Truace
Subscribe
68/100

Moderate evidence

Cite
More
Export CSVExport JSONEmailLinkedInX

Personalized medical chatbots got patients to share more, except on sensitive conditions

Study title: Encouraging disclosure: the role of personalization in pre-consultation chatbots

Evidence type
Controlled or validated comparison
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2641cite or check it

Study title: Encouraging disclosure: the role of personalization in pre-consultation chatbots

A peer-reviewed study in the Asia Pacific Journal of Marketing and Logistics tested whether personalized service from pre-consultation chatbots makes patients more willing to share information. Three scenario experiments and a field survey found that higher personalization increased willingness to self-disclose, and that the effect ran through greater trust in the AI.

Read the study abstract

Purpose This study aims to explore how personalized services provided by pre-consultation chatbots influence patients' intentions to self-disclose and the boundary conditions of this effect. Drawing on the Communication Privacy Management Theory, it identifies AI interactive trust as a mediator and examines how disease sensitivity moderates this mechanism. Design/methodology/approach Three scenario experiments and a field survey were conducted to test hypotheses. Experiments 1 and 2 were between-subjects experim…

60Gain side

How the two sides scoreClose call

56Problem side

The gain

In three scenario experiments and a field survey, researchers found that a more personalized pre-consultation chatbot increased patients' willingness to self-disclose by building trust in the AI, for low-sensitivity diseases.

The problem

In three scenario experiments and a field survey on pre-consultation chatbots, the trust-driven boost in patients' willingness to self-disclose weakened or disappeared for high-sensitivity diseases.

A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.

What the scores mean

G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.

P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.

In brief

The effect depended on the condition. It held for low-sensitivity diseases, but for high-sensitivity conditions the trust-based effect weakened or disappeared. Where patients most need to share delicate details, personalization alone did not reliably encourage disclosure.

The work draws on Communication Privacy Management Theory and treats AI interactive trust as the mediating mechanism, with disease sensitivity as the boundary condition. The fourth study, a survey, reconfirmed all the hypotheses.

Main points

  1. The research combined three between-subjects scenario experiments with one field survey.
  2. Experiments 1 and 2 manipulated chatbot service personalization (low vs. high) in different disease contexts.
  3. Experiment 3 used a 2 (personalization: low vs. high) × 2 (disease type: low vs. high sensitivity) design.
  4. Personalization raised willingness to self-disclose by increasing AI interactive trust.
  5. The effect appeared only for low-sensitivity diseases; for high-sensitivity conditions it weakened or vanished.
  6. Survey 4 reconfirmed all of the study's hypotheses.

The full story

The study examined pre-consultation chatbots, which collect information from patients before they see a clinician. It asked whether personalized service changes patients' intention to disclose personal information, and under what conditions.

Experiments 1 and 2 varied personalization between low and high in different disease contexts. Experiment 3 crossed personalization with disease sensitivity, allowing the researchers to test whether the type of illness changes the effect.

Results pointed to AI interactive trust as the mechanism: personalization built trust, and trust raised willingness to disclose. That chain held for low-sensitivity diseases but weakened or disappeared for high-sensitivity ones, and a field survey reconfirmed the pattern.

What this doesn’t fix

The main experiments used scenario designs, and the trust-based effect weakened or disappeared for high-sensitivity conditions, so findings do not show that personalization encourages disclosure across all diseases.

Sources

1 on file
  1. Asia Pacific Journal of Marketing and Logisticsdoi:10.1108/apjml-01-2026-0238
    Peer-reviewed2026-10-22

Talk it over

Four questions to start onThis record found a gain and a problem in the same place. Which matters more to you, and who might answer differently?

Start with a question

Personalized medical chatbots got patients to share more, except on sensitive conditionshttps://truvace.com/item/encouraging-disclosure-the-role-of-personalization-in-pre-consultation-chatbots

  1. This record found a gain and a problem in the same place. Which matters more to you, and who might answer differently?

    The gain In three scenario experiments and a field survey, researchers found that a more personalized pre-consultation chatbot increased patients' willingness to self-disclose by building trust in the AI, for low-sensitivity diseases.

    The problem In three scenario experiments and a field survey on pre-consultation chatbots, the trust-driven boost in patients' willingness to self-disclose weakened or disappeared for high-sensitivity diseases.

    Answer in the thread

  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a controlled comparison, from Asia Pacific Journal of Marketing and Logistics.

    Answer in the thread

  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat The main experiments used scenario designs, and the trust-based effect weakened or disappeared for high-sensitivity conditions, so findings do not show that personalization encourages disclosure across all diseases.

    Answer in the thread

  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

What readers say

More from Health