Article
Predictive analytics in clinical practice
Predictive analytics im klinischen Alltag
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Published: | May 25, 2022 |
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Outline
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Objective: Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient-outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practice remains challenging today.
Methods: We performed a topic review to highlight several advantages and disadvantages of the application of predictive analytics in clinical practice.
Results: To foster the progress of predictive analytics into the clinical workflow of the neurosurgeon, 1) the used data sets should be more refined to the clinical scenario studied, 2) predictive analytics should ideally be used to study patients in equipoise regarding optimal management, not to study the available data, and 3) neurosurgeons should have knowledge on effective implementation of the designed predictive tools for the right patients.
Conclusion: To flourish and reach its potential, predictive analytics need data that is of adequate quantity and quality, ideally tailored to clinical scenarios in equipoise regarding optimal management. Adequate reporting of predictive analytic tools is incumbent for uptake into clinical workflows. At least for now, the neurosurgeons' knowledge, experience and vigilance remain imperative for applying predictive analytics in clinical practice.
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Notes: The paper related to this abstract has been published recently [1].
References
- 1.
- Mijderwijk HJ, Steiger HJ. Predictive Analytics in Clinical Practice: Advantages and Disadvantages. In: Staartjes VE, Regli L, Serra C, editors. Machine Learning in Clinical Neuroscience. (Acta Neurochirurgica Supplement; 134). Cham: Springer. pp. 263-268. DOI: 10.1007/978-3-030-85292-4_30