Digital Health - Executive DIALOG

The realities of emerging AI/Machine learning healthcare products

This article is divided into three parts: 1) Overview of terminology; 2) Successful healthcare AI examples; 3) Realities, Limitations, and Challenges.

Roadmap to benchmarked transformation: Digital maturity models

Measurement allows quantification and quantification raise awareness. The internationally recognized HIMSS maturity model provides prescriptive frameworks to healthcare organizations to measure progress as they build their digital health ecosystems.

Imagining a Health Benefits Network facilitated by Health Claims Exchanges (HCX)

A digital backbone for healthcare payments is coming!

AI: Interplay among humans, organisations & technology

AI in health care is improving diagnosis and clinical care, as they said in the report from W.H.O. At the same time, artificial intelligence may change the practice of medicine, increasing efficiency and accuracy of diagnosis, especially in specialities that rely on imaging such as radiology and pathology.

New digital health opportunities for personalized healthcare

In these 75 years, almost all major aspects of human society have been transformed, but healthcare remains the same as it used to be before the above definition – healthcare remains disease-care.

Digital transformation is predicated on several critical catalysts for these changes: enabling standards and regulatory environment, favorable funding models, availability of safe, trusted, and reliable technology, well designed end-to-end integration and a workforce with skills to embark on and sustain every aspect of the digital transformation journey.

Workforce and the digital healthcare enterprise

Improving medication safety in the modern era

Medication errors are common, but most of them have little or no potential for harm. A small proportion of the potential adverse drug events do have the potential to harm a patient, but most of those do not harm patients.

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