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Design+ AI’s role in medical history taking

Table 1. Similarity matrix
Document name PH 09 PH 08 PH 07 PH 06 PH 05 PH 04 PH 03 PH 02 PH 01
PH 09 1 0.73 0.61 0.79 0.88 0.73 0.48 0.58 0.67
PH 08 0.73 1 0.52 0.64 0.67 0.76 0.45 0.55 0.58
PH 07 0.61 0.52 1 0.58 0.73 0.7 0.64 0.61 0.58
PH 06 0.79 0.64 0.58 1 0.79 0.64 0.58 0.42 0.7
PH 05 0.88 0.67 0.73 0.79 1 0.79 0.61 0.58 0.73
PH 04 0.73 0.76 0.7 0.64 0.79 1 0.58 0.73 0.64
PH 03 0.48 0.45 0.64 0.58 0.61 0.58 1 0.67 0.7
PH 02 0.58 0.55 0.61 0.42 0.58 0.73 0.67 1 0.55
PH 01 0.67 0.58 0.58 0.7 0.73 0.64 0.7 0.55 1
Note: The shades of grey indicate the degree of similarity.
Abbreviation: PH: Physician.

This efficiency gain is further exemplified by another research should consider both short-term and long-term
interviewee: financial impacts on health-care systems and individual
“I could imagine that it is impressive for patients if practices.
the doctor has a lot of information in a differentiated “I have high hopes that patient care can be improved
context right from the start and you can delve deep through AI support. Improvement can be achieved.
into their health problems during the consultation.” I wonder whether this necessarily only has to
(I07, 59ff) be in terms of taking medical histories. I also see
“Therefore, the ultimate question is: will health-care potential in administrative areas. When it comes
organizations successfully adopt AI?” The present study to reducing bureaucracy, writing applications and
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paperwork and things like that. Documentation. To
at least gives clear indications that the doctors responsible be honest, I have more hope there than with regard
are open to the use of AI and goes some way to answering to taking medical histories.” (I07, 72 – 77)
the core question of Sezgin’s 2023 study. 38
The meta-study by Khanna et al. shows that the
39
However, the integration of AI in medical anamnesis
is not without challenges. Concerns were raised regarding combined evaluation of diagnostic and therapeutic models
data protection, patient constitution, and potential impacts offers greater savings. The review of the literature on the
economic impact of implementing AI in health-care shows
on the doctor–patient relationship. As one respondent clear evidence that the use of AI in health-care sectors
noted:
such as ophthalmology, radiology, and disease screening
“Well, if a kind of data protection could be has shown positive economic impacts. The area of medical
guaranteed if the patients could simply reserve the history is not reflected as a single area of application. 40
right for the doctors to maintain confidentiality The economic challenges of using AI in the practices
toward the insurance companies.” (I09, 87 – 89)
of registered doctors suggest an even greater challenge
These findings, while insightful, are limited by the than can be expected in inpatient facilities with an existing
study’s small sample size, self-selection of participants, IT support infrastructure. This is therefore an area where
and regional constraints. To address these limitations further research is needed.
and further explore the potential of AI in health care, we (B). Patient acceptance and trust (Technology acceptance:
propose several areas for future research: Skepticism and technology anxiety)
The prepositions are aligned to our analytic model Exploring patients’ perceptions and acceptance of AI
combining the elements of the TUI concept with the in medical anamnesis is vital. Studies should examine how
situative demands within the anamnesis situation. patients feel about interacting with AI systems and whether
(A). Economic impact and cost-benefit analysis (Technology this influences their trust in the medical care they receive.
acceptance: perceived usefulness)
“And I think (.) I think it also increases patient
Economic studies evaluating the cost-effectiveness of satisfaction because they have the feeling that,
implementing AI in medical anamnesis are necessary. This firstly, they can deal with something, they are taken

Volume 2 Issue 1 (2025) 8 doi: 10.36922/dp.7675