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Introduction: Data from an electronic medical record (EMR) system can provide valuable insight regarding health consequences in the aftermath of a disaster. In January of 2010, the U.S. Department of Health and Human Services (HHS) deployed medical personnel to Haiti in response to a crippling earthquake. An EMR system was used to record patient encounters in real-time and to provide data for decision support during response activities. Problem: During the Haiti response, HHS monitored the EMR system by recoding diagnoses into seven broad categories. At the conclusion of the response, it was evident that a new diagnosis categorization process was needed to provide a better description of the patient encounters that were seen in the field. After examining the EMRs, researchers determined nearly half of the medical records were missing diagnosis data. The objective of this study was to develop and test a new method of categorization for patient encounters to provide more detailed data for decision making. Methods: A single researcher verified or assigned a new diagnosis for 8,787 EMRs created during the Haiti response. This created a new variable, the Operational Code, which was based on available diagnosis data and chief complaint. Retrospectively, diagnoses recorded in the field and Operational Codes were categorized into eighteen categories based on the ICD-9-CM diagnostic system. Results: Creating an Operational Code variable led to a more robust data set and a clearer depiction emerged of the clinical presentations seen at six HHS clinics set up in the aftermath of Haiti’s earthquake. The number of records with an associated ICD-9 code increased 106% from 4,261 to 8,787. The most frequent Operational Code categories during the response were: General Symptoms, Signs, and Ill-Defined Conditions (34.2%), Injury and Poisoning (18.9%), Other (14.7%), Respiratory (4.8%), and Musculoskeletal and Connective Tissue (4.8%). Conclusion: The Operational Code methodology provided more detailed data about patient encounters. This methodology could be used in future deployments to improve situational awareness and decision-making capabilities during emergency response operations.

Objective The Florida Department of Health (FDOH), Bureau of Epidemiology, partnered with the U.S. Department of Health and Human Services (HHS) Office of the Assistant Secretary for Preparedness and Response (ASPR) to improve surveillance methods in post disaster or response events. A new process was implemented for conducting surveillance to monitor injury and illness for those presenting for care to ASPR assets such as Disaster Medical Assistance Team (DMAT) sites when they are operational in the state. The purpose of the current work was to field test and document the operational experience of the newly implemented ASPR data module in ESSENCE-FL (syndromic surveillance system) to receive near real-time automated data feeds when ASPR federal assets were deployed in Florida during the 2012 Republican National Convention (RNC). Introduction Florida has implemented various surveillance methods to augment existing sources of surveillance data and enhance decision making with timely evidence based assessments to guide response efforts post-hurricanes. Historically, data collected from deployed federal assets have been an integral part of this effort. However, a number of factors have made this type of surveillance challenging: logistical issues of field work in a post-disaster environment, the resource intensive manual data collection process from DMAT sites, and delayed analysis and interpretation of these data to inform decision makers. The ESSENCE-FL system is an automated and secure web-based application accessed by FDOH epidemiologists and staff at participating hospitals. Methods ESSENCE-FL was configured by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to receive ASPR electronic medical record (EMR) data. A scheduled program to generate data files for FDOH was created using SAS Enterprise Business Intelligence (EBI) software and a script was set up on the ASPR server to send an updated file via secure file transfer protocol (sftp) every 15 minutes. A case definition was created by ASPR field teams to identify which encounter visits would be entered into the electronic medical record (EMR) and received in ESSENCE-FL. To assess completeness of data elements and total patient encounters received in ESSENCE-FL, DMAT field teams maintained Excel line lists of patient encounters and emailed them to FDOH three times daily during the RNC. ASPR data were reviewed and analyzed by FDOH staff multiple times a day in near real time utilizing the existing ESSENCE-FL robust analysis tools. Results Three separate ASPR missions were deployed to Florida to support the RNC. ASPR EMR data files were received at 15-minute intervals by ESSENCE-FL from the ASPR central server during each day of the 2012 RNC (August 26–31). Reduced patient counts within ESSENCE-FL as compared with DMAT-maintained Excel line lists indicated an incomplete input, upload, or transfer of patient data from one of two ASPR sites to the central ASPR servers. Although only 11 of 34 total patient encounters were received by ESSENCE-FL during the event, the system design enabled users to run specific queries and display the results of their queries in time series graphs, pie and bar charts, GIS maps, dashboards, and statistical tables. Conclusions There is a great need to have timely access to data sources to enhance disease surveillance efforts and help guide decision makers’ situational awareness and disease control efforts during a response. The FDOH, Bureau of Epidemiology’s collaboration with JHU/APL and ASPR takes advantage of ASPR’s EMR-S to make data sharing and analysis efficient as evidenced during the RNC. Automated data feeds to ESSENCE-FL removed resource intensive manual data collection by public health, improved standardization of syndrome and demographic categorizations, increased access to these data by local, state, and federal epidemiologists in a timely manner, and expedited analysis and interpretation for situational awareness. Future recommendations include pre-event testing of the entire data flow process, establishing an on-site specialist to immediately assist with any issues, greater understanding of the field team use of the EMR-S, and ensuring field staff is aware of data quality needs for effective public health surveillance.

Objective The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decision-making in disasters. Introduction Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing web-based data analysis and visualization tools. Methods Fusion Analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within ASPR. The 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. The Fusion Analytics data integration system was built using off-the-shelf EBI software. Fusion Analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. Fusion Analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. It also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. Conclusions We are currently in a unique position within public health. One the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. On the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. Fusion Analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. It also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities.

Objective U.S. Department of Health and Human Services (HHS) Office of the Assistant Secretary for Preparedness and Response (ASPR) partnered with the Florida Department of Health (FDOH), Bureau of Epidemiology, to implement a new process for the unidirectional exchange of electronic medical record (EMR) data when ASPR clinical assets are operational in the state following a disaster or other response event. The purpose of the current work was to automate the exchange of data from the ASPR electronic medical record system EMR-S into the FDOH Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) system during the 2012 Republican National Convention (RNC). Introduction ASPR deploys clinical assets, including an EMR system, to the ground per state requests during planned and no-notice events. The analysis of patient data collected by deployed federal personnel is an integral part of ASPR and FDOH’s surveillance efforts. However, this surveillance can be hampered by the logistical issues of field work in a post-disaster environment leading to delayed analysis and interpretation of these data to inform decision makers at the federal, state, and local levels. FDOH operates ESSENCE-FL, a multi-tiered, automated, and secure web-based application for analysis and visualization of clinical data. The system is accessible statewide by FDOH staff as well as by hospitals that participate in the system. To improve surveillance ASPR and FDOH engaged in a pilot project whereby EMR data from ASPR would be sent to FDOH in near real-time during the 2012 hurricane season and the 2012 RNC. This project is in direct support of Healthcare Preparedness Capability 6, Information Sharing, and Public Health Preparedness Capability 13, Public Health Surveillance and Epidemiological Investigation. Methods In 2011, FDOH approached ASPR about securely transmitting raw EMR data that could be ingested by ESSENCE-FL during ASPR deployments in the state. Upon conclusion of an agreement for a date exchange pilot, data elements of interest from the ASPR EMR were identified. Due to the modular design ESSENCE-FL Microsoft SQL databases were easily adapted by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to add a new module to handle receipt of ASPR EMR data including code to process the files, remove duplicates and create associations with existing reference information, such as system-defined geographic regions and age groups. Scripts were developed to run on the ASPR server to create and send updated files via secure file transfer protocol (SFTP) every 15 minutes to ESSENCE-FL. Prior ASPR event deployment data was scrubbed and sent to ESSENCE-FL as a test dataset to ensure appropriate receipt and ingestion of the new data source. Results EMR data was transmitted through a central server at ASPR to ESSENCE-FL every 15 minutes during each day of the 2012 RNC (August 26–31). In ESSENCE-FL, configuration allowed the data to be queried, analyzed, and visualized similar to existing ESSENCE-FL data sources. In all, data from 11 patient encounters were successfully exchanged between the partners. The data were used by ASPR and FDOH to simultaneously monitor in near real-time onsite medical response activities during the convention. Conclusions Timely access to patient data can enhance situational awareness and disease surveillance efforts and provide decision makers with key information in an expedient manner during disaster response and mass gatherings such as the RNC. However, data are siloed within organizations. The collaboration between FDOH, ASPR and JHU/APL made EMR data sharing and analysis more expeditious and efficient and increased timely access to these data by local, state, and federal epidemiologists. The integration of these data into the ESSENCE-FL system created one location where users could go to access data and create epidemiologic reports for a given region in Florida, including the RNC. To achieve these successes with partners in the future, it will be necessary to develop partnerships well in advance of intended data exchange. Future recommendations include robust pre-event testing of the data exchange process and planning for a greater amount of lead-time between enacting the official agreement and beginning data exchange.