Either approach limits the adoption of AI into key analytics workflows.Ĭhallenge 2: Integrating AI Expertise Within the OrganizationĮffective AI integration requires data science expertise, and the leaders who attempt to fill this expertise gap find it challenging for myriad reasons: However, most AI tools are standalone applications requiring analysts to learn and adopt yet another program, language, or integration point. Ideally, analysts want AI capabilities integrated into their existing workflows and business intelligence (BI) tools. Healthcare.AI dramatically broadens the use and use cases for effective AI within your organization.Ĭhallenge 1: Integrating AI into Current Tools The Healthcare.AI offering from Health Catalyst is an improved, transformational suite of products and expert services that address this wider array of critical business issues. However, this approach was often ineffective when applied without expert guidance (self-service), and the approach was also too narrow in scope, limiting the effective use of AI in healthcare.
The first release of Healthcare.AI by Health Catalyst focused primarily on point of care or service predictive model use cases. Leaders have struggled to integrate AI into current tools, integrate or change workflows, and demonstrate a positive impact of AI. Too often, these efforts have been less than successful. While these attempts may even incorporate augmented intelligence (AI), which goes beyond artificial intelligence by enabling human decision makers to make the best choices, they often fall short.Īs a result, many business and analytics leaders are trying to integrate augmented intelligence (AI) into their analytic processes to better address these critical issues. These leaders are turning to artificial intelligence as the key to better decision-making, believing it will deliver faster turnaround of insights with small margins for error. Healthcare leaders face an unprecedented list of increasingly critical issues across revenue, cost, and quality.
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