Currently submitted to: JMIR Formative Research
Date Submitted: Jan 27, 2026
Open Peer Review Period: Jan 28, 2026 - Mar 25, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited.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.
An AI-Based Smart Nursing Ward Model for Enhanced Recovery After Thoracic Surgery: A Historical Controlled Trial
ABSTRACT
Background:
Due to surgical trauma and the impact of the disease, patients undergoing thoracic surgery often experience a series of postoperative symptom burdens, which affect their recovery. Traditional perioperative care has drawbacks.
Objective:
To evaluate the impact of an AI-based personalized smart nursing ward management model on postoperative recovery outcomes in patients undergoing thoracic surgery.
Methods:
According to patients' admission sequence, patients who met the inclusion criteria were divided into a control group (n=303) and an intervention group (n=240). The control group adopted the routine nursing mode of general wards, while the intervention group implemented the AI-based personalized smart nursing ward management model on the basis of the routine nursing provided to the control group.
Results:
Data from all 543 enrolled patients were analyzed. Compared with the control group (n=303) receiving routine care, the intervention group (n=240) had a significantly shorter median hospital stay (9.0 days vs 12.0 days) and chest tube indwelling time (5.0 days vs 7.0 days), as well as lower total hospitalization costs (¥61,032.87 vs ¥72,859.90) (all P < .001). The postoperative pulmonary complication rate was also significantly lower in the intervention group (3.8% vs 12.2%, P < .001). Furthermore, patient satisfaction was higher (98.53% vs 91.28%), and nurses' daily step count was reduced (12,359.52 vs 18,692.74 steps) in the intervention group (both P < .001)
Conclusions:
The AI-based smart nursing model effectively promotes postoperative recovery and offers an innovative management approach for thoracic surgery.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.