Development of Automated Data Quality Indicators and Visualizations using Florida's ESSENCE System

Understanding your data is a fundamental pillar of disease surveillance success. With the increase in automated, electronic surveillance tools many public health users have begun to rely on those tools to produce reports that contain processed results to perform their daily jobs. These tools can focus on the algorithm or visualizations needed to produce the report, and can easily overlook the quality of the incoming data. The phrase “garbage in, garbage out” is often used to describe the value of reports when the incoming data is not of high quality.

March 02, 2018

Disease Mapping with Spatially Uncertain Data

Uncertainty introduced by the selective identification of cases must be recognized and corrected for in order to accurately map the distribution of risk. Consider the problem of identifying geographic areas with increased risk of DRTB. Most countries with a high TB burden only offer drug sensitivity testing (DST) to those cases at highest risk for drug-resistance. As a result, the spatial distribution of confirmed DRTB cases under-represents the actual number of drug-resistant cases.

March 02, 2018

Disease Surveillance and Achieving Synergy In Public Health Quality Improvement

National efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. Measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. They also reveal opportunities for innovations and improvements. Similar quality improvement efforts in the health care system are beginning to bear fruit.

March 02, 2018

Evaluating the Variation on Public Health's Perceived Field Need of Communicable Disease Reports

Communicable disease surveillance is a core Public Health function. Many diseases must be reported to state and federal agencies (1). To manage and adjudicate such cases, public health stakeholders gather various data elements. Since cases are identified in various healthcare settings, not all information sought by public health is available (2) resulting in varied field completeness, which affects the measured and perceived data quality.

March 19, 2018

Collaborative Automation Reliably Remediating Erroneous Conclusion Threats (CARRECT)

Analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. It often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. To address this challenge, Aptima began development of CARRECT, the Collaborative Automation Reliably Remediating Erroneous Conclusion Threats system.

May 22, 2018

Using Change Point Detection for Monitoring the Quality of Aggregate Data

Data consisting of counts or indicators aggregated from multiple sources pose particular problems for data quality monitoring when the users of the aggregate data are blind to the individual sources. This arises when agencies wish to share data but for privacy or contractual reasons are only able to share data at an aggregate level. If the aggregators of the data are unable to guarantee the quality of either the sources of the data or the aggregation process then the quality of the aggregate data may be compromised. This situation arose in the Distribute surveillance system (1).

July 06, 2018

Introducing the HHS Framework for Quality in the Public Health System

Join ISDS for a presentation on public health quality measures in the context of surveillance activities by Peggy Honoré from the US Department of Health and Human Services. Quality in healthcare and public health must serve as a catalyst for improving the health of the nation. While organized efforts to address healthcare quality have advanced rigorously in recent years, progress in public health quality is beginning to emerge as well. Contributing to this effort is a framework for public health quality released by the US Department of Health and Human Services.

October 17, 2017

Using Cultural Modeling to Inform a NEDSS-Compatible System Functionality Evaluation

The National Notifiable Disease Surveillance System (NNDSS) comprises many activities including collaborations, processes, standards, and systems which support gathering data from US states and territories. As part of NNDSS, the National Electronic Disease Surveillance System (NEDSS) provides the standards, tools, and resources to support reporting public health jurisdictions (jurisdictions). The NEDSS Base System (NBS) is a CDC-developed, software application available to jurisdictions to collect, manage, analyze and report national notifiable disease (NND) data.

July 13, 2018

Using Information Entropy to Monitor Chief Complaint Characteristics and Quality

Health care processes consume increasing volumes of digital data. However, creating and leveraging high quality integrated health data is challenging because large-scale health data derives from systems where data is captured from varying workflows, yielding varying data quality, potentially limiting its utility for various uses, including population health. To ensure accurate results, it’s important to assess the data quality for the particular use.

July 13, 2018

In data we trust? An evaluation of the quality of influenza hospital admissions data gathered by automated versus manual reporting

The Washington Comprehensive Hospital Abstract Reporting System (CHARS) has collected discharge data from billing systems for every inpatient admitted to every hospital in the state since 1987 [1]. The purpose of the system is to provide data for making informed decisions on health care. The system collects age, sex, zip code and billed charges of the patient, as well as hospital names and discharge diagnoses and procedure codes.

May 02, 2019

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