Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications
Routine verbal communication between patients and nurses contains critical linguistic and interactional indicators for identifying cognitive impairment. Integrating audio-recorded patient-nurse communication with EHR data provides a more comprehensive and accurate method for early detection of cognitive decline, potentially improving patient outcomes through timely interventions. This combined approach could revolutionize cognitive impairment screening in home healthcare settings.
Authors
- James M. Noble (ORCID: https://orcid.org/0000-0003-0648-6702)
- Kathryn H. Bowles (ORCID: https://orcid.org/0000-0001-7740-5725)
- Maxim Topaz (ORCID: https://orcid.org/0000-0002-2358-9837)
- Sridevi Sridharan (ORCID: https://orcid.org/0000-0002-6674-4315)
- Sasha Vergez (ORCID: https://orcid.org/0000-0003-4733-7180)
- Suzanne Bakken (ORCID: https://orcid.org/0000-0001-6202-6001)
- Julia Hirschberg (ORCID: https://orcid.org/0000-0003-0689-7616)
- Nicole Onorato (ORCID: https://orcid.org/0000-0002-0466-6147)
- Maryam Zolnoori (ORCID: https://orcid.org/0000-0003-4484-2990)
- Margaret V. McDonald (ORCID: https://orcid.org/0000-0003-1045-0956)
- Ali Zolnour (ORCID: https://orcid.org/0009-0007-0668-5169)
- Ian Spens
Institutions
- Columbia University Irving Medical Center (US)
- Columbia University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1093/jamia/ocae300
- Citations
- 7
- Primary Topic
- Nursing Diagnosis and Documentation
- Type
- article
- Field-Weighted Citation Impact
- 10.19
Funders
- National Institute on Aging