Telehealth and chronic pain management from rapid adaptation to long-term implementation in pain medicine: A narrative review
Bibliographic record
Abstract
The COVID-19 pandemic called for drastic changes to expand and rapidly implement telehealth to prevent breach of care for chronic patients. Responding to the challenge of implementing remote care in chronic pain services, a specialty highly dependent on doctor-patient rapport, physical examination, and frequent follow-up visits requires extensive adaptation involving administrative processes and clinical routines. We present our experience of a successful rapid adaptation to telemedicine paradigm as a response to the COVID-19 pandemic during a time of marked restriction of access to ambulatory hospital services for pediatric and adult chronic pain patients. This narrative review covers current scientific evidence for the use of telehealth for chronic pain management and describes in detail the challenges to implement telemedicine in ambulatory clinics from different perspectives. Best practices for telehealth use are recommended. A proposal for remote physical examination of pain patients is made, based on available evidence in the fields of musculoskeletal medicine and neurology comparing in-person vs remote physical examination. As an internal quality control process, an informal online survey was conducted to assess thoughts and experiences among patients and caregivers using telemedicine consultation services at the pediatric pain clinic. Providing chronic pain management services through telehealth is a viable option for many patients and health care professionals. This is reliant on the availability of appropriate materials and training, with guidelines for both patients and health care workers. With the rapid pace of technological advancements, even further integration of telehealth into routine health care is possible.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".