Preliminary Analysis of Worldwide Usage Patterns in a Mobile Palliative Care Reference App
Bibliographic record
Abstract
Background: Fast Facts and Concepts for iOS and Android is the world’s most downloaded point of care mobile reference application for palliative care providers. This free mobile app leverages the Fast Facts and Concepts article repository that was started in 1999 at the End-of-Life/Palliative Education Resource Center at the Medical College of Wisconsin. Our team released the initial iOS version of the app in summer of 2014. Since then, it has been downloaded over 13,000 times. Objective: The purpose of this project is to evaluate and describe user behaviors of palliative care clinicians on a global scale using an analytics layer integrated into Fast Facts and Concepts. Methods: An analytics layer was integrated and disclosed with version 1.0.3; an analytics event is triggered when an article is read or when a search is made. The event, along with anonymous user metadata, was sent to a Web server where it was segmented. Summary statistics were generated using Python scripts and include category weight, article rank, and search term clusters. We evaluated user behavior of the Fast Facts and Concepts app during a 3-month window to better understand the needs of the userbase. Results: Our dataset had 26,733 events and 1461 unique users from 41 countries collected over 3 months. Prognosis was the most active category, with searches for Palliative Performance Scale accounting for a third of prognosis reads. Articles about dosage featured heavily, especially on methadone titration. On the spectrum of illness, physiological categories such as gastrointestinal and renal diseases were generally more popular than psychiatric disorders. Articles about interpersonal skills from categories such as Communication; Ethics, Law, Policy; and Psychosocial and Spiritual Experience were the least read. All of our conclusions are supported by chi-squared tests with P values <.01. More detailed results are included in the poster. Conclusions: This analysis shows that most users consult the app for guidance in symptom management and prognosis. The usage patterns described above suggest that the app is likely being used at the point of care as a clinical reference for medical decision making and therapeutic guidance. Our study provides evidence that mobile applications can be effective tools to distribute quality palliative care resources on a global scale. Our results also indicate which topics should be emphasized in medical education and how increased vigilance about these topics can optimize patient care; with a large active user base, we have the opportunity to make even more precise conclusions in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".