The Need for Electronic Health Records in Long-Term Care
Bibliographic record
Abstract
Long-term care (LTC) settings serve an important proportion of seniors and vulnerable populations that require 24-hour nursing care. Deployment of interoperable electronic health records (iEHRs) to these settings lag. There is little evidence on the availability of patient information from across the continuum of care. To fill this knowledge gap this study examines the prevalence and nature of information gaps experienced in LTC during patient encounters (n=1050). Overall, more than one-third (34%) of all LTC patient encounters were missing at least one item of information that was needed for the encounter. Approximately 59% of missing information during patient encounters was documented or ordered by a clinician external to the LTC facility; 41% were within the LTC facility itself. These information gaps have an adverse consequence for nearly 3 out of every 10 (31%) patient encounters in LTC. Extending iEHRs to LTC has the potential to support timely, appropriate, and better quality of patient care and improve provider experience.
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 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 | 0.043 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".