Evaluating the Preferences and Tradeoffs of AI-based Electronic Consultations for Older Adults in Primary Care

NCT07766694 | Not Yet Recruiting

• 1 other identifier: 26-5862
• Study Type: interventional
• Target: 220
• Locations: 1 country
• Sites: 1

Brief Summary

Electronic consultations, or e-consults, let a primary care doctor request medical advice from a specialist without requiring the patient to attend a separate visit. Artificial intelligence (AI) systems may be able to provide this type of advice, potentially making e-consults faster and less costly. However, whether AI-based e-consults are acceptable may depend on how patients and clinicians weigh factors such as who provides the advice, how quickly it is received, its cost, and its quality. The purpose of this study is to examine how older adult patients and primary care clinicians weigh these factors when comparing e-consults produced by a human specialist with those produced by an AI system. In the study, participants will complete a one-time survey in which they compare sets of two hypothetical e-consults. Each e-consult will include a different combination of features, such as whether the advice comes from a human or AI, the expected wait time, the cost, and the quality of the advice. This study will estimate how much patients and clinicians value each feature in an e-consult. The findings will inform the potential future AI e-consults, so they better reflect patient and clinician preferences.

Conditions

Primary Care Patients; Primary Care Provider

Outcome Measures

Primary Outcomes (1)

• Preferences and Tradeoffs of Attributes of Electronic Consults

  Participants will review 12 sets comparing 2 hypothetical e-consults. Each e-consult is defined by five attributes: the source (i.e., AI vs. human), wait time, cost, and quality of the e-consult, as well as, whether follow-up questions can be asked. For each set, the outcome is the participant's selection of one e-consult over the other. We will analyze the results of the discrete choice experiment using mixed-effects logistic regression adjusted for each attribute value with crossed random effects (intercepts) for participant and task.

  From enrollment to the end of the survey, approximately 15 minutes

Secondary Outcomes (1)

• Health Numeracy Measured by the Short Numeracy Understanding in Medicine Instrument

  From enrollment to the end of the survey, approximately 15 minutes

Study Arms (1)

Electronic Consultation Systems

EXPERIMENTAL

Participants will review 2 different e-consult systems, which will differ in terms of quality, cost, wait time, and source of the e-consult. Participants will then indicate a preference for 1 e-consult system in each set.

Other: Discrete Choice Experiment Survey

Interventions

Discrete Choice Experiment Survey OTHER

Participants will review these e-consults systems via a one-time survey lasting about 15 minutes.

Electronic Consultation Systems

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)

You may qualify if:

• Adult, 65 years of age or older
• Had at least two office visits at Penn Medicine in the past 12 months
• Has an upcoming appointment in a Penn Medicine primary care setting, including family medicine, internal medicine, and geriatric medicine

You may not qualify if:

• Documented Alzheimer's, dementia, or related condition
• CLINICIANS:
• Adult, 18 years of age or older
• Works at Penn Medicine as one of the following: physician, nurse practitioner, or physician's assistant
• Works in a primary care Penn Medicine setting including: family medicine, internal medicine, geriatrics medicine
• Actively in medical school, internship, or residency

Sponsors & Collaborators

• University of Pennsylvania: lead

Study Sites (1)

University of Pennsylvania

Philadelphia, Pennsylvania, 19104, United States

Location

Central Study Contacts

Nicholas Bishop

CONTACT

215-573-0779; nicholas.bishop@pennmedicine.upenn.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Medicine and Informatics

Study Record Dates

• First Submitted: August 9, 2026
• First Posted: August 14, 2026
• Study Start: August 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: August 14, 2026

Record last verified: 2026-08

Locations

US (1)