Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.

Sadel J, Grant NV, Burkhardt H, Kunze C

DOI: 10.2196/81824
Published: 2026 Mar 24
Container: JMIR formative research
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.2196/81824,
  title = {Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.},
  author = {Sadel J and Grant NV and Burkhardt H and Kunze C},
  year = {2026},
  journal = {JMIR formative research},
  doi = {10.2196/81824},
  url = {https://doi.org/10.2196/81824}
}

RIS

TY  - JOUR
TI  - Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.
AU  - Sadel J
AU  - Grant NV
AU  - Burkhardt H
AU  - Kunze C
PY  - 2026
JO  - JMIR formative research
DO  - 10.2196/81824
UR  - https://doi.org/10.2196/81824
ER  -

APA

J, S., NV, G., H, B., & C, K. (2026). Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.. JMIR formative research. https://doi.org/10.2196/81824

Source records

pubmed · retrieved 2026-09-26T02:38:09.950Z