AI-based Product Management for Improving Cloud-based Healthcare Information Systems

Summary

Public assistance healthcare programs like Medicaid and SNAP have been associated with increasing costs for years. Medicaid and SNAP costs have increased 9.9% year over year from 2021 to 2023, and average annual spending is expected to peak in FY2024 by going well beyond $800Bn. These are symptoms of broader problems, such as recurring costs, inefficient benefit and case-based processing, legacy information systems, program bottlenecks, time-consuming healthcare services delivery, lack of digitization, and low-quality patient and provider experiences.

Introduction

In this article, I discuss the most-critical business and technology challenges faced by public-assistance healthcare programs like Medicaid and SNAP, why their digitization is an increasingly important decision, and how AI-driven product management can aid in successful digital transformations of legacy healthcare information systems and processes. I also highlight product management frameworks, strategies, principles, AI models, AI strategies and use cases to digitize healthcare information systems across the US.

Challenges

Several critical problems associated with public assistance healthcare programs like Medicaid and SNAP emphasize the need for digitization. This section discusses these challenges and their impact on Medicaid, SNAP, and the recipients.

Manual and Paper-Based Processes: Public assistance programs often rely on manual and paper-based processes, which are time-consuming, prone to errors, and result in significant administrative burdens. The manual processing of applications, eligibility verification, and benefits distribution leads to delays and inefficiencies in providing timely assistance to those in need and heightened risks of fraud and abuse.

Insufficient Data Insights: Limit program administrators from making informed decisions and providing high-quality services to beneficiaries. Without the ability to identify trends in healthcare service utilization, administrators may be unable to understand the needs of patients, leading to wasted resources, suboptimal healthcare outcomes, and even denied access to necessary care.

Eligibility Determination Complexity: Determining eligibility for public assistance programs can involve extensive documentation, income
verification, and compliance with specific criteria. Manual processing and lack of automation contribute to delays and errors in eligibility determination, potentially leaving eligible individuals without access to necessary healthcare or food assistance.

Legacy Platforms: Hinder the efficiency and effectiveness of healthcare services. Outdated technological systems can pose several challenges for public-assistance healthcare programs, including security risks, data integration issues, and compatibility problems.

Administrative Burdens: Additional delays in accessing care, higher costs, and the utilization of additional staff resources further create administrative burdens that increase program and operational
expenses.

Difficulty in Adapting to Changing Needs: Public assistance programs must adapt to changing socioeconomic conditions, legislative changes, and emerging healthcare challenges. Manual or legacy processes make it difficult to respond quickly and efficiently to these evolving needs, resulting in delayed policy implementations and potentially leaving vulnerable populations without essential support.

Far from being impossible, the solution lies in cutting-edge, product-management-driven methodologies that leverage digitization. This allows for next-gen solutions that deliver improved end-user experiences, streamline processes, improve accessibility, enhance data integration and analytics, reduce fraud, promote user engagement, and reduce operational costs.

In the subsequent sections, I detail key product management strategies and frameworks that product managers can leverage with a combination of AI business processes to effectively drive the digitization of public assistance healthcare programs, improve patient and provider experiences, and ensure better outcomes for all end-users and stakeholders.

Product Management Strategies

User-Centric Approach: This involves understanding the needs and challenges of the beneficiaries and stakeholders of public assistance healthcare programs. Product managers can conduct user research, interviews, and usability testing to gather insights and ensure that the digitization efforts are aligned with the users' requirements. AI can be integrated by leveraging machine learning algorithms to analyze user data, identify patterns, and personalize the user experience, thus enhancing the effectiveness of the digital product.

Agile Development Methodologies: Agile methodologies enable iterative and adaptive approaches to software development, which are well-suited for the dynamic nature of public assistance programs. Implementing agile practices like Scrum or Kanban allows for flexibility and responsiveness to changing requirements. Frequent feedback loops and short development cycles facilitate the early detection of issues and provide opportunities for quick iterations to enhance digital solutions for meeting evolving healthcare landscape needs.

Continuous Improvement Cycles: Continuous improvement is essential for the long-term success of digitized public assistance programs. Regular evaluation and feedback mechanisms enable programs to identify areas for enhancement and optimization. Performance metrics, user analytics, and user feedback surveys can provide valuable insights into the effectiveness of digitized systems.

AI-Driven Decision Making: Leverage AI models and algorithms to analyze large volumes of healthcare data, identify patterns, and generate actionable product feature insights. AI can aid in optimizing healthcare services (think chatbots, intelligent eligibility predictions, automated case-based processing), personalized patient care, and predicting health outcomes. Good examples are AI models or LLMs that help build virtual agents, RAG for forecasting and generating case volumes for eligibility processing.

Data Privacy and Security: Prioritize robust data privacy and security measures to safeguard patient information. Comply with regulations like HIPAA and implement encryption, access controls, and secure transmission protocols.

Product Management Frameworks

The Value-Proposition Canvas: Used to pinpoint the specific needs and preferences of target customers, develop a product that addresses those needs, and identify the particular features and benefits that underserved populations are looking for in a digital solution. This framework is essential because it helps deliver mission-critical features to optimize healthcare information system processes. It is beneficial for integrations with providers and data interfaces. For example, it can be used to modify eligibility determination calculation parameters to predict which applicant is eligible intelligently and needs premium payments VS which applicant does not require premium payments due to Type of Assistance
(TOA).

The Kano Model: Used to determine the most important features to customers, prioritize development efforts accordingly, and identify the features that had maximum user value within shortened time constraints and that could require the least product development efforts. This framework is essential because it provides an approach to building digital Self-Service and Worker Portals that are in sync for healthcare applications and application processing with real-time insights on case flows, requests for more information, and case document verifications. For example, it can be used to prioritize different case flows for role-based caseworker and supervisor evaluations to reduce incorrect processing and inconsistencies in case information and expedite responses to RFIs.

Lean Startup: The Lean Startup frameworkemphasizes a build-measure-learn approach, where product managers build minimum viable products (MVPs), measure their impact, and learn from user feedback. Product managers can use the Lean Startup framework to iteratively develop and
refine requirements for healthcare information systems based on real-time data and insights. Such an approach can also allow for the building of optimal flows that simulate case processing, managed care interactions, benefits determination, and eligibility predictions for targeted interventions to improve patient and provider experiences.

Finally, I want to highlight use cases that integrate the above strategies and frameworks for allowing product leaders to digitize public assistance programs through healthcare information systems successfully. The use cases allude to mission-critical end-to-end product management AI-based product management initiatives I led at Deloitte for helping multiple US States digitally transform their public-assistance information systems and operations using advanced AI-based techniques. They also provide insights on the results, the impact created, and why AI-based digital product management is critical in improving public assistance information systems and subsequently mission-critical healthcare programs.

Conclusion

By combining product management principles with appropriate strategy frameworks, public assistance programs can evolve to meet the changing needs of beneficiaries, deliver more efficient services, and achieve positive social impact. As discussed in this article, digital product management’s role in next-gen service delivery is instrumental in advancing health equity and reducing healthcare disparities. Digital product managers should continue innovating and refining product offerings and systems implementation to prioritize enhanced healthcare services delivery, reduction in operational costs, and state-of-the-art user experiences.