TY - GEN
T1 - Characterizing Patient-Generated Clinical Data and Associated Implications for Electronic Health Records
AU - Arsoniadis, Elliot G.
AU - Tambyraja, Rabindra
AU - Khairat, Saif
AU - Jahansouz, Cyrus
AU - Scheppmann, Daren
AU - Kwaan, Mary R.
AU - Hultman, Gretchen
AU - Melton, Genevieve B.
N1 - Publisher Copyright:
© 2015 IMIA and IOS Press.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - Patient-facing technologies are increasingly utilized for direct patient data entry for potential incorporation into the electronic health record. We analyzed patient-entered data during implementation of a patient-facing data entry technology using an online patient portal and clinic-based tablet computers at a University-based tertiary medical center clinic, including entries for past medical history, past surgical history, and social history. Entries were assessed for granularity, clinical accuracy, and the addition of novel information into the record. We found that over half of patient-generated diagnoses were duplicates of lesser or equal granularity compared to previous provider-entered diagnoses. Approximately one fifth of patient-generated diagnoses were found to meet the criteria for new, meaningful additions to the medical record. Our findings demonstrate that while patient-generated data provides important additional information, it may also present challenges including generating inaccurate or less granular information.
AB - Patient-facing technologies are increasingly utilized for direct patient data entry for potential incorporation into the electronic health record. We analyzed patient-entered data during implementation of a patient-facing data entry technology using an online patient portal and clinic-based tablet computers at a University-based tertiary medical center clinic, including entries for past medical history, past surgical history, and social history. Entries were assessed for granularity, clinical accuracy, and the addition of novel information into the record. We found that over half of patient-generated diagnoses were duplicates of lesser or equal granularity compared to previous provider-entered diagnoses. Approximately one fifth of patient-generated diagnoses were found to meet the criteria for new, meaningful additions to the medical record. Our findings demonstrate that while patient-generated data provides important additional information, it may also present challenges including generating inaccurate or less granular information.
KW - Electronic Health Record
KW - Patient facing technology
KW - Patient generated healthcare data
UR - http://www.scopus.com/inward/record.url?scp=84951970490&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951970490&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-564-7-158
DO - 10.3233/978-1-61499-564-7-158
M3 - Conference contribution
C2 - 26262030
AN - SCOPUS:84951970490
T3 - Studies in Health Technology and Informatics
SP - 158
EP - 162
BT - MEDINFO 2015
A2 - Georgiou, Andrew
A2 - Sarkar, Indra Neil
A2 - de Azevedo Marques, Paulo Mazzoncini
PB - IOS Press
T2 - 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
Y2 - 19 August 2015 through 23 August 2015
ER -