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Role of Big Data in Healthcare

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Role of Big Data in Healthcare

In this article, see the role of big data in healthcare and look at the new healthcare dynamics.

· Big Data Zone ·
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Big Data is creating a revolution in healthcare, providing better outcomes while eliminating fraud and abuse, which contributes to a large percentage of healthcare costs.

 

Much like the ‘complexity’ involved in a surgery, the volatility involved in healthcare data is so uniquely complex, that a holistic approach is needed to handle the structured and unstructured data from disparate sources within an ever-changing regulatory environment.

With the future beckoning more sources of healthcare data from patient-generated tracking from electronic devices like monitors and sensors, healthcare delivery organizations (HDOs) are under tremendous pressure to reduce the ‘cost’ of operations and storage; ‘control’ escalating costs to improve revenue & profitability; improve governance & risk management through ‘compliance,’ resulting in improved ‘cash’ flows.

Given the plethora of healthcare systems, data aggregation is essential. Organizations have been analyzing patient cost and quality data for years. Still, as healthcare moves from volume-based to value-based payment, HDOs are on the threshold of exploiting the population health hype provided they can build critical data and analytic capabilities.

Organizations work with data aggregators to pull in data from internal systems and external partners (e.g., providers pulling insurance claim data) so they can have a complete view of a patient’s or population’s data on which they can risk, stratify, and analyze quantitative and qualitative data.

Payers and providers, with an increasing need to understand complex data sets, are rapidly installing data aggregators. The industry is moving from simply looking at structured data to incorporating unstructured data, which has driven organizations to evaluate current solutions and, in many cases, rip and replace current installations with new technologies. 

Organizations see value in data aggregation, but the amount of work it takes to move the data into actionable insights is substantial. The desire to infuse unstructured data and the growth of new technologies in big data in general, coupled with an industrywide need to better understand patient data, will drive significant growth. Health clouds provide a cost-effective and secure way for healthcare organizations to scale.

Information life cycle management (ILM) is finally starting to be put in practice within the HDO in targeted, practical ways. ILM used to be about controlling precipitous storage costs through better storage resource management — now it is more about recognizing that the value of information changes over time, and about how systems of record will enforce information life cycle policies.

Payers and providers rely on a variety of analytic solutions to help them understand business performance, patient populations, and provider performance. These tools allow for qualitative and quantitative analysis retrospectively, and increasingly, predictively. This area is seeing significant growth from both payers and providers, particularly as organizations seek to analyze unstructured data and do more with predictive modeling. When well implemented, analytic capabilities allow organizations to identify gaps in care and performance readily and to quantify dollars at risk.

Analytics, more than EHRs, will drive insights to action for payers and providers and help move the industry forward.

Patients or customers can get a 360-degree view of their healthcare data, thanks to digitalization. It is a win-win situation both for hospitals and the patients as they taste success using smartphones with healthcare apps and wearables. Devices that are not expensive are now part of patients’ day-to-day life. It is time HDOs expand their horizons and focus on integration and analytics rather than focusing on internal systems. 

Bid data will help HDOs create a plethora of opportunities to enhance customer value and revenue as they face the ‘customer explosion’ in healthcare. As the numbers rise, big data will be the key cost differentiator either in providing care, managing population, or detecting fraud. 

The New Healthcare Data Dynamics 

The industry is moving from merely looking at structured data to incorporating unstructured data, which has driven organizations to evaluate current solutions and, in many cases, rip and replace current installations with new technologies. Underlying technologies are being augmented or replaced by new systems like Hadoop, MapReduce, and HIVE. 

The desire to infuse unstructured data and the growth of new technologies in big data in general, coupled with an industrywide need to better understand patient data, will drive significant growth.

Telemedicine uses telecommunication technology to provide virtual care for patients in remote clinics or home settings. Telehealth can extend the reach of care delivery settings. Use cases include remote monitoring of hospital, skilled nursing facility (SNF), an inpatient rehabilitation facility (IRF) beds; home health monitoring of chronic comorbid patient populations; virtual visits for low-acuity issues, dermatology and other specialties; and second-opinion services for specialties, including oncology, stroke, and neurology.

Digital healthcare requires that medical professionals have immediate, direct, and natural-language access to analysis of all the data in its original formats. 

They need tools that can answer ad hoc questions and provide recommendations based on all the relevant data by incorporating the latest medical research, be at the bedside, or while the patient is sitting in the doctor’s office.

The new definition of data includes free-form text such as doctors’ notes, radiologists’ reports and medical journal articles, e-mails, still images such as CAT scans, video, recorded speech, patient historical data, genome files, biometric and other scientific data from clinical research and drug development. It also includes the Internet of Things (IoT) data from wearables, medical devices, respirators, blood pressure monitors, and other connected devices. Data from various social media channels such as Facebook and Twitter is adding to the increasing volumes. 

All this data, besides, data that resides in separate, stand-alone systems — EMR, PACS, RTHS, EMPI, LIS, and PMS, is also part of the new healthcare data.

Big data technology is required to gather and manage these large volumes of data involved and provide accurate answers that reflect the latest medical research, again from multiple credible sources. Big Data and advanced analytics promise answers to some of the significant problems the healthcare industry faces today.

Role of Big Data in Healthcare

Big data and advanced analytics can improve basic to critical decisions on patient care to the real-time health systems (RTHS) while dealing with digital medicine. The shift to value-based from evidence-based service to creating effective patient-centric care, improved clinical outcomes, fraud detection, and real-time continuous patient monitoring outside the clinical setting using personal and IoT sensors are all important trends in healthcare. And, can be achieved by real-time analysis of large data volumes.

Hadoop Data Lakes and advanced analytics can add significant value to organizations who are weak in data insights and who heavily depend on their EHRs and data warehouses.

Organizations need to start small and build competencies over time to take advantage of big data for enhanced insight and decision making.

Conclusion

Digital healthcare requires intelligent integration and consolidation of available patient information and machine data, structured, semi-structured, and unstructured, in their original formats. The good news for providers, payers, and patients is that big data is creating a revolution in healthcare, providing better outcomes while eliminating fraud and abuse, which contributes to a large percentage of the healthcare cost.

Topics:
big data, big data analtics, big data analysis, big data in healthcare

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