Using Health Information Technology to Reach Patients in Underserved Communities: A Pilot Study to Help Close the Gap With Health Disparities
Bibliographic record
Abstract
INTRODUCTION: In the current era of medical education and curriculum reform, medical schools across the United States are launching innovative approaches to teaching students in order to improve patient outcomes and increase patient safety. One such innovation is the use information technology (IT) that can be used to disseminate health information, especially for patients with limited access to care. Strategies for using health IT to enhance communication between providers and patients in low-income communities can be incorporated into undergraduate medical education (UME) curriculum. METHODS: A pilot study was conducted to determine if IT could serve as an effective means of communication with patients at a free clinic where 100% of the patients are uninsured; the clinic is located in an urban setting and primarily serves Latinos, the working poor, and the homeless. An anonymous survey was administered to patients to assess rates of IT ownership, general IT use, and IT use for health and medical information. RESULTS: The majority of study participants owned a cell phone (92%); one-third used their cell phone to access health or medical information (38%). Most study participants reported using the Internet (72%), and two-thirds had used the Internet to obtain health and medical information (64%). CONCLUSION: Given the difficulties faced by low income and medically underserved communities in accessing healthcare services, the use of IT tools may improve their' access to health information in ways that could enhance patient knowledge and self-management, and perhaps positively impact health outcomes. Therefore, it is essential to incorporate use of IT tools in training for medical students and residents to enhance communication with patients in underserved communities.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".