Engaging Patients and Caregivers Managing Rare Diseases to Improve the Methods of Clinical Guideline Development: A Research Protocol
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines provide systematically developed recommendations for deciding on appropriate health care options for specific conditions and clinical circumstances. Up until recently, patients and caregivers have rarely been included in the process of developing care guidelines. OBJECTIVE: This project will develop and test a new online method for including patients and their caregivers in this process using Duchenne muscular dystrophy (DMD) care guidelines as an example. The new method will mirror and complement the RAND/UCLA Appropriateness Method (RAM)-the gold standard approach for conducting clinical expert panels that uses a modified Delphi format. RAM is often used in clinical guideline development to determine care appropriateness and necessity in situations where existing clinical evidence is uncertain, weak, or unavailable. METHODS: To develop the new method for engaging patients and their caregivers in guideline development, we will first conduct interviews with experts on RAM, guideline development, patient engagement, and patient-centeredness and engage with Duchenne patients and caregivers to identify how RAM should be modified for the purposes of patient engagement and what rating criteria should patients and caregivers use to provide their input during the process of guideline development. Once the new method is piloted, we will test it by conducting two concurrently run patient/caregiver panels that will rate patient-centeredness of a subset of DMD care management recommendations already deemed clinically appropriate and necessary. The ExpertLens™ system-a previously evaluated online modified Delphi system that combines two rounds of rating with a round of feedback and moderated online discussions-will be used to conduct these panels. In addition to developing and testing the new engagement method, we will work with the members of our project's Advisory Board to generate a list of best practices for enhancing the level of patient and caregiver involvement in the guideline development process. We will solicit input on these best practice from Duchenne patients, caregivers, and clinicians by conducting a series of round-table discussions and making a presentation at an annual conference on Duchenne. RESULTS: The study protocol was reviewed by RAND's Human Subjects Protection Committee, which determined it to be exempt from review. Interviews with RAM experts have been completed. The projected study completion date is May 2020. CONCLUSIONS: We expect that the new method will make it easier to engage large numbers of patients and caregivers in the process of guideline development in a rigorous and culturally appropriate manner that is consistent with the way clinicians participate in guideline development. Moreover, this project will develop best practices that could help involve patients and caregivers in the clinical guideline development process in other clinical areas, thereby facilitating the work of guideline developers.
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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 | 0.174 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".