10 Big Data Analytics Use Cases for Healthcare IT
Big data means a lot of things to a lot of different people, but what is becoming increasingly clear as the largest market players strategies start to unfold, 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 along with big data consulting services 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.
It isn’t easy, namely because the demand for healthcare IT skills far outpaces the supply of workers able to fill job openings, but a better grasp of that data means knowing more about individual patients as well as large groups of them and knowing how to use that information to provide better, more efficient and less expensive care.
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. Also, the current solution takes several minutes to search historical EHR records. CMS will investigate the hospitals and systems, especially if they suspect, or have a report, that they are not compliant. CMS audit hospital to see if those facilities are in compliance with the price transparency policy.
A data warehouse is great, says John D’Amore, founder of clinical analytics software vendor Clinfometrics, but it’s the healthcare equivalent of a battleship that’s big and powerful but comes with a hefty price tag and isn’t suitable for many types of battles. It’s better to use lightweight drones—in this case, applications—which are easy to build in order to accomplish a specific task.
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, says Brian Dixon, assistant professor of health informatics at Indiana University and research scientist with the Regenstrief Institute, 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. The benefits of this process—in addition to meeting regulatory requirements—include research that takes into account demographic information as well as corollary tests related to specific treatments. This eliminates gaps in records that public health agencies often must fill with phone calls to already burdened healthcare organizations
4. Make Healthcare IT Vendors Articulate SOA Strategy
Dr. Mark Dente, managing director and chief medical officer for MBS Services, recommends that healthcare organizations “aggregate clinical data at whatever level you can afford to do it,” then normalize that data. This capability to normalize data sets in part explains the growth and success of providers such as Kaiser Permanente and Intermountain Healthcare, he says.
Service oriented architecture is the answer, Dente says, because it can be built to host today’s data sets—as well as tomorrow’s, from sources that organizations don’t even know they need yet. (This could range from personal medical devices to a patient’s grocery store rewards card.) Challenge vendors on their SOA strategy, Dente says, and be wary of those who don’t have one.
5. 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, Blood Pressure and ECG 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
If growth in concentration of BP with statistical significance found for male in the age group 30-45 in a specific zip code say 08837 from chi square test then hold road shows to sensitize the inhabitants in the zip code on healthy eating habits. If the number of patient segment migrations> 10 % based on actual diagnosis events moves from cluster-2 to cluster-5 then proactively import preventive medicine in bulk to cater to growing needs
6. 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.
7. Apriori sequence analysis to define new clinical pathways
Apriori algorithms can be used to unearth interesting sequences in data occurring close to each other before a clinical outcome. These could be time ordered sequence of events. This would help us create episode rules like “If ‘restlessness’ & ‘insomnia’ occurs in the transcripts there is a 60 % chance that a coronary episode is imminent”. These can trigger proactive interventions which can help reduce the chances of an adverse event or a hospital admission event.
8. 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 a Web-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.” To combat this, Beth Israel is encoding physician notes using the SNOMED CT standard. In addition to the benefit of standardization, using SNOMED CT makes data more searchable, which aids the research query process.
9. Give Everyone a Chance to Participate
The practice of medicine cannot succeed without research, but the research process itself is flawed, says Leonard D’Avolio, associate center director of biomedical informatics for MAVERIC within the U.S. Department of Veterans Affairs. Randomized controlled trials can last many years and cost millions of dollars, he says, while observational studies can suffer from inherent bias.
The VA’s remedy has been the Million Veteran Program, a voluntary research program that’s using blood samples and other health information from U.S. military veterans to study how genes affect one’s health. So far, more than 150,000 veterans have enrolled, D’Avolio says. All data is available to the VA’s 3,300 researchers and its hospital academic affiliates. The idea, he says, is to embed the clinical trial within VistA, the VA EHR system, with the data then used to augment clinical decision support.
10. 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.