The PULSE (Public Health Use Case and Learning Scaling Engine) program represents a significant step in the adoption of generative artificial intelligence within the U.S. public health sector. Involving OpenAI, Anthropic, and Accenture, ten state, local, tribal, or territorial jurisdictions will test AI models across five critical use cases: biosurveillance and drug-wave prediction, social determinants of health, operations efficiency and community feedback analysis, public communications with multilingual translation, and automated clinical data retrieval via FHIR. However, the announcement by the Coalition for Health AI (CHAI) leaves numerous questions unanswered regarding governance, privacy, and evaluation. For tech and development companies, such initiatives highlight the need for robust and customized AI systems that integrate securely into healthcare environments.
From a technical perspective, it is striking that CHAI has not specified which versions of OpenAI models (GPT-4, GPT-4o, etc.) or Anthropic models (Claude 3.5 Sonnet, Opus) will be used, nor how they will be assigned to the different pilots. Likewise, the evaluation, privacy, security, and human review requirements for each use case remain undefined. This contrasts with the NIST AI Risk Management Framework recommendations, which emphasize evaluating systems according to their intended use, operating environment, and affected parties. The lack of clear criteria can create uncertainty for participating agencies, which need to ensure that AI outputs—from public communications to automated diagnostics—are reviewed by qualified personnel before use. Here, the development of custom software that incorporates human validation and auditing layers becomes essential to mitigate risks.
Another critical aspect is data protection. The program will operate under HIPAA in cases where electronic protected health information (ePHI) is handled. However, CHAI has not clarified whether the pilots will use identifiable records, anonymized data, synthetic data, or aggregated datasets. Nor have retention periods, access controls, storage requirements, or rules for submitting protected information been defined. In this context, cybersecurity becomes a fundamental pillar: any cloud infrastructure hosting these models, whether AWS or Azure, must comply with the technical safeguards required by regulations. Companies like Q2BSTUDIO offer consulting services in cloud AWS/Azure that enable the design of secure architectures with encryption at rest and in transit, role-based access controls, and audit logs—aspects that health agencies should consider from the outset.
The proposed use cases have profound implications for public health management. Biosurveillance and drug-wave prediction, for example, could leverage language models to analyze large volumes of epidemiological surveillance data, but accuracy and potential biases require constant validation. Automated clinical data retrieval via FHIR is another promising front: generative models could build queries, extract records, summarize information, or combine all these functions. However, if the model generates incorrect queries or incomplete summaries, who verifies the quality before that data is used in clinical or administrative decisions? A viable solution is to integrate AI agents that act as supervised assistants, where the system proposes results but a healthcare professional reviews them before execution. This hybrid approach combines AI efficiency with human judgment, something many software companies are already implementing in their platforms.
From a business intelligence perspective, tools like Power BI could enhance the visualization of data generated by AI models. For example, social determinants of health (SDoH) maps would benefit from interactive dashboards that correlate demographic, geographic, and clinical variables. Combining generative models with BI/Power BI platforms would allow public health officials to quickly identify patterns and trends, provided the underlying data is reliable and well-governed. Q2BSTUDIO, as a software and technology development company, offers integration services between AI, cloud, and BI systems, facilitating such synergies while ensuring data pipelines meet quality and privacy standards.
Scalability is another relevant point. The PULSE program will only reach ten jurisdictions, but the intention is to generate implementation guides for hundreds of agencies nationwide. Differences in size, budget, technical infrastructure, and staff capabilities are enormous. A tribal agency with limited resources does not have the same needs as a large city health department. The playbooks to be published in 2027 must be flexible enough to adapt to diverse contexts. Here, custom software development makes sense: each agency may need specific customizations in workflows, integrations with legacy systems, or adaptations to local regulations. Standard solutions rarely fit perfectly, and companies offering custom applications are better positioned to address this heterogeneity.
Governance remains the major pending issue. The announcement does not detail how model outputs will be reviewed, whether AI-generated public communications must be approved by staff, whether multilingual translations will be validated, or whether biosurveillance results will be used only for testing or incorporated into real operational workflows. The NIST guidance on generative AI recommends identifying which functions require human oversight and training users to understand system limitations. Without these mechanisms, the risk of errors with serious health consequences is real. Agencies will need to implement dashboards with performance, accuracy, and bias indicators, which can be achieved through customized BI platforms and monitoring agents.
Finally, the role of technology providers like Q2BSTUDIO in this ecosystem is twofold: on one hand, advising on the selection of appropriate models and cloud configurations; on the other, developing the integration, security, and governance layers that are missing from the initial proposal. The company, with experience in AI projects, cloud AWS/Azure, cybersecurity, and BI, can help health agencies translate the ambitions of the PULSE program into robust and responsible implementations. While the Coalition for Health AI defines evaluation and data protection criteria, the software industry must be prepared to offer solutions that are not only technically sound but also ethical and aligned with real public health needs.
