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Completeness

Electronic laboratory reporting (ELR) was demonstrated just over a decade ago to be an effective method to improve the timeliness of reporting as well as the number of reports submitted to public health agencies. The quality of data (inc. completeness) in information systems across all industries and organizations is often poor, and anecdotal reports in the surveillance literature suggest that ELR may not improve the completeness of the data in the submitted reports.

 

Objective 

To examine the completeness of data submitted from clinical information systems to public health agencies as notifiable disease reports.

Referenced File
Submitted by elamb on

Completeness of public health information is essential for the accurate assessment of community health progress and disease surveillance. Yet challenges persist with respect to the level of completeness that public health agencies receive in reports submitted by health care providers. Missing and incomplete data can jeopardize information reliability and quality resulting in inaccurate disease evaluation and management (1). Additionally, incomplete data can prolong the time required for disease investigators to complete their work on a reported case. Thus, it is important to determine where the scarcity of information is coming from to recognize the characteristics of provider reporting.

Objective

To examine the completeness of data elements required for notifiable disease surveillance from official, provider-based reports submitted to a local health department.

Submitted by knowledge_repo… on

One of the first county-wide syndromic surveillance systems in the nation, the Syndromic Tracking and Reporting System (STARS) has been in operation since 11/01/2001, and now covers Hillsborough, Pinellas and Collier counties. STARS uses hospital emergency department visit data to detect aberrations of non-specific syndromes and serves as an earlier warning system for public health threats. Patient’s syndrome is collected upon arrival, separately from routine collection of clinical and administrative data; but in some hospitals the process is being streamlined with routine data collection. Aberration detection is done twice daily using the statistical system EARS developed by the CDC. Upon flagging of an aberration, follow-up investigation is conducted to verify cases, and identify source of exposure following a sequence of decision procedure. After several years of operation and some instituted enhancements, a systematic evaluation was called to (1) assess if STARS has met the operation specifications and (2) characterize system efficacy and effectiveness.

 

Objective

To evaluate STARS with respect to quality of syndrome diagnoses, timeliness and completeness of data collection and processing, performance of aberration detection methods, and aberration investigation.

Referenced File
Submitted by elamb on

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. To better understand this variation, we evaluated public health practitioners’ perceived value of these fields to initiate or complete communicable disease reports.

Objective:

To assess communicable disease report fields required by public health practitioners and evaluate the variation in the perceived utility of these fields.

 

Submitted by Magou on

The National Syndromic Surveillance Program (NSSP) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. These data, captured in nearly real time, are intended to improve the nation's situational awareness and responsiveness to hazardous events and disease outbreaks. During CDC’s previous implementation of a syndromic surveillance system (BioSense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. The BioSense Governance Group Data Quality Workgroup paved the way to rethink surveillance data flow and quality. Their work and collaboration with state and local partners led to NSSP redesigning the program’s data flow. The new data flow provided a ripe opportunity for NSSP analysts to study the data landscape (e.g., capturing of HL7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. In addition, NSSP extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. Even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. However, these issues were now identified. The newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. Therefore, an important component of the NSSP data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings.

Objective:

Review the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance.

Submitted by elamb on
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