Choose one track. Master’s proposals target Spring 2027. Final scope and programme approval are agreed with the supervisor; bachelor scopes are suggested adaptations.
TRACK 01 · IMPLEMENTATION INCLUDED
Technical track
Develop a working solution and test whether it addresses the problem.
Possible research question
Can source-linked AI documentation reduce verification effort and unnoticed errors compared with ordinary AI drafts?
Suggested tasks
Read research on AI-supported clinical documentation and compare existing solutions.
Identify one problem faced by healthcare professionals.
Write a research question and define what the solution should do.
Create scripted consultations and use an AI model to draft notes from them.
Build a review screen that links each statement to the transcript and lets users correct or approve it.
Compare it with an AI note editor without source links. Measure errors found, correction time and remaining mistakes.
Explain what worked, what did not, and how the results compare with earlier research.
Evaluation, degree scope and deliverables
Study and evaluation
Compare the implemented solution with a conventional AI-drafting interface with the same underlying model. Combine reproducible technical tests with an appropriate empirical evaluation.
Unsupported statements and important omissions
Correction time
Errors remaining after human review
Degree scope
Bachelor’s
Build source highlighting and a review workflow for one note type using scripted transcripts. Evaluate whether users can find seeded documentation errors.
Master’s
Compare evidence-linked and ordinary drafts while controlling the model, prompts and cases; examine verification behaviour as well as accuracy.
Background
Programming
Language-model integration
Basic text processing
Possible deliverables
A focused literature review, justified problem and research question
A working prototype with source code and setup instructions
A reproducible comparison and an appropriate study of use
A report explaining design lessons, results and limitations
TRACK 02 · NO IMPLEMENTATION REQUIRED
Non-technical track
Study existing systems, information or work practices. You do not need to develop software.
Possible research question
How do healthcare professionals verify AI-drafted notes, and what makes evidence links useful or misleading?
Suggested tasks
Read earlier studies of AI-supported clinical documentation.
Choose one problem and write a research question the study can answer.
Use scripted consultation transcripts and existing or researcher-prepared draft notes.
Observe how participants check statements, detect omissions and correct errors in a think-aloud study.
Interview participants about professional accountability and the information they need before approving a note.
Analyse the interviews, observations or documents using a clearly described method. Look for disagreements as well as common patterns.
Explain the findings, compare them with earlier research and suggest practical improvements.
Evaluation, degree scope and deliverables
Study and evaluation
Use a bounded empirical study of AI-supported clinical documentation. Justify case selection, recruitment and the analysis method. Distinguish observed behaviour from participants’ perceptions; use triangulation or a comparison where it serves the research question.
Verification strategies and overlooked errors in reviewed cases
Perceived accountability and correction effort
Evidence for the findings, conflicting cases and limits of the study
Degree scope
Bachelor’s
Study one case or a small set of existing materials. Agree the interviews, documents or scenario tasks with the supervisor. Describe the method, analyse the findings and give practical recommendations.
Master’s
Use a clear research question and relevant IS theory. Justify the cases, participants and analysis method. Explain what the findings add to earlier research and where they may apply. No software development is required.
Background
Literature review and academic writing
Qualitative or quantitative research methods
Interest in AI-supported clinical documentation; no programming prerequisite
Possible deliverables
A literature review and research question
A study plan and approved research material
An analysis supported by interviews, observations, documents or scenario results
A thesis with findings, recommendations and limitations
Scope and access
Use scripted consultations rather than patient recordings. The artifact drafts documentation; it does not diagnose or recommend treatment. A source link is not proof of support. These implementation-related limits apply when developing or testing a technical solution. For a non-technical study, agree access to participants or existing materials early, use approved or fictional cases where appropriate, and distinguish perceptions from observed outcomes.
Agree access to data, participants or existing materials and any required ethics or privacy review before committing. A non-technical track needs a systematic study, not a working prototype.
Full academic proposal
Working topic
Evidence-Linked AI Documentation for Healthcare Professionals
Brief outline
This proposal examines AI-supported clinical documentation in the work and information needs of healthcare professionals. The technical track combines a literature review and justified gap with requirements, design, implementation and evaluation of a bounded solution. The non-technical track investigates practices, experiences or organisational conditions through a systematic study of existing systems, documents or scenarios, without requiring implementation. Choose one track and agree the final research question, degree scope and contribution with the supervisor.
Programme fit
Information Systems. These are suggested research approaches, not a statement of confirmed programme policy. Agree the final title, track, degree scope and contribution with the supervisor and programme.
Shared research foundation
Review the literature; identify and justify a gap; formulate research questions; conduct a systematic study; analyse the evidence; explain the contribution relative to prior research and discuss limitations. The technical track additionally includes requirements, design, implementation and evaluation of an artifact.
