Automated problem list generation and physicians perspective from a pilot study
| dc.contributor.author | Devarakonda, Murthy V | |
| dc.contributor.author | Mehta, Neil | |
| dc.contributor.author | Tsou, Ching-Huei | |
| dc.contributor.author | Liang, Jennifer J | |
| dc.contributor.author | Nowacki, Amy S | |
| dc.contributor.author | Jelovsek, John Eric | |
| dc.date.accessioned | 2017-08-01T13:15:24Z | |
| dc.date.available | 2017-08-01T13:15:24Z | |
| dc.date.issued | 2017-09-01 | |
| dc.description.abstract | 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problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date.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 Watson 1 | |
| dc.identifier.eissn | 1872-8243 | |
| dc.identifier.issn | 1386-5056 | |
| dc.identifier.uri | ||
| dc.publisher | Elsevier BV | |
| dc.relation.ispartof | International Journal of Medical Informatics | |
| dc.relation.isversionof | 10.1016/j.ijmedinf.2017.05.015 | |
| dc.title | Automated problem list generation and physicians perspective from a pilot study | |
| dc.type | Journal article | |
| duke.contributor.orcid | Jelovsek, John Eric|0000-0002-7196-817X | |
| pubs.begin-page | 121 | |
| pubs.end-page | 129 | |
| pubs.organisational-group | Clinical Science Departments | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Obstetrics and Gynecology | |
| pubs.organisational-group | Obstetrics and Gynecology, Urogynecology | |
| pubs.organisational-group | School of Medicine | |
| pubs.publication-status | Accepted | |
| pubs.volume | 105 |
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