Implementering av IT-losningar i aldreomsorgen : Hur nystartade e-halsoforetag kan skapa en lonsam position med innovativa IT-losningar
Bibliographic record
Abstract
The elderly care in Sweden needs to change because predictions states that proportion of elderly is expected to rise with 30 % between 2010 and 2050. This means that a quarter of the entire population in Sweden will be at the age of 65 years or older by 2050. With this background it is clear that the efficiency in the elderly care is an important issue. The Swedish government has therefore presented a report which states the goal of Sweden being the number one in e- health by the year of 2025. This will be achieved by letting entrepreneurs create tools to make the healthcare more efficient. Because of that, this project will investigate how a start- up company can create a lucrative position with e- health products for the elderly care market. This was done by conducting a qualitative research study based on 31 interviews and three focus groups with stakeholders in the elderly care. The stakeholders were elderly people, staff and head of divisions at retirement homes and Uppsala city officials. The study was conducted at the e-health start-up company Cenvigo which is located in the city of Uppsala. From the result it is shown that it exist a difference between how different healthcare providers implement IT. As of today there exist a lot of different IT- systems in the elderly care which are difficult to work with because they are poorly build and not compliant with other systems. The effect is that the systems are difficult to work with and that the staff needs to document the same data twice. Even though the reality looks like that our findings show that people working in the elderly care has a positive attitude towards IT solutions. But still, as an e-health company, it will be an good idea to make the product easy to use and compliant with other systems because it affects how it can perform on the market. The products also need to add value to the elderly care by for example make the working process more efficient. The findings show that staff put a lot of time in surveillance which probably can be digitalized. In order to gain a lucrative position a company also needs to identify customer groups. Potential customers are the elderly, the retirement homes or the government. The elderly has shown no interest in paying for e-health solutions themselves and they are also skeptical to use the product though they do not object to have them in the organizations. The government purchase routines are highly regulated by laws in contrast with privately driven retirement homes which do not have as strict routines. An e-health company therefore needs to be able to adapt to the demands of the market.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".