The NBS: Digital Public Health Reporting A Brief History January, st ELR Information Public Health Meeting January, nd ELR Information for Public Health Meeting October, 1999 NEDSS Launched March, 2000 Construction of NBS Begins January, 2003 NBS Launched into Production May, 2006 Public Health ASP Offering Launched December, th State in Production with NBS May, 2010 Ability to build disease modules at the state February, 2005 NBS User Group Launched The NBS continually evolves to meet the challenges of public health surveillance.

The NBS: Digital Public Health Reporting Utilization in the United States NBS is implemented in 16 states and is used by over 1,000 public health practitioners

The NBS: Digital Public Health ReportingThe NBS: Digital Public Health Reporting and H1N1 Flu At the Intersection of Policies, Standards and Technologies Policies PHIN Initiative NEDSS Infrastructure Health People 2010 State PH Law Trust for Americas Health Standards Health Level Seven SNOMED LOINC PHIN Vocabulary Electronic Data Exchange Security Public Health Data Model Technologies J2EE architecture WC3 PHIN Messaging (MS) Enterprise Application Integration (EAI) Analytics

The NBS: Digital Public Health Reporting An Agent for Change in Public Health Surveillance Paper-BasedWeb-Based Data Islands and One-Off Solutions Integrated and Interoperable Manual EntryElectronic Messaging Centralized Data Entry and Access Distributed Data Entry and Access Home GrownStandards-Based The NBS has facilitated the movement from paper to digital surveillance.

The NBS: Digital Public Health Reporting Addressing Public Health Issues The NBS: Digital Public Health Reporting and H1N1 Flu

The NBS: Digital Public Health ReportingThe NBS: Digital Public Health Reporting and H1N1 Flu Real World Usage TX: Responding to H1N1 SC: Empowering Providers AL: Bidirectional Avenues of Collaboration ID: Efficiency Through ELR VA: Analysis/Visualization

The NBS: Digital Public Health Reporting What We Now Know About H1N1 Source: Centers for Disease Control and Prevention TX: Responding to H1N1

The NBS: Digital Public Health Reporting Standards Based Surveillance Lets Go Back to April 2009 –What will be the source of truth for data related to H1N1? –At what level are we going to track the disease? –What questions need to be captured? –How are we going to analyze our data? –How will we notify CDC of disease occurrence? –How can we share our work with other public health departments? TX: Responding to H1N1

The NBS: Digital Public Health Reporting Responding to H1N1 Identify: Early recognition that the swine flu emerging in Texas was actually Novel Influenza A Collect: Immediate need was to develop a mechanism to capture data Disseminate: Share information based on analysis of data collected using data warehouse Adapt: Adjust surveillance based on disease trends and public health outcomes Control: Surveillance to document the existence and potential for development of severe sequelae TX: Responding to H1N1

The NBS: Digital Public Health Reporting Before Electronic Laboratory Reporting Workload: Labor intensive and prone to error Timeliness: Reporting delays; dependency on fax, mail and phone Accountability: Lost in bottom of the drawer Completeness: Difficult to analyze the completeness of reporting ID: Efficiency Through ELR

The NBS: Digital Public Health Reporting NBS > Point of Care Reporting to Public Health Public Health Data Repository Integrated Data Repository Electronic Laboratory Reporting to Public Health: 1.Salmonella is detected in a specimen submitted for a patient with symptoms of a Foodborne illness 2.County public health practitioner receives the electronic laboratory report and begins an investigation 3.State public health practitioner analyzes received ELRs NEDSS Base System Laboratory Secure Portal Laboratory Information System Analysis ID: Efficiency Through ELR

The NBS: Digital Public Health Reporting Progress with ELR Between 1/2007 and 6/2010, the number of lab reports received via ELR increased from ~22/month to ~500/month /07 Mayo 5/08 ARUP 4/08 Quest 11/08 St. Lukes /09 IBL H1N1 results Time period 7/09 – 10/10, includes H1N1 results from Idaho Bureau of Laboratories ID: Efficiency Through ELR 2010 <2007 LabCorp 3/10 Interpath

The NBS: Digital Public Health Reporting Percentage of reportable disease lab reports received via ELR, 2007–2009* *Does not include STDs 3% ELR 97% Non- ELR 25% ELR 75% Non- ELR 40% ELR 60% Non- ELR ~1,000 lab reports ~1,200 lab reports (20% increase) ID: Efficiency Through ELR Increased ELR between 2007 and 2009 led to a 40% statewide reduction in data entry time (from 195 hrs/yr to 120 hrs/yr) = more time for prevention and control activities. Efficiency Through ELR

The NBS: Digital Public Health Reporting Improvements in Timelines and Completeness Timeliness. –Since implementation of the NBS and ELR, disease reporting timeliness has increased Example: Elapsed time between PHD receiving reports and reporting to the state has decreased from 5.0 days (95% C.I., ) to 3.4 days (95% C.I., ) Completeness –Completeness varies by public health jurisdiction, but the percentage of required fields completed in the NBS in 2009 was between 88% and 100%: 99.8% basic demographic fields (e.g., age, sex) 98.7% onset and diagnosis date Race and ethnicity are least likely to be complete (67%) ID: Efficiency Through ELR

The NBS: Digital Public Health Reporting Bidirectional Avenues of Collaboration State Health Department Local Health Departments Resource intensive processing of morbidity/laboratory reports Lost Investigations Limited local staff development Limited feedback on investigations Lack of analytic capabilities Inability to monitor disease trends Paper Business Process = Disconnected AL: Bidirectional Avenues of Collaboration

The NBS: Digital Public Health Reporting Bidirectional Avenues of Collaboration Electronic receipt of laboratory and morbidity reports Timely investigations and interventions Real-time, electronic feedback to field staff Continual feedback on investigations On demand analysis capabilities Access to multi-year data sets for trend analysis Digital Business Process = Connected Local Health Departments State Health Department AL: Bidirectional Avenues of Collaboration

The NBS: Digital Public Health Reporting Bidirectional Avenues of Collaboration Paper-BasedDigital Prepare Paper files received at state levelElectronic or paper files received Files manually sorted/saved according to jurisdiction. ELR process consumes, translates, and loads messages into the NBS Sorted files prepared to be sent to local jurisdiction with investigation form. Paper files keyed in by State Review Files sent to jurisdiction by mailMessages appear in the dashboard accessible according to users permissions Indicate records needing follow-up and begin investigation Records reviewed within NBS, electronically stamping records not needing further follow-up Respond Investigation completed and mailed to State and state manually enter records into data silo Create Investigation. Once completed, create a notification to state for review. Weekly flat files transmitted to CDCState level electronically reviews investigations for transmission to CDC AL: Bidirectional Avenues of Collaboration

The NBS: Digital Public Health Reporting Paper Based Approach to Disease Reporting SC: Empowering Providers

The NBS: Digital Public Health Reporting NBS > Point of Care Reporting to Public Health National Center for Public Health Data Repository Business Process Provider to Public Health: 1.Infection Control Nurse at a hospital enters a patients lab report directly in the NBS to alert public health to a positive finding for Tuberculosis 2.Public health practitioner receives the laboratory report within their dashboard and follows up with the Infection Control Nurse for additional information 3.Upon investigation, CDC is electronically notified of a confirmed case of Tuberculosis NEDSS Base System Provider Interoperability 2 31 Secure Portal Interoperability SC: Empowering Providers

The NBS: Digital Public Health Reporting Empowering Providers SC: Empowering Providers GA NC Examples of Provider Based Disease Reporting SC NBS facilitates private – public joint surveillance. Provider Site

The NBS: Digital Public Health Reporting VA: Analysis/Visualization Centralized database and analytic tools enhance use of reportable disease data All health departments have a database for analysis Data is available for analysis sooner because it is in the database sooner Working from the same database results in consistent and reliable statistics Opportunity for more integrated analysis Information is used in decision making when it is readily available Once a report is developed it can be shared by all users (and across states) Users collaborate to find solutions and build skills

The NBS: Digital Public Health Reporting VA: Analysis/Visualization You mean you want to be able to use the data? Easy-to-use queries encourage non-epidemiologists to run reports Datamarts support queries and analysis by power users –Disease specific datamarts provide access to all relevant fields –Data from multiple ODS tables are combined, including administrative data –Flattened data structures simplify use –Data transformations simplify analysis –Exportable files support analysis through other tools (Aberration detection, geospacial mapping, SAS, Logi-XML) Reports prioritize action –Identify cases needing investigation when many reports are received –Identify potential clusters from review of linelists Evaluation of workflow improves use of resources Analysis to evaluate data quality and completeness –improve surveillance strategies –support data improvement –ensure appropriate interpretation Incorporation of legacy data in a datamart to support historical analysis

The NBS: Digital Public Health Reporting VA: Analysis/Visualization We collect data to guide public health action We need robust tools to analyze and visualize the data Need more access to disease-specific data Need more capacity for graphic presentation of data Need more capacity for ad-hoc analysis by users Need more ability to incorporate statistical calculations, including rates Do we build the capacity in NBS or build interfaces with other tools and applications?