More, More, More: Removing data inefficiencies will remove healthcare roadblocks

October 2, 2019

Realizing the benefits of health data interoperability at scale will necessitate broad, multidisciplinary collaboration and coordination

In today’s incredibly connected world, more health data is available than ever before-but how much of it is being transformed into actionable information?

Not enough.

From routine patient care to record keeping to requisite regulatory compliance details, the healthcare industry generates enormous amounts of directionless data. All that data on its own may not hold any tangible value, but the future of healthcare will be reshaped if we can leverage it to improve care.

Massive collections of big data have already permeated many components of the health ecosystem and are fueling new forms of service delivery. The rising ability to collect patient data and transmit from remote locales accelerates the reach of virtual healthcare and telemedicine. Innovative predictive models fed by troves of imaging data can better anticipate disease. Data from wearables such as a Fitbit or Apple watch can aid in developing more personalized, lasting healthcare approaches. 

That’s the good news.

The bad news is that the vast majority of these developments transpire in isolated, unscalable, limited-purpose silos. There are very real barriers to translating health-data-based innovation broadly into improved healthcare practice. An American Hospital Association study identified key roadblocks such as a lack of compatible technology among providers, difficulty in data exchange across different vendor platforms, challenges with matching/identifying patients between systems, and the prohibitive costs of customized interface development and data exchange with outside systems.

Meanwhile, frustration over clumsy, one-size-fits-all approaches to fundamental data technologies such as EHRs continues to undermine the physician-patient relationship and hinder greater data literacy in the industry. It turns out that striking a balance between sharing and protecting health data is extremely complicated, as is doing so without alienating or overburdening those who need to use it.

Beyond meeting explicit regulatory or contractual requirements, healthcare organizations still struggle to define data collection intent and purpose within their own confines, much less properly analyze and exchange data insights industry-wide. 

There’s a void in data usability standardization-and that void is an enormous obstruction. Filling it requires a means to make health data more purposeful and portable regardless of provider system or interface. It requires that health data, itself, be made interoperable.

Realizing the benefits of health data interoperability at scale will necessitate broad, multidisciplinary collaboration and coordination. That’s no small feat in our necessarily cautious and complex sector.

But it is possible, as data innovation in other highly complex and/or regulated ventures such as geographic information science or algorithmic finance have shown.

The rewards of such an effort would be swift and copious. Consider just four areas that would be immediately advanced by more effectively interoperable data:

Physician Decision Support

Knowledge transfer at the point of care is perhaps the most underutilized application of health data’s transformative potential. The kind of assistive data-fed reference/EHR/prescribing technology that could actually bridge gaps between physician skill, standardized domain intelligence, and applied treatment-at the right time and at the practice level-requires interoperable data to ever reach fruition.

Clinical Trials

Clinical research, especially in the field of drug development, is completely driven by data. Yet when it comes to planningoperating, and reporting on clinical trials, suboptimal data management increases time, cost, and complexity and imposes unnecessary limitations on endpoints and protocols. More interoperable data would aid in determining relevant and cost-effective ways to run trials, and surface more impactful discoveries efficiently for ongoing development.  

Credentialing

An often overlooked issue that has a large impact on many aspects of healthcare is credentialing. Organizations are routinely unable to collect, evaluate, and verify qualifications expeditiously, which delays care provision. While credential data is conceptually straightforward, it is largely unstandardized, making it difficult to manage and maintain. Interoperable data for caregiver credentialing validation would immediately streamline provider enrollment processes and increase patient access to quality care. 

Hospital Operation 

Healthcare providers invest stunning amounts in data management and analytics tools to make better decisions, reduce waste, and control cost associated with duplication of services and the administration of inferior treatment. However, these efforts tend to be siloed within individual institutions or networks. More interoperable data on service provision across multiple settings will provide greater insight into operational function and serve to improve business administration, patient satisfaction, and care outcomes-as well as drive new revenue enhancement goals.

As we continue to evolve toward a proactive and value-based healthcare system, data remains essential in powering this shift-but only if it is unlocked from its current silos.

Interoperable health data will enable more well-rounded care by enabling providers to better track and treat patients throughout the care continuum at scale. And for myriad distinct and essential healthcare endeavors like managing clinical trials, credentialing, or hospital operation-interoperable data is key to conquering seemingly insurmountable challenges and generating greater tangible value.

Lawrence Cohen is CEO at Health2047 Inc. An experienced biotechnology leader, he thrives in the development of drugs and platforms to treat illnesses. Department of Biochemistry at Harvard Medical School. He earned his B.A. from Grinnell College.

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