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Big data and fast data analytics have already proved their value to healthcare providers and patients.  The early benefits included improving hospital profits and cutting down on wasted overhead. Now, advanced analytics help providers predict disease and epidemics, cure disease and avoid preventable deaths.

Here are some use cases, which show important advances in the healthcare sector:

  • Preventing disease:

An ever-growing stash of public health data enables healthcare providers to spot diseases before they occur. For example, the Pittsburgh Health Data Alliance collects data from medical and insurance records, wearable sensors and genetic data.

The data is compared and analyzed against information from thousands of other patients. The ability to centralize and analyze formerly isolated data enables sophisticated predictive modelling.

  • Providing proactive care:

Healthcare professionals want to provide more proactive care by constantly monitoring, collecting and analyzing patients’ vital signs. For example, the MapR Converged Data Platform streams this data in real time and sends actionable alerts that support proactive care. Data scientists constantly improve algorithms, which indicate when patients might have an emergency.

  • Providing sophisticated remote care

Telemedicine doesn’t grab the headlines but, increasingly, it is taking its place as a standard item in the healthcare services menu.

When patients cannot come to a hospital (or go to a regional hospital with more sophisticated services), telemedicine platforms can go to the patient. These platforms provide a remote consultation office via telecommunications and collect and analyze vital signs such as heart rate, blood pressure and ECG. Local doctors or those regional hospitals can analyze the data and consult with patients.

  • Improving in-inpatient safety

Healthcare organizations are adding bedside medical device data to sensitive algorithms, which detect and interpret out-of-range vital signs hours before humans notice a problem.

UC Davis researchers use this approach to detect sepsis. This difficult-to-detect condition has a 40 percent mortality rate and is difficult to notice until it’s too late. The procedure provides a quick, accurate way to determine which patients require aggressive treatment.

  • Managing public health and risk

Predictive analytics give healthcare providers tools they need to anticipate patients’ disease and other dangerous conditions. For example, to curb the rate of veteran suicides, the U.S. Army uses predictive analytics and a risk model that identifies patients, who might harm themselves. Program managers estimate that by coordinating data-driven care and following up after a hospital stay for a psychiatric episode, four lives of every hundred veterans treated can be saved.

  • Preventing hospital readmissions.

Data scientists can use hospitalization histories and predictive analytics to flag patients, who are likely to return to inpatient care within 30 days. Real-time EHR data analytics helped a Texas hospital cut readmissions by five percent by drawing on nearly 30 data elements included in each patient’s chart.

  • Personalizing Medical Treatment

Personalized treatment planning is a customized method, in which patients continuously monitor the effects of their medication. High-speed analytics platforms provide real-time access to summary and detailed patient data, so treatment decisions can changed quickly when needed.

  • Detecting fraud

As sophisticated healthcare services grow, so do fraudulent claims. Healthcare organizations can now detect fraud based on analysis of out-of-range billing data, procedural benchmark data or patient information.

For example, administrators can analyze irregularities in patient records to detect a hospital’s overuse of services in short time periods or identical prescriptions for the same patient filled in several locations. Modern analytics platforms detect irregular data in real time and alert providers to investigate unusual events before payment is made.


Interested to learn more about our expertise in helping healthcare companies with Big Data analytics? Contact us today!

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Shikha Kashyap
Chief Technology Officer
About Shikha: Shikha is a tech leader with deep expertise in emerging technologies such as Big Data analytics using MapR, Hortonworks, Tableau, and Spotfire. Her experience includes working with Fortune 500 companies, implementing solution design, architecting, and project managing. Shikha leads Technology for Syntelli and is passionate about non-profit causes and giving back to the community.

Connect with Shikha on LinkedIn: [social_list linkedin_url=”https://www.linkedin.com/in/shikhabkashyap”]


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