Information management flow for tele-homecare for the elderly; An emerging need for continuity of care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Tele-homecare methods can be used to provide home care for the elderly, if information management is provided. The aim of this study was to compare the places and methods of the data collection and media that use Tele-homecare for the elderly in selected countries in 2015. METHODS: A comparative-applied library study was conducted in 2015. The study population were five countries, including Canada, Australia, England, Denmark, and Taiwan. The data collection tool was a checklist based on the objectives of study. Persian and English papers from 1998 to 2014, related to the Electronic Health Record, home care and the elderly were extracted from authentic journals and reference books as well as academic and research websites. Data were collected by reviewing the papers. After collecting data, comparative tables were prepared and the weak and strong points of each case were investigated and analyzed in selected countries. RESULTS: Clinical, laboratory, imaging and pharmaceutical data were obtained from hospitals, physicians' offices, clinics, pharmacies and long-term healthcare centers. Mobile and tablet-based technologies and personal digital assistants were used to collect data. Data were published via Internet, online and offline databanks, data exchange and dissemination via registries and national databases. Managed care methods were telehealth management systems and point of service. CONCLUSION: For continuity of care, it is necessary to consider managed care and equipment with regard to obtaining data in various forms from various sources, sharing data with registries and national databanks as well as the Electronic Health Record. With regard to the emergence of wearable technology and its use in home care, it is suggested to study the integration of its data with Electronic Health Records.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".