Cohesion in the Fragmented World of Healthcare Data

Discussing public health data issues as they pertain to Truebit’s potential is Dr. Steven Teutsch, MD, MPH. Teutsch is an adjunct professor at the UCLA Fielding School of Public Health; and a Senior Fellow at the Leonard D. Schaeffer Center for Health Policy and Economics at the University of Southern California. His experience in the fields of public health spans a career over 40 years. Teutsch has published over 200 articles and eight books on topics that include technology assessment, health services research, and surveillance. The aptly named CDC Steven M. Teutsch Prevention Effectiveness Fellowship provides economists, policy analysts, and decision scientists the opportunity to apply quantitative methods to the science of health protection and promotion, and disease prevention. Teutsch is a leading name in the world of public health surveillance and we are thrilled to have him write for us.

Cohesion in the Fragmented World of Healthcare Data

By Steven M. Teutsch

Obtaining accurate public health data means the difference between a healthy, functioning society that is responsive to rising threats and one where lives are shortened and endangered due to inaction and mismanagement. Truebit could provide the solution to more timely, complete, and accurate information through data verification and management by decentralized stakeholders who carry out the functions currently handled by numerous, often disconnected agencies. This trustless system has the potential to improve the current complex processes used to not only interpret data but also communicate it with the proper channels to ensure timely and effective action.

Public health touches our lives every day. Safe food and water, control of infectious disease, sanitation and environmental protection, oversight of the medical care system, maternal and child health, regulation of drugs and much, much more. To do its job effectively, it needs accurate, timely, and meaningful data.

The COVID pandemic exemplifies the importance of data. Consider all the ways data are needed and where that data could come from:

• Identification of an outbreak (emergency rooms, death records, laboratory tests)
• Monitoring the spread of the outbreak (case reports)
• Monitoring the severity of the outbreak (death records, intensive care unit admissions)
• Monitoring the virus (laboratory testing)
• Control efforts (policies on mask wearing, social distancing, school closures, business practices)
• Behaviors (observations of mask wearing)
• Public attitudes (surveys)
• Vaccination rates
• Non-observable data, i.e. how many people are infected with COVID-19 but asymptomatic

And that information is usually needed about ethnicity, socio-economic status, gender, age, and detailed geographic information.

To enable public health agencies to take action, that information is needed rapidly and at each jurisdictional level. Constitutionally, health is the responsibility of the states and each functions largely independently, leaving a complex web of data systems with a plethora of data sources. Even such basic information as case counts requires information from physicians and clinics, laboratories and hospitals each having its own data system. Cases can be provisional or confirmed (e.g., with laboratory confirmation). Public health often needs identifiable patient data, for example, to follow up on individual cases, and, hence, is exempt from Health Information Portability and Accountability Act (HIPAA) privacy provisions. Nonetheless, it is important to respect the confidentiality of patient information and maintain the confidence of the clinical care system which is required to adhere to HIPAA requirements as well as public health reporting requirements. The willingness or ability of these providers to submit data is highly variable and missing data are common.

Public health has developed a variety of mechanisms for addressing underreporting and biases in data streams as well as ways to reduce duplication and correct records. All of these efforts, though, are hampered by the chronic underfunding and understaffing of public health agencies. Delays are common and lead to unnecessarily slow response. In the COVID pandemic, early efforts to follow up cases and contain the spread were doomed to failure and gave way to more population-wide measurement and containment efforts.

Technologies, such as Truebit, can efficiently harness decentralized data streams and the strengths of decentralized stakeholders to perform many of the data curation and analytic tasks currently handled in weakly connected government agencies. In addition to improving current processes, stakeholders can find innovative solutions to enhance analyses as well as interpret and communicate results.

Public health surveillance (the ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning, implementation, and evaluation of public health practice) is a core function of public health. Improving the systems to make them responsive to changing needs, assuring the timeliness and accuracy of the data, analyzing and interpreting the data, providing it to those who need to act on it, and monitoring the actions taken and their effectiveness can greatly enhance the performance of public health systems. Truebit has the potential to bridge the gap between inefficient data reporting and timely public health action.