Automated problem list generation and physicians perspective from a pilot study

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

doi:10.1016/j.ijmedinf.2017.05.015

Automated problem list generation and physicians perspective from a pilot study

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

Health Solutions, College of (CHS)

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date.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 language	English (US)
Pages (from-to)	121-129
Number of pages	9
Journal	International Journal of Medical Informatics
Volume	105
DOIs	https://doi.org/10.1016/j.ijmedinf.2017.05.015
State	Published - Sep 2017

Keywords

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

ASJC Scopus subject areas

Health Informatics

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10.1016/j.ijmedinf.2017.05.015

Cite this

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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problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date.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{\textquoteright} 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 = "Devarakonda, {Murthy V.} and Neil Mehta and Tsou, {Ching Huei} and Liang, {Jennifer J.} and Nowacki, {Amy S.} and Jelovsek, {John Eric}",

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doi = "10.1016/j.ijmedinf.2017.05.015",

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

AU - Devarakonda, Murthy V.

AU - Mehta, Neil

AU - Tsou, Ching Huei

AU - Liang, Jennifer J.

AU - Nowacki, Amy S.

AU - Jelovsek, John Eric

PY - 2017/9

Y1 - 2017/9

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

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Automated problem list generation and physicians perspective from a pilot study

Abstract

Keywords

ASJC Scopus subject areas

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