1// Thomas Hagemeijer : Thomas brings up a topic of discourse that usually fades from view.What about his works? I thoroughly enjoy. His visually captivating tale on a single slide makes it incredibly simple and bearable to read.

The question he's answering - Is a prevention model in healthcare really possible? He believes so and brings in the model of ChenMed . To read more, follow his blogpost shared in the link

2// Raj Grover : Raj is a prolific writer on deep technology and digital revolutions. Here he brings in a pertinent discussion around Data Science Maturity Models. Articulates his perspectives around a published report authored by Oracle 's Mark Hornick . To read more, follow the link

3// Andrew Rebhan : A digital health advocate and a researcher shares insights from a survey that was administered amongt 1,004 US adults by Stericycle Communication Solutions and Ipsos about respondents who received care from a "non-traditional" care setting.

4// Frank Kumli : For someone who is keen to comprehend the diabetes sector, Frank has written a tremendously in-depth and captivating thread of work that explores business models and identifies participants in the diabetic care niche.A necessary read for everyone interested in comprehending the preventive / non-communicable illness area and the operating models > player heat map

5// Elad Walach :One of the most respected CEOs in the deep tech health sector, Elad, recounts his expertise with radiology artificial intelligence.He emphasises the need of creating "value" that extends beyond a specific workstation and how it affects the larger health system.The "downstream impact," in essence.

6// Jane Sarasohn-Kahn : Jane is an economist and a prolific thinker who offers thoughts on the health system, influence, and scale. She calls our attention to a just-released list of the industries that consumers find most appealing. Surprisingly, consumers give healthcare the third-lowest rating. Follow the link for further information.

7// Dale Sanders : Dale shared his thoughts about data, maximizing performance against a metric vs the probability of creating a negating effect to the intent of the metric. To read more, follow the link