November 10, 2020

Signaling transformation: Why AI will reshape healthcare for the better

Bill Moroz

Topic:   Data

Last updated: February 2nd, 2021

Among the myriad forces transforming healthcare, the application of artificial intelligence (AI) technology throughout the industry holds considerable potential to rapidly and fundamentally change how healthcare is experienced, utilized, and managed.

The COVID-19 pandemic response offers a microcosm of AI’s advancing role within healthcare. Examples of how the adoption of – and trust in – this technology signifies healthcare’s progression to a data-driven, insights-enabled industry include:

  • Chatbots guiding individuals through self-triage of symptoms to conserve clinical resources
  • Digital workers augmenting epidemiological resources to improve public health surveillance
  • Predictive models forecasting patient utilization and outcomes trends at facility and community levels to optimize resource planning
  • Intelligent therapeutics discovery supporting rapid pharmaceutical lifecycle management

And while initiatives focused on value-based care models, digital transformation, and interoperability rightfully occupy top spots on healthcare leaders’ strategic agenda, the ascendancy of AI technology uniquely reflects the centrality of data’s value in today’s healthcare ecosystem.

Fundamentally, AI’s contribution to improved outcomes is defined by the data that calibrates its application. A better data foundation enables better outcomes.”

The accelerant: sparking innovation and delivering value

AI is built on ever-evolving techniques, including machine learning (ML), artificial neural networks (ANN), and natural language processing (NLP), and therein lies its transformative value. To fully understand AI’s innovative potential, consider AI’s three primary categories of application:

  • Operational and administrative-oriented AI
  • Clinician-oriented AI
  • Patient and member-oriented AI

AI applications can range in complexity from simple administrative task automation and fraud detection to advanced social determinants of health (SDOH) analysis, computer-aided diagnostic decision-making, and clinical data mining to reduce clinical variation. When applied to these categories, AI can increase patient satisfaction, forestall looming physician shortages, and reduce provider burnout by allowing clinicians to work at the top of their license – while also identifying waste and improving care quality, access, and equity.

AI is also recalibrating how we define and deliver healthcare in ways that demand more of the data fueling AI’s advancements. Examples include:

  • Population health management and risk prediction
  • Biometric-enabled health monitoring, virtual care assistants, and personalized medicine
  • AI-powered data exchange across the care spectrum and public health

Transforming big data into smart data

The near-ubiquitous applicability of AI technology across healthcare can eclipse the data management strategy necessary to fully realize its organizational impact and value. Healthcare leaders must consider these fundamentals to achieve AI success:

  • Align initiative investment to business use cases and expected return on investment (ROI) to demonstrate tangible measurement of gains and outcomes
  • Ensure data quality with data sets that reflect sufficiency, scale, and representation
  • Enable algorithmic and data model transparency by demystifying the inputs driving insights
  • Evaluate data context to account for biases and disparity
  • Nurture a data-first organizational culture that prioritizes insights, enterprise agility, and iterative innovation

Intelligent data management is at the heart of optimizing AI applications. Improving decision quality at scale requires an AI-ready data foundation built to onboard, organize, and operationalize data. To function as a catalyst for enterprise transformation, AI initiatives must be deployed within a robust, integrated data ecosystem marked by:

  • Business-led and IT-supported processes and data governance
  • Stakeholder-driven applications that democratize insights derived from a trusted source of data truth

Fundamentally, AI’s contribution to improved outcomes is defined by the data that calibrates its application. A better data foundation enables better outcomes, both from the ROI in AI initiatives and for the intended beneficiaries of AI-enabled healthcare – patients, clinicians, and communities.

During this industry shift from a focus on the quantity of action to the outcomes of effort, AI’s vast potential to help organizations “hear the signal amid the data noise” by harnessing and then acting on insights can’t be overstated. And it all begins with data.

This blog originally appeared in Healthcare Dive.

Bill Moroz is the senior solutions architect for ibi. He has been helping healthcare organizations to derive value from data for more than 20 years. His experience includes interoperability, data management, analytics, and artificial intelligence (AI).


As an innovative, results-driven professional, he participated in developing the HL7 standards and HIE/RHIO networks. Bill has experience in the AI/machine learning space, working on projects for clinical variation management and claims FWA.

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