Why Not Give Big Data A Chance To Improve Treatment Adherence?

Today, it is difficult to deny the potential of new technologies in the field of health, although if reference is made to big data and adherence to treatment, it seems that this union is not yet fully realized.

Big Data And Health

The term big data refers to a large and complex set of data, as well as the specific treatment techniques of that large volume of data, according to the Institute of Knowledge Engineering (IIC). If big data were used specifically to improve the great problem posed by lack of adherence to treatments, it would not only be possible to know what is happening with the attitude of that patient, but predictive models could be developed to classify by profiles (age, sex, sociocultural environment, etc.), analyze behaviors and, based on all this, make the most appropriate decisions when, for example, prescribing a drug. But for now, it seems that all this is still impossible.

Big data and Artificial Intelligence (AI) in health is a support to medical practice. However, in the case of adherence to treatments and within the Health System, there are very few examples; it is an anecdotal reality. It is the apps that are taking charge of supporting the patient in these issues from different perspectives, considering adherence within their different functions.

Why It Hasn’t Happened Yet

For those in charge of delivering healthcare, this is so given the complexity that each autonomous community or state has its health system in addition to the fact that patient information (clinical history) is found in hospitals, and it is something that would have to be simplified to be able to undertake solutions of this type. Also, you have to have resolution and initiative to launch these innovative projects and achieve results that help with this type of problem.

Related:   7 Different Types of Proteins and Their Functions

If we extrapolate what is in the health and big data area, we can say that, as of today, the real deployment of this type of solution is scarce or almost nil, so the health sector is very far from the advances achieved in other areas, for example, the financial sector or that of large technology.

Why It Needs To Happen

However, the application of the big data paradigm to the health environment will suppose a magnitude improvement, not yet predictable, in the quality of patient care, as well as in the prevention, diagnosis, and treatment of diseases, together with a notable reduction in healthcare costs. To achieve these achievements, the integration of all the data from very different sources is essential, as well as the development of new technologies that allow the exploitation of said data.

Great Room For Improvement

The adherence to treatments is a complex area with numerous variables at stake, which in turn would allow a great margin of improvement if big data were used. Big data can offer solutions such as the development of algorithms that are capable of predicting which individuals will have poor adherence to treatment, as well as offering solutions to minimize non-adherence.

Specifically, the area of big data and AI helps to know what is happening, and for example, in combination with electronic devices or IoT Internet of Things, it could be identified if a patient is taking the medication and with what pattern of behavior. Another technique could be the application of language processing to build virtual assistants and that they establish an interaction with the patient, for example, to remind them of the medication. Also, through a conversation with questions, evidence of what the behavior is like could be offered and how the medication is being taken. These same assistants could interact with the caregivers and warn of anomalies in the patient’s routine.

Related:   How do CDNs work 

Boosting Confidence

All this helps to gain confidence in the treatment, which is one of the characteristics that most support adherence, because the more confidence, the more likely the prescribed treatment will be followed by the patient.

In this sense, experts have developed an MS Model, a personalized recommendation tool for treatment in multiple sclerosis (MS) based on Artificial Intelligence. To do this, variables that influence the evolution of MS were studied to indicate the best treatment based on the characteristics of each patient. In this way, the tool offers a personalized recommendation to the specialist to decide what to prescribe in each case.

Choosing the best treatment means stopping the development of any disease. However, there are cases, such as multiple sclerosis, in which patients do not react in the same way to the same treatment, but their clinical or demographic characteristics determine that response. Technology can be of help in this regard.

Complement, Not Substitute

However, Artificial Intelligence is a complement in decision-making, another tool to help professionals in their day-to-day life. This makes it necessary for there to be training among them to be able to work with it and with big data since in this way they will gain confidence and transmit it to the patients themselves so that they feel involved in that transformation.

It is necessary to approach these projects with seriousness, depth, and professionalism so that the results are as expected. Data and AI is a complex environment, like many other disciplines, and you have to be accompanied by professionals who have demonstrated their experience; It is not an environment for amateurs.

Related:   How to Improve the Condition of Your Skin This Summer

An Integrated Effort

Also, it requires the involvement not only by experts in this area but also by administrations, private stakeholders, locum tenens companies, hospitals, etc. The true value of big data in health will be achieved only if the different actors involved in the process commit themselves to this project jointly to take the health field into a new era.

This can only happen within the framework of a healthcare big data ecosystem that is integrated with technology, appropriate policies on privacy and confidentiality, infrastructures, and a culture of data sharing. All this entails a series of challenges that must be faced from different perspectives and degrees of depth.

Be the first to comment

Leave a Reply

Your email address will not be published.