Healthcare

With the help of artificial intelligences and data analytics, data can be analyzed to achieve a significant awareness that are essential for efficient healthcare services.

 

Data science in the healthcare sector can help improve diagnostics and treatment, as well as the management of physical and human resources.

The digital transformation that a large number of organizations from all productive sectors go through and the enormous amount of data (structured, unstructured, video and images) generated by it in multiple channels (physical, online, mobile and social) results in what we know as Big Data, which in turn has triggered the popularity of data analyics around the world. 

The applications of Big Data in medicine and in the health sector in general, have a place in practically all areas: genomics, epidemiology, clinical trials, clinical operations, citizen collaboration, telecare, and administrative management. In all these areas, the future of the health and health systems and models involves collecting, storing, screening, analyzing and consulting the avalanche of data that technological advances are generating in a structured, secure and duly anonymized way. The data in the health sector on which to apply Big Data analytical techniques are very varied (personal, clinical, administrative data, data from social media). The information extracted from all this data has the potential to achieve a more effective medicine: a personalized, participatory, preventive, predictive and population-based medicine.

 

Being able to identify and anticipate the needs of patients, health centers or clinical laboratories is the result of applying Big Data technology in healthcare. Analyzing the impact of the social and the study of historical data allows defining specific preventive policies. 

The rise of the internet and the possibilities it offers patients, doctors, family members and other stakeholders to create and participate in eHealth online communities is associated with the concepts of e-patient, online influence and the ability to jointly develop proposals based on Big Data techniques of free text analysis, sentiment analysis and automatic normalization and coding of medical data to achieve: 

• Test with better clinical results. 

• Design of new treatments. 

• Reduction of medical errors. 

• More effective prevention measures. 

• Personalized healthcare. 

 

Participatory medicine makes citizens more responsible for their own health. With the information generated by the data analysis, the healthy individual decides about their health and the patient decides about their disease. With this, a greater involvement of all those affected is achieved, which facilitates, among other things, a better diagnosis and greater adherence to treatments.

• Obtain a centralized and structured file with all the data collected.

• Segment the population into groups according to similar social health characteristics and risk ranges.

• Analyze the efficacy or adverse effects of a drug or treatment.

• Produce statistics on patients to choose those who are most suitable for clinical studies.

• Previously detect the needs of patients to receive specialized care.

• Achieve behavior patterns in patients and health professionals.

• Environmental and occupational health campaigns.

• Public policies for the development of health education.

• Studies of probability of appearance or continuity of a disease.

• Surveillance of epidemic outbreaks.

• Development of research programs in public health.

• Evaluate the quality of medical services.

• Test with better clinical results.

• Design of new treatments.

• Reduction of medical errors.

• Much more effective prevention measures.

• A personalized treatment for each patient.

• Analytics allows access to comprehensive information about the current business environment and its wider perspective.

• Optimizing performance management incorporates the full spectrum of people, processes, technology, and governance.

• Timely and accurate information is available to move from reactive to proactive decisions.

• Low-risk processes can be automated in order to concentrate more time on more complex risk scenarios and decisions with high relevance.

• The company becomes more flexible so that it can react more quickly to changing regulatory market and economic conditions.