Big data means a lot of things to a lot of different people, but what is becoming increasingly clear is that big data is about real-time analysis and data driven decision-making. Now Big Data is playing key role in Healthcare and several health institutes are seeking a solution that could bring the very latest drug interaction data right to patients’ bedsides.

Now the Big data analytics in healthcare is playing vital role in analyzing massive data volumes and conduct multiple drug studies simultaneously—allowing researchers to design, test and apply brand-new algorithms to quickly identify drug risk warning signals.

Today in huge amounts of patient data is generating every day that healthcare organizations possess requires extracting it from legacy systems, normalizing it and then building applications that can make sense of it. The increasing digitization of healthcare data means that organizations often add terabytes’ worth of patient records to data centers annually. Large volumes of unstructured data have to be analyzed to offer better treatment for patients.  However, as speakers at the inaugural Medical Informatics World conference suggest, a little bit of data analytics know-how can go a long way.

Big Data is playing key role in Health & Life Sciences and is very useful healthcare organizations in  Health Insurance fraud detection, Campaign and sales program optimization, Brand management, Patient care quality and program analysis, Medical Device and Pharma Supply-chain management,  Drug discovery and development analysis and etc.

Here are some examples big data analytics use cases in healthcare.

 

1. Analyzing Electronic Health Records (EHR).
The use case is aimed at aggregating and analyzing all of the patient Electronic Health Records (EHR) from hospitals and other healthcare providers and make them available online to doctors as they are examining the patients.  This aims to bring down the cost of providing healthcare by sharing patient information between providers to reduce ordering duplicate tests and reduce the time taken to provide patient care.  Current EPIC solution does not allow having more than a few months of historical patient information available online.

 

2. Big Data in Hospital Network.
Instead of taking readings every few hours, a hospital continuously recorded data from all the medical instruments in a pediatrics ward.  By capturing data and analyzing it and looking at it from maybe five or six different points of view, the analytics team was able to help the physicians spot an infection trends 12 to 24 hours earlier than they may have spotted it.  That allowed doctors start a course of treatment that let them save the lives or shorten stays.

3. Control Data for Better Public Health Reporting, Research.
Stage 2 of meaningful use requires organizations to submit syndromic surveillance data, immunization registries and other information to public health agencies. This offers a great opportunity to “normalize” raw patient data by mapping it to LOINC and SNOMED CT, as well as by performing real-time natural language processing and using tools such as the Notifiable Condition Detector to determine which conditions are worth reporting.
Healthcare organizations no longer need to hunt for and gather data; now, the challenge is to domesticate and tame the data for an informaticist’s provision and control.

4. Telemedicine Analytics.
Telemedicine platforms can go to the patient when it is difficult for the patient to come to the hospital. A telemedicine platform can capture various vitals of the patient like temperature, Heart rate and Blood Pressure which can streamed to a central repository in real time via satellite. Once collated a series of triggers can be placed on the data to sense and respond to real world health conditions

 5. Use Free Public Health Data For Informed Strategic Planning.
Strategic plans for healthcare organizations often resort to reactive responses to the competitive market and a “built it and they will come” mentality, says Les Jebson, director of the Diabetes Center of Excellence within the University of Florida Academic Health System. Taking a more proactive approach requires little more than a some programming know-how.

Using Google Maps and free public health data, the University of Florida created heat maps for municipalities based on numerous factors, from population growth to chronic disease rates, and compared those factors to the availability of medical services in those areas. When merged with internal data, strategic planning becomes both visually compelling (critical for C-level executives) and objective (critical for population health management), Jebson says. With this mapping, for example, the university found three Florida counties that were underserved for breast cancer screening and thus redirected its mobile care units accordingly.

6. Move to Evidence-Based Medicine
Cookbook medicine refers to the practice of applying the same battery of tests to all patients who come into the emergency department with similar symptoms. This is efficient, but it’s rarely effective. As Dr. Leana Wan, an ED physician and co-author of When Doctors Don’t Listen, puts it, “Having our patient be ‘ruled out’ for a heart attack while he has gallstone pain doesn’t help anyone.”

Dr. John Halamka, CIO at Boston’s Beth Israel Deaconess Medical Center, says access to patient data—even from competing institutions—helps caregivers take an evidence-based approach to medicine. To that end, Beth Israel is rolling out a smartphone app that uses aWeb-based- drag-and-drop UI to give caregivers self-service access to 200 million data points about 2 million patients.  Even when data’s in hand, analytics can be complicated; what one electronic health record (EHR) system calls “high blood pressure” a second may call “elevated blood pressure” and a third “hypertension.”

7. Location aware application analytics for enhancing customer experience and optimizing nurse/doctor deployment.
A range of new solutions within hospitals have  RFID chips which are embedded to patient’s card or Doctors card or nurse which can relay the location information of the patient/doctor in real time. This location data is a real new data pool with huge implications for effectively managing patients experience and optimizing resource within a hospital. For example we can create a simple vectors like nurse/patient ratio, nurse mobility index etc. We can also create models to see the strength of the relationships between patient satisfaction index and nurse/patient ratio. We can then define optimal nurse/patient ratios for different sections of the hospital – OPD/cardiology/pediatric wards for example may need higher nurse/patient ratios than say for example dental department. Once set anytime this goes crosses a threshold an alert can be send to the Head nurse to alleviate the risk of a under serviced patient. We can also use the nurse mobility index to decide how the various departments must be co-located within the hospital to improve patient outcomes and optimize use of expensive health care equipment.