Just as the consumerization of healthcare is upon us, perhaps the consumerization of predictive modeling could grow to meet the demands of the public for their own good.
Patient data certainly is the new currency of this healthcare marketplace, as Validic’s Ryan Beckland has noted. In fact, the use of business and clinical analytics may be the engine that drives all progress. The extraction and analysis of Big Data, as well the development and implementation of predictive models is no longer a luxury in healthcare. Increasingly, it’s the essential missing piece that providers will need to both optimize care and concomitantly decrease costs.
Thankfully, today’s healthcare world isn’t just focused on the needs of healthcare systems and providers—but increasingly on the consumerization of all things health. It’s why direct to consumer services continue to grow—since savvy consumers will continue to demand more, and semantic interoperability across vendors, systems, regions, countries and continents will most likely continue to lag the rest of technological progress.
These dynamics could mold a future in which consumers tap into the benefits of Big Data, and apply them to their own needs as well.
Drivers, Drivers, Everywhere
There are a number of drivers impacting the use of Big Data in healthcare, as reflected in a recent Research and Markets report, “Healthcare Analytics Global Market – Forecast to 2020.” The report predicts a global CAGR for healthcare analytics of more than 25% during this period—due to issues such as increasing health IT adoption, centralized healthcare mandates, the emergence of Big Data with predictive and prescriptive analytics, advanced technologies, digitization of world commerce, and venture capital investments. The source of such growth is expected to be within various market segments:
- Products: Descriptive, predictive and prescriptive analytics
- Components: Hardware, software and services
- Delivery Modes: On-promise, web-hosted and cloud-based models
- End-users: Healthcare and others
From Reflection to Prediction
Using the vast potential of Big Data, evidence-based care is moving from a retrospective research model to a prospective model geared toward prevention first with treatment as needed. It’s a significant shift in thinking, and is already being incentivized by financial concerns.
The ability to access a variety of data sources will be integral to the validity of care models. Predictive models built upon data solely from within a single EHR system will not be enough. It’s why the ability to aggregate and integrate data from outside sources will be essential as the focus on population health grows—which is significantly influenced by a variety of factors outside of the acute-care setting. It’s also why the persistent lag in semantic interoperability will continue to impact the quality and cost of care.
Big Data Competency among Providers
It’s clear that Big Data competency will most likely be the deciding factor in financial success for healthcare providers and systems. Those with the ability to optimize the use of predictive analytics will be able to see the risks before they happen, intervene appropriately, optimize patient outcomes and reduce costs.
Healthcare systems and ACOs which own both the greatest financial risk and most extensive resources to align with increasingly sophisticated tools for predictive analytics will lead the way.
They’ll be followed by individual providers who will be forced to accept the fact that Big Data use is essential for survival, as well as the requirement for membership in larger entities that can help them stay afloat.
Big Data Ownership among Consumers
Finally, consumers will accept Big Data’s value to both enhance their own care and reduce the cost of making it happen—since their financial stake in the matter continues to grow.
It may be several years down the road, but just as the consumerization of healthcare is upon us, so may the consumerization of predictive modeling grow to meet the demands of the public for their own good.
As predictive modeling and population health efforts merge, geo-socioeconomic factors will be increasingly integrated into clinical analytics algorithms. Where such factors are individualized through the integration of patient-centric data from peripherals such as mhealth applications and social media, accuracy will be enhanced.
However, the integration of localized factors—such as the location of the healthcare setting and/or location of primary residence—may skew instances of care provided in other geographical areas.
Dynamics such as these—including interoperability limitations—may mold a future in which we carry both our Personal Health Information (PHI), and our “Personal Predictive Models” (PPMs) as well.
As we can now pay to have our genomes assessed, what if we could also pay for our own individualized algorithms for care?
What if we could have direct-to-consumer subscription services for dynamic and personalized predictive care models based on real-time information with algorithm adjustments supported by machine learning?
What if we could access our integrated PPMs on-the-go and in-the-Cloud to shop the healthcare scene anywhere in the world to find out where we could plug in for the best and most cost-effective care?
As informed consumers continue to demand more, the advances in clinical analytics combined with the consumerization of healthcare may provide a predictive model of its very own—where savvy patients take charge of their own data and become savvy shoppers as well.
It may seem like a magical prospect now, but so were many of the realities on today’s digital health landscape. In such a scenario, future decisions about what treatments you receive may not be decided by the physician at your side—but your very own PPM in your very own Cloud.
The nuviun blog is intended to contribute to discussion and stimulate debate on important issues in global digital health. The views are solely those of the author. This article was first published October 16, 2014.