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Accepted for/Published in: JMIR Formative Research

Date Submitted: Aug 4, 2025
Open Peer Review Period: Aug 4, 2025 - Sep 29, 2025
Date Accepted: Dec 24, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

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

JMIR Form Res 2026;10:e81824

DOI: 10.2196/81824

PMID: 41875195

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Optimizing Discharge Management: Empirical Insights and Technological Requirements

  • Johanna Sadel; 
  • Natalie Victoria Grant; 
  • Heinrich Burkhardt; 
  • Christophe Kunze

ABSTRACT

Hospital discharge management is a critical yet complex practice at the intersection of care continuity, administrative coordination, and organizational constraints. This paper presents findings from an empirical field study of discharge management workflows in two German hospitals, highlighting the roles, routines, and challenges faced by healthcare professionals in orchestrating patient transitions. Drawing from qualitative interviews and ethnographic observations, we identify key tensions in communication, documentation, and inter-professional collaboration that shape discharge processes. Based on these insights, we derive design implications for the development of supportive health technologies that align with real-world practices and responsibilities. We argue that understanding discharge management as a sociotechnical process is essential for designing responsible and context-sensitive AI-based systems in healthcare. Our study contributes to ongoing discussions on care transitions, assistive system design, and the responsible integration of technology into clinical work.


 Citation

Please cite as:

Sadel J, Grant NV, Burkhardt H, Kunze C

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

JMIR Form Res 2026;10:e81824

DOI: 10.2196/81824

PMID: 41875195

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