One of the esential requirements for a useful implementation of the Health 4.0 using or not IoT MD (medical devices) is the requirement to manage the HSD (health small data) analytics.
As clearly stated by most of the studies the main drawback not to use HSD analytics is the lack of knowledge.
Therefore it is necessary to find out less complex tools for data analysis that allow a DIY self analytics, considering the complexity of knowledge require in analytic tools and the absolutely lack of this knowledge among healthcare workers.
We have been supporting to change the requirement to train doctors and completely modify medical and health-related training carriers. We require to face XXI century knowledge: (1) on what everything is available on line even in 3D reconstructions and in where (2) diagnosis is based in the coincidence of symptoms and signs and analytics that can easily carried out by a computer and where (3) computers can build individual anatomic & physiologic models. Under those circumstance a carrier of medicine should contain a mix of self-surveillance tools guided by ML (machine learning), emergency and survival procedures (never tough in the carrier) and physio pathology and metabolic knowledge (superficially tough) together with a number of diagnostic diseases, syndromes and genetic conditions and its associated treatment.
The rest should be left for the computers to update agreement treatments, prognostic factors, available resources…
See also AI versus MD.