Healthcare Analytics, Population Health Management, Healthcare Big Data

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Will Big Data Analytics Rescue Lackluster Electronic Health Records?

Electronic health records are still producing widespread frustration, despite executive optimism about big data analytics.

Big data analytics and electronic health records

Source: Thinkstock

By Jennifer Bresnick

- Big data analytics are “extremely important” for helping healthcare organizations see a return on their electronic health records (EHRs) investments, according to 83 percent of stakeholders participating in a recent Health Catalyst survey.

Frustration and disappointment with EHRs remains high, the poll of 1100 professionals revealed, but providers are eagerly anticipating a future in which cutting-edge analytics, including machine learning, natural language processing, and neural networks, create a seamless pipeline of actionable insights.

Nearly 20 percent of respondents believe that that the return on the EHR Incentive Programs has been “terrible” for provider organizations, while an additional 42 percent said the program has produced “poor” results thus far.

Only ten percent believe that the more than $30 billion spent on incentives to implement EHRs has been truly worth it, indicating that widespread dissatisfaction with the current capabilities of health IT tools isn’t likely to go away without some significant alterations to the landscape.

The poll was released soon after an AMA and University of Wisconsin report indicating that primary care physicians spend almost six hours per day on EHR documentation tasks, including 1.5 hours of work after their shifts have ended.

READ MORE: Can $10B Solve Healthcare’s Big Data Analytics, Productivity Woes?

Researchers warned that unintuitive workflows and burdensome documentation requirements could contribute to provider burnout in addition to being unhelpful to organizations seeking to use the EHR as a point of data collection for analytics projects.

“This study reveals what many primary care physicians already know – data entry tasks associated with EHR systems are significantly cutting into available time for physicians to engage with patients,” said AMA President David O. Barbe. “Unfortunately, clerical and administrative demands are not being reconciled with patient priorities and clinical workflow.”

“Poorly-designed and implemented EHRs have physicians suffering from a growing sense that they are neglecting their patients and working more outside of clinic hours as they try to keep up with an overload of type-and-click tasks.”

Improved data management and analytics capabilities are likely to be a main driver of positive change – if healthcare organizations can successfully implement the tools and strategies required to transition away from reactive decision-making hampered by poor interoperability, inadequate information governance, and cumbersome workflows that dampen provider morale.

At the moment, a quarter of organizations are experimenting with implementing “analytics 2.0” capabilities, including open source coding and data visualization tools.

READ MORE: Transparency is Key for Clinical Decision Support, Machine Learning Tools

Seventeen percent have started to develop a data driven organizational culture by exploring how to integrate change management with machine learning and other more advanced methodologies. 

Just five percent believe they have already tapped into the potential of true cognitive computing by enabling some form of natural language processing tools, deep learning, or neural networks.

Those percentages are likely to shift quickly as healthcare organizations snap up billions of dollars in advanced big data management and analytics offerings.  Many market reports predict massive surges in these market sectors as providers race to equip themselves with intelligent insights to help them cope with changing reimbursement structures and population health management needs.

Between 2017 and 2024, the healthcare artificial intelligence marketplace is slated to see a 40 percent compound annual growth rate, said one recent analysis, creating a $10 billion juggernaut focused on diagnostics, genomics, drug discovery, medical imaging, and the development of personal AI assistants.

Participants in the Health Catalyst survey are ready and eager to help this market develop.  More than 40 percent of respondents said they were advocates of big data analytics who want to “help lead the change and make a difference,” while 35 percent said they were optimistic about the field’s potential to improve outcomes and bolster financial stability.

READ MORE: The Difference Between Clinical Decision Support, Big Data Analytics

Nine percent stated that they were taking a “wait and see” approach to analytics, but less than 20 percent said they were worried about succeeding with their big data initiatives or unsure that the promises of data analytics would come to fruition.

While optimism and excitement may be high, the skeptics may be among the many organizations struggling to secure the talent, infrastructure, data integrity, and funding they need to make the most of their data assets. 

Organizations have only made grudging progress over the past few years against the many challenges of developing a seamless big data ecosystem that supports actionable, timely decision-making, especially for the purposes of risk-based reimbursements and value-based care.

Earlier in 2017, CIOs taking part in a CHIME survey were much more hesitant to express confidence that they could overcome their EHR woes to create successful analytics programs.

Close to 40 percent said they were still focused on the basic EHR optimization tasks that could enable data extraction and analytics, mirroring the discontent of providers who do not feel as if their EHRs are as valuable as they could be for broader improvement initiatives.

Sixty-three percent of participants in the CHIME poll also noted that they are not expecting budget increases over the next two years to help them work on the problems they are facing, which may make it even more difficult to push through their health IT barriers.

Compounding the challenges are a pervasive lack of available talent, as evidenced in a cross-industry survey by Harvey Nash at the end of 2016. 

Sixty-five percent of executives and IT professionals responding to the poll said that they are missing the manpower to engage in meaningful infrastructure development, including 39 percent who specifically cited a shortage of skilled analytics workers.

The healthcare industry must apply a multi-pronged strategy to these interconnected problems, the AMA has said.  Improving EHR usability by redesigning systems to support team-based care in a more modular, streamlined fashion can reduce stress for providers, making it easier for physicians, nurses, and other end-users to deliver higher quality care to their patients.

More intuitive workflows with fewer clicks and cleanly presented interfaces can improve the data collection process, which will in turn bolster the integrity and reliability of the organization’s information assets.

Patient safety and coordinated care stand to benefit from these fundamental improvements to the end-user experience – and organizations may soon find themselves in possession of large quantities of trusted, complete, and more accurate patient data.

These data sets can then be used to fuel more granular and meaningful analytics, returning actionable insights to executives and providers for enhanced financial and clinical decision support capabilities.

Changing the EHR usability equation is at the root of this process, stresses the AMA. 

“Making clinical care improvements the primary focus of EHRs requires a more open platform that not only contributes to ‘big data’ and gains benefits from big data analytics but also facilitates individual patient encounters through the use of ‘small data,’” the society said. “Instead of focusing on the EHR as the centerpiece of the health IT ecosystem, the EHR should be viewed as one of many contributors to the future health IT landscape.”

“To achieve this, vendors must develop EHRs not as an application that serves ‘all things for all people’ (i.e., multiple functions fit the broader needs of users) but as a more nimble, supportive application that facilitates the data capture and displays data, tailored to the end-user.”

Repositioning EHRs in this manner may be the key to reducing frictions between dissatisfied end-users and uncertain executives whose excitement over the potential of big data doesn’t yet match the industry’s ability to deliver actionable insights.

As organizations sprint to equip themselves with the tools required to succeed with value-based reimbursement and innovative care models, big data analytics may only be a truly valuable investment if providers also sufficiently attend to the basics of data collection and display.

Improving EHR usability and reducing aggravation with day-to-day health IT interactions is the first step towards creating a technology ecosystem that can take full advantage of next-generation machine learning and artificial intelligence tools.

Less stressful EHR workflows, coupled with heightened attention to data integrity and governance, will prepare healthcare organizations with the underlying foundations of clean data and engaged providers that will help them translate their excitement about big data into measurable results.


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