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Looking Forward: How AI and Automation Can Transform Health and Human Services



As we navigate the transformative era of health and human services (HHS), artificial intelligence (AI) and automation are emerging as powerful tools that are reshaping how agencies operate and deliver services. According to the report “How AI & Automation Are Shaping the Next Era of Health & Human Service” a Government Technology and Governing Guide, made in collaboration with APHSA, and the 2024 “Digital Counties Survey” by the Center for Digital Government, AI is making significant strides in enhancing customer and employee experiences, improving service delivery and addressing complex challenges.

AI’s potential to revolutionize HHS is seen in its ability to streamline constituent data, aiding the no ‑wrong ‑door approach, and to enhance accessibility through features like real-time translations and adaptive learning tools. It empowers agencies to target resources more effectively, ensuring underserved communities receive the support they need.

Understanding AI’s Potential

According to the 2024 ”Digital Counties Survey,” counties are exploring various applications of AI, with notable areas of focus and experimentation. The percentage of counties surveyed exploring the focus areas is shown in the table:

Area

Category

Percent

Focus

Personal productivity

23.9%

AI-augmented application development

20.9%

Fraud detection and prevention

14.9%

Natural language processing

13.4%

Decision support

11.9%

Image detection and classification

10.6%

Adaptive learning and personalized instruction

10.4%

Testing and Pilots

AI/machine learning

36.4%

Low-code/no-code development

19.4%

Robotic process automation

9.1%

Enhancing Customer Experience

AI enhances customer experience in HHS by streamlining constituent data and implementing a no-wrong-door approach. It allows residents to easily find necessary forms without navigating complex departmental systems. Agencies benefit by using AI to target resources effectively, improving outcomes in underserved areas.

AI also improves accessibility, integrating advanced tools like screen readers and audio aids for users with hearing or vision impairments. Additionally, real-time AI translations help overcome language barriers, making services more inclusive.

Use Cases

  • Unified data connection: Implement AI to connect constituent data across departments

  • Simplified access: Use AI to simplify document access for residents

  • Improved resource allocation: Identify areas where AI can enhance resource allocation to improve community outcomes

  • Enhanced accessibility: Integrate accessibility features, such as screen readers and audio aids

  • Overcome language barriers: Use AI-driven, real-time translations to break language barriers

Improving Employee Experience

HHS agencies are struggling with workforce shortages amid rising demand for social services. To address these challenges, agencies can implement several AI-driven solutions:

  • Sandboxed large language models (LLMs): Develop AI models tailored to agency policies to help staff find specific answers efficiently.

  • Modernized human-capital management: Use AI to speed up hiring, tailor training programs, and automate routine administrative tasks.

  • Case management: Leverage AI to review case records, analyze data, and support caseworkers.

  • Unified information technology service management (ITSM) platforms: Digitize workflows, automate benefit applications, and summarize case files with AI-enabled systems.

  • Policy and regulation assistance: Create an AI-powered chatbot that provides instant answers to staff questions about agency policies and procedures.

  • Complex case queries: Develop an LLM that helps caseworkers quickly find and interpret information related to complex or uncommon cases.

  • Case record review: Deploy AI to analyze case records, flag important details, and provide summaries that help caseworkers make informed decisions.

Reducing Administrative and Regulatory Burdens

Agencies can leverage AI to update and enhance HHS systems, shifting from merely transactional platforms to ones that address the varied needs of customers across the full spectrum of services like housing, food assistance, and healthcare. AI assists agencies in reviewing and refining their rules, policies, and procedures. Additionally, it improves outcomes by managing routine but essential tasks in delivering health and human services.

Use Cases

  • Personalized service delivery: An AI system analyzes client data to tailor support services based on individual needs.

  • Policy and procedure optimization: AI reviews and suggests improvements to agency rules, policies, and procedures. AI analyzes historical data and current procedures to identify inefficiencies and compliance issues, recommending changes to streamline processes and enhance program effectiveness.

  • Routine task automation: AI handles repetitive administrative tasks to free up human resources for more complex duties.

Promoting System Efficiency and Effectiveness

AI can enhance program integrity and reduce errors in HHS agencies by detecting fraud and irregular activities, thus safeguarding public funds. It helps minimize inconsistent decision-making in areas such as child welfare and eligibility determinations. However, AI should not be used for making final eligibility decisions. Additionally, AI can address issues like benefits cliffs—unexpected reductions in public benefits due to minor increases in earnings—by offering insights and guidance to help clients and employers navigate these changes without disrupting benefits.

Use Cases

  • Fraud detection and prevention: AI identifies fraudulent activities and irregular patterns in benefit application.

  • Consistency in decision-making: AI assists caseworkers by providing data-driven recommendations based on historical decisions and best practices, helping to ensure fair and consistent outcomes in child welfare cases.

  • Managing benefits cliffs: AI helps clients navigate benefits cliffs caused by small increases in earnings by monitoring client earnings and benefits, providing timely alerts and guidance to help clients understand and manage the impact of a pay raise on their benefits, ensuring a smoother transition and avoiding lapses.

Predicting Patient and Community Health Needs

AI can enhance public health outcomes by predicting future needs and preparing agencies to address them effectively. It holds promise for personalized treatment plans and precision medicine, though these applications may be more advanced. Currently, AI is used to drive analytics and insights, aiding in the development of models and simulations to understand health interventions’ potential outcomes. For example, AI can inform strategies for preventing substance and opioid misuse.

Use Cases

  • Predictive health needs forecasting: AI forecasts future public health needs to better allocate resources and plan interventions. An AI system analyzes historical health data and trends to predict potential outbreaks or increases in specific health issues, enabling agencies to proactively prepare and allocate resources.

  • Personalized treatment plans: AI assists in creating customized treatment plans for patients based on their individual health data.

  • Substance and opioid misuse prevention: AI informs the development and implementation of substance and opioid misuse prevention strategies.

Understanding the Challenges and Considerations

AI has the potential to transform the HHS sector by streamlining operations, improving service delivery, and enhancing client experiences. However, if we do not proactively address the challenges AI presents—such as ethical considerations, data privacy, and equitable access—we risk creating unintended consequences that could harm the very customers we aim to serve.

Data Privacy and Security

Governments must ensure the protection of sensitive health and personal data used for AI predictions. This requires robust AI governance with a focus on:

  • Strong encryption

  • Secure data storage

  • Strict access controls

These measures help guard against breaches and unauthorized access.

Algorithmic and Data Bias

AI systems can inadvertently perpetuate or worsen existing biases present in the training data. To mitigate this risk:

  • Use diverse and representative data sets

  • Conduct regular audits of AI/ML models

These practices help reduce bias and ensure fairer outcomes.

Perpetuating Inequity

AI outputs may unintentionally lead to inequitable access to HHS services, particularly in marginalized communities or rural areas. To address this:

  • Establish policies to assess the impact of AI models on different populations

  • Monitor and adjust strategies to avoid reducing access to essential services

  • Evaluate how AI affects various client groups to prevent unintended consequences

The “Black Box” Problem

AI systems, especially complex neural networks, can lack transparency and accuracy, leading to challenges in understanding decision-making processes. Issues include:

  • Combatting potential AI-generated hallucinations that misrepresent or falsify information

  • Prioritizing AI explainability in governance policies

  • Holding third-party tools and vendors to the same standard

  • Ensuring that AI systems’ decision-making processes are transparent is crucial

Transparency and Accountability

Agencies must clearly define responsibility for AI system outputs, especially when issues arise. Effective strategies include:

  • Robust AI governance

  • Human-in-the-loop review

These mechanisms provide critical checks and balances, ensuring accountability and addressing problems as they occur.

Employing Best Practices

Collaborating with Peers

Agencies should collaborate with counterparts in other jurisdictions to exchange knowledge about AI tools and emerging solutions. This collaboration helps in understanding different approaches and enhancing AI implementation strategies.

Leveraging Available Tools

Most HHS agencies will use third-party AI tools, but those seeking custom solutions can benefit from reference AI coding and language models to speed up development. Using these tools can help tailor AI solutions to specific needs.

Prioritizing Human-Centered Design

Incorporating feedback from constituents and frontline staff from the beginning is essential. Agencies should use mechanisms like customer- and community-advisory groups to gather input and involve staff who have in-depth knowledge of policies and processes in the design of AI systems.

Focusing on Empathetic Messaging

Effective change management involves empathetic communication. Leaders must articulate how AI will enhance interactions with government for constituents and internally share their vision for AI with staff to demonstrate its benefits.

Working with AI Implementation Partners

Agencies should carefully vet potential technology providers to ensure their commitment to responsible AI practices. This involves reviewing past projects, certifications, and adherence to industry standards. Contracts with third-party providers should clearly define expectations for data privacy, fairness, transparency, and accountability.

Conclusion

As we stand at the forefront of a new era in HHS, the transformative power of AI and automation offers unprecedented opportunities to enhance service delivery, improve outcomes, and address pressing challenges. From streamlining customer interactions and personalizing care to optimizing employee workflows and predicting future health needs, AI has the potential to revolutionize how HHS agencies operate.

However, the successful integration of AI requires careful consideration of several factors. Ensuring data privacy and security, addressing algorithmic biases, and maintaining transparency are crucial to building trust and ensuring equitable access to services. Furthermore, prioritizing human-centered design, fostering collaboration with peers, and selecting responsible AI partners will be pivotal in navigating this complex landscape.

By embracing these best practices and remaining vigilant about the ethical implications of AI, agencies can leverage these powerful tools to not only enhance their operations but also improve the lives of the individuals they serve. As we continue to explore and innovate, we must remain committed to using AI in ways that promote fairness, efficiency, and inclusivity, paving the way for a future where technology and human services work hand in hand to create a better world for all.

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