How AI in Health Systems Improves Upon Traditional Business Intelligence Tools

| May 16, 2024

By Health Catalyst Editors—Healthcare industry complexities, such as labor costs, overworked resources, and inadequate technology, are major concerns for healthcare leaders who strive to prioritize how data is accessed and managed across teams and departments. Aligning clinical and financial information at the organizational level to make strategic decisions is one of healthcare’s biggest challenges.

Historically, executives managed data using separate tools, including the following:

  1. Business intelligence (BI) solutions: Consolidates structured data from multiple sources and employs basic statistical measures to provide a comprehensive view of key performance indicators, such as population health, operational and fiscal performance, and clinical outcomes.
  2. Advanced predictive analytics applications: Manages both structured and unstructured data from various databases across a healthcare system, as well as real-time data, to anticipate future trends and enable data-driven decisions.

What’s missing from BI and advanced healthcare analytics tools today is the ability to extract data insights from a single clinical or business decision and apply these insights to the healthcare organization. That’s where AI becomes the linchpin of outcomes improvement. AI analyzes large datasets to extract the most relevant insights in the form of patterns, anomalies, and potential risks that fly under the radar, going unnoticed by clinicians, analysts, or data scientists. The healthcare industry has strived to enhance performance and quality for decades. Now, with the power of AI, healthcare leaders can make decisions with greater speed and accuracy.

AI capabilities enable rapid processing of incoming data – whether it’s operational, patient-generated, clinical, or administrative and billing. Such tools can calculate and analyze big data with precision and without bias in the fraction of the time it used to take, which could be months or even longer, which ties up precious resources. In addition, the cost savings that AI yields are an estimated $150 billion in 2026—an important driver for leadership buy-in.

When applied correctly, AI should act as a collaborator—not a replacement—in human decision-making so that the tools are ethically applied and leveraged correctly throughout the organization. Some healthcare provider organizations are eagerly embracing AI due to its seamless integration into their current technology infrastructure and effectively solving their unique clinical and financial challenges, including:

  1. Reducing unnecessary tests for improved clinical pathway adherence in areas like urgent care settings.
  2. Enhancing decision-making at the C-suite level by selecting appropriate incentive metrics for setting executive compensation packages.
  3. Improving health equity by removing unintentional bias and upholding fair and more inclusive practices.
  4. Identifying patients at high or rising risk for unnecessary or preventable healthcare utilization.
  5. Protecting data privacy and safeguarding sensitive patient information.

By combining human knowledge and skills with AI computations and analysis, AI creates endless opportunities for healthcare executives and providers to make an impact faster and with analytic precision.

Additionally, health systems need to continuously monitor their technology usage and performance to detect any instances of non-compliance or unethical behavior.

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