Healthcare orgs value data analytics for improved care quality

A new survey conducted by Arcadia and the Healthcare Information and Management Systems Society (HIMSS) revealed that healthcare stakeholders view data analytics as key to meeting many of their strategic goals, but struggle with issues like data quality and consolidation.

The report polled 100 US-based healthcare leaders – defined as those in director positions or above – that have roles in the data analytics platform selection or innovation processes. Respondents were asked a series of questions to uncover their perspectives on the use of these platforms, including their perceived value, how organizations use data to support decision-making and challenges to the adoption of analytics tools.

The survey found that the use of data and analytics platforms varies significantly among healthcare organizations, but that data is considered key to driving a host of business objectives.

Just over 55 percent of respondents indicated that improving care quality is one of the top three strategic goals that data empowers within their organizations, with 30 and 29 percent selecting improving workforce productivity and identifying cost-saving opportunities, respectively.

However, making data useable remains a challenge that stakeholders are actively working to solve.

When asked what would make their organization’s data more useable, 62 percent of respondents indicated cross-team collaboration, 58 percent selected enhancing data literacy and 53 chose leveraging predictive analytics.

Further, respondents reported that data platforms are also important for helping organizations adopt technologies – such as AI and machine learning – and unlock data’s potential across the enterprise.

A 55 percent majority of respondents indicated that they strongly agreed that “data analytics platform(s) are critical to creating a trustworthy data asset to enable stakeholders across our organization,” while 51 percent strongly agreed that data analytics will be key to the success of their organization in the coming years.

Alongside building trustworthy data assets, organizations are also prioritizing analytics to help them take advantage of advanced technologies like AI. Nearly 45 percent of organizations with more than 15,000 employees reported that they have plans to make “significant improvements” to their analytics infrastructure to keep pace with AI innovation. Just under a quarter of organizations with fewer than 15,000 employees report that they are making similar plans.

To help organizations meet these goals, data analytics platforms must offer key features.

Over 80 percent of those surveyed indicated that features to improve data quality are must-haves in a data platform, whereas 65 percent highlighted the need for a comprehensive enterprise data solution. Over three-fifths prioritized features that enable clinical staff to use data to enhance their productivity, and 60 percent wanted the ability to consolidate data across multiple disparate platforms.

The ability to utilize unstructured or non-traditional healthcare data was one of the most significant features respondents selected as a “nice to have” within their data analytics platform.

The aggregation of non-traditional and unstructured data is a major priority for 56 and 55 percent of respondents.

This is particularly true for multi-facility organizations, such as integrated delivery networks (IDNs) and multi-hospital systems. Of these, 70 percent are interested in non-traditional data aggregation, compared to 44 percent of academic medical centers, stand-alone hospitals and specialty hospitals.

Despite these potential benefits of an analytics platform, stakeholders highlighted that adopting such a tool presents significant barriers.

The most common barriers included competing priorities, the perceived complexity of integration and a lack of internal resources, which were cited by 48, 37 and 36 percent of organizations, respectively. Just over 30 percent of respondents reported that there is no budget available to fund the adoption of a data platform, and a similar segment of the cohort indicated that security and privacy concerns were holding their organizations back.

Insufficient IT infrastructure, resistance to change among staff and lack of internal resources were also major roadblocks for many.

Increasing awareness around the value of data has led many healthcare organizations to pursue advanced technologies and solutions, including generative AI.

Insights from a recent John Snow Labs survey highlighted that many healthcare and life sciences organizations are putting funding toward these efforts, with 34 percent of respondents reporting a 10-50 percent budget increase. The upper end of this curve saw 13 percent claiming to see increases of over 300 percent.

These investments seem to be driving the adoption of small, task-specific language models optimized for specific use cases, including helping users streamline clinical workflows and improve patient communication.