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Towards better healthcare: What could and should be automated?

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  • Fruehwirt, Wolfgang
  • Duckworth, Paul

Abstract

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesirable consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated using current technology, but also what should be automated. We investigate these research questions by utilizing probabilistic machine learning models trained on thousands of expert ratings, provided by both healthcare practitioners and automation experts. To the best of our knowledge, the present study is the first to analyze the desirability of automating healthcare work activities (human workforce preferences) in combination with current technological capabilities. We present a succinct four quadrant model, the Automatability-Desirability Matrix, based on our findings. It can be used to support policymakers and organizational leaders in developing practical strategies on how to harness the positive power of AI, while accompanying change and empowering stakeholders in a participatory fashion.

Suggested Citation

  • Fruehwirt, Wolfgang & Duckworth, Paul, 2021. "Towards better healthcare: What could and should be automated?," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521003991
    DOI: 10.1016/j.techfore.2021.120967
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    2. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.

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