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Big Data and its importance to revolutionize the tracking of patients with implantable medical devices

No organization plans and controls its production or labor capacity, currently without considering the technological elements available. Technology is increasingly present in the life of every individual, in production management and in company´s strategy.

The competitive environment often requires these organizations, in different sectors, to incorporate existing, or to develop new technologies. An organization focused on growth, survival of the business, knows that to be engagement and satisfaction of actors attached to it, is important that it has the capacity to perform their functions focused on continuous monitoring and reassessment of quality, speed in its response capacity during crisis management, reliability maintenance, adaptive capacity reflected in flexibility, and process analysis costs.

Long time ago, at least almost 4 and a half years, the perspective of a registration and real-time monitoring system of implantable electronic medical devices implants (MDI’s) is a recurrent topic in my reflexion work front to questions whose magnitude is beyond much knowledge of those actors involved in this scenario. The legitimacy of this concern is the result of my experience with the theme infoveillance and Infodemiology, in which I worked with in my post-doctoral residency. Access to these issues it just gives by my daily work with those who are most affected by the absence of this technology: the patients.

The need to make accessible to professionals an online registration system from paperless registration information of cardiac device implants, or other medical devices, from these patients, has always been important to make dynamic data and also to advance in applicable statistics in health surveillance, as in cases of devices alerts (recall), for instance.

Specific applications such as the identification of users in risk, and also the important need to turn data into Information, are paradigms that still have to advance so the statistics on artificial cardiac pacing can mature even further its descriptive level, and improve ability to better understand the correlation between variables to increase the reliability of results and consequently enhance its usefulness to support decision making.

In addition, traceability in real time, updated data and with artificial intelligence systems (AI) integrated, can democratize opportunities for professionals and patients, to establish efficient and effective behaviors to deal with certain contingencies. In AI we have the area of Machine Learning. This is an area of computer science that evolved from pattern recognition study and the theory of machine learning in artificial intelligence. Machine Learning is “intimately” connected with the big data as constitutes the basis from extremely important predictive analysis to make estimates in Health (predictive analytics or predictive modeling).

Apart from these issues, the operational cost of paper-based systems are high, inefficient and ineffective. The loss of data for the most part are prohibitive to be able to generate highly reliable information, since countless bias causes may relate unacceptably on the results found, and leave a researcher adrift on the path and future decisions to be taken. That is, large amount of data, however incomplete, run the risk of not enabling the generation of coherent and useful knowledge to support decisions for policymakers. Big Data on health is of interest to many stakeholders: governments, operators, health insurance, hospitals, pharmaceutical field, among others.

The difficulty of finding a user of a generator or leads in need of follow-up to ensure the safety of the patient’s health, by ANVISA (Anvisa in Brazil is like FDA in USA) notification for example, certainly becomes an arduous task and most of the times impossible, when the records arrive in paper, and are not measured at various levels of complexity of the impact of the gap, for example, between the date of implant data registration or generator type for example, and inserting these data into the system, if the data is ever filled in.

Big Data Systems in Health concern several “stakeholders”: governments, health insurance, hospitals, pharmaceutical, among others.

Although this is just a kick start to think and mature issues here with the actors in this scenario, an achievement of which Pacemaker Users  is proud, is to have thought and created a registration system model, with full paperless approach to medical device users patients in Brazil, and disseminated to members of medical societies and industry the importance of tools like this one. I’m happy because this initiative is an example to motivate the development of a registration tool for professionals.

As an e-patient and representant  of  patients who use implantable medical device, I’m  happy to contribute this perspective to think about the importance of the implementation of computerized registration systems for medical devices, and its real-time monitoring in Brazil, including, motivate for a critical reflection about the meaning of this in different clinical settings.

Dr. Luciana Alves PhD e-Patient Advisor | Founder and Blogger at PACEMAKERusers | President/CEO at Clube do Marcapasso (Nonprofit Organization – Brazil) | Member of  Society for Participatory Medicine

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