The CDC’s DMI: A turning point for data quality in public health

The CDC has advanced national health threat surveillance via the Data Modernization Initiative (DMI), but there's still room for progress.
data quality in healthcare

For decades, public health departments relied on pen and paper to collect patient information. This was the standard for a variety of reasons, primarily because so many departments lacked the resources to invest in technology and the personnel necessary to ease the transition to a digital system. However, the COVID-19 pandemic illuminated an immediate and urgent need to electronically gather, track, and share this information. To help resolve the gaps in public health systems, the Centers for Disease Control and Prevention (CDC) launched the Data Modernization Initiative (DMI) – a multi-year, billion-dollar plus effort to modernize core data and surveillance infrastructure across the federal and state public health landscape. The ultimate goal is to create a sustainable, response-ready public health ecosystem that will empower strategies to improve health across the country.

Through DMI, the government has made significant improvements across healthcare information systems, allowing thousands of facilities to exchange patient data near real-time through electronic case records (eCR). And while this is progress, the abundance of patient information available, mixed with the complexity of the healthcare industry, can reduce data quality and pose unique challenges to public health agencies (PHA).  

Challenges with public health data

Public health has been chronically underfunded, resulting in siloed and brittle data systems. The COVID-19 pandemic prompted the CDC to create the DMI to transform PHAs’ disparate data systems into a connected surveillance network. It is worth noting that up to 70% of healthcare organizations report information to PHAs via fax, adding an additional layer of difficulty to accessing reliable data.

While the government has implemented file structure standards, like HL7 and FHIR, to ease data exchange and improve interoperability, details can still go missing in the process of gathering and sharing information. In addition, clinical documentation can become disorganized with non-standardized terminology and the complexity of mapping to various code systems. This can lead to distortion of patient data, making it unreliable and challenging for the government to use for decision-making on issues such as health inequity and crisis response.

To address the problem, PHAs often assign their informaticists to enter and clean data. This process is time-consuming and delays their ability to quickly uncover health risks in communities and implement strategies to protect the public. According to the CDC, epidemiologists may spend up to 80% of their time cleansing, standardizing, and enriching clinical data. This mundane task can also lead to job dissatisfaction as many data professionals invested in extensive training to predict, analyze and solve health threats – not manually organize data.

High data quality requires the right foundation

The DMI was launched with the aim of utilizing patient information to devise health measures that protect the public. However, poor data quality can hamper the timely response of public health to emerging threats. By leveraging IMO’s normalization engine and our industry-leading clinical terminology, PHAs can streamline their data management process. IMO Precision Normalize transforms messy data into a clean, reliable, and analysis-ready format, enabling informaticists to better focus on unearthing actionable insights to advance public health initiatives.

To learn how IMO Precision Normalize can help you meet your DMI goals by standardizing disparate clinical data, click here.

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