Embracing big data – the future of healthcare | Willem Herter & Wouter Kroese | TEDxSaxionUniversity

Big data is already changing the way business decisions are made — and it’s still early in the

game. However, because big data exceeds thecapacity and capabilities of conventional storage,

reporting and analytics systems, it demands new problem-solving approaches. With the convergence

of powerful computing, advanced database technologies, wireless data, mobility and social

networking, it is now possible to bring together and process big data in many profitable ways.

Big data solutions attempt to cost-effectively solve the challenges of large and fast-growing

data volumes and realize its potential analytical value. For instance, trend analytics allow you to

figure out what happened, while root cause and predictive analytics enable understanding of why

it happened and what is likely to happen in the future. Meanwhile, opportunity and innovative

analytics can be applied to identifying opportunities and improving the future.

All healthcare constituents — members, payers, providers, groups, researchers, governments,

etc. — will be impacted by big data, which can predict how these players are likely to behave,

encourage desirable behavior and minimize less desirable behavior. These applications of big data

can be tested, refined and optimized quickly and inexpensively and will radically change healthcare delivery and research. Leveraging big data will certainly be part of the solution to controlling spiraling healthcare costs. Simply by witnessing how big data has transformed consumer IT, it is clear that the promise of big data in healthcare is immense (think Google, Facebook and Apple’s Siri, which all rely on processing and transmitting massive amounts of data). While its potential in healthcare has not been fulfilled, the question is not if, but when. This white paper will define big data, explore the opportunities and challenges it poses for healthcare, and recommend solutions and technologies that will help the healthcare industry take full advantage of this burgeoning trend.

Bringing the Patient into the Loop

The healthcare model is undergoing an inversion.

In the old model, facilities and other providers were incented to keep patients in treatment —

that is, more inpatient days translated to more revenue. The trend with new models, including

accountable care organizations (ACO), is to incent and compensate providers to keep patients


At the same time, patients are increasingly demanding information about their healthcare

options so that they understand their choices and can participate in decisions about their care.

Patients are also an important element in keeping healthcare costs down and improving outcomes.

Providing patients with accurate and up-to-date information and guidance rather than just data

will help them make better decisions and better adhere to treatment programs.

In addition to data that is readily available, such as demographics and medical history,

another data source is information that patients divulge about themselves. When combined with outcomes, high-quality data provided by patients can become a valuable source of information for researchers and others looking to reduce costs, boost outcomes and improve treatment. Several challenges exist with self-reported data: • Accuracy: People tend to understate their weight and the degree to which they engage in negative behaviors such as smoking; meanwhile, they tend to overstate positive behaviors, such as exercise. These inaccuracies can be accounted for by adjusting these biases and — through big data processing — improve accuracy time. • Privacy concerns: People are generally reluctant to divulge information about themselves because of privacy and other concerns. Creative ways need to be found to encourage and incent them to do so without adversely impacting data quality. Effective mechanisms and assurances must be put into place to ensure the privacy of the data that patients submit, including de-identification prior to external access. • Consistency: Standards need to be defined and implemented to promote consistency in selfreported data across the healthcare system to eliminate local discrepancies and increase the usefulness of data. Usage guidelines follow standards. • Facility: Mechanisms based on e-health and m-health — such as mobility and social networking — need to be creatively employed to ease members’ ability to self-report. Providing access to some de-identified data can simultaneously improve levels of self-reporting as a community develops among members.

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