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Healthcare 4.0 digital applications: An empirical study on measures, bundles and patient-centered performance

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  • Tortorella, Guilherme Luz
  • Fogliatto, Flavio S.
  • Kurnia, Sherah
  • Thürer, Matthias
  • Capurro, Daniel

Abstract

This paper aims to identify bundles of Healthcare 4.0 (H4.0) digital applications and measure the impacts of their adoption on patient-centered performance. We conducted an empirical-exploratory study based on a survey with 119 middle/senior managers from different hospitals. Multivariate data techniques were applied to identify the H4.0 bundles, create combined adoption measures and analyze associations between H4.0 adoption and performance. Configuration theory provided the theoretical underpinnings to explain the relationships among the investigated elements. Four bundles of H4.0 digital applications were proposed: Smart Healthcare Supply Chain, Smart Patient Diagnosis, Smart Patient Treatment, and Smart Patient Follow-up. We found that high H4.0 adoption scores in hospitals positively impact patient-centered performance, measured through five indicators (patient readmission rate, mortality rate, length-of-stay, satisfaction, and safety). As the utilized measures for H4.0 digital applications were initially raised from academic works and validated based on practitioners' perceptions, the establishment of bundles brings a more practical oriented framework with a strong theoretical background, which is still scarce in the literature. Instead of addressing a wide range of concerns related to H4.0, the proposed bundles focus on the most popular and relevant aspects in a hospital with a significant impact on patients' interests.

Suggested Citation

  • Tortorella, Guilherme Luz & Fogliatto, Flavio S. & Kurnia, Sherah & Thürer, Matthias & Capurro, Daniel, 2022. "Healthcare 4.0 digital applications: An empirical study on measures, bundles and patient-centered performance," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:tefoso:v:181:y:2022:i:c:s0040162522003043
    DOI: 10.1016/j.techfore.2022.121780
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