Analysis of Internet Usage Among Cancer Patients in a County Hospital Setting: A Quality Improvement Initiative
Bibliographic record
Abstract
BACKGROUND: Cancer is one of the most common diseases that patients research on the Internet. The Commission on Cancer (CoC) recommended that Parkland Memorial Hospital (PMH) improve the oncology services website. PMH is Dallas County's public health care facility, serving a largely uninsured, minority population. Most research regarding patient Internet use has been conducted in insured, Caucasian populations, raising concerns that the needs of PMH patients may not be extrapolated from available data. The PMH Cancer Committee, therefore, adopted a quality improvement initiative to understand patients' Internet usage. OBJECTIVE: The objective of the study was to obtain and analyze data regarding patients' Internet usage in order to make targeted improvements to the oncology services section of the institutional website. METHODS: A task force developed an 11-question survey to ascertain what proportion of our patients have Internet access and use the Internet to obtain medical information as well as determine the specific information sought. Between April 2011 and August 2011, 300 surveys were administered to newly diagnosed cancer patients. Multivariate analyses were performed. RESULTS: Of 300 surveys, 291 were included. Minorities, primarily African-American and Hispanic, represented 78.0% (227/291) of patients. Only 37.1% (108/291) of patients had Internet access, most (256/291, 87.9%) having access at home. Younger patients more commonly had Internet access, with a mean age of 47 versus 58 years for those without (P<.001). Education beyond high school was associated with Internet access (P<.001). The most common reason for Internet research was to develop questions for discussion with one's physician. Patients most frequently sought information regarding cancer treatment options, outcomes, and side effects. CONCLUSIONS: Less than one-half of PMH oncology patients have Internet access. This is influenced by age, educational level, and ethnicity. Those with access use it to obtain information related to their cancer diagnosis. The most effective way of addressing our patients' needs using the institutional website is to provide links to reputable disease-specific sites.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".