A Web-Based, Population-Based Cirrhosis Identification and Management System for Improving Cirrhosis Care: Qualitative Formative Evaluation
Bibliographic record
Abstract
BACKGROUND: Cirrhosis, or scarring of the liver, is a debilitating condition that affects millions of US adults. Early identification, linkage to care, and retention of care are critical for preventing severe complications and death from cirrhosis. OBJECTIVE: The purpose of this study is to conduct a preimplementation formative evaluation to identify factors that could impact implementation of the Population-Based Cirrhosis Identification and Management System (P-CIMS) in clinics serving patients with cirrhosis. P-CIMS is a web-based informatics tool designed to facilitate patient outreach and cirrhosis care management. METHODS: Semistructured interviews were conducted between January and May 2016 with frontline providers in liver disease and primary care clinics at 3 Veterans Health Administration medical centers. A total of 10 providers were interviewed, including 8 physicians and midlevel providers from liver-related specialty clinics and 2 primary care providers who managed patients with cirrhosis. The Consolidated Framework for Implementation Research guided the development of the interview guides. Inductive consensus coding and content analysis were used to analyze transcribed interviews and abstracted coded passages, elucidated themes, and insights. RESULTS: The following themes and subthemes emerged from the analyses: outer setting: needs and resources for patients with cirrhosis; inner setting: readiness for implementation (subthemes: lack of resources, lack of leadership support), and implementation climate (subtheme: competing priorities); characteristics of individuals: role within clinic; knowledge and beliefs about P-CIMS (subtheme: perceived and realized benefits; useful features; suggestions for improvement); and perceptions of current practices in managing cirrhosis cases (subthemes: preimplementation process for identifying and linking patients to cirrhosis care; structural and social barriers to follow-up). Overall, P-CIMS was viewed as a powerful tool for improving linkage and retention, but its integration in the clinical workflow required leadership support, time, and staffing. Providers also cited the need for more intuitive interface elements to enhance usability. CONCLUSIONS: P-CIMS shows promise as a powerful tool for identifying, linking, and retaining care in patients living with cirrhosis. The current evaluation identified several improvements and advantages of P-CIMS over current care processes and provides lessons for others implementing similar population-based identification and management tools in populations with chronic disease.
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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 | 0.136 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".