Key environmental challenges from the qualitative data
| Specific environmental barrier | Key impact | Empirical evidence |
|---|---|---|
| Ongoing organizational changes are consuming resources | Prevents focus on development and innovation initiatives | “Wellbeing services counties are in such turmoil with the national reform that there’s hardly time for development” (R52) |
| Unclear guidance on regulation compliance | Creates risk-averse behavior and implementation delays | “there are problems in interpreting legislation and uncertainty in connecting new products” (R25) |
| Complex and ineffective data access system | Blocks research and algorithm development projects | “Findata currently does not serve at all what it was established for” (R29) |
| Insufficient public national investment in AI development | Limits organizational capacity for innovation | “Money is always the big problem” (R17) |
| Massive divide between healthcare and vendors | Results in unsuitable commercial solutions | “massive gap between public social and healthcare actors and private application vendors” (R64) |
| Limited vendor knowledge of public sector constraints | Products fail to address real organizational requirements | “System vendors don’t always have an understanding of public sector operations and needs” (R1) |
| Regulations preventing the use of patient data for AI training | Blocks the development of contextualized AI models | “Regulatory situation prevents AI training with real customer and patient data” (R61) |
| Price-focused and rigid acquisition procedures | Favors traditional vendors over innovative solutions | “procurement processes are so rigid and favor price competition and traditionally used operators” (R2) |
| Specific environmental barrier | Key impact | Empirical evidence |
|---|---|---|
| Ongoing organizational changes are consuming resources | Prevents focus on development and innovation initiatives | “Wellbeing services counties are in such turmoil with the national reform that there’s hardly time for development” (R52) |
| Unclear guidance on regulation compliance | Creates risk-averse behavior and implementation delays | “there are problems in interpreting legislation and uncertainty in connecting new products” (R25) |
| Complex and ineffective data access system | Blocks research and algorithm development projects | “Findata currently does not serve at all what it was established for” (R29) |
| Insufficient public national investment in AI development | Limits organizational capacity for innovation | “Money is always the big problem” (R17) |
| Massive divide between healthcare and vendors | Results in unsuitable commercial solutions | “massive gap between public social and healthcare actors and private application vendors” (R64) |
| Limited vendor knowledge of public sector constraints | Products fail to address real organizational requirements | “System vendors don’t always have an understanding of public sector operations and needs” (R1) |
| Regulations preventing the use of patient data for AI training | Blocks the development of contextualized AI models | “Regulatory situation prevents AI training with real customer and patient data” (R61) |
| Price-focused and rigid acquisition procedures | Favors traditional vendors over innovative solutions | “procurement processes are so rigid and favor price competition and traditionally used operators” (R2) |
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