Using Mobile Health Applications in Postoperative Care: Patients’ and Healthcare Providers’ Experiences
Bibliographic record
Abstract
Mobile health applications have revolutionized healthcare, enhancing patient engagement, providing health information, and improving clinical outcomes. However, inclusivity and monitoring of technology's impact on workflow are crucial for full realization. The purpose of this study is to describe the experiences of both patients and healthcare providers in utilizing mobile health applications in postoperative care and the aim is to produce knowledge that could be used to improve the practices of both patients and healthcare providers when using mobile health applications in postoperative care. This study employs a qualitative, descriptive review method to explore patients' and healthcare providers' experiences with mobile health applications in postoperative care, focusing on experiences and inductive and dynamic research techniques. Through inductive content analysis, two main categories were identified: patients' experiences and providers' experiences. The content was further subdivided into five generic categories. The study used a Boolean Technique to tailor a search strategy for databases CINAHL Complete and PubMed, utilizing AND, OR, and NOT commands for specific results. This study gathered 13 articles from 2015-2023, published in various countries, including the USA, Canada, Sweden, Australia, the Netherlands, Switzerland, and the UK/Ireland. The population included patients who underwent postoperative surgeries, including breast reconstruction, abdominal surgery, orthopedic surgery, etc, and healthcare providers who performed these surgeries. The literature review examines the use of mobile health applications in postoperative care, focusing on obtaining acceptance, healthcare communications, postoperative care, accessibility, and future preferences. It highlights the benefits of these apps in enhancing patient engagement, promoting accessibility, and empowering patients. Mobile health applications are gaining popularity in postoperative care for improved recovery management. However, challenges include privacy, security, usability, and information overload. Recommendations include prioritizing patient data privacy, providing training, involving patients and healthcare providers in app design, and implementing continuous evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".