The health model and the health sector in general, is one of the sectors where Big Data will have the greatest impact.
The health model and the health sector in general, is one of the sectors where big data will have the greatest impact and where its applications will grow dramatically, both for the medical area, as well as for data analysis areas (stories medical, clinical analysis ...), the management of health centers, hospital administration, scientific documentation (generation, storage and exploitation) ...
The potential of big data in medicine lies in the possibility of combining traditional data with other new forms of data at both the individual and population levels; that is, perform the integration of structured and unstructured data. Indeed, in the health sector an immense quantity and variety of structured, semi-structured and unstructured or unstructured data is generated.
Structured data is data that can be stored, consulted, analyzed and manipulated by machines, usually in data table mode. Unstructured or unstructured data is the opposite. Structured data is the classic patient data (name, age, sex ...) and unstructured data are paper prescriptions, medical records, handwritten notes from doctors and nurses, voice recordings, X-rays, scanners, MRIs, CT scans, and other medical images. To these data and belonging to both categories as well, the electronic files of accounting and administrative management, clinical data, etc. can be considered.
In the Health sector there are numerous heterogeneous data sources that provide a large amount of information related to patients, diseases and health centers.
YRISH Technology will use Big data in healthcare to predict, prevent and personalize diseases and thus affected patients. The fields will be practically almost all the health sectors, but in particular we can already cite some in which the greatest challenges are found:
· Genomic research
· Genome sequencing
· Clinical Operation.
· Self-help and citizen collaboration.
· Improved patient care
· Remote patient monitoring
· Personalized medicine for everyone
· Virtual autopsies
· Follow-up of chronic patients
· Improvements in medical processes
The segmentation of the population into groups with similar health characteristics and risk levels will make it possible to assign the most appropriate services and resources based on the real needs of patients.
The application of Big Data allows us to infer an intelligence layer, in which the application of predictive models that help anticipate health needs and offer more effective medical care is of special relevance.
Health data is obtained from electronic medical records, telemedicine devices, clinical tests, and even wearables. Also from epidemiological, nutritional and genomic data, which is known as Real World Data (RWD).
A proactive control in primary care allows the stratification of the population based on risk levels and early detection of those patients who would not need to be referred to specialized care services.
Social Health Optimization
The social health optimization of tele-assistance services entails segmenting patients or users in a situation of dependency, predicting their possible evolution, anticipating their needs, better covering them and improving the quality of the service at a lower cost.
Predictive models can predict behavior patterns based on common characteristics and the reasons that lead an individual to need health care in any emergency, permanent care or personalized follow-up.