It wasn't long ago that to feed a family for the week, someone needed to travel to a dozen different businesses to purchase all the necessary ingredients and supplies. Now, a single trip to the local supermarket covers everything. Imagine if making data-informed public health decisions were this easy. But an often antiquated and heavily siloed data landscape has made this task nearly impossible. Much like how businesses have realized efficiencies from integration, whether through the adoption of an Enterprise Resource Planning system or by simply selling bread and produce in the same store, public health can also benefit from increased interoperability. Achieving this will require a new approach and the adoption of some best practices from the private sector, specifically a business model for public health data, better partnerships with health care, and sustained funding. If public health truly benefits us all, there needs to be more effective ways of communicating these benefits to increase support and funding for its critical infrastructure and gain efficiency with external data partners. There are examples of data integrating effectively and efficiently for benefits that extend beyond public health. For example, before anyone can take their first international trip, a state vital records program (which issues and maintains birth records) securely and confidentially verifies citizenship with the US Department of State to issue a passport. However, suboptimal data transactions provide a better use case example for where a business plan could help. Suboptimal data ecosystems are created by many legitimate complex factors—some by design to protect patient privacy and some that are simply bureaucratic governmental barriers or constructs of a bygone past that require leadership and culture change to overcome. 1 Anecdotally, we can't find anyone that would build from scratch the governmental public health data ecosystem that has evolved over decades to the one we have today. And we are certain there are those who feel that way about the private sector health and consumer data ecosystem as well. Our challenges are significant, will take years to work through, and require a commitment to sustained funding. Only then will we realize the data future that will make our communities safer and our children and grandparents live healthier, longer lives. Knowing the complexities, it will take time to gain an agreed definition and detail. Any plan moving forward should include the following considerations. Define and Build the Public Health Data Business Model In a snapshot, a business model is the way an organization creates, delivers, and captures value. Why is it important for the public health data ecosystem to have a well-defined business model? In short, because the health of our communities depends on it. We have seen the effects of a nebulous governmental public health business model that lacks better inclusion of health care, industry partners, and payers such as Medicaid. In fact, there is no explicitly defined formal public health data business model, which is contributing to the challenges that public health faces when it tries to advocate for funding. Public health's continued capacity limitations, fragmented systems, and a lack of trust in data hold government officials back from being able to make the best decisions for public health intervention. As has been demonstrated throughout history, the "public health system" has experienced challenges that arise from its amorphous delimited siloed systems. 2 That's not to say that we haven't made progress, but to truly make long-term, system-wide gains, public health data needs a plan—specifically a business model. Developing a public health data business model begins with a well-defined business strategy. This strategy should reflect deliberate choices about what the "public health data business" will do and not do, and how it will create an advantageous mutually beneficial framework for collaboration among health care, tech, and governmental public health sectors, especially in a rapidly changing technology landscape. This is important because resources are limited, and it's critical that public health is clear on how it will best use its resources to serve its customers (ie, our communities) and decide whether strategy will drive structure or structure will drive strategy in the public health data business model. The key components of actualizing a business model require that the organization (ie, public health) formally and in written form (1) defines a value proposition; (2) understands and establishes how it will interact with its customers; (3) identifies channels for how value is delivered to its customers; (3) identifies revenue streams; (4) allocates assets and resources; (5) defines activities that deliver value; (6) establishes key partnerships that support the model and its value proposition; (7) and implements a cost structure. 3 The public health data business model should be designed to meet the needs of its core customers while remaining adaptable to the broader public health ecosystem. To be effective, it must be supported by strong leadership and workforce. The model should also shift its focus to actively engage with health care systems and technology firms, while strategically leveraging federal partners and funding pathways, including Medicaid, to ensure long-term sustainability and impact. Likewise, given the structure and governance of public health, sustainability depends on a clearly defined public health data business model. Such a model provides a foundation for navigating shifting funding landscapes by establishing reliable revenue streams, cost structures, and adaptable resources aligned with the evolving governmental funding environment. Through leadership and an empowered workforce, a public health data business model will effectively create a robust health ecosystem that brings tech firms and technology leaders, health care, payers, and community organizations together. Where do we start? Involve Health Care Providers and Technology Leaders in a Meaningful Way The age-old adage of "together we are stronger" is also true in this situation. There are significant benefits to health care, governmental, and the tech industry working together to tackle the health ecosystem challenges that we face today to bridge interoperability, privacy and security, analytics, and ethical use of data for action. But how do you integrate systems and thinking that often have very different goals (eg, the health of a population vs. the health of an individual vs. profits)? Collaboration that centers on sharing data and resources to improve everyone's health outcomes and providing systems that continue to inform decision-making across both sectors is the answer. Public health, technology firms, community organizations, and health care providers must be at many tables together. It is paramount that public health begins to express how wins benefit not just populations, but health systems, providers, and taxpayers. Fund Public Health Data Systems The silos of public health data not only encompass the individual data systems and programs but also extend to the funding structure. States are often forced to rely on individual, disease- or system-specific federal grants to modernize or maintain their data infrastructure. 4 This model never prioritizes interoperability or incentivizes leveraging the data systems that already exist in the health care sector. When large-scale, broad-scoped funding is made available to states, we know they can make bold advances. Both Michigan and Washington utilized resources from CDC's Data Modernization Initiative (DMI) to implement electronic case reporting systems and integrate hospital and laboratory data for enhanced interoperability and near real-time public health monitoring. 5-7 The DMI funding was a multi-year, multi-billion-dollar effort to modernize surveillance and data infrastructure across federal, state, tribal, local, and territorial agencies. 7 Since fiscal year 2020, CDC has directed over 1 billion to state, local, tribal, and territorial jurisdictions via DMI. 8 Expanding flexible funding models like the Public Health Infrastructure Grant removes funding siloes. Similarly, integration of Medicaid as a sustainable funding channel—through the CMS enhanced federal match program- offers significant support for eligible health IT projects. This program reimburses a percentage of approved development costs and covers a percentage of ongoing operations systems such as immunization registries and health information exchanges, making it a viable source of funding to support broader systems change. 9 Our call to action is for leaders from across health care, technology industry, community organizations, and governmental public health to work collaboratively to clearly define public health's data business model—the future of public health relies on it. With this business model, the nation can optimally fund our health ecosystem. Then, ultimately, we can (1) effectively measure and drive community and neighborhood health improvement; (2) support sustainable sources of funding, through Medicaid programs, providers, and health insurance plans with a quality and health measurement focus; and finally (3) ensure accountability for improving patient health, and the life-critical real-time, actionable data that mean the difference between no illness, an illness requiring hospitalization, or eventually premature death.
Offutt-Powell et al. (Tue,) studied this question.