Big Data: Chancen und Risiken aus Sicht der Bürger

Knorre, Susanne; Müller-Peters, Horst; Wagner, Fred

doi:10.1007/978-3-658-27258-6_3

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Other challenges could be solved in principle, but solutions are not yet well established. Firstly, acceptance of the collection and processing of personal data by state institutions is low, and health-related data are particularly worthy of protection [ 28 ]. If they become freely available, assumptions about future restrictions could have significant negative consequences for the individuals in question and, for instance, reduce promotion opportunities or the likelihood that temporary jobs become permanent jobs.…”

Section: Discussionmentioning

confidence: 99%

“…If they become freely available, assumptions about future restrictions could have significant negative consequences for the individuals in question and, for instance, reduce promotion opportunities or the likelihood that temporary jobs become permanent jobs. However, resistance to institutional data collection and data analysis is significantly lower when people expect a health benefit from it [ 28 ]. This benefit must be communicated in a comprehensible way.…”

Section: Discussionmentioning

confidence: 99%

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Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study

2020

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Purpose Unmet rehabilitation needs are common. We therefore developed a risk score using administrative data to assess the risk of permanent work disability. Such a score may support the identification of individuals with a high likelihood of receiving a disability pension. Methods Our sample was a random and stratified 1% sample of individuals aged 18–65 years paying pension contributions. From administrative records, we extracted sociodemographic data and data about employment and welfare benefits covering 2010–2012. Our outcome was a pension due to work disability that was requested between January 2013 and December 2017. We developed a comprehensive logistic regression model and used the model estimates to determine the risk score. Results We included 352,140 individuals and counted 6,360 (1.8%) disability pensions during the 5-year follow-up. The area under the receiver operating curve was 0.839 (95% CI 0.834 to 0.844) for the continuous risk score. Using a threshold of ≥ 50 points (20.2% of all individuals), we correctly classified 80.6% of all individuals (sensitivity: 71.5%; specificity: 80.8%). Using ≥ 60 points (9.9% of all individuals), we correctly classified 90.3% (sensitivity: 54.9%; specificity: 91.0%). Individuals with 50 to < 60 points had a five times higher risk of a disability pension compared to individuals with low scores, individuals with ≥ 60 points a 17 times higher risk. Conclusions The risk score offers an opportunity to screen for people with a high risk of permanent work disability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study

2020

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“…They can free up or create the necessary treatment capacities and financial resources—and withdraw them where they do not need them. Thus, the medical use of AI can mitigate prevalent cost pressures and sustain healthcare systems (Knorre et al, 2020 ). In all these sectors, AI can and will increasingly take over functions ranging from pure data analysis and decision support to the complete assumption of decision-making.…”

Section: How Does the Use Of Medical Ai Produce New Medical Certainties?mentioning

confidence: 99%

Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them

Ulmenstein¹,

Tretter²,

Ehrlich³

et al. 2022

Front. Artif. Intell.

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Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.

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Big Data: Chancen und Risiken aus Sicht der Bürger

Cited by 2 publications

References 5 publications

Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study

Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study

Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them

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