How healthcare data technology is leveraged by leaders

Arcadia believes in the power and potential of data, and we’re determined to help the healthcare industry optimize these resources. So, we partnered with HIMSS to survey healthcare decision-makers to understand how they utilize data today and how they plan to leverage it in the future. Our findings also revealed the top challenges and opportunities facing the industry today.

In this report, we’ll share those insights and analyze the data; the full results are also available if you’d like to download and explore them. We’ll also discuss strategies and best practices the industry can leverage to turn data into action, specifically through healthcare data technology, and we’ll share some examples of how health leaders are doing this today.

The scale of healthcare data

The scale of data in healthcare is hard to measure, much less envision. Hospitals produce an average of 50 petabytes of data each year, with as much as 97% of that data going unused. Today, approximately 30% of the world’s data volume is being generated by the healthcare industry.

By 2025, the compound annual growth rate of data for healthcare will reach 36%. That's 6% faster than manufacturing, 10% faster than financial services, and 11% faster than media & entertainment.

And for those working to build happier, healthier days for all, unlocking this fast-growing big data is critical to achieving healthcare transformation. This encompasses access to the right data, ensuring that data is high-quality and accurate, and establishing the right governance, infrastructure, and analytics to make the data effective and useful.

What is the role of technology in healthcare data?

Demand for healthcare data continues to grow, as does the ways in which it’s used. 93% of survey respondents agree access to quality data across all platforms and workflows is critical to an organization's performance. However, only 57% of an organization’s data is being used to make intelligent business decisions. There’s a need to close this delta, and it can be done through intelligent healthcare data platforms stacked with additional tools to make the data actionable.

Previous research shows that 82% of those who use such tools are highly satisfied with them, and leaders have clear plans to implement more tools with a healthcare data platform in the future.

The most common tools healthcare leaders plan to implement include:

• Patient monitoring
• Templates for medical conditions
  
• Clinical decision support tools
• Patient data aggregation throughout the care cycle

There are also less common (but still impactful) tools some healthcare leaders are implementing, such as:

• Population health management
• Communications platforms that connect hospitals and care teams
  
• VBC payment models
• Decision-making tools for preferred provider networks
• AI and machine learning

A quarter of healthcare leaders have plans to integrate a ‘communications platform that connects hospitals with outside care teams’ and ‘population health management’ within the next 12 months. 34% plan to combine data for a patient throughout the care cycle within 24 months, and 38% of leaders plan to implement value-based care tools within the next two years or so. This is especially relevant as CMS chases the goal to get all Medicare patients onto an accountable care plan by 2030, which will necessitate data and tools to support that goal.

Organizations’ data analytics and technology plans vary by care setting and size. For example, larger entities are significantly more likely to report having plans to implement a ‘decision-making tool for preferred provider networks’ in the next 12 months.

How does healthcare data technology leverage artificial intelligence and machine learning?

Artificial intelligence is immensely powerful in the quest to unlock big data. It can offer predictive analytics, help cleanse and normalize data, analyze data, and suggest actions for optimal outcomes. AI supplements human skill and ability by helping turn data into action, and it helps decision-makers back their conclusions with evidence.

Over a quarter of organizations have already implemented AI/ML, and 15% plan to in the next 12 months. 43% plan to implement it in 12-24 months or more, while 13% don’t plan to implement it at all. Interestingly, those in a C-Suite role are more likely to have plans to implement AI/ML within the next 12 months.

The bottom line? As an industry, we’re more aware of the potential advancements AI can bring — including the ability to drive a much more efficient workforce that’s able to practice at the top of their license. That’s not exclusive to care delivery, such as a medical license. It applies to everyone’s license — an analytics leader might be able to provide the most valuable outputs, or AI and ML could help an executive have the right inputs to be the most effective in their role.

What’s the barrier to entry for embracing healthcare data technology?

Challenges remain and must be overcome for the industry to advance. The most significant barrier is resources:

• 70% say competing priorities are a challenge to invest in or upgrade an analytics platform.
• 58% say staffing to implement and train for new technologies is a barrier their organization faces to modernize.
• 47% don’t have the budget for improved healthcare data technology. Those that defer run the risk of snowballing costs over the long term.

This tells us that healthcare leaders see the transformative value of healthcare data technology. However, ambitious plans and competing priorities can make it challenging to move the needle in any one direction.

Others say it’s a matter of trust and accuracy:

• 22% cite inaccurate data.
• Nearly a third stated that their organization’s data is less than 76% accurate on average.
• 20% cite delayed response times, or an inability to produce in real-time.

There’s also a matter of adoption. 40% say clinicians and analysts are resistant to adopting new solutions; 18% say leadership is resistant to new solutions.

Often, the key to success can be zeroing in on a specific goal, such as improving operations or patient outcomes, and going all in on achieving that work. Focusing efforts more narrowly can enable organizations to see progress sooner than they might by focusing on a broader set of efforts. This is especially true for organizations with limited resources, such as staffing or budget.

Optimizing outcomes with healthcare data technology

There’s an opportunity to overcome many of these challenges with the right cohesive technology stack. Healthcare leaders must act now to consider whether to build or buy a platform and the tools it enables in order to succeed in a market led by increased consumerism and overhauls led by CMS. To do so, there are three key steps:

1. Aggregate

Trust and accuracy of data can be solved through an interoperable platform that combines siloed data in one location, in (near) real-time. This gives users confidence in the completeness and timeliness of their data and inspires trust to use it to guide decision-making. Information must be fast and useful. A way to earn trust and utilization of data is to measure the outcomes it can drive.

One example of this is quantifying how the application of big data helps solve a problem, such as improvement in quality. Organizations that can successfully aggregate their data and analyze it to tell a story and inform business actions can demonstrate the application of data actually works.

2. Analyze

The key word is information (and not data!). Curating data is the first step, but you must have tools that sit on top of it to help analyze and make meaning out of the data. Drive adoption by curating user-specific insights to build proven interventions that are tailored to specific patients, markets, and conditions.

3. Act

These tools must be role and workflow specific — and customizable. Workflow efficiencies, for example, position clinicians to avoid time-consuming repetitive tasks that lead to burnout, allowing them to focus on patient care, which has ripple effects across the organization. But the best-case scenario is to make them actionable at all levels — what a C-suite leader needs is different from what a clinician needs. Where clinicians need integrated tools in their EHRs, a Chief Medical Officer might want a larger view of population health metrics.

Ensuring the aggregation of quality data, having the tools in place to analyze the data, and empowering all stakeholders in an organization to act on the insights provided by these tools also unlock a fourth step that optimizes outcomes and workflows even further: Automate. With advanced algorithms and AI, tools powered by a healthcare data platform not only augment decision-making and improve efficiencies for clinicians, analysts, and executives, they have the potential to completely transform the way organizations deliver care and operate their healthcare businesses in a coordinated and strategic way.

Leverage technology for healthier populations and healthier workflows

What did we discover as we conducted this study in partnership with HIMSS? The best ways to succeed in contemporary healthcare are adopting a data platform, then stacking tools on top that will help it perform the specific functions your organization needs.

Additionally, it’s key that the technology is scalable and can be broadly applied. Whether you’re squarely in value-based care or exploring that model while operating under traditional fee-for-service models, it’s possible to expand a healthcare data analytics platform’s use case beyond VBC or strictly population health. While these use cases may be among the reasons why a health system, CIN, or ACO adopts a data platform, it’s only the beginning in terms of how it can be used and the value it can drive for an organization.