenter data collection', International Journal of Cardiology, vol. 196, pp. 178-182. https://doi.org/10.1016/j.ijcard.2015.05.140
Broberg, Craig ; Mitchell, Julie ; Rehel, Silven ; Grant, Andrew ; Gianola, Ann ; Beninato, Peter ; Winter, Christiane ; Verstappen, Amy ; Valente, Anne Marie ; Weiss, Joseph ; Zaidi, Ali ; Earing, Michael G. ; Cook, Stephen ; Daniels, Curt ; Webb, Gary ; Khairy, Paul ; Marelli, Ariane ; Gurvitz, Michelle Z. ; Sahn, David. / Electronic medical record integration with a database for adult congenital heart disease : Early experience and progress in automating multicenter data collection. In: International Journal of Cardiology. 2015 ; Vol. 196. pp. 178-182.
@article{84dc7c4773a443c3afff040fd4c9aa39,
title = "Electronic medical record integration with a database for adult congenital heart disease: Early experience and progress in automating multicenter data collection",
abstract = "Background The adoption of electronic health records (EHR) has created an opportunity for multicenter data collection, yet the feasibility and reliability of this methodology is unknown. The aim of this study was to integrate EHR data into a homogeneous central repository specifically addressing the field of adult congenital heart disease (ACHD). Methods Target data variables were proposed and prioritized by consensus of investigators at five target ACHD programs. Database analysts determined which variables were available within their institutions' EHR and stratified their accessibility, and results were compared between centers. Data for patients seen in a single calendar year were extracted to a uniform database and subsequently consolidated. Results From 415 proposed target variables, only 28 were available in discrete formats at all centers. For variables of highest priority, 16/28 (57{\%}) were available at all four sites, but only 11{\%} for those of high priority. Integration was neither simple nor straightforward. Coding schemes in use for congenital heart diagnoses varied and would require additional user input for accurate mapping. There was considerable variability in procedure reporting formats and medication schemes, often with center-specific modifications. Despite the challenges, the final acquisition included limited data on 2161 patients, and allowed for population analysis of race/ethnicity, defect complexity, and body morphometrics. Conclusion Large-scale multicenter automated data acquisition from EHRs is feasible yet challenging. Obstacles stem from variability in data formats, coding schemes, and adoption of non-standard lists within each EHR. The success of large-scale multicenter ACHD research will require institution-specific data integration efforts.",
keywords = "Congenital heart disease, Electronic health records, Healthcare information systems, Multicenter data collection",
author = "Craig Broberg and Julie Mitchell and Silven Rehel and Andrew Grant and Ann Gianola and Peter Beninato and Christiane Winter and Amy Verstappen and Valente, {Anne Marie} and Joseph Weiss and Ali Zaidi and Earing, {Michael G.} and Stephen Cook and Curt Daniels and Gary Webb and Paul Khairy and Ariane Marelli and Gurvitz, {Michelle Z.} and David Sahn",
year = "2015",
month = "7",
day = "23",
doi = "10.1016/j.ijcard.2015.05.140",
language = "English (US)",
volume = "196",
pages = "178--182",
journal = "International Journal of Cardiology",
issn = "0167-5273",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Electronic medical record integration with a database for adult congenital heart disease

T2 - Early experience and progress in automating multicenter data collection

AU - Broberg, Craig

AU - Mitchell, Julie

AU - Rehel, Silven

AU - Grant, Andrew

AU - Gianola, Ann

AU - Beninato, Peter

AU - Winter, Christiane

AU - Verstappen, Amy

AU - Valente, Anne Marie

AU - Weiss, Joseph

AU - Zaidi, Ali

AU - Earing, Michael G.

AU - Cook, Stephen

AU - Daniels, Curt

AU - Webb, Gary

AU - Khairy, Paul

AU - Marelli, Ariane

AU - Gurvitz, Michelle Z.

AU - Sahn, David

PY - 2015/7/23

Y1 - 2015/7/23

N2 - Background The adoption of electronic health records (EHR) has created an opportunity for multicenter data collection, yet the feasibility and reliability of this methodology is unknown. The aim of this study was to integrate EHR data into a homogeneous central repository specifically addressing the field of adult congenital heart disease (ACHD). Methods Target data variables were proposed and prioritized by consensus of investigators at five target ACHD programs. Database analysts determined which variables were available within their institutions' EHR and stratified their accessibility, and results were compared between centers. Data for patients seen in a single calendar year were extracted to a uniform database and subsequently consolidated. Results From 415 proposed target variables, only 28 were available in discrete formats at all centers. For variables of highest priority, 16/28 (57%) were available at all four sites, but only 11% for those of high priority. Integration was neither simple nor straightforward. Coding schemes in use for congenital heart diagnoses varied and would require additional user input for accurate mapping. There was considerable variability in procedure reporting formats and medication schemes, often with center-specific modifications. Despite the challenges, the final acquisition included limited data on 2161 patients, and allowed for population analysis of race/ethnicity, defect complexity, and body morphometrics. Conclusion Large-scale multicenter automated data acquisition from EHRs is feasible yet challenging. Obstacles stem from variability in data formats, coding schemes, and adoption of non-standard lists within each EHR. The success of large-scale multicenter ACHD research will require institution-specific data integration efforts.

AB - Background The adoption of electronic health records (EHR) has created an opportunity for multicenter data collection, yet the feasibility and reliability of this methodology is unknown. The aim of this study was to integrate EHR data into a homogeneous central repository specifically addressing the field of adult congenital heart disease (ACHD). Methods Target data variables were proposed and prioritized by consensus of investigators at five target ACHD programs. Database analysts determined which variables were available within their institutions' EHR and stratified their accessibility, and results were compared between centers. Data for patients seen in a single calendar year were extracted to a uniform database and subsequently consolidated. Results From 415 proposed target variables, only 28 were available in discrete formats at all centers. For variables of highest priority, 16/28 (57%) were available at all four sites, but only 11% for those of high priority. Integration was neither simple nor straightforward. Coding schemes in use for congenital heart diagnoses varied and would require additional user input for accurate mapping. There was considerable variability in procedure reporting formats and medication schemes, often with center-specific modifications. Despite the challenges, the final acquisition included limited data on 2161 patients, and allowed for population analysis of race/ethnicity, defect complexity, and body morphometrics. Conclusion Large-scale multicenter automated data acquisition from EHRs is feasible yet challenging. Obstacles stem from variability in data formats, coding schemes, and adoption of non-standard lists within each EHR. The success of large-scale multicenter ACHD research will require institution-specific data integration efforts.

KW - Congenital heart disease

KW - Electronic health records

KW - Healthcare information systems

KW - Multicenter data collection

UR - http://www.scopus.com/inward/record.url?scp=84937418300&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84937418300&partnerID=8YFLogxK

U2 - 10.1016/j.ijcard.2015.05.140

DO - 10.1016/j.ijcard.2015.05.140

M3 - Article

VL - 196

SP - 178

EP - 182

JO - International Journal of Cardiology

JF - International Journal of Cardiology

SN - 0167-5273

ER -