Health Systems Tackle Organizational Challenges with In-House AI Product Design

Lina Scroggins, Mercy's chief product officer, believes that building AI tools in-house is the key to solving organizational challenges. According to Scroggins, this approach allows health systems to create tailored solutions rather than relying on generic vendor products. This shift not only addresses specific needs but also enhances control and flexibility.

When healthcare organizations invest in AI and develop new digital products, the real risk lies not in the technology itself but in its application, according to additional insights from healthcare IT professionals. The challenge is ensuring that these tools are effectively integrated into existing workflows and aligned with organizational goals.

Building Tailored Solutions

Mercy, a leading health system, has adopted product development principles used by large technology companies. This approach emphasizes agility, continuous improvement, and user-centric design. Scroggins highlights several key strategies:

• User-Centric Design: Engaging clinicians and staff in the development process to ensure that AI tools meet their specific needs.
• Agile Development: Using iterative cycles to rapidly test and refine products, allowing for quick adjustments based on feedback.
• Data Integration: Ensuring that AI systems can seamlessly integrate with existing data sources, such as electronic health records (EHRs), to provide accurate and actionable insights.

By focusing on these principles, Mercy has been able to develop AI tools that directly address the unique challenges of its operations. For example, they have created predictive models for patient readmissions, which help identify high-risk patients early and intervene proactively. These models are trained on internal data, making them more accurate and relevant compared to off-the-shelf solutions.

In Practice

The benefits of in-house AI development extend beyond just addressing specific challenges. Here are some practical outcomes observed by Mercy and other health systems:

• Increased Control: By developing tools internally, organizations have greater control over the features and functionality, ensuring they align with clinical workflows.
• Cost Efficiency: While initial investment can be high, long-term costs are often lower due to reduced dependency on external vendors and the ability to customize solutions without additional licensing fees.
• Data Privacy and Security: In-house development allows for tighter control over data privacy and security, which is crucial in healthcare where patient information must be protected.

However, this approach also comes with its own set of challenges. Scroggins notes that building a strong AI team requires significant investment in talent and resources. Maintaining and updating these tools can be resource-intensive. Despite these hurdles, the benefits often outweigh the costs, especially for large health systems with complex needs.

By leveraging in-house AI product design, health systems like Mercy are not only addressing their unique organizational challenges but also setting a precedent for how healthcare organizations can innovate and improve patient care through technology.

Key Takeaways

• Tailored Solutions: In-house AI development allows health systems to create tools that directly address their specific needs.
• User-Centric Design: Engaging clinicians in the development process ensures that AI tools are practical and effective.
• Agile Development: Iterative cycles help refine products quickly based on real-world feedback.
• Data Integration: Seamless integration with existing data sources enhances the accuracy and relevance of AI models.
• Increased Control and Cost Efficiency: In-house development provides greater control over features and can be more cost-effective in the long run.
• Data Privacy and Security: Tighter control over data management is crucial for protecting patient information.