HealthCare

Health

Big Data has changed the way we manage, analyze and leverage data in any industry. One of the most promising areas where it can be applied to make a change is healthcare. Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Average human lifespan is increasing along world population, which poses new challenges to today's treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers. In this article, we would like to address the need of big data in healthcare: why and how can it help? What are the obstacles to its adoption? We will then provide you with 12 big data examples in healthcare that already exist and that we benefit from.

Sample Architecture

Analyzing Electronic Health Records (EHRs)

Doctors sharing EHRs can aggregate and analyze data for trends that can reduce healthcare costs. Sharing data between physicians and healthcare providers as they examine patients can reduce duplicate tests and improve patient care. Most EHR data is siloed, largely for security and regulatory compliance reasons, but finding a secure way to mine patient data can improve the quality of care while reducing costs.

Analyzing Hospital Networks

Consider the power of analyzing trends in hospital care. For example, centralizing analysis of medical instruments in a pediatric ward can isolate possible infant infection trends earlier. Or consider the case of one hospital that was able to reduce post-operative staph infection: by using big data, the administration was able to determine that one surgeon was prescribing post-operative antibiotics that were less effective against infection.

Control Data for Public Health Research

The medical profession is drowning in data. Medical offices and hospitals submit data about medical conditions and immunizations, but without big data, those data are meaningless. Using analytics normalizes raw patient data to fill gaps in public health records that can affect regulations as well as providing better care.

Evidence-Based Medicine

Most hospitals and emergency rooms practice "cookbook medicine," where a patient is admitted, and the physician uses the same battery of tests in order to identify the cause for symptoms. Using evidence-based medicine, the doctor can match symptoms to a larger patient database in order to come to an accurate diagnosis faster and more efficiently. Where big data plays a role is assimilating information from different sources and normalizing the data, so one record that describes "high blood pressure" maps to another that describes "elevated blood pressure."

Reducing Hospital Readmissions

Hospital costs are rising partially because of high readmission rates within 30 days of patient release. Using big data analytics in order to identify at-risk patients based on past history, chart information, and patient trends, hospitals can identify at-risk patients and provide the necessary care to reduce readmission rates.

Protecting Patient's Identity

Insurers like UnitedHealthcare are using big data analytics in order to detect medical fraud and identity theft. The company uses analytics on speech-to-text records from calls to the call center to identify potential fraudsters. The insurance company also uses big data in order to predict which types of treatment plans are more likely to succeed.