Arcadia believes in the power and potential of data, and we’re determined to help the healthcare industry optimize this critical resource. So, we partnered with HIMSS to survey healthcare decision-makers to understand how they currently utilize data and how they plan to leverage it in the future. This study also revealed the top challenges and opportunities facing the industry today.
In this article, we’ll share our findings and analyze the data. 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.
Key takeaways:
The scale of data in healthcare is hard to measure, much less envision. However, healthcare data is growing at a rate of 63% annually, and total global healthcare information surpassed 4,200 exabytes in 2025. Today, approximately 30% of the world’s data volume is generated by the healthcare industry.
Unlocking and leveraging this ever-growing amount of data is critical to achieving healthcare transformation. This encompasses access to the right data, ensuring all information is high-quality and accurate, and establishing the right governance, infrastructure, and analytics to make data effective and useful.
Demand for healthcare data continues to grow, as do the ways in which it’s used. In our study with HIMSS, 93% of survey respondents agree that access to quality data across all platforms and workflows is critical to an organization's performance. However, only 57% of respondents’ organizations’ data is being used to make intelligent business decisions.
Closing this gap is possible through intelligent healthcare data technology, complemented by 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:
There are also less common (but still impactful) tools some healthcare leaders are implementing, such as:
Our study with HIMSS identified the following trends:
Artificial intelligence is immensely powerful in unlocking big data. It can provide predictive analytics, help cleanse and normalize data, analyze data, and suggest actions to achieve optimal outcomes. AI supplements human skill and ability by turning data into action, helping decision-makers back their conclusions with evidence.
Challenges in healthcare technology adoption remain and must be overcome for the industry to advance. Our survey of healthcare leaders found that the most significant barrier is resources:
These responses demonstrate 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.
Other respondents say it’s a matter of trust and accuracy:
There is also the matter of internal pushback: 40% of respondents say clinicians and analysts are resistant to adopting new solutions, and 18% say leadership is resistant to them.
Often, the key to success is zeroing in on a specific goal, such as improving operations or patient outcomes, and going all in to achieve it. Focusing efforts more narrowly can enable organizations to see progress sooner than they might by pursuing a broader set of initiatives. This is especially true for organizations with limited staffing or budget resources.
Overcoming many healthcare data challenges is possible with the right cohesive technology stack. Healthcare leaders must act now to decide whether to build or buy a platform and which features they need to succeed in a market driven by increased consumerism and CMS-led overhauls.
To do so, there are three key steps:
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.
The keyword here is information (not data!). Curating data is the first step, but you must have tools that efficiently analyze and make meaning out of the data. Leverage user-specific insights to build care interventions that are tailored to specific patients, markets, and conditions.
Tools must be role and workflow-specific — and customizable. Workflow efficiencies, for example, position clinicians to avoid time-consuming repetitive tasks, allowing them to focus on patient care. The best-case scenario is to make data actionable at all levels, depending on what the user needs. For instance, where clinicians need chronic care management tools to monitor certain conditions, a payer might want a larger view of population health metrics.
Aggregating quality data, analyzing it, and empowering stakeholders to act on insights may unlock a fourth step that optimizes patient outcomes and improves performance even further: Automate.
With advanced algorithms and AI, tools powered by a healthcare data platform not only augment decision-making and improve efficiency for clinicians, analysts, and executives, but also have the potential to completely transform the way organizations deliver care and operate their healthcare businesses in a coordinated, strategic way.
What did we discover as we conducted this study with HIMSS? The best way to succeed in contemporary healthcare is to adopt a data platform, then stack additional tools to help it perform the specific functions your organization needs.
Additionally, your platform needs to be scalable and broadly applicable. Whether you’re squarely operating under value-based care or exploring that model while still using traditional fee-for-service models, you can expand healthcare data technology’s use case beyond VBC or strictly population health. While these use cases may be the primary reason a health system, CIN, or ACO adopts a data platform, it’s only the beginning of how this technology can be used and the value it can drive for an organization.