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Decentralized applications- Dapps & Healthcare.

Decentralized applications- Dapps & Healthcare.

A decentralized app, or Dapp, is a web-based application that employs a decentralized, public and cryptographically empowered database system to facilitate the exchange of information among its users.

Dapp data isn’t owned or managed by any central body. All participants maintain an identical copy of all the application’s data, and any change to the data is made through a consensus. When new information is added, it is permanently stored on all computers, regardless of how many they are in the network.

dApps offer peer-to-peer data exchange, based on cryptographically enforced consensus.

Today, decentralized applications have gone from storing and managing financial transactions to virtually all forms of information, including personal identity, intellectual property, smart contracts, and of course, healthcare data.

  1. Decentralized apps promise to link hospital data systems across a shared network, so that the exchange of such information can happen in real-time, from one end to the next.
  2. In a decentralized world, all information is distributed and shared across many points on the network, which means there’s no single point of failure. Hacker attacks would therefore become a lot more difficult.
  3. Dapps could also facilitate the sharing of research, clinical trials, advanced directives and safety analyses, and therefore enhance collaboration,
  4. Patients could use personal health devices ( IoT devices) to record data and share it with medical practitioners in real time. Alternatively, instead of storing patient data, Dapps can store access controls — such as who a patient authorizes to see their health data — even when the data itself is stored in an EHR.

We are creating the foundations for a true Health 4.0 with personal small health data (SHD) feeding to a real preventive health care.

Origin information: Here’s How Dapps Can Revolutionize the Healthcare Industry

AI make better doctors

Heart arrythmias can be detected more accuratly with MACHINE LEARNING techniques

“Deep learning involves feeding large quantities of data into a big simulated neural network, and fine-tuning its parameters until it accurately recognized problematic ECG signals”.

And the case is that AI is better and detect irregularities before a doctor can detect it. Furthermore the machine is always with the patient and therefore is not left just to the moment where a doctor visit is programmed.

From Indrustry 4.0 to Health 4.0

iot internet of things public domain

The fourth industrial revolution is linked to digitalization production processes by means of TICs and IoTs as well as new materials. Now the cyberphisical systems are the main issues being possible personalized mass production.

Industry 4.0 is a fusion between virtual and real world. Machines become indepent and autonomous and can be reprogramm themselves to more fast and easy reach market demands. Furthermore all this information is fully evailable on real time in all production areas.

Products are software tested and arrive to the market improving 50% speed with similar quality control. Double digital product simulation allow to test several designs in the production chain.

This increase by Exabytes de amount of information expecting to reach  up to 15.000 exabytes by 2020. For this we require extremly powerful systems capable of processing such amount of data to be able to obtain relevant information from it.

An quick and intelligent data analysis of sensors of actuators are required

Factories are getting fused and automatized allowing machines to rearange by themselves in an intelligent manner. On this environment man intelligence is over it, Ideas and functions come from humans.

This is well understood in the Industry 4.0 but it is totally disregarded in Health 4.0.

The amount of standardized processed and data and patients are imposible to be absorved by a healthcare system based on people personal work. No one is capable to face all medical innovation capable to influence peoples’s health, no one is capable to reduce waiting list, no one is capable to make decisions based on prior Artificial Intelligence analysis of all possible health data. No one want to take care of the data coming from IoT of users for health purposes, no ones is capable to fuse for the benefit of patients data coming from genetics, microbiomics, pharmacocinetics, epigenomics, image analysis data etc…

Medicine is stack with the massive new coming relevant data that specialialization requires super and extrasuper specialized people.

Machine Learning and deep learning techniques are now democratized, open source, easy to incoorporate to regular computer but the healthcare worker not only do not follow AI innovations but they fear it, blocking the possibility to speed up hospital management, follow up patients 24 hours a day, preventing complications, indicating treatment, finding solutions and relevant coincidences that could improve health, life and patients treatment in an efficient manner.

Anonimized hospital data is not shared. And personal data that belong to users for its own benefit is not used at all.

Huge changes have to be introduced in Medical training and techniques of what to do with data and how to take benefit of it is an essential one.

Virtual medicine and ubiquitous health support is the base of Health 4.0.

Why no-one is changing the way to provide health? 



2.- Health 4.0.

3.- MedTech

4.- Predictive analytics


DIY self analytics

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 or the ACDS (Advanced Clinical Decision Support).

If you do not beleave it, just check the situation in NHS, but always take into consideration that the average concept of training have to be updated.

Edge computing vs Fog computing

Diferences between Mobile Edge Computing (MEC) and OpenFog is based mainly in

  • Fog covers mobile but also wireline
  • Fog covers edge but also access and wearable/things and intermediate layers between edge and cloud
  • Fog addresses verticals beyond the mobile/service provider
  • MEC standards are largely compute oriented, where the OpenFog Consortium’s reference architecture also embraces storage and deep packet networking
  • MEC focuses on single layer of nodes in the RAN (Radio Access Network)- Open RAN MEC– or base transceiver station (BTS), whereas fog computing can have a deeper hierarchy
  • Fog computing has a stronger emphasis on security/privacy
  • Finally, fog is inclusive of cloud, while edge excludes the cloud.

One key element of 5G is likely to be Mobile Edge Computing (MEC), an emerging standard that extends virtualized infrastructure into the radio access network (RAN). ETSI has created a separate working group for it — the ETSI MEC ISG — with about 80 companies involved.  MEC uses a lot of NFV infrastructure to create a small cloud at the edge,”

The 8 Technical pilars on which the Architecture of the OpenFog is based are summarized on the image above taken from the  OpenFog_Reference_Architecture_2_09_17-FINAL .

See also The 10 myths of Fog computing.

QR codes. Care for elders.

This is the solution found in Iruma-Japan for an elderly population  that in some places overpass young people.

Welcome Fog computing, bye bye cloud computing

This is the tesis of Peter Levine that we agree with:

Most of power computation will be carried out by IoTs in the Edge computing, particularly in Health. Changes of management and architecture have to drastically change in healthcare in order to allow personalized help and coaching in the HFOG with the use of a real intelligence with the Deep Learning working in the cloud.

This is the approach of the Humanization of Healthcare (H2O) using Personal Health Assistants (PHAs) as essential in H2H interation.




Cognitoys. More important for doctors then for children.

Artificial Intelligence Will Redesign Healthcare. Yes, but several goals should be achieved first. We mention here what we consider the most important:

  1. creation of ethical standards which are applicable to and obligatory for the whole healthcare sector
  2. The acquirement of basic knowledge about how AI works in a medical setting  and both patients and doctots should getting accustomed to artificial intelligence and discovering its benefits. With or without the aids of Cognitoys, to change medical training is extraordinary urgent.
  3. Not in vane this will be the next industrial revolution and will be specially sensitive for healthcare based upon Small data (not big data).

Ciber-physical systems

Innovacion en Health 4.0

La Curva “S” nos permite reflexionar sobre los comportamientos frente a las discontinuidades tecnológicas (ver Juan Carrion).


Las etapas de la Curva “S” son:

Etapa 1: Aparece una nueva tecnología que se encuentra todavía en fases de desarrollo y mejora, por lo que no ofrece competencia directa a la anterior. En este punto parece que su aplicación será muy limitada en el futuro y su éxito es incierto. Esta fase se suele prolongar un largo periodo de tiempo, durante el cual la nueva tecnología es perfeccionada.

Etapa 2: Finalmente el uso de la nueva tecnología se desarrolla a gran velocidad, ofreciendo grandes ventajas y superando a la anterior en varias, sino en todas, las dimensiones de rendimiento. Las empresas que utilizan la antigua tecnología reaccionan y se defienden intentando mejorar su producto / proceso, por lo que pueden aparecer ciertas innovaciones incrementales, que no permiten superar a la larga a la nueva tecnología.

En Health 4.0 y telemedicina, no sabemos donde estamos ya que en la cultura innovadora de Gary Hamel los dones que las potencian no se comparten y la empresa sanitaria tan legislada-controlada posee gran dificultad en introducir innovaciones duraderas en el tiempo ya que solo pueden ir de la mano de grandes empresas comerciales capaces de sortear y dominar la totalidad de los controles de calidad con el tiempo.


Quiza la solucion para acelerar el cambio esté en la filosofia open-source, la del software libre que permita que todos llevemos a cabo una mejoría sin los limites y cortapisas de patentes e intereses comerciales y que cultivarian los 3 dones innovadores de Iniciativa+Creatividad+Pasion.