Screening tools to identify patients with complex health needs at risk of high use of health care services: A scoping review
Bibliographic record
Abstract
BACKGROUND: Many people with chronic conditions have complex health needs often due to multiple chronic conditions, psychiatric comorbidities, psychosocial issues, or a combination of these factors. They are at high risk of frequent use of healthcare services. To offer these patients interventions adapted to their needs, it is crucial to be able to identify them early. OBJECTIVE: The aim of this study was to find all existing screening tools that identify patients with complex health needs at risk of frequent use of healthcare services, and to highlight their principal characteristics. Our purpose was to find a short, valid screening tool to identify adult patients of all ages. METHODS: A scoping review was performed on articles published between 1985 and July 2016, retrieved through a comprehensive search of the Scopus and CINAHL databases, following the methodological framework developed by Arksey and O'Malley (2005), and completed by Levac et al. (2010). RESULTS: Of the 3,818 articles identified, 30 were included, presenting 14 different screening tools. Seven tools were self-reported. Five targeted adult patients, and nine geriatric patients. Two tools were designed for specific populations. Four can be completed in 15 minutes or less. Most screening tools target elderly persons. The INTERMED self-assessment (IM-SA) targets adults of all ages and can be completed in less than 15 minutes. CONCLUSION: Future research could evaluate its usefulness as a screening tool for identifying patients with complex needs at risk of becoming high users of healthcare services.
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 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 | 0.023 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.042 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".