Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications
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Presenter
Maryam Zoolnori, PhD
Columbia University School of Nursing
Moderator
Jifan Gao
University of Wisconsin-Madison
Statement of Purpose
Alzheimer's disease and related dementias (ADRD) are escalating public health challenges, with many cases remaining undiagnosed until advanced stages. While previous research has focused on Electronic Health Record (EHR) data and structured cognitive assessments, early and subtle indicators—such as linguistic and interaction cues during routine patient-clinician conversations—often go unnoticed. Building on emerging evidence that speech features (e.g., semantic coherence, syntactic complexity, and turn-taking patterns) are highly sensitive to early cognitive decline, our study leverages natural language processing (NLP) and machine learning (ML) methods to analyze patient-nurse verbal communication in home healthcare.
This approach advances the field by integrating real-world audio-recorded patient-clinician verbal communications with EHR data (including NLP driven risk factors from clinical notes), enabling a more comprehensive detection of early-stage cognitive decline. Our findings demonstrate that linguistic and interaction cues from verbal communications provide valuable insights that significantly improve screening accuracy when combined with informative features from EHR data. By offering a scalable pipeline to record, analyze, and fuse these data sources, this work paves the way for earlier interventions, potentially improving patient outcomes and reducing healthcare costs associated with late-stage diagnosis.
Additional Information
The target audience for this activity includes physicians, nurses, other healthcare providers, and medical informaticians.
No commercial support (funding from a governmental agency, ineligible company or in-kind donation) was received for this activity.
Completion of this “Enduring Material” is demonstrated by participating in the live webinar or viewing the on-demand recording, engaging with presenters during the live session by submitting questions, and completing the evaluation survey at the conclusion of the course.
Learners may claim credit and download a certificate upon submission of the evaluation. Participation in additional resources and the course forum is encouraged but optional.
ACCME Accreditation Statement
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Designation Statement
The American Medical Informatics Association designates this Enduring activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
ANCC Accreditation Statement
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
Approved Contact Hours: 1 CME/CNE
*Learners may earn 1 contact hour for each monthly Journal Club session, for a maximum of 6 contact hours per year. To receive the full 6 contact hours, participants must either attend the live webinar or view the on-demand recording for each Regularly Scheduled Series (RSS) Journal Club presentation.
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In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to the planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
Faculty and planners who refuse to disclose any financial relationships with ineligible companies will be disqualified from participating in the educational activity.
For an individual with no relevant financial relationship(s), course participants must be informed that no conflicts of interest or financial relationship(s) exist.
Disclosures
Disclosures of relevant financial relationships of all planners and presenters of the Journal Club.
Planning Committee
The planning committee and reviewers reported that they have no relevant financial relationship(s) with ineligible companies to disclose.
- Joanna Abraham, PhD, FACMI, FAMIA
- Jifan Gao, MS
- Frances Hsu, BS, MS
- Sonish Sivarajkumar
- Song Wang, MS
- Faisal Yaseen
AMIA Staff
The following staff have no relevant financial relationship(s) with ineligible companies to disclose.
- Jennifer Wahl
- Melissa Kauffman