Challenges in the Implementation of a Mobile Application in Clinical Practice: Case Study in the Context of an Application that Manages the Daily Interventions of Nurses
Bibliographic record
Abstract
BACKGROUND: Working in a clinical environment requires unfettered mobility. This is especially true for nurses who are always on the move providing patients' care in different locations. Since the introduction of clinical information systems in hospitals, this mobility has often been considered hampered by interactions with computers. The popularity of personal mobile assistants such as smartphones makes it possible to gain easy access to clinical data anywhere. OBJECTIVE: To identify the challenges involved in the deployment of clinical applications on handheld devices and to share our solutions to these problems. METHODS: A team of experts underwent an iterative development process of a mobile application prototype that aimed to improve the mobility of nurses during their daily clinical activities. Through the process, challenges inherent to mobile platforms have emerged. These issues have been classified, focusing on factors related to ensuring information safety and quality, as well as pleasant and efficient user experiences. RESULTS: The team identified five main challenges related to the deployment of clinical mobile applications and presents solutions to overcome each of them: (1) Financial: Equipping every care giver with a new mobile device requires substantial investment that can be lowered if users use their personal device instead, (2) Hardware: The constraints inherent to the clinical environment made us choose the mobile device with the best tradeoff between size and portability, (3) Communication: the connection of the mobile application with any existing clinical information systems (CIS) is insured by a bridge formatting the information appropriately, (4) Security: In order to guarantee the confidentiality and safety of the data, the amount of data stored on the device is minimized, and (5) User interface: The design of our user interface relied on homogeneity, hierarchy, and indexicality principles to prevent an increase in data acquisition errors. CONCLUSIONS: The introduction of nomadic computing often raises enthusiastic reactions from users, but several challenges due to specific constraints of mobile platforms must be overcome. The ease of development of mobile applications and their rapid spread should not overshadow the real challenges of clinical applications and the potential threats for patient safety and the liability of people and organizations using them. For example, careful attention must be given to the overall architecture of the system and to user interfaces. If these precautions are not taken, it can easily lead to unexpected failures such as an increased number of input errors, loss of data, or decreased efficiency.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".