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The goal of this study is to evaluate the feasibility and preliminary efficacy of an artificial intelligence (AI)-generated personalized question prompt list (a list of suggested questions to ask during outpatient appointments) for patients with hematologic cancers. The intervention will involve tailoring a standardized prompt to patients' individual characteristics and concerns. This prompt will then be used to ask Washington University's (WashU) HIPAA compliant ChatGPT to generate personalized question lists for outpatient appointments. Analyses will assess the impact of personalized QPLs on patients' question-asking behavior; communicative self-efficacy; and self-reported amount and satisfaction with information obtained about their disease and its treatment. Sub-analyses will explore patterns in questions generated by WashU ChatGPT. Patients will also provide feedback pertaining to the perceived helpfulness and ease-of-use of WashU-ChatGPT-generated question lists, as well as their attitudes and intentions regarding use of AI chatbots and whether they would engage in pre-appointment AI-assisted question brainstorming independently in the future.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| GPT-QPL: An AI-Generated, Personalized Question Prompt List | Experimental | Participants will complete surveys at appointment 1 (pre-intervention) and after being provided the AI-Generated, Personalized Question Prompt List at appointment 2 (post-intervention). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| GPT-QPL | Other | A research team member will generate a QPL that is personalized to the patient's demographics (from EHR screening and Baseline Demographic Survey) and concerns (from Distress Thermometer Problem Checklist and Interview). |
| Measure | Description | Time Frame |
|---|---|---|
| Changes in communicative self-efficacy |
| Appointment 1 (pre-intervention) and appointment 2 (post-intervention) (estimated to be 12 weeks) |
| Measure | Description | Time Frame |
|---|---|---|
| Changes in perceived amount of cancer information received as measured by an abbreviated version the EORTC QLQ - INFO25 |
|
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Eligibility Criteria as determined by Electronic Health Record (EHR) Screening:
Documented diagnosis of lymphoma, as defined by ICD-10 codes C81-C88 or multiple myeloma, as defined by ICD codes C90.0-C90.02
Has a scheduled follow-up appointment at a participating outpatient oncology clinic within the next month. Participating clinics include:
Undergoing infusion or injection-based systemic therapy intended to cure or manage the disease, as opposed to regimens delivered solely for palliative purposes, at a frequency of once every 1-4 weeks.
Between the ages of 18-99 at enrollment (Patients older than 99 will be excluded so that the study team is able to elicit questions from WashU ChatGPT using a standardized prompt that describes the patient's age only as specifically as a 10-year decade range (i.e., "I am a patient in my 20's, 30's, 40's, etc. Patients who are 18 or 19 years old will be described in the following manner: "I am a young adult patient in my late teens").
Able to consent to medical care independently (does not have a legally authorized representative or documented diagnosis of cognitive impairment)
Eligibility Criteria as determined by subsequent recruitment phone call or In-person approach after a clinic appointment:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jessica Hahne, MA, MPH | Contact | 314-935-6173 | hahne.j@wustl.edu |
| Name | Affiliation | Role |
|---|---|---|
| Jessica Hahne, MA, MPH | Washington University School of Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Washington University School of Medicine | Recruiting | St Louis | Missouri | 63110 | United States |
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| Label | URL |
|---|---|
| Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine | View source |
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| ID | Term |
|---|---|
| D008223 | Lymphoma |
| D009101 | Multiple Myeloma |
| D009369 | Neoplasms |
| D003142 | Communication |
| ID | Term |
|---|---|
| D009370 | Neoplasms by Histologic Type |
| D008232 | Lymphoproliferative Disorders |
| D008206 | Lymphatic Diseases |
| D006425 | Hemic and Lymphatic Diseases |
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|
| Appointment 1 (pre-intervention) and appointment 2 (post-intervention) (estimated to be 12 weeks) |
| Changes in satisfaction with cancer information received as measured by the EORTC QLQ - INFO25 |
| Appointment 1 (pre-intervention) and appointment 2 (post-intervention) (estimated to be 12 weeks) |
| Changes in the number of questions asked during appointments |
| Appointment 1 (pre-intervention) and appointment 2 (post-intervention) (estimated to be 12 weeks) |
| Changes in self-reported degree of intention to use AI chatbots for healthcare tasks |
| Appointment 1 (pre-intervention) and appointment 2 (post-intervention) (estimated to be 12 weeks) |
| D007160 | Immunoproliferative Disorders |
| D007154 | Immune System Diseases |
| D054219 | Neoplasms, Plasma Cell |
| D020141 | Hemostatic Disorders |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D010265 | Paraproteinemias |
| D001796 | Blood Protein Disorders |
| D006402 | Hematologic Diseases |
| D006474 | Hemorrhagic Disorders |
| D001519 | Behavior |