Bibliographic record
Abstract
Linked Comment: Aungst et al. Int J Clin Pract 2014; 68: 155–62. In this issue of the International Journal of Clinical Practice, Aungst et al. give us an overview of mobile medical applications, ‘apps’, intended to enhance medical decision-making and potentially improve therapeutic outcomes 1. One of the undersigned (LC) had the good fortune of virtually meeting Tim by being a peer reviewer of his manuscript on apps that he wrote for his fellow pharmacists 2, and he kindly agreed to work on a similar paper for the International Journal of Clinical Practice. Tim is Assistant Professor at MCPHS University (formerly Massachusetts College of Pharmacy and Health Sciences) and an Editor for iMedicalapps.com, an online publication for medical professionals, patients and analysts interested in mobile medical technology and healthcare apps. By way of full disclosure, among his co-authors, Iltifat Husain is Editor in Chief of iMedicalapps.com, Satish Misra is Managing Editor and Tom Lewis, an Editor; Kevin Clauson is Director, Center for Consumer Health Informatics Research (see cchir.org). Finding good medical apps can be like searching for a platypus. You know they are out there thriving, but how do you find them? This paper can get you started. Apps are hot. This past month, I have received many emails touting one app or another, including one ‘Physician iPhone/iPad App of the Week’ that promised ‘full-text reading for journal subscribers, including society member subsubscriptions, for over 500 [competitor's name deleted] published journals in the medical, nursing, dental, allied health and veterinary field.’ Apps have not escaped the notice of regulators. The US Food and Drug Administration (FDA) recently issued a guidance document regarding mobile medical applications 3, after initially issuing a draft in July 2011. Although the FDA does not intend to regulate apps that can inform consumers about healthcare options (or to allow patients to measure and track their own vital signs), the FDA does plan to regulate apps that are essentially medical devices and ‘whose functionality could pose a risk to a patient's safety if the mobile app were to not function as intended.’ This can include sensor-based ‘electronic stethoscopes.’ The guidance states that ‘When the intended use of a mobile app is for the diagnosis of disease or other conditions, or the cure, mitigation, treatment, or prevention of disease, or is intended to affect the structure or any function of the body of man, the mobile app is a device.’ The FDA does not intend to regulate or enforce regulations if the medical app poses a low risk to the public 4. Nor have apps escaped the notice of managed care executives, with the promise of reduced overall costs when quality of care is enhanced and processes made more efficient 5. Practitioners may be left in the dust if they do not adapt to these technological changes. Just as the electronic health record is becoming a standard of care for hospitals, other best practices continue to be rapidly shared, facilitated by the internet and social media. Some of these best practices will involve using medical apps. Handwriting-to-text apps can be addictive. Medical records can be stored and/or accessed securely in an iPad and practitioners can have instant access to their information wherever they are. Camera apps can be used to image paper reports before they get misplaced. Although everything is electronic, it is accessible, searchable and usable, and now, inexpensive. Consistent with its aim, Aungst's paper provides a framework to help physicians identify, evaluate and implement medical apps. It covers challenges in how to identify what is an actual medical app intended for use by a physician, and raises the issue of bias and quality of reviews of apps. Readers are reminded of the importance of patient privacy, data security, the need to backup data and to keep apps updated. Some of the points raised may seem obvious to an experienced user but, to the physician who is new to medical apps, this paper is a useful read before setting out to acquire medical apps. No disclosures relevant to the subject of this editorial other than a fascination with technology.
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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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".