Barriers to timely data collection and exchange hindered health departments throughout COVID-19, from fax machines creating bottlenecks for disease monitoring to inconsistent reporting of race and ethnicity. Modernizing public health data systems has become a bipartisan postpandemic imperative, with President Trump engaging the US Digital Service to improve data exchange and President Biden issuing an Executive Order on his second day in office to advance public health data and analytics.
These initiatives should be informed by the experience of digitizing health care delivery. The Health Information Technology for Economic and Clinical Health (HITECH) Act drove the near-universal adoption of certified electronic health records (EHRs). However, progress was not without pitfalls, from regulatory requirements affecting EHR usability, to new reporting, billing, and patient engagement processes disrupting workflows, to proprietary standards hindering interoperability.1 This Viewpoint explores lessons from HITECH for public health data modernization for COVID-19 and beyond.
The State of Public Health Data Systems
While public health pioneered the collection, standardization, and application of population-level data, the field remains reliant on 20th-century tools. Systems are siloed, limiting information exchange between public health and medicine. Although HITECH sought to incentivize hospitals to submit public health data electronically, a report based on 2015 information indicated that 970 of 4043 (24%) hospitals either claimed exclusion or did not report data for syndromic surveillance, and the majority did not actively report to specialized registries.2Even when data exist, public health is unequipped to leverage them, with informatics specialists accounting for only approximately 1% of health department roles.3
The National Academy of Medicine recently highlighted how underinvestment in digital capabilities severely constrained the pandemic response in the US.4 Delayed laboratory reporting hindered surveillance, while data sharing barriers among health systems, health departments, and government impeded decision-making. Furthermore, gaps in race and ethnicity data exacerbated health inequities.
In response, health departments formed cross-sector partnerships, collaborating with academia to construct dashboards and leveraging anonymized data sets created by technology companies. However, these were stopgap measures, not sustainable solutions. Even though Congress has appropriated more than $1 billion for the CDC’s Data Modernization Initiative (DMI), such overdue resources are the means, not the ends. Policy makers need to learn from HITECH—which had 30-fold more funding and still encountered roadblocks—to ensure data systems meet the needs of the public health community, and in turn, the US population.
Like public health today, health care in 2009 predominantly relied on paper-based systems despite the increasingly digital nature of US society. The HITECH Act sought to address this gap by accelerating EHR adoption. However, building EHRs was no guarantee of adoption by hospitals and use by clinicians. Thus, it was imperative for policy makers to invest in support systems, incentive mechanisms, and standards development. To this end, Congress established the mandate for the Office of the National Coordinator (ONC) for Health IT through HITECH, which provided authorities for voluntary certification of EHRs, establishment of data standards, and funding for technical support and workforce training. HITECH also allocated an estimated $35 billion for the Meaningful Use program, which offered incentive payments to clinicians and health care organizations whose adoption and utilization of EHRs and related health IT systems met predefined criteria and reporting requirements (eg, electronic reporting of quality data).5
Although HITECH achieved its core goal of digitizing US health care, the historic effort encountered notable challenges and missed opportunities. With the benefit of hindsight, 5 lessons from HITECH are pertinent for achieving effective public health data modernization.
Focus on outcomes over process. The key shortcoming of HITECH was the way timelines and process measures reduced EHR adoption to the lowest common denominator of regulatory requirements as opposed to emphasizing the meaningful integration of IT systems for clinical benefits. For public health, policy makers must first define the long-term outcomes of DMI and craft a strategy that accounts for the variation in starting capabilities across the local, state, territorial, and federal level before imposing new requirements on health departments. Public Health 3.0—the federal government’s existing roadmap for upgrading public health infrastructure—provides a key guide for orienting data and informatics investments, emphasizing the integration of public health data, clinical information, and metrics related to the social determinants of health.6
Align governance to foster accountability. Data modernization initiatives are as much an exercise in change management as they are in technology deployment. The challenge for public health is the decentralization of governance across 50 states and 8 territories and freely associated states and the District of Columbia. HITECH addressed coordination challenges for health care by establishing ONC as the leader for implementing regulatory reforms and infrastructure investments. Public health should use these existing authorities to align work across jurisdictions. Specifically, CDC should leverage appropriated funds to describe and prioritize the use cases for modernization, which can then serve as guideposts for ONC to drive standards adoption consistently throughout the country.
Build for the future instead of preserving the present. Public health must avoid digitizing where the field is today and instead build systems to support where the field is going. In health care, the rush to implement HITECH meant that the care experience was digitized, but not transformed to support population health, care coordination, and intuitive user interfaces. As a specific example, the proprietary standards used when translating paper-based records into electronic form replicated existing inefficiencies, drove needless repetition, and prevented near-term interoperability. This lack of redesign is partially responsible for health IT’s productivity paradox.7 As consumers increasingly shift to mobile platforms and health care increasingly transitions to the cloud, desktops and hard drives will become as outdated as fax machines. Public health must therefore build for the future, designing systems capable of integrating traditional (eg, vital statistics), expanded (eg, health records), and novel (eg, social media) data signals. The application of artificial intelligence and machine learning methods to these data can help generate the kind of timely, granular, and actionable information needed to better tailor the essential services of public health (eg, disease monitoring) to specific community and population contexts.8
Invest in public health and informatics workforce development. HITECH sought to address the variation in the digital capabilities of health systems by creating regional extension centers, which funded technical support for EHR installation and utilization and reached nearly half of all primary care physicians in the US. HITECH also helped train nearly 20 000 new health IT professionals by funding workforce development programs at universities and community colleges. Similar capacity gaps exist in public health, with poor uptake of EHRs and low participation in Digital Bridge, a CDC public-private partnership for interoperability that has piloted electronic case reporting between departments, health systems, and EHR vendors but has been limited to 8 sites.9 Consequently, public health may benefit from an analogous approach for engaging health agencies about adopting new tools and platforms. Likewise, HITECH’s university partnerships and curriculum development initiatives may offer a useful model for building a pipeline of informatics professionals for health departments.
Take a portfolio approach to incentives and implementation. HITECH’s flaw was anchoring the law to a single program, ie, Meaningful Use, such that digitization became an exercise in compliance rather than an evolution in system capabilities. To avoid collapsing data modernization into a checkbox, policy makers should be clearer about the goals of reforms and less prescriptive about the means for change. Indeed, scaling data modernization programs will require using diverse policy, program, and financial levers, from incorporating interoperability into Public Health Accreditation Board standards to adjusting the ease of grant making to incentivize program participation. As recommended by the National Academy of Medicine, the Council of State and Territorial Epidemiologists, and the Association of State and Territorial Health Officials, this portfolio of strategies should include public-private partnerships.10 For HITECH, the private sector played a critical role in defining both national standards-based networks and regional health information exchange networks, which helped accelerate progress for interoperability. This partnership model, which is a key pillar of Public Health 3.0 and the National Academy of Medicine’s postpandemic recommendations for public health, can enhance the public health community’s data and analytics capabilities, from open access data sets for disease forecasting, to application programming interfaces for data exchange, to bespoke tools for specific public health needs.
Just as digitization of care delivery under HITECH was integral for transforming the health care system following the Affordable Care Act, so too will the modernization of data systems and IT practices be a critical component of reimagining US public health following COVID-19. By treating information as a determinant of health, public health will be better equipped to manage the everyday challenges and emergency situations facing health departments in the 21st century.
Corresponding Author: Karen B. DeSalvo, MD, MPH, MSc, Google Health, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (firstname.lastname@example.org).
Conflict of Interest Disclosures: Mr Kadakia reported previous employment at Cleveland Clinic London, Blue Cross Blue Shield of North Carolina, and the FDA and has received consulting fees from the National Academy of Medicine, unrelated to this article. Dr Howell reported employment by Google and owns equity in Alphabet; and receives royalties from McGraw-Hill for the textbook Understanding Healthcare Delivery Science. Dr DeSalvo reported employment by Google as Chief Health Officer, serving on the board of directors for HL7 and Welltower, and previous employment as the National Coordinator for Health Information and on the board of directors for Humana.
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