Automated problem list generation and physicians perspective from a pilot study

Murthy Devarakonda, Neil Mehta, Ching Huei Tsou, Jennifer J. Liang, Amy S. Nowacki, John Eric Jelovsek

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Objective An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson1

Original languageEnglish (US)
Pages (from-to)121-129
Number of pages9
JournalInternational Journal of Medical Informatics
Volume105
DOIs
StatePublished - Sep 1 2017
Externally publishedYes

Fingerprint

Natural Language Processing
Physicians
Patient-Centered Care
Electronic Health Records
Machine Learning

Keywords

  • Electronic health records
  • IBM Watson
  • Longitudinal patient records
  • Machine learning
  • Natural language processing
  • Problem list

ASJC Scopus subject areas

  • Health Informatics

Cite this

Automated problem list generation and physicians perspective from a pilot study. / Devarakonda, Murthy; Mehta, Neil; Tsou, Ching Huei; Liang, Jennifer J.; Nowacki, Amy S.; Jelovsek, John Eric.

In: International Journal of Medical Informatics, Vol. 105, 01.09.2017, p. 121-129.

Research output: Contribution to journalArticle

Devarakonda, Murthy ; Mehta, Neil ; Tsou, Ching Huei ; Liang, Jennifer J. ; Nowacki, Amy S. ; Jelovsek, John Eric. / Automated problem list generation and physicians perspective from a pilot study. In: International Journal of Medical Informatics. 2017 ; Vol. 105. pp. 121-129.
@article{2b43817a9ac74b02b0e0166a8c21a310,
title = "Automated problem list generation and physicians perspective from a pilot study",
abstract = "Objective An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson1",
keywords = "Electronic health records, IBM Watson, Longitudinal patient records, Machine learning, Natural language processing, Problem list",
author = "Murthy Devarakonda and Neil Mehta and Tsou, {Ching Huei} and Liang, {Jennifer J.} and Nowacki, {Amy S.} and Jelovsek, {John Eric}",
year = "2017",
month = "9",
day = "1",
doi = "10.1016/j.ijmedinf.2017.05.015",
language = "English (US)",
volume = "105",
pages = "121--129",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Automated problem list generation and physicians perspective from a pilot study

AU - Devarakonda, Murthy

AU - Mehta, Neil

AU - Tsou, Ching Huei

AU - Liang, Jennifer J.

AU - Nowacki, Amy S.

AU - Jelovsek, John Eric

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Objective An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson1

AB - Objective An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson1

KW - Electronic health records

KW - IBM Watson

KW - Longitudinal patient records

KW - Machine learning

KW - Natural language processing

KW - Problem list

UR - http://www.scopus.com/inward/record.url?scp=85021248932&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021248932&partnerID=8YFLogxK

U2 - 10.1016/j.ijmedinf.2017.05.015

DO - 10.1016/j.ijmedinf.2017.05.015

M3 - Article

VL - 105

SP - 121

EP - 129

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

ER -