The Virtual Physician: Clarifying Medical Liability Issues in the Use of Remote Patient Monitoring
Bibliographic record
Abstract
More than ever before, information and communication technologies are playing an important role in the provision of health care services. As a form of telehealth, remote patient monitoring (RPM) uses information technologies and telecommunication tools to collect health data from patients outside of traditional health care institutional settings and transmit the data to health care providers for monitoring and evaluation. There are many challenges to RPM’s greater implementation in health care, including the potential for risk of harm for patients, and uncertainty regarding the liability of physicians utilizing RPM. Uncertain medical liability may have a chilling effect on the greater clinical use of RPM. To date, medical liability issues regarding RPM have not been addressed by courts and there is a paucity of literature on the topic. This article attempts to clarify some of the liability issues raised by RPM. To help guide physicians in their use of RPM, I propose the adoption of professional guidelines specific to RPM that courts can use in determining whether physicians have breached relevant standards of practice. Furthermore, by providing evidence-based standards, guidelines can mitigate risks of patient injury and reduce physicians’ reticence to adopt RPM.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".