Exploiting Big Data

Over recent years the collection and storage of vast quantities of data, in combination with advances in data science, has opened up a new era of big data.
Big data refers to database whose size, complexity and dynamic nature are beyond the scope of traditional data collection and analysis methods. Transforming such unstructured contents into a structured format for later analysis is a major challenge. Creating a scalable process to find, harvest, and curate the unstructured universe of data, to predict outcomes, is exceptionally challenging.
Healthcare systems produce an enormous amount of data which can be effectively used to improve quality and supporting prevention. Clinical data analysis is fundamental to understand trends in diseases on a personal and company scale. Clinical data analysis supports real time identification of best practice treatments to patients on the basis of specific pattern analysis strategies able to enhance pre-diagnosis and early disease detection. An outcome-based analysis allows the definition of the optimal treatment for specific patients by analyzing comprehensive patient and outcome data to compare the effectiveness of various procedures.
The outcomes of Big data exploiting produce predictions with the aim of improve the efficiency and efficacy of personal care. The potential benefits to critical care are significant, with faster progress in improving health and better value for money. Although not replacing clinical trials, big data can improve their design and advance the field of precision medicine.

ELECTRONIC HEALTH RECORDS

It is the most widespread application of Big Data in healthcare. Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results etc. Records are shared via secure information systems and are available for healthcare providers from both public and private sector. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication. Electronic Health Records (EHRs) can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders.

PREDICTIVE ANALYSIS IN HEALTH CARE

Predictive analytics is already recognized as the biggest business intelligence trend for 2016 but the potential applications reach far beyond business and much further in the future. Patient information can be collected to create databases for predictive analytics tools that will improve the delivery of care. The goal is to help doctors make Big Data-informed decisions within seconds and improve patients’ treatment. This is particularly useful in case of patients with complex medical histories, suffering from multiple conditions. New tools would also be able to predict, for example who is at risk of diabetes and who is advised to make use of additional screenings or weight management.

PATIENT STRATIFICATION

In the clinical trials workflow, one of the most important logistical and statistical challenges is ensuring that your data accurately reflects the population you are studying. Not enough resources are available to test a drug on the entire human race, but we can test it on a group that reflects the drug’s potential patients in order to have confidence in the results.
Of course a clinical trial of a drug for use on the elderly only needs results that reflect its effects on the elderly, and the same for any treatment with a specific patient pool. But whatever the group of patients you are aiming for, you want to ensure that the results accurately predict what will happen once the drug goes into use. This is where patient stratification comes in.
Stratification of clinical trials is the partitioning of subjects and results by a factor other than the treatment given. Stratification can be used to ensure equal allocation of subgroups of participants to each experimental condition. This may be done by gender, age, or other demographic factors. Stratification can be used to control for confounding variables (variables other than those the researcher is studying), thereby making it easier for the research to detect and interpret relationships between variables. For example, if doing a study of fitness where age or gender was expected to influence the outcomes, participants could be stratified into groups by the confounding variable.

WHAT DO WE OFFER?

Examples of Big Data in healthcare prove that the development of medical applications of data should be the apple in the eye of data science, as they have the potential to safe people’s lives. Already today Big Data allows for early identification of illnesses of individual patients and socioeconomic groups and taking preventive actions because, as we all know, prevention is better than cure.

The analysis Big data requires specific non-conventional methods in order to predict outcomes and to extract a trend from an unstructured mass of data. Prometheus Metabolomics LTD has developed algorithms (e.g., KODAMA, www.kodama-project.com) to deal specifically with business problems: Key solutions for your needs.

Combine disparate data from administrative, clinical, pharmaceutical, and survey-based sources to measure outcomes, quality of life, cost of care, and adherence to care pathways at the individual level
Measure variance of care by patient demographics, site of cancer, provider, and geographic area
Determine opportunities to reduce costs without adversely affecting patient outcomes
Match patients to appropriate pathways of care and track patients as their cancer evolves
Characterize care gaps and potential overtreatment to help determine care priorities
Provide transparency in the cost of care and use cost as a part of decision making
Provide patient education and engagement to enable shared decision making, particularly regarding the potential transition to palliative care.

Our services are focused on the critical issues driving clinical research organization performance, including quality of care, member acquisition and retention, operational efficiency, and financial performance.
Through our patented machine learning and advanced analytics approach, we reveal the uncaptured risk within your population and use advanced analytics to determine which programs will be best received by the consumer.