Healthcare Analytics, Population Health Management, Healthcare Big Data

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Why Healthcare Big Data Analytics Needs the Internet of Things

By Jennifer Bresnick

- There are many ways to define the concept of healthcare big data analytics.  At its core, “big data analytics” is simply the joining of two or more previously disparate sources of information, structured in such a way that insights can be drawn from the comparison or examination of the new, expanded data set. 

Healthcare big data analytics and the Internet of Things

However, the term has moved far beyond its dictionary parameters in the healthcare industry.  For pioneering providers taking their first steps into leveraging information as a clinical and business asset, healthcare big data analytics is the marriage of EHR and HIE data, public health resources, patient engagement metrics, clinical outcomes, revenue cycle statistics, and patient-generated data produced by continuously monitoring medical devices, wearables, smartphones, and bedside monitors.

Bringing together this big data has not been an easy task so far, and despite its critical importance to the strategic vision of healthcare organizations, relatively few are currently able to achieve their objectives.  Success with healthcare big data analytics relies on competent vendors, a highly skilled team of experts, and an understanding of the population health management goals an organization hopes to conquer.

But there’s one more piece to the puzzle that providers must understand: the importance of the ever-expanding Internet of Things, that nebulous network of data collection nodes that governs so much more of a patient’s daily life than one might think.

What is the Internet of Things, anyway?

READ MORE: IBM Watson Expands Role in Imaging Analytics, Population Health

Six years ago, the number of internet-connected electronic devices surpassed the number of people who own them, the FTC said earlier this year.  From smartphones to cameras, Bluetooth scales to insulin pumps, almost everything with batteries or a wall charger is connected to something larger than itself. 

These devices and their unique internal identifiers, taken together with the cloud storage banks that support them and the apps that allow consumers to interface with them, are known as the Internet of Things (IoT).  By exchanging meaningful data across this vast network of devices, developers and consumers alike achieve greater value from their objects through personalization of services automated and governed by data analytics systems instead of humans.

While some may view the IoT as the perfect set-up for a post-apocalyptic novel, it has real power for healthcare.  Analytics systems that integrate medical devices like imaging machines and beside monitors can reduce unnecessary spending, improve diagnostic accuracy, and slash repeated tests.  Monitoring hand hygiene through internet-connected sanitizer stations can cut infection rates and save lives.  Increasing patient engagement through smartphones and patient-generated health data doesn’t just improve satisfaction and overall health, but it also helps providers get paid.

The IoT is already here, the FTC has declared, and it’s up to healthcare to make the best possible use of it.

Why does the IoT matter to healthcare big data analytics?

READ MORE: Healthcare Orgs Struggle to Meet EHR, Big Data Workforce Needs

It’s not exactly a secret that healthcare suffers from a lack of seamless, large-scale health data interoperability.  With the ONC frowning on vendors that actively block health information exchange, and a concerted industry effort to make data a more accessible asset for a larger number of healthcare organizations, the spirit of interoperability will open up new opportunities for healthcare big data analytics.

The problem is that EHRs contain a relatively limited proportion of the data that could be useful for generating actionable insights.  Allergy lists, vital signs, and demographics are all very well, but patients are demanding more nimble reactions and deeper relationships from their providers.  Clinical data is where the most successful hospitals start their analytics problems, but to deliver a higher level of consumer satisfaction, providers must know a lot more about their patients and their needs than the EHR can tell.

The IoT can provide that data, if healthcare organizations build the infrastructure to accept it.  Many providers have bee able to integrate financial and utilization data to create a portrait of organizational operations, but these sources do not give a clear idea of what patients do on their own time.

The answer is in their devices.  Few patients are ever more than arm’s length away from their smartphones.  They will wear their FitBits or new Apple Watches everywhere; they may have daily interaction with smart pill bottles, food intake apps, sleep monitors, and blood pressure cuffs.  These ubiquitous tools are becoming popular with patients who expect simplicity and smarts from their daily interactions with electronic tools, and they are important for tracking how patients behave while healthcare providers aren’t looking.  Harnessing this data can give savvy organizations an edge when it comes to population health management. 

Automated patient-generated data collection systems will stream data directly into the provider’s repository of choice.  Once architected, they require little to no action on the part of the patient or the clinician, but simply produce reports that can be analyzed or tracked when necessary.  While developing this type of automation can be a difficult task that requires some careful negotiation around physician workflows, researchers and leading organizations have seen success from mining social media data and wearable devices through algorithms that predict behaviors and allow providers a leg up on allocating resources or preempting poor patient choices.

READ MORE: IBM Watson Gets SNOMED CT Infusion to Enhance Data Analytics

As population health management takes on a greater financial significance and patient-centered care is rewarded with bonuses and incentives, healthcare big data analytics can’t afford to ignore these devices as a rich source of pertinent information.

How can healthcare organizations embrace the IoT perspective?

Many providers have had a rough time accepting that the EHR is a critical, central tool for collecting and viewing patient data, but healthcare big data analytics requires organizations to take one step beyond thinking the EHR is important for patient care.  In the Internet of Things, the EHR is just one small piece of a broader vision of data as a strategic asset: just one point of attachment in a spider’s web of information.

Embracing the centrality of the Internet of Things means relinquishing the idea that the provider is the only pillar around which healthcare revolves.  While hospitals and physicians will always feature prominently in a patient’s quest for wellness, the decisions she makes in her daily life have just as much, if not more, impact on her likelihood of developing diabetes, heart disease, or lung cancer.  These decisions are supported by and conducted through her connected devices, and healthcare providers must recognize the influence of these new technologies on the way patients view their health and the role of their providers.

Healthcare organizations that wish to succeed in an era where healthcare big data analytics is a necessity instead of a luxury must be able to acknowledge that the Internet of Things knows more about their patients than their basic EHRs ever will.  Providers must put in the effort to utilize this information as a vital tool for strategic achievements instead of a burden on workflows and an annoyance to overwhelmed physicians. 

As the big data analytics industry matures, vendors and developers will need to take the lead in creating products and services that make IoT data meaningful, relevant, and easy to consume in the consult room or through a dashboard.  While this seems like an insurmountable challenge to many clinicians still struggling through alerts and check boxes, the necessary innovation may be close at hand, spurred by an increasing reliance on value as a measure for payment. 

Collecting and leveraging patient-generated health data from IoT devices will be the key to population health management in a changing landscape where reimbursement and outcomes are inexorably intertwined, even as healthcare big data analytics becomes a key competency for organizations seeking a deeper understanding of their responsibilities and opportunities to provide quality patient care.


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