Patient- and family-centered performance measures focused on actionable processes of care for persistent and chronic critical illness: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Approximately 5 to 10% of critically ill patients transition from acute critical illness to a state of persistent and in some cases chronic critical illness. These patients have unique and complex needs that require a change in the clinical management plan and overall goals of care to a focus on rehabilitation, symptom relief, discharge planning, and in some cases, end-of-life care. However, existing indicators and measures of care quality, and tools such as checklists, that foster implementation of best practices, may not be sufficiently inclusive in terms of actionable processes of care relevant to these patients. Therefore, the aim of this systematic review is to identify the processes of care, performance measures, quality indicators, and outcomes including reports of patient/family experience described in the current evidence base relevant to patients with persistent or chronic critical illness and their family members. METHODS: Two authors will independently search from inception to November 2016: MEDLINE, Embase, CINAHL, Web of Science, the Cochrane Library, PROSPERO, the Joanna Briggs Institute and the International Clinical Trials Registry Platform. We will include all study designs except case series/reports of <10 patients describing their study population (aged 18 years and older) using terms such as persistent critical illness, chronic critical illness, and prolonged mechanical ventilation. Two authors will independently perform data extraction and complete risk of bias assessment. Our primary outcome is to determine actionable processes of care and interventions deemed relevant to patients experiencing persistent or chronic critical illness and their family members. Secondary outcomes include (1) performance measures and quality indicators considered relevant to our population of interest and (2) themes related to patient and family experience. DISCUSSION: We will use our systematic review findings, with data from patient, family member and clinician interviews, and a subsequent consensus building process to inform the development of quality metrics and tools to measure processes of care, outcomes and experience for patients experiencing persistent or chronic critical illness and their family members. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016052715.
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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 | 0.084 | 0.118 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.024 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".