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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 was received for this activity.

Completion of this “Other Activity (Regularly Scheduled Series – RSS)” 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.

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

The American Medical Informatics Association designates this Other activity (Regularly Scheduled Series (RSS)) for a maximum of 12 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

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: 12 CME/CNE

It is the policy of the American Medical Informatics Association (AMIA) to ensure that Continuing Medical Education (CME) activities are independent and free of commercial bias. To ensure educational content is objective, balanced, and guarantee content presented is in the best interest of its learners and the public, the AMIA requires that everyone in a position to control educational content disclose all financial relationships with ineligible companies within the prior 24 months. An ineligible company is one whose primary business is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients. Examples can be found at accme.org.

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
Dates and Times: -
Type: JAMIA Journal Club
Course Format(s): On Demand
Credits:
1.00
CME
,
1.00
CNE
Price: Free
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