Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating Digital Health Capability at Scale Using the Digital Health Indicator
21
Zitationen
8
Autoren
2022
Jahr
Abstract
Abstract Background Health service providers must understand their digital health capability if they are to drive digital transformation in a strategic and informed manner. Little is known about the assessment and benchmarking of digital maturity or capability at scale across an entire jurisdiction. The public health care system across the state of Queensland, Australia has an ambitious 10-year digital transformation strategy. Objective The aim of this research was to evaluate the digital health capability in Queensland to inform digital health strategy and investment. Methods The Healthcare Information and Management Systems Society Digital Health Indicator (DHI) was used via a cross-sectional survey design to assess four core dimensions of digital health transformation: governance and workforce; interoperability; person-enabled health; and predictive analytics across an entire jurisdiction simultaneously. The DHI questionnaire was completed by each health care system (n = 16) within Queensland in February to July 2021. DHI is scored 0 to 400 and dimension score is 0 to 100. Results The results reveal a variation in DHI scores reflecting the diverse stages of health care digitization across the state. The average DHI score across sites was 143 (range 78–193; SD35.3) which is similar to other systems in the Oceania region and global public systems but below the global private average. Governance and workforce was on average the highest scoring dimension (x̅= 54), followed by interoperability (x̅ = 46), person-enabled health (x̅ = 36), and predictive analytics (x̅ = 30). Conclusion The findings were incorporated into the new digital health strategy for the jurisdiction. As one of the largest single simultaneous assessments of digital health capability globally, the findings and lessons learnt offer insights for policy makers and organizational managers.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.778 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.690 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.259 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.901 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.