An Aging Population: Challenges to the Electronic Health Record Development and Health Informatics Community
Bibliographic record
Abstract
Baycrest Centre is one of the largest Academic Health Centres in Canada serving the aging population. As such, it has very complex information management (IM) requirements. Recently, a research project was carried out to determine the extent to which electronic health record (EHR) technologies are available and implemented within long-term care (LTC) organizations of comparable dimensions. Data collection included Internet searches and telephone interviews with targeted technology vendors and facilities. Results showed that although there are many superficial similarities between LTC and acute care, care delivery models and processes are so different, and the IM and EHR needs so unique, as to require different technology solutions and information management approaches. However, progress in development of relevant LTC solutions has been slow – 70% of vendors have chosen not to participate in LTC applications development. LTC facilities also expressed frustration with the fact that implementing an EHR is an extensive and expensive process, and yet there is minimal evidence to lobby for its implementation. Research to date has shown that benefits cannot be measured on a return-on-investment basis. Empirical data remain limited, and most benefits have historically been of a qualitative nature. Given the lack of evidence and a viable technical solution, it is not surprising that most LTC facilities have struggled to advance in the implementation of EHRs. This article presents a number of challenges to both the vendor and health informatics communities. Without appropriately addressing these challenges, relevant solutions for IM in LTC will fail to meet the well-established and much-discussed demographic of an aging population that is growing exponentially.
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 | 0.053 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".