Evaluating Patient Experience With Genomic Medicine: A Content Analysis of National Cancer Institute–Designated Cancer Centers’ Websites
Bibliographic record
Abstract
Background: National Cancer Institute-designated cancer centers (NCI-CCs) throughout the United States are mandated to translate state-of-the-art cancer research to communities and enhance clinical care for patients within their catchment areas. NCI-CCs play a vital role in national cancer initiatives focused on optimizing cancer care via personalized medicine in which improved risk assessment, screening, and genetic testing are foundational. In this era of targeted personalized care, although genetics has been incorporated into cancer centers, it is unknown how these innovations are being communicated to the public and communities served on cancer center websites. There is particularly limited knowledge surrounding how NCI-CCs publicly communicate their efforts to integrate patient-reported experiences with genomics to fulfill their overall mission and reduce the cancer burden in their catchment areas. Objective: The objective of this study was to evaluate how NCI-CCs publicly share information on their websites related to cancer center programming and activities to measure and incorporate patients' experiences with the use of genetics to guide cancer care. Methods: For all NCI-CCs providing clinical care (N=65), we conducted a review of publicly available and published information and assessed five domains relevant to patients' experiences with genomic medicine: whether NCI-CCs (1) provided genetic testing, (2) directly expressed a goal of delivering personalized care, (3) provided pharmacogenomic testing, (4) assessed patient-reported experience measures with genomic medicine (including patient-reported outcomes [PROs] and other patient experience measures [OPEMs]), and (5) indicated an established infrastructure or set of resources to evaluate patient experience. We conducted a content analysis of the publicly available websites of NCI-CCs using the validated directed approach to content analysis. We quantified the results of our content analysis using count measures based on a binary (yes or no) coding scheme. Results: While almost all the NCI-CCs (64/65, 98%) discussed providing personalized care and performing genetic testing on their websites, we found that 58% (38/65) indicated online that they assessed PROs or other patient experience measures with genomic medicine. Fewer centers (25/65, 38%) discussed on their websites having a mechanism for evaluating patients' experiences with genomic medicine that captured broader types of information beyond PROs, such as measures of patient education or care team communication. Finally, approximately 1 in 3 NCI-CCs (23/65, 35%) indicated having an established infrastructure with departmental resources dedicated to monitoring patients' experiences. These centers reflecting a built-in infrastructure were 8% to 12% more likely to publicly communicate targeted activities to assess patients' experiences with genomic medicine. Conclusions: With the burgeoning use of genomics in research and clinical care, comprehensive evaluation and incorporation of measures of patients' experiences with genomic medicine present a key opportunity to enhance cancer care at NCI-CCs.
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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 | 0.015 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".