Charting the Future of Healthcare Data: Critical Market Insights

Article Summary

Health Catalyst’s Leslie Falk and Tim Zenger discuss crucial healthcare insights, navigating information overload, and trends impacting the future of data and analytics in healthcare.

Insight Market Trends Falk Zenger

Editor’s Note: This Q&A article is based on a recent webinar entitled, Looking Ahead: Market Trends Impacting Key Healthcare Issues, presented by Leslie Falk, DNP, MBA, Chief Client Success Officer, and Tim Zenger, Vice President of Research and Strategy, at Health Catalyst.

As we immerse ourselves in a vast ocean of data, distinguishing meaningful insights from noise has become a crucial skill. Last year, I had the opportunity to sift through over 87,000 pieces of content.

While many of these content pieces offer value, only a fraction warrants the attention of busy health tech leaders. This begs the question: How do we navigate this sea of information effectively, especially in healthcare, where every moment counts?

The evolving landscape of data and analytics in healthcare is also an area where drowning in data is no longer an option. This shift towards quality over quantity has us thinking about whether we’ve accepted our current predicament or catapulting to a new norm.

Today, I am excited to share some insights I gained from a conversation with Leslie Hough Falk, the Chief Client Success Officer at Health Catalyst. She has a wealth of experience in healthcare and data analytics. I sat down with her to get her thoughts on this evolving landscape and discuss other trends in healthcare.

The following is an edited excerpt of our recent conversation.

Zenger: We no longer exist in a world where more data is the only answer. We live in an overwhelming sea of data, a tsunami of data, and clinicians and healthcare administrators often feel overwhelmed with trying to identify signals from noise in the data. As a nurse and talking with our clients, Les, is this issue more or less relevant today?

Falk: I think it’s even more relevant. During my tenure at Hewlett-Packard, my colleague, who happened to be an analyst, made a bold statement in one of our team meetings. He declared that he would withhold any further data from the team until they could justify why they needed it and what purpose it would serve. Initially taken aback by his approach, I soon realized the wisdom behind his actions. His words prompted me to reflect on the significance of data and its utilization.

Drawing parallels with the well-known Plan-Do-Study-Act methodology, it became evident that while we often excel in healthcare in executing and implementing actions, we often fall short in the planning and analysis stages. Statistics indicating that a significant portion of collected data remains unused raise crucial questions about our data acquisition practices. We must clearly define the questions we seek to answer through data analysis and understand the analytics methods we’ll use before embarking on data collection efforts.

Zenger: In a recent cross-industry research study focusing on analytics maturity, healthcare providers were at the bottom of the maturity scale. This indicates that the healthcare industry lags behind other sectors, like retail, eCommerce, finance, commercial air transportation, and others, in adopting effective strategies to meet evolving patient needs and addressing economic headwinds.

Despite efforts and good work being done within the healthcare sector, there is a notable delay in progress compared to other industries. This slower pace of advancement ultimately hurts patients.

How should we address the industry’s lack of maturity, and what implications does this have for healthcare organizations and patients?

Falk:  Analytics maturity significantly correlates with long-term financial performance. This connection is not merely theoretical; it directly impacts economic outcomes three, five, and ten years from now. Analysts should advocate for analytics maturity and tout its benefit for the organization, both financially and from the clinical quality perspective.

Health systems must address barriers to becoming data-driven, whether from people, processes, culture, or technology.

Zenger: Data from Gartner tracking IT spending reveals that much of a health system’s budget is spent “maintaining” systems. How do you eliminate so much time spent keeping the lights on when you see so many opportunities for improvement?

Falk: One prime example is automation, specifically streamlining tasks like software upgrades and data validation. Some health systems have made notable strides in this area.

One of which has freed up valuable analyst resources by redirecting their focus from labor-intensive EHR report development to creating intuitive business intelligence tools and dashboards. This shift empowered end-users to access answers efficiently and enabled analysts to contribute to transformative projects within the organization.

Such strategic utilization of resources drives meaningful progress across various initiatives — a testament to the power of innovative problem-solving approaches.

Zenger: Despite predictions made in 2013 and 2016 about the rapid shift to value-based care contracts, the reality is that value-based care has progressed slowly over the past decade. Data from 2022 reveals that only a small percentage of payments are currently made through population health models with downside risk, indicating a reluctance to embrace full-risk payment structures, particularly in the commercial sector. This stagnation suggests that significant changes in healthcare payment systems may still be decades away. What are your thoughts on that?

Falk: We also conducted a survey at the 2018 Healthcare Analytics Summit seeking opinions on the future of value-based care in five years. Respondents anticipated an acceleration in its adoption.

However, three key stakeholders are associated with value-based care: patients, payers, and providers. Existing models predominantly focused on two of these three groups, highlighting the need for models that consider all stakeholders.

Insights from the Thomas Jefferson School of Population Health further supported this perspective by indicating a shift from full-risk arrangements to more balanced structures. While some may interpret this trend as a sign of stagnation in value-based care, it is emphasized that organizations are likely to continue transitioning from fee-for-service toward performance-based payment models tied to quality metrics.

The overarching concern for many remains profitability, with deliberations revolving around the financial viability of embracing full-risk arrangements.

Zenger: The latest analysis by Gartner projects that Generative Artificial Intelligence (AI) will soon reach a phase of critical evaluation known as the trough of disillusionment. This phase will bring forth a clearer understanding of the actual offerings in the market while potentially sidelining less substantial developments over the next 12 to 24 months. Les, how do you view the ongoing discourse surrounding AI?

Falk:  I maintain a positive outlook for the future due to three specific instances where data and analytics driven by AI are making significant impacts. One such example involves enhancing patient engagement through digital solutions. For those unfamiliar with this concept, let me illustrate two scenarios: addressing appointment no-shows in ambulatory settings and managing surgery cancellations effectively using personalized AI-generated messaging based on individual patient preferences and behaviors.

Another notable application is seen in chart abstraction tasks, which traditionally involve the laborious processing of unstructured data. Here, AI can suggest answers and bring up supporting evidence that skilled professionals can review. This could improve efficiency over time.

The third area worth highlighting is Healthcare.AI, a comprehensive suite that integrates statistical tools and machine learning techniques within business intelligence workflows to deliver practical ROI outcomes in healthcare operations.

Incorporating this approach into your business intelligence tools enables thorough data analysis and accurate insights through the Plan, Do, Study, Act framework. Organizations that embrace lean principles and implement technology, people, processes, and culture effectively alongside the Plan, Do, Study Act method will likely utilize data analytics and AI efficiently.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

The Intersection of AI, Cybersecurity, and Data Platforms in Healthcare

AI in Healthcare: Hype, Hope, and Missed Opportunities

Three Reasons Augmented Intelligence Is the Future of AI in Healthcare

How WakeMed’s Path to Digital Maturity Led to Measurable Outcomes

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