The Characteristics and Effectiveness of Interventions for Frequent Emergency Department Utilizing Patients With Chronic Noncancer Pain: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Patients with chronic noncancer pain (CNCP) present unique challenges to emergency department (ED) care providers and administrators. Their conditions lead to frequent ED visits for pain relief and symptom management and are often poorly addressed with costly, low-yield care. A systematic review has not been performed to inform the management of frequent ED utilizing patients with CNCP. Therefore, we synthesized the available evidence on interventional strategies to improve care-associated outcomes for this patient group. METHODS: We searched Medline, EMBASE, CINAHL, CENTRAL, SCOPUS, and Web of Science from database inception to June 2018 for eligible interventional studies aimed at reducing frequent ED utilization among adult patients with CNCP. Articles were assessed in duplicate in accordance with methodologic recommendations from the Cochrane Handbook for Systematic Reviews of Interventions. Outcomes of interest were the frequency of subsequent ED visits, type and amount of opioids administered in the ED and prescribed at discharge, and costs. Methodologic quality was assessed using the Cochrane Risk of Bias in Non-Randomized Studies of Interventions and Risk of Bias tools for nonrandomized and randomized studies, respectively. RESULTS: Thirteen studies including 1,679 patients met the inclusion criteria. Identified interventions implemented pain policies (n = 4), individualized care plans (n = 5), ED care coordination (n = 2), chronic pain management pathways (n = 1), and behavioral health interventions (n = 1). All of the studies reported a decrease in ED visit frequency following their respective interventions. These reductions were especially pronounced in studies whose interventions were focused around individualized care plans and primary care involvement. Interventions implementing opioid restriction and pain management policies were largely successful in reducing the amounts of opioid medications administered and prescribed in the ED. CONCLUSIONS: Multifaceted interventions, especially those employing individualized care plans, can successfully reduce subsequent ED visits, ED opioid administration and prescription, and care-associated costs for frequent ED utilizing patients with CNCP.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".