COVID-19 has underscored some of the major challenges faced by stakeholders and health professionals looking to leverage personal health information to stop the spread of COVID-19. These challenges have been heightened by the need to integrate the "the conditions in which people are born, grow, live, work and age that shape health. Social determinants of health include factors like socioeconomic status, education, neighborho... More data (SDOH) into patient EHRs. This section highlights some of those major challenges.
Privacy and trust concerns are in conflict with the fluid exchange of information. Existing privacy rules – via HIPPA and other federal, state, and local laws and regulations may hinder data collection and sharing. Additionally, individuals may be hesitant to share personal data or data about their community, regardless of privacy rules because of a reticence to share sensitive health issues or not feeling comfortable with government monitoring key movements or actions. This may lead to existing processes resulting in incomplete data collection. For example, if data is collected only in clinical settings patients may be less willing to share information – particularly if they are dealing with new providers or those that they have not built trusting relationships with. Community groups may be more comfortable sharing data, but often lack the technological know-how or infrastructure to do so.
Data on immediate social needs should be considered independently from broader, long-term SDOH indicators. Public health data collection during the COVID-19 pandemic is more dynamic than SDOH data collection, making it more complicated – and at the same time vital – to understand how the situation is evolving. Meanwhile, social needs are likely to evolve during a crisis in ways that may not be reflected in more static SDOH data. It is important for policymakers and practitioners to respond rapidly to immediate social needs. SDOH data can be used to gain a baseline understanding of social needs, while public health data collected during the pandemic can help understand how those needs may be changing.
Data interoperability is limited by legacy system disparities across geographic levels. There are challenges associated with data interoperability, file formatting, and structure due to the variety of legacy systems at play across the federal, state, and local levels. For example, many states maintain different platforms to collect, clean, aggregate, and share key health data metrics during a disaster that are not standardized across systems. As a result, data must be reformatted once submitted to the federal government, which can be a cumbersome process requiring time and money. Outside of government, it is also difficult for community-based organizations and other entities to seamlessly share data, especially on the social determinants of health.
Data flows and situational awareness. The Federal Government needs to improve its situational awareness through improved data systems that can aggregate systems for data applicability in local communities. Given the range of data systems that currently exist across the United States and the changing requirements of data reporting, many state governments do not successfully gather information from local clinics and public health authorities. This creates difficulties in maintaining robust surveillance systems across the United States and only highlights disparities between states with different COVID infection rates.