A National Assessment of Access to Technology Among Nursing Home Residents: A Secondary Analysis
Bibliographic record
Abstract
BACKGROUND: According to the National Center for Health Statistics, there are over 1.7 million nursing home residents in the United States. Nursing home residents and their family members have unique needs and stand to benefit from using technology empowering them to be more informed and engaged health care consumers. Although there is growing evidence for benefits of patient-facing technologies like electronic patient portals on patient engagement in acute and outpatient settings, little is known about use of this technology in nursing homes. OBJECTIVE: The purpose of this study was to report findings from a secondary analysis of data from a national nursing home study of information technology (IT) adoption, called IT sophistication. We describe the extent to which nursing homes (n=815) allow residents or their representatives to access technology including electronic health records, patient portals, and health information-exchange systems as well as the ability of the residents or representatives to self-report data directly into the electronic health record. METHODS: We used descriptive statistics and regression techniques to explore relationships between information technology adoption (IT sophistication) and residents' or their representatives' access to technology. Covariates of location, bed size, and ownership were added to the model to understand their potential influence on the relationship between IT sophistication and resident access to technology. RESULTS: Findings revealed that resident access to technology was a significant predictor of the nursing home IT sophistication (P<.001). The inclusion of covariates-nursing home location, bed size, and ownership-with their interactions produced a nonsignificant effect in the model. Residents' or their representatives' use of electronic health records and personal health records were both significant predictors of overall IT sophistication (P<.001). CONCLUSIONS: As nursing homes continue to progress in technological capabilities, it is important to understand how increasing IT sophistication can be leveraged to create opportunities to engage residents in their care. Understanding the impact of health information technology on outcomes and which technologies make a difference will help nursing home administrators make more informed decisions about adoption and implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".