Putting Patient Experience Data into Action! A Workshop to Explore the Collection and Use of Patient Experience Data in Practice
Bibliographic record
Abstract
Health systems globally are in pursuit of the quintuple aim – better population health, improved patient and caregiver experiences, improved clinician experiences, sustainable per capita costs1 and greater health equity. Patients and their care partners (family and friends who provide care) have unique insights on the quality of healthcare delivery, yet, few organizations are poised to meaningfully integrate these voices into care delivery transformation efforts. Patient experience (PE) data comes in many forms including (and not limited to) structured and unstructured survey data, narratives of patient and family complaints or accolades, adverse event reporting, or qualitative interviews and focus groups. These data are often temporally lagging the patient’s interaction with the health system, rarely includes the voices of equity deserving groups (i.e, individuals who have been historically marginalized and underrepresented in research) and are seldom used for improving care. To meet the expectations of patients and care partners, we require a comprehensive, systematic, and sustainable approach to integrating patient and care partner voices into how we reform, design and deliver care. Through a scoping review and stakeholder engagements we have highlighted that methods for the collection and implementation of PE data should be multidimensional and have identified four components of a PE Tool-kit (PET) to move from knowledge to action: 1) engage the community meaningfully; 2) identify and curate multiple forms of PE data; 3) aggregate and synthesize PE data; and 4) integrate PE data into quality improvement efforts. We are conducting this workshop to further explore domains within our proposed Patient Experience Tool-Kit that resonates with an international audience and identify gaps and implementation considerations. The workshop will be grounded by a brief theory burst about the challenges the PET and its components aim to address followed by interactive sessions in small groups to address the following questions: 1)How will the proposed components support the link between the collection and use of PE data into health system improvement efforts? What tool-kit components are missing? 2)What are the barriers and facilitators to implementing the PE Tool-Kit? Note: we will direct participants to use a sociotechnical systems approach when identifying barriers and facilitators which consists of: people, environment (macro/micro), tools (i.e., technology), and tasks (i.e., process). 3)What are the considerations/adaptations to consider that will support the Tool-Kit’s adoption across different sectors of the integrated care system? Introduction to Workshop/ Overview of Activities – 5 mins Presentation of Patient Experience Tool-Kit Components- 10 mins Small group discussions to address the three questions (each table to start with a different question)- 30 mins Large group activity to report back and synthesis small group discussion, including the l use Mentimeter to capture core insights). The facilitators will summarize key insights and highlight core themes captures in the discussion at the end of the activity – 20 minutes Wrap up to assess interest in developing a community of practice for continued learning and sharing- 5 mins
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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 | 0.218 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".