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CASE STUDY
CASE STUDY

Enhancing Patient Care Through AI Innovation with the AlignAI Hub

Enhancing Patient Care Through AI Innovation with the AlignAI Hub

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Highlights

  • The AI COE team achieved up to 10x productivity gains by reducing reliance on manual workflows.
  • Expanded from managing a handful of scattered AI ideas to consistently tracking hundreds of AI use cases.
  • Increased adoption by enabling clinicians and analysts to self-serve, using a centralized platform to replace fragmented processes for locating AI resources.
  • Provided leadership with end-to-end visibility into AI initiatives, establishing confidence in the quality of AI governance and solution deployments.
  • Team engagement shifted from isolated efforts to a system of people across departments, involving clinicians, analysts, security teams, and leaders.

  • The AI COE team achieved up to 10x productivity gains by reducing reliance on manual workflows.

  • Expanded from managing a handful of scattered AI ideas to consistently tracking hundreds of AI use cases.

  • Increased adoption by enabling clinicians and analysts to self-serve, using a centralized platform to replace fragmented processes for locating AI resources.

  • Provided leadership with end-to-end visibility into AI initiatives, establishing confidence in the quality of AI governance and solution deployments.

  • Team engagement shifted from isolated efforts to a system of people across departments, involving clinicians, analysts, security teams, and leaders.

Challenges

A nationwide healthcare system with over 25,000 employees, including strategists, physicians, and clinicians, is dedicated to providing exceptional patient care through advanced treatments, cutting-edge research, and operational efficiency. The organization prioritizes data-driven decision making to deliver positive experiences that foster patient loyalty. However, as the organization sought to scale its AI initiatives to remain competitive, it encountered significant hurdles that hindered progress. This limited their ability to fully utilize data and AI insights to increase patient and member engagement.

A nationwide healthcare system with over 25,000 employees, including strategists, physicians, and clinicians, is dedicated to providing exceptional patient care through advanced treatments, cutting-edge research, and operational efficiency. The organization prioritizes data-driven decision making to deliver positive experiences that foster patient loyalty. However, as the organization sought to scale its AI initiatives to remain competitive, it encountered significant hurdles that hindered progress. This limited their ability to fully utilize data and AI insights to increase patient and member engagement.

They faced several challenges:

  • Fragmented AI Processes: AI ideas were scattered across teams and managed in static Excel sheets, resulting in duplicated efforts and outdated information.

  • Low AI Awareness and Adoption: Many employees and clinicians were unaware of AI resources or how to engage with the internal AI center of excellence (COE), while leadership lacked confidence in the AI solutions and data insights being produced.

  • Siloed Departments: Teams working on AI under the CIO, CISO, COO and CDO operated independently, creating misaligned priorities and inconsistent workflows.

  • Operational Delays: Without a scalable framework, AI use case collection and prioritization required extensive manual effort, which prolonged implementation timelines.

They faced several challenges:

  • Fragmented AI Processes: AI ideas were scattered across teams and managed in static Excel sheets, resulting in duplicated efforts and outdated information.

  • Low AI Awareness and Adoption: Many employees and clinicians were unaware of AI resources or how to engage with the internal AI center of excellence (COE), while leadership lacked confidence in the AI solutions and data insights being produced.

  • Siloed Departments: Teams working on AI under the CIO, CISO, COO and CDO operated independently, creating misaligned priorities and inconsistent workflows.

  • Operational Delays: Without a scalable framework, AI use case collection and prioritization required extensive manual effort, which prolonged implementation timelines.

The organization’s current process included the use of existing tools such as Excel and SharePoint. They also conducted standalone training sessions to improve AI literacy. However, the reliance on traditional office tools and inconsistent workflows made it difficult to scale, while the lack of centralized visibility hindered collaboration, alignment and proper governance across teams.
The organization needed a solution that could streamline AI transformation across departments. Their ultimate goal was to equip clinicians, employees and technical teams alike with an ‘AI Source of Truth’ that would improve patient care and drive operational efficiency with AI system-wide.

The organization’s current process included the use of existing tools such as Excel and SharePoint. They also conducted standalone training sessions to improve AI literacy. However, the reliance on traditional office tools and inconsistent workflows made it difficult to scale, while the lack of centralized visibility hindered collaboration, alignment and proper governance across teams.

The organization needed a solution that could streamline AI transformation across departments. Their ultimate goal was to equip clinicians, employees and technical teams alike with an ‘AI Source of Truth’ that would improve patient care and drive operational efficiency with AI system-wide.

Solution

The healthcare system adopted the AlignAI Hub to centralize and streamline the entire lifecycle of AI initiatives, from idea collection to prioritization, development, deployment and monitoring. This included custom Playbooks to drive role-specific AI literacy and process adherence, intelligent Use Case Manager to organize and prioritize AI ideas, and Control Center to ensure governance by design, by embedding regulations, standards and policies directly into AI development & approval workflows. The AlignAI Hub also integrated with key applications to unite teams and tools under one hub. By providing employees with a single source of real-time information on all things AI in the company, the organization aligned AI initiatives with their strategic priorities of increasing patient care and engagement.

Key features of the AlignAI Hub that addressed the organization's needs included:

The healthcare system adopted the AlignAI Hub to centralize and streamline the entire lifecycle of AI initiatives, from idea collection to prioritization, development, deployment and monitoring. This included custom Playbooks to drive role-specific AI literacy and process adherence, intelligent Use Case Manager to organize and prioritize AI ideas, and Control Center to ensure governance by design, by embedding regulations, standards and policies directly into AI development & approval workflows. The AlignAI Hub also integrated with key applications to unite teams and tools under one hub. By providing employees with a single source of real-time information on all things AI in the company, the organization aligned AI initiatives with their strategic priorities of increasing patient care and engagement.

Key features of the AlignAI Hub that addressed the organization's needs included:

AI Use Case Manager

This feature automatically captured hundreds of AI ideas from employees & physicians, intelligently organizing use cases for the review team within the AlignAI Hub. It categorized ideas by AI capability, estimated value, and alignment with strategic goals, streamlining prioritization. Features like automated de-duplication and filtering reduced manual effort, enabling the team to focus on advancing high-priority initiatives.

Playbooks & Control Center

The Playbooks provided guided workflows for clinicians, physicians, and leaders, ensuring clarity and consistency in their roles plus adherence to AI best-practices. The Control Center tracked adherence to regulations, company policies, and essential standards. Ensuring AI solutions had gone through the proper process and monitoring over time. By minimizing reliance on spreadsheets, meetings and emails and local files, it significantly enhanced governance, throughput and peace-of-mind for AI developers and users alike.

Integrations with Existing Tools

The AlignAI Hub connected to existing tools like Jira, Sharepoint, Excel, Epic, Axon, and Tableau. These integrations simplified the experience for employees and clinicians, by consolidating processes and creating a single source of truth for AI initiatives while maintaining familiarity and security with the existing tech stack.

Implementation Process

The healthcare system deployed the AlignAI Hub in just a couple weeks. This rapid implementation enabled the organization to begin influencing clinicians, prioritizing use cases, preparing AI solutions for deployment, and establishing a strong foundation for new AI technologies and business-aligned solutions within the first month.

The healthcare system deployed the AlignAI Hub in just a couple weeks. This rapid implementation enabled the organization to begin influencing clinicians, prioritizing use cases, preparing AI solutions for deployment, and establishing a strong foundation for new AI technologies and business-aligned solutions within the first month.

The main focus of the implementation process was to transform how AI initiatives were managed by improving the intake and prioritization of AI ideas, fostering collaboration across departments, and embedding governance to align projects with strategic goals. Ensuring high standards of data quality and stewardship was a critical aspect of the process, enabling leaders to trust data insights to inform better decision-making and feel confident in data and AI adoption.

After identifying these priorities, AlignAI integrated data from Excel, Word, PDFs, Epic, Axon, and Tableau into the platform. The initial rollout was completed in a couple weeks, with further enhancements implemented over the following months.

The main focus of the implementation process was to transform how AI initiatives were managed by improving the intake and prioritization of AI ideas, fostering collaboration across departments, and embedding governance to align projects with strategic goals. Ensuring high standards of data quality and stewardship was a critical aspect of the process, enabling leaders to trust data insights to inform better decision-making and feel confident in data and AI adoption.

After identifying these priorities, AlignAI integrated data from Excel, Word, PDFs, Epic, Axon, and Tableau into the platform. The initial rollout was completed in a couple weeks, with further enhancements implemented over the following months.

Results

The implementation of AlignAI Hub yielded significant results for the healthcare system:

The implementation of AlignAI Hub yielded significant results for the healthcare system:

Efficiency Gains

  • The AI COE team achieved up to 10x productivity gains by reducing reliance on manual workflows for idea intake, prioritization, and deployment.

  • Increased adoption by enabling clinicians and analysts to self-serve, using a centralized platform to replace fragmented processes for locating AI resources.

Scalability

  • Expanded from managing a handful of scattered ideas to consistently tracking hundreds of AI use cases.

  • Processes were structured to manage a growing number of AI use cases without adding headcount.

Confidence in Strategic Goals

  • Centralized tracking and prioritization accelerated AI solution deployment, boosting leadership confidence in alignment to strategic initiatives around patient care, loyalty, and increased membership engagement.

  • Provided leadership with end-to-end visibility into AI initiatives, establishing confidence in the quality of data insights and solution deployments.

Improved Governance

  • Governance and compliance requirements were embedded directly into workflows, meeting internal company policies and external standards like NIST and ISO during AI solution design and deployment.

  • Structured compliance processes via playbooks ensured a consistent approach to oversight around data stewardship and quality for AI initiatives.

Cross-Team Collaboration

  • Real-time dashboards gave teams centralized visibility, improving monitoring, priority alignment, and cross-department collaboration.

  • Team engagement shifted from isolated efforts to a fully coordinated process involving clinicians analysts, security teams and executive leaders.

"We needed AlignAI to help guide the design, initialization and deployment of the AI COE to support the AI use cases, governance and self-service shift we identified in our competitive gap assessment."

– SVP, Chief Information Officer