TY - JOUR
T1 - Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
AU - Moon, Sungrim
AU - Liu, Sijia
AU - Chen, David
AU - Wang, Yanshan
AU - Wood, Douglas L.
AU - Chaudhry, Rajeev
AU - Liu, Hongfang
AU - Kingsbury, Paul
N1 - Funding Information:
The Center for Innovation (CFI) and Center for the Science of Healthcare Delivery at Mayo Clinic sponsored the OCR processing portion of this study. The research team is partially supported by the National Institutes of Health (NIH) grants: R01LM11934, U01TR02062, and R01GM102282. The authors also thank Andrew Wen MS for insightful comments.
Funding Information:
The Center for Innovation (CFI) and Center for the Science of Healthcare Delivery at Mayo Clinic sponsored the OCR processing portion of this study. The research team is partially supported by the National Institutes of Health (NIH) grants: R01LM11934, U01TR02062, and R01GM102282. The authors also thank Andrew Wen MS for insightful comments.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/6/15
Y1 - 2019/6/15
N2 - Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients.
AB - Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients.
KW - Electronic health record
KW - Medical concept evaluation
KW - Medical concept matching
KW - Natural language processing
KW - Optical character recognition
KW - Outside medical records
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U2 - 10.1007/s41666-019-00044-5
DO - 10.1007/s41666-019-00044-5
M3 - Article
AN - SCOPUS:85088160311
SN - 2509-498X
VL - 3
SP - 200
EP - 219
JO - Journal of Healthcare Informatics Research
JF - Journal of Healthcare Informatics Research
IS - 2
ER -