I am trying to introduce healthcare informtics (such as machine learning)to help hospitals or clinics improve health care quality in innovative ways. Before that, I would like to learn what issues concern you (or the institute you work for) most and what issues you consider are currently critical and potential to the healthcare management.
Any comment is more than welcomed.
Hi,
A number of important benefits, including: Increased quality of care and enhanced health outcomes. Avoidance of errors and adverse events. Improved efficiency, cost-benefit, and provider and patient satisfaction.
for more info ..kindly refer below link:
Article Ten Key Considerations for the Successful Implementation and...
If you are interested in introducing analytics ML or AI, then a key issue to negotiate is the trustworthiness of the system and analytic products. The analytical algorithms and output are often based on the commercial or market relevant outcomes and measures. These are often not relevant to health or defined differently to well established epidemiological and biostatisticsl terms used in medicine. This may cause problems for interpretation by the clinical team who are trained in medical stats. Also non-medical analytic processes do not understand or take into account the wide range of biases present in clinical data or understand biological mechanisms and criteria for causation. These are important issues that need to be addressed.
Dear Allen Tsai ,
There is plenty of literature which address different solutions for improving the quality of healthcare systems. Everyone looks at this from their own point of view, depending on their interests and field of work. One way to improve the healthcare quality is combining knowledge-based practices (such as knowledge management and organizational learning) with the healthcare policies (patient safety culture and clinical governance). More information can be found here:
Article The effect of combining knowledge-based practices and health...
Thanks for your reminder, knowledge-based management / knowledge-based clinical decision support system are always in need of further development.
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