A user‐centered, learning asthma smartphone application for patients and providers
Bibliographic record
Abstract
PROBLEM: Smartphone applications are an increasingly useful part of patients' self-management of chronic health conditions. Asthma is a common chronic health condition for which good self-management by patients is very helpful in maintaining stability. User-centered design and intelligent systems that learn are steps forward in building applications that are more effective in providing quality care that is scalable and tailored to each patient. METHODS: A literature and application store search to review historic and current asthma smart phone applications. User-centered design is a methodology that involves all stakeholders of a proposed system from the beginning of the design phase to the end of installation. One aspect of this user-centered approach involved conducting focus groups with patients and health care providers to determine what features they desire for use in applications and create a model to build smart infrastructure for a learning health care system. A simple prototype for an asthma smartphone application is designed and built with basic functionality. OUTCOMES: Only one publication in the literature review of asthma smartphone applications describes both user-centered design and intelligent learning systems. The authors have presented a set of user-desired attributes for a smart health care application and a possible data flow diagram of information for a learning system. A prototype simple user-centered designed asthma smartphone application that better assists patients in their care illustrates the value of the proposed architecture. DISCUSSION: Our user-centered approach helped design and implement a learning prototype smart phone application to help patients better manage their asthma and provide information to clinical care providers. While popular in other industries, user-centered design has had slow adoption in the health care area. However, the popularity of this approach is increasing and will hopefully result in mobile application that better meets the needs of both patients and their care providers.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".