Influence of Initial Health Care Provider on Subsequent Health Care Utilization for Patients With a New Onset of Low Back Pain: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The aim of this research was to examine the scope of evidence for the influence of a nonmedical initial provider on health care utilization and outcomes in people with low back pain (LBP). METHODS: Using scoping review methodology, we conducted an electronic search of 4 databases from inception to June 2021. Studies investigating the management of patients with a new onset of LBP by a nonmedical initial health care provider were identified. Pairs of reviewers screened titles, abstracts, and eligible full-text studies. We extracted health care utilization and patient outcomes and assessed the methodological quality of the included studies using the Joanna Briggs Institute checklist. Two reviewers descriptively analyzed the data and categorized findings by outcome measure. RESULTS: A total of 26,462 citations were screened, and 11 studies were eligible. Studies were primarily retrospective cohort designs using claims-based data. Four studies had a low risk of bias. Five health care outcomes were identified: medication, imaging, care seeking, cost of care, and health care procedures. Patient outcomes included patient satisfaction and functional recovery. Compared with patients initiating care with medical providers, those initiating care with a nonmedical provider showed associations with reduced opioid prescribing and imaging ordering rates but increased rates of care seeking. Results for cost of care, health care procedures, and patient outcomes were inconsistent. CONCLUSIONS: Prioritizing nonmedical providers at the first point of care may decrease the use of low-value care, such as opioid prescribing and imaging referral, but may lead to an increased number of health care visits in the care of people with LBP. High-quality randomized controlled trials are needed to confirm our findings. IMPACT: This scoping review provides preliminary evidence that nonmedical practitioners, as initial providers, may help reduce opioid prescription and selective imaging in people with LBP. The trend observed in this scoping review has important implications for pathways of care and the role of nonmedical providers, such as physical therapists, within primary health care systems. LAY SUMMARY: This scoping review provides preliminary evidence that nonmedical practitioners, as initial providers, might help reduce opioid prescription and selective imaging in people with LBP. High-quality randomized controlled trials are needed to confirm these findings.
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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) | 0.002 | 0.001 |
| 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".