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1.  SAGES Update: Electronic Disease Surveillance in Resource-Limited Settings 
Objective
The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, open-source software tools for electronic disease surveillance in resource-limited settings. This demonstration will illustrate several new innovations and update attendees on new users in Africa and Asia.
Introduction
The new 2005 International Health Regulations (IHR), a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance with particular emphasis on resource-limited settings. More than a decade ago, in collaboration with the US Department of Defense (DoD), the Johns Hopkins University Applied Physics Laboratory (JHU/APL) developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE collects, processes, and analyzes non-traditional data sources (i.e. chief complaints from hospital emergency departments, school absentee data, poison control center calls, over-the-counter pharmaceutical sales, etc.) to identify anomalous disease activity in a community. The data can be queried, analyzed, and visualized both temporally and spatially by the end user. The current SAGES initiative leverages the experience gained in the development of ESSENCE, and the analysis and visualization components of SAGES are built with the same features in mind.
Methods
SAGES tools are organized into four categories: 1) data collection, 2) analysis & visualization, 3) communications, and 4) modeling/simulation/evaluation. Within each category, SAGES offers a variety of tools compatible with surveillance needs and different types or levels of information technology infrastructure. SAGES tools are built in a modular nature, which allows for the user to select one or more tools to enhance an existing surveillance system or use the tools en masse for an end-to-end electronic disease surveillance capability. Thus, each locality can select tools from SAGES based upon their needs, capabilities, and existing systems to create a customized electronic disease surveillance system. New OpenESSENCE developments include improved data query ability, improved mapping functionality, and enhanced training materials. New cellular phone developments include the ability to concatenate single SMS messages sent by simple or Smart Android cell phones. This ‘multiple-SMS’ message ability allows use of SMS technology to send and receive health information exceeding normal SMS message length in a manner transparent to the users.
Conclusions
The SAGES project is intended to enhance electronic disease surveillance capacity in resource-limited settings around the world. We have combined electronic disease surveillance tools developed at JHU/APL with other freely-available, interoperable software tools to create SAGES. We believe this suite of tools will facilitate local and regional electronic disease surveillance, regional public health collaborations, and international disease reporting. SAGES development, funded by the US Armed Forces Health Surveillance Center, continues as we add new international collaborators. SAGES tools are currently deployed in locations in Africa, Asia and South America, and are offered to other interested countries around the world.
PMCID: PMC3692858
software; surveillance; electronic; open-source
2.  The Biosurveillance Resource Directory - A One-Stop Shop for Systems, Sources, and Tools 
Objective
The goal of this project is to identify systems and data streams relevant for infectious disease biosurveillance. This effort is part of a larger project evaluating existing and potential data streams for use in local, national, and international infectious disease surveillance systems with the intent of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.
Introduction
Local, national, and global infectious disease surveillance systems have been implemented to meet the demands of monitoring, detecting, and reporting disease outbreaks and prevalence. Varying surveillance goals and geographic reach have led to multiple and disparate systems, each using unique combinations of data streams to meet surveillance criteria. In order to assess the utility and effectiveness of different data streams for global disease surveillance, a comprehensive survey of current human, animal, plant, and marine surveillance systems and data streams was undertaken. Information regarding surveillance systems and data streams has been (and continues to be) systematically culled from websites, peer-reviewed literature, government documents, and subject-matter expert consultations.
Methods
A relational database has been developed and refined to allow for detailed analyses of data streams and surveillance systems. To maximize the utility of the database and facilitate one-stop-shopping for biosurveillance system information, we have expanded our scope to include not only biosurveillance systems, but also data sources, tools, and biosurveillance collectives. Captured in the information collected about the resource (if available) is the name and acronym of the resource, the date the resource became available, the accessibility of the resource (is it open to all, or are there limitations to access), the primary sponsors, if the resource is associated with GIS functionality, and if the focus is health. Also collected is contact information, information regarding the scope and domain of the resource, the pertinent diseases or disease categories, and the geographic and population coverage of the resource. Websites associated with the resource are directly accessible from the database. Data stream information is also captured based on our developed data stream framework. If the resource uses other specified systems/sources/tools for data gathering or analysis, then that is also captured and directly linked within the database.
Results
The Biosurveillance Resource Directory (BRD) is in the process of being tested by multiple potential end users in the public health, biosecurity, and biosurveillance communities. Feedback from these testers is being used to refine the database to maximize functionality and utility. Additionally, methods for dynamically updating and maintaining the database are being evaluated. Automated and semi-automated queriable reports have been developed and are integral to demonstrating specific use-case scenarios in which the BRD would be beneficial for end-users.
Conclusions
A need for a biosurveillance one-stop shop has been increasingly called for to help in evaluating what data streams and systems are available and relevant for many different biosurveillance needs and goals. The prototype Biosurveillance Resource Directory is a search-able, dynamic database for biosurveillance systems, sources, and tools information.
PMCID: PMC3692785
infectious disease; biosurveillance; database
3.  Operational Experience: Integration of ASPR Data into ESSENCE-FL during the RNC 
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.
PMCID: PMC3692919
surveillance; response; disaster
4.  Sharing Public Health Information with Non-Public Health Partners 
Objective
The objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health ESSENCE users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. This capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. These interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. This project provides tools that can enhance emergency management situational awareness of public health events. It also allows external partners a mechanism for providing feedback to support public health investigations.
Introduction
Automated Electronic Disease Surveillance has become a common tool for most public health practitioners. Users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. The insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other non-public health officials. Disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. This abstract describes the development of tools necessary to support information sharing between public health and partner organizations.
Methods
The project initially brought together public health and emergency management officials to determine current gaps in technology and policy that prevent sharing of information on a consistent basis. Officials from across the National Capital Region (NCR) in Maryland, Virginia, and the District of Columbia determined that a web portal in which public health information could be securely posted on and captured by non-public health users (humans and computer systems) would be best. The development team then found open source tools, such as the Pebble blogging system, that would allow information to be posted, tagged, and searched in an easily navigable site. The system also provided RSS feeds both on the site as whole and specific tags to support notification. The team made modifications to the system to incorporate spring security features to allow the site to be securely hosted requiring usernames and passwords for access. Once the Pebble system was completed and deployed, the NCR’s aggregated ESSENCE system was adapted to allow users to submit daily reports and post time series images to the new site. An additional feature was created to post visualizations every evening to the site summarizing the day’s reports.
Results
The system has been in testing since March of 2012 and users of the system have provided valuable feedback. Based on the success of the tests, public health users in the NCR have begun working on the policy component of the project to determine when and how it should be used. Modifications to the system since deployment have included a single sign on capability for ESSENCE users and the desire to allow other features of ESSENCE to be posted beyond time series graphs, such as GIS maps and statistical reports.
Conclusions
Having tools that can promote exchange of information between public health and non-public health partners such as emergency management, law enforcement, and first responders can greatly enhance the situational awareness and impact overall preparedness and response. By having tools embedded in ESSENCE, users are able to integrate the information sharing aspects into their daily routines with a small amount of effort. With the use of open source tools, the same type of capability can be easily replicated in other jurisdictions. This presentation will describe the lessons learned and potential improvements the project will incorporate in the future.
PMCID: PMC3692892
Open Source; Emergency Management; Information Sharing
5.  Enabling ESSENCE to Process and Export Meaningful Use Syndromic Surveillance Data 
Objective
The objective of this project is to enable the ESSENCE system to read in, utilize, and export out meaningful use syndromic surveillance data using the Health Level 7 (HL7) v2.5 standard. This presentation will detail the technical hurdles with reading a meaningful use syndromic surveillance data feed containing multiple sources, deriving a common meaning from the varying uses of the standard and writing data out to a meaningful use HL7 2.5 format that can be exported to other tools, such as BioSense 2.0 (2). The presentation will also describe the technologies employed for facilitating this, such as Mirth, and will discuss how other systems could utilize these tools to also support processing meaningful use syndromic surveillance data.
Introduction
In order to utilize the new meaningful use syndromic surveillance data sets (3) that many public health departments are now receiving, modifications to their systems must be made. Typically this involves enabling the storage and processing of the extra fields the new standard contains. Open source software exists, such as Mirth Connect, to help with reading and interpreting the standard. However, issues with reliably reading data from one source to another arise when the standard itself is misunderstood. Systems that process this data must understand that while the data they receive is in the HL7 v2.5 standard format, the meaning of the data fields might be different from provider to provider. Additional work is necessary to sift through the similar yet disjoint fields to achieve a consistent meaning.
Methods
This project utilized 3 separate instances of ESSENCE and BioSense 2.0. For both importing and exporting HL7 v2.x standard files, the project used the open source tool Mirth Connect. For importing data the project adapted versions of Tarrant County and Cook County ESSENCE systems in the Amazon GovCloud to receive meaningful use syndromic surveillance data files sent from BioSense 2.0. For exporting data to BioSense 2.0, the project used Mirth Connect to poll the local version of Cook County’s ESSENCE database and export the data into an HL7 v2.5 file. The resultant file was sent over secure file transfer protocol (SFTP) to BioSense 2.0. The team then evaluated the process by comparing the data in the local instances of ESSENCE and the corresponding instances hosted on the Internet cloud.
Results
Many issues were encountered during the reading of the HL7. While the standard suggests that hospitals and hospital systems would all send data in the same fields for the same data, the reality was far different. Although HL7 v2.5 is a standard and there is a defined use for each field, it can be interpreted in many ways. A large portion of time was spent communicating with the local health department to determine exactly what each field meant for a particular hospital. Comparing the Internet cloud and local versions did have some difficulties due to local filtration rules that eliminated non-ER related records from the local Tarrant County system. The project was able to utilize new query features in ESSENCE to filter down to only ER related records on the Internet cloud version to support the comparisons. The project was able to re-use much of the configuration that was created when moving from one jurisdiction to the other. This will help when describing how others may use the same technology in their own systems.
Conclusions
Reading and interpreting the data consistently from a data feed containing multiple sources can be challenging. Confusion with the HL7 v2.3 or 2.5 standards causes many health organizations to transmit data in inconsistent ways that betrays the notion of a messaging standard. However, with the tools this project have created and the lessons we have learned, the pain of implementing meaningful use syndromic surveillance data into a system can be reduced.
PMCID: PMC3692901
Analytics; Electronic Medical Records for Public Health; Interoperability; Meaningful Use; Internet Cloud
6.  Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System 
Objective
To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).
Introduction
Telephone triage is a relatively new data source available to biosurveillance systems.1–2 Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, over-the-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.
Methods
Veterans phoning VA’s TT system, and those admitted or seen at a VA facility with influenza or influenza-like-illness (ILI) diagnosis were included in this analysis. Influenza-specific ICD-9-CM coded emergency department (ED) and urgent care (UC) visits, hospitalizations, TT calls, and ILI outpatient visits were analyzed covering 2010–2011 and 2011–2012 influenza seasons (July 11, 2010–April 14, 2012). Data came from 80 VA Medical Centers and over 500 outpatient clinics with complete reporting data for the time period of interest. We calculated Spearman rank-order coefficients, 95% confidence intervals and p-values using Fisher’s z transformation to describe correlation between TT data and other influenza healthcare measures. For comparison of time trends, we plotted data for hospitalizations, ED/UC visits and outpatient ILI syndrome visits against TT encounters. We applied ESSENCE detection algorithms to identify high-level alerts for influenza activity. ESSENCE aberration detection was restricted to the 2011–2012 season because limited historical TT and outpatient data from 2009–2010 was available to accurately predict aberrancy in the 2010–2011 season. We then calculated the peak measure of healthcare utilization during both influenza seasons (2010–2011 and 2011–2012) for each data source and compared timing of peaks and alerts between TT and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance.
Results
There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ED/UC visits (0.85); and ILI-outpatient visits (0.88), respectively (P< 0.0001 for all correlations). Peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. For the 2011–2012 season, TT alerted with increased influenza activity before all other settings.
Conclusions
Data from VA telephone care correlates well with other VA data sources for influenza activity. TT may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. As a national health care system with a large patient population, VA could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with TT data. Additional analyses are needed to understand and correlate TT with healthcare utilization and severity of illness.
PMCID: PMC3692747
Surveillance; Influenza; Telephone triage; Veterans
7.  National Collaborative for Bio-Preparedness 
Objective
Demonstrate the functionality of the National Collaborative for Bio-Preparedness system.
Introduction
The National Collaborative for Bio-Preparedness (NCB-Prepared) was established in 2010 to create a biosurveillance resource to enhance situational awareness and emergency preparedness. This joint-institutional effort has drawn on expertise from the University of North Carolina- Chapel Hill, North Carolina State University, and SAS Institute, leveraging North Carolina’s role as a leader in syndromic surveillance, technology development and health data standards. As an unprecedented public/private alliance, they bring the flexibility of the private sector to support the public sector. The project has developed a functioning prototype system for multiple states that will be scaled and made more robust for national adoption.
Methods
NCB-Prepared recognizes that the capability of any biosurveillance system is a function of the data is analyzes. NCB-Prepared is designed to provide information services that analyze and integrate national data across a variety of domains, such as human clinical, veterinary and physical data. In addition to this one-health approach to surveillance, a primary objective of NCB-Prepared is to gather data that is closer in time to the event of interest. NCB-Prepared has validated the usefulness of North Carolina emergency medical services data for the purposes of biosurveillance of both acute outbreaks and seasonal epidemics (1).
A unique model of user-driven valuing of data-providing value back to the provider in their terms-motivates collaboration from potential data providers, along with timely and complete data. NCB-Prepared approaches potential data providers, partners and users with the proposition that enhanced data quality and analysis is valuable to them individually and that an integrated information system can be valuable to all. With the onboarding of new data sources, NCB-Prepared implements a formal process of data discovery and integration. The goal of this process is three-fold: 1) to develop recommendations to enhance data quality going forward, 2) to integrate information across data sources, and 3) to develop novel analytic techniques for detecting health threats. NCB-Prepared is committed to both utilizing standard methods for event detection and to developing innovative analytics for biosurveillance such as the Text Analytics and Proportional charts method (TAP). The sophisticated analytic functionality of the system, including improved time to detection, query reporting, alert detection, forecasting and predictive modeling, can be attributed to collaboration between analysts from private industry, academia and public health.
NCB-Prepared followed the formal software development process known as agile development to create the user interface of the system. This method is based on iterative cycles wherein requirements evolve from regular sessions between user groups and developers. The result of agile development and collaborative relationships is a system which visualizes signals and diverse data sources across time and place while providing information services across all levels of users and stakeholders.
Conclusions
Lessons Learned: Understand the functionality of new biosurveillance system, NCB-PreparedIdentify the benefit of creating collaborative relationships with data providers and usersAppreciate the value of a public/private partnership for biosurveillance and bio-preparedness
PMCID: PMC3692851
Biosurveillance; Analytics; Preparedness; Emergency
8.  Using Cloud Technology to Support Monitoring During High Profile Events 
Objective
In May 2012, thousands of protesters, descended on Chicago during the NATO Summit to voice their concern about social and economic inequality. Given the increased numbers of international and domestic visitors to the Windy City and the tension surrounding protesting during the summit, increased monitoring for health events within the city and Chicago metropolitan region was advised. This project represents the first use of cloud technology to support monitoring for a high profile event.
Introduction
Hospital emergency departments in Cook and surrounding counties currently send data to the Cook County Department of Public Health (CCDPH) instance of ESSENCE on CCDPH servers. The cloud instance of ESSENCE has been enhanced to receive and export all meaningful use data elements in the meaningful use format. The NATO summit provided the opportunity for a demonstration project to assess the ability of an Amazon GovCloud instance of ESSENCE to ingest and process meaningful use data, and to export meaningful use surveillance data to the Cook County Locker in BioSense 2.0.
Methods
In the three weeks leading up to the NATO Summit, HL7 data extracts were sent to BioSense 2.0 and a data feed was established to the Amazon GovCloud instance of ESSENCE. Queries specific to anticipated health events associated with the summit such as injuries, tear gas exposure, and general exposure, were developed. Several features of the cloud instance of ESSENCE enhanced the ability of CCDPH staff epidemiologists to conduct analyses, including the sharing capabilities of queries and the myESSENCE dashboard feature. The sharing capabilities within the cloud instance of ESSENCE allowed queries to be easily shared with multiple staff epidemiologists and across health jurisdictions. The myESSENCE dashboard feature was used to create dashboards of surveillance results, including time series graphs, maps, and records of interest for relevant queries, that were shared with public health staff monitoring population health during the summit. This information was used to provide situational awareness on a daily basis in the Chicago Metropolitan region.
Results
Data feeds to BioSense 2.0 and the Amazon GovCloud instance of ESSENCE were successful. The NATO Summit did not produce any remarkable public health concerns in suburban Cook County. The use of the cloud instance of ESSENCE enhanced the timeliness of generating situational awareness reports for distribution to public health partners in the Chicago Metropolitan region.
Conclusions
While further evaluation of cloud resources to conduct syndromic surveillance is warranted, use of the cloud instance of ESSENCE during the NATO Summit demonstrated the ability of the cloud to support surveillance for both routine and high profile events.
PMCID: PMC3692921
Cloud; Meaningful Use; Surveillance; Public Health
9.  SMART Platforms: Building the App Store for Biosurveillance 
Objective
To enable public health departments to develop “apps” to run on electronic health records (EHRs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. We describe a novel health information technology platform with substitutable apps constructed around core services enabling EHRs to function as iPhone-like platforms.
Introduction
Health care information is a fundamental source of data for biosurveillance, yet configuring EHRs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations.
Despite a $48B investment in HIT, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. An alternative approach is to reimagine EHRs as iPhone-like platforms supporting substitutable apps-based functionality. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality.
Methods
Substitutability requires that the purchaser of an app can replace one application with another without being technically expert, without requiring re-engineering other applications that they are using, and without having to consult or require assistance of any of the vendors of previously installed or currently installed applications. Apps necessarily compete with each other promoting progress and adaptability.
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project is funded by a $15M grant from Office of the National Coordinator of Health Information Technology’s Strategic Health IT Advanced Research Projects (SHARP) Program. All SMART standards are open and the core software is open source.
The SMART project promotes substitutability through an application programming interface (API) that can be adopted as part of a “container” built around by a wide variety of HIT, providing readonly access to the underlying data model and a software development toolkit to readily create apps. SMART containers are HIT systems, that have implemented the SMART API or a portion of it. Containers marshal data sources and present them consistently across the SMART API. SMART applications consume the API and are substitutable.
Results
SMART provides a common platform supporting an “app store for biosurveillance” as an approach to enabling one stop shopping for public health departments—to create an app once, and distribute it everywhere.
Further, such apps can be readily updated or created—for example, in the case of an emerging infection, an app may be designed to collect additional data at emergency department triage. Or a public health department may widely distribute an app, interoperable with any SMART-enabled EMR, that delivers contextualized alerts when patient electronic records are opened, or through background processes.
SMART has sparked an ecosystem of apps developers and attracted existing health information technology platforms to adopt the SMART API—including, traditional, open source, and next generation EHRs, patient-facing platforms and health information exchanges. SMART-enabled platforms to date include the Cerner EMR, the WorldVista EHR, the OpenMRS EHR, the i2b2 analytic platform, and the Indivo X personal health record. The SMART team is working with the Mirth Corporation, to SMART-enable the HealthBridge and Redwood MedNet Health Information Exchanges. We have demonstrated that a single SMART app can run, unmodified, in all of these environments, as long as the underlying platform collects the required data types. Major EHR vendors are currently adapting the SMART API for their products.
Conclusions
The SMART system enables nimble customization of any electronic health record system to create either a reporting function (outgoing communication) or an alerting function (incoming communication) establishing a technology for a robust linkage between public health and clinical environments.
PMCID: PMC3692876
Electronic health records; Biosurveillance; Informatics; Application Programming Interfaces
10.  Establishing a Federal and State Data Exchange Pilot for Public Health Situational Awareness 
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.
PMCID: PMC3692893
Syndromic surveillance; Public health informatics; Data exchange; Federal and state collaboration
11.  Incorporation of School Absenteeism Data into the Maryland Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) 
Objective
The state of Maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. This session will discuss the process, challenges, and best practices for expanding the ESSENCE system to include school absenteeism data as part of disease surveillance. It will also discuss the plans that Maryland has for using this new data source, as well as the potential for further expansion.
Introduction
Syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. The Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) conducts syndromic surveillance using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Since its inception, ESSENCE has been a vital tool for DHMH, providing continuous situational awareness for public health policy decision makers. It has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. Schools have the potential to play a major role in the spread of disease during an epidemic. Therefore, having school absenteeism data in ESSENCE would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease.
Methods
DHMH partnered with the Maryland State Department of Education (MSDE), local health departments, and local school systems to incorporate school absenteeism data into the syndromic surveillance program. There are 24 local public school systems and 24 local health departments in the state of Maryland. OP&R contacted each local school superintendent and each local health officer to arrange a joint meeting to discuss the expansion of the ESSENCE program to include school absenteeism data. Once the meetings were arranged, OP&R epidemiologists traveled to each local jurisdiction and presented their plan for the ESSENCE expansion. At each meeting were representatives from the local health department, as well as school health, school attendance, and school IT staff. This allowed all questions and concerns to be addressed from both sides. In addition to the targeted meetings and presentations, the Secretary of Health issued an executive order which required all local school systems to sign a memorandum of understanding (MOU) with DHMH. This MOU detailed the data elements to be shared with the ESSENCE program and the process by which this would be shared. While this order made data contribution mandatory, the site visits by the OP&R staff created a working relationship and partnership with the local jurisdictions. Data was collected from all public schools in the state including elementary, middle, and high schools.
Results
As of June 30, 2012, Maryland became the first state in the United States to incorporate 100% of its public school systems (1,424 schools) into ESSENCE. Each school system reports absenteeism data daily via an automated secure FTP (sFTP) transfer to DHMH. Due to its unique properties, Johns Hopkins Applied Physics Laboratory (JHUAPL) designed a new detection algorithm in ESSENCE specifically for this data source. OP&R epidemiologist review and analyze this data for disease surveillance purposes in conjunction with other data sources in ESSENCE (emergency department chief complaints, poison control center data, thermometer sales data, and over-the-counter medication sales data). Integrating school absenteeism data will provide a more complete analysis of potential public health threats. The process by which Maryland incorporated their public school systems’ data could potentially be used as a best practice for other jurisdictions. Not only was DHMH able to obtain data from all public schools in the state, but the process also enhanced collaboration between local health departments and public school systems.
PMCID: PMC3692827
ESSENCE; Surveillance; Absenteeism
12.  Evaluation of ESSENCE in the Cloud Using Meaningful Use Syndromic Surveillance Data 
Objective
This project represents collaboration among CDC’s BioSense Program, Tarrant County Public Health and the ESSENCE Team at the Johns Hopkins University APL. For over six months the Tarrant County Public Health Department has been sending data through the BioSense 2.0 application to a pilot version of ESSENCE on the Amazon GovCloud. This project has demonstrated the ability for local hospitals to send meaningful use syndromic surveillance data to the Internet cloud and provide public health officials tools to analyze the data both using BioSense 2.0 and ESSENCE. The presentation will describe the tools and techniques used to accomplish this, an evaluation of how the system has performed, and lessons learned for future health departments attempting similar projects.
Introduction
In November of 2011 BioSense 2.0 went live to provide tools for public health departments to process, store, and analyze meaningful use syndromic surveillance data. In February of 2012 ESSENCE was adapted to support meaningful use syndromic surveillance data and was installed on the Amazon GovCloud. Tarrant County Public Health Department agreed to pilot the ESSENCE system and evaluate its performance compared to a local version ESSENCE they currently used. The project determined the technical feasibility of utilizing the Internet cloud to perform detailed public health analysis, necessary changes needed to support meaningful use syndromic surveillance data, and any public health benefits that could be gained from the technology or data.
Methods
This project investigated database and visualization changes necessary to support meaningful use syndromic surveillance data in ESSENCE. It evaluated the Internet cloud environment and determined the benefits and disadvantages to using this technology as a platform for ESSENCE. This included scalability, performance, and cost analysis of the Internet cloud platform. After using the system for a period of time, the Tarrant County users evaluated the Internet cloud version of the system.
Results
Many technical adaptations to the ESSENCE system were made to support the new meaningful use syndromic surveillance elements. Several optimizations, including a new database schema and cube table structures, were developed to improve performance of ESSENCE in the Internet cloud and incorporating the meaningful use requirements. The Internet cloud platform offered many levels of performance that could alter the ESSENCE user experience. Smaller configurations allowed for 100 concurrent users to experience 16 second response times, whereas larger configurations supported experiences of 2 second response times.
Conclusions
Public health departments are dealing with new meaningful use syndromic surveillance data elements and the cost of maintaining local systems. This collaborative team have researched and evaluated tools, technologies, and solutions that can be used throughout the country.
PMCID: PMC3692900
Electronic Medical Records for Public Health; Interoperability; Meaningful Use; Syndromic Surveillance; Internet Cloud
13.  An ISDS-Based Initiative for Conventions for Biosurveillance Data Analysis Methods 
Objective
The panel will present the problem of standardizing analytic methods for public health disease surveillance, enumerate goals and constraints of various stakeholders, and present a straw-man framework for a conventions group.
Introduction
Twelve years into the 21st century, after publication of hundreds of articles and establishment of numerous biosurveillance systems worldwide, there is no agreement among the disease surveillance community on most effective technical methods for public health data monitoring. Potential utility of such methods includes timely anomaly detection, threat corroboration and characterization, follow-up analysis such as case linkage and contact tracing, and alternative uses such as providing supplementary information to clinicians and policy makers.
Several factors have impeded establishment of analytical conventions. As immediate owners of the surveillance problem, public health practitioners are overwhelmed and understaffed. Goals and resources differ widely among monitoring institutions, and they do not speak with a single voice. Limited funding opportunities have not been sufficient for cross-disciplinary collaboration driven by these practitioners. Most academics with the expertise and luxury of method development cannot access surveillance data. Lack of data access is a formidable obstacle to developers and has caused talented statisticians, data miners, and other analysts to abandon the field. The result is that older research is neglected and repeated, literature is flooded with papers of varying utility, and the decision-maker seeking realistic solutions without detailed technical knowledge faces a difficult task.
Regarding conventions, the disease surveillance community can learn from older, more established disciplines, but it also poses some unique challenges. The general problem is that disease surveillance lies on the fringe of disparate fields (biostatistics, statistical process control, data mining, and others), and poses problems that do not adequately fit conventional approaches in these disciplines.
In its eighth year, the International Society of Disease Surveillance is well positioned to address the standardization problem because its membership represents the involved stakeholders including progressive programs worldwide as well as resource-limited settings, and also because best practices in disease surveillance is fundamental to its mission. The proposed panel is intended to discuss how an effective, sustainable technical conventions group might be maintained and how it could support stakeholder institutions.
Methods
Members of a Technical Conventions Group would have experience and dedication to advancing the science of disease surveillance. Primary functions would include: Specify and communicate technical problems facing professionals involved in routine monitoring of population health. Alternative use applications would also be considered, such as the use of epidemiological findings to inform clinical diagnoses.Independently evaluate the utility of proposed analytical solutions to well-defined problems in public health surveillance and confer approval or certification, perhaps on several levels, such as whether results can be replicated with shareable data. Approved solutions might be restricted to commonly available software such as the R language or Microsoft EXCEL.Facilitate sharing of tools and methodologies to evaluate methods and to visualize their results
The framework to be discussed in the proposed panel would be a means of keeping open lines of collaboration and idea-sharing. Overcoming obstacles toward this goal is worthy of a conference panel discussion whether or not it concludes that a conventions group is a viable approach.
Results
Three 15-minute panelist talks are proposed: Background: in-depth description of the dimensions of the problem aboveConstraints facing public health practitioners and requirements for practical analytic toolsStrawman conventions group: role, logistics, inclusiveness, methods of communicating with stakeholders and related organizations and producing/disseminating output.
For the 45 minutes of discussion, the first 15–20 will invite reactions to the first two talks to sharpen the scope of the effort. The remainder of the session will cover the advisability, feasibility, and logistics of an ISDS-based conventions group, and modify the straw-man group concept.
PMCID: PMC3692949
Standards; Data Analysis; Statistical Algorithms; Certification
14.  Biosurveillance Ecosystem (BSVE) Workflow Analysis 
Introduction
The Defense Threat Reduction Agency Chemical and Biological Technologies Directorate (DTRA CB) has initiated the Biosurveillance Ecosystem (BSVE) research and development program. Operational biosurveillance capability gaps were analyzed and the required characteristics of new technology were outlined, the results of which will be described in this contribution.
Methods
Work process flow diagrams, with associated explanations and historical examples, were developed based on in-person, structured interviews with public health and preventative medicine analysts from a variety of Department of Defense (DoD) organizations, and with one organization in the Department of Health and Human Services (DHHS) and with a major U.S. city health department. The particular nuanced job characteristics of each organization were documented and subsequently validated with the individual analysts. Additionally, the commonalities across different organizations were described in meta-workflow diagrams and descriptions.
Results
Two meta-workflows were evident from the analysis. In the first type, epidemiologists identify and characterize health-impacting events, determine their cause, and determine community-level responses to the event. Analysts of this type monitor information (primarily statistical case information) from syndromic or disease reporting system or other sources to determine whether there are any unusual diseases or clusters of disease outbreaks in the jurisdiction. This workflow involved three consecutive processes: triage, analysis and reporting. Investigation and response processes to disease outbreaks are both parallel and overlapping in many circumstances. In the second meta-workflow type, analysts monitor for a potential health event through text-based sources and data reports within their particular area of responsibility. This surveillance activity is often interspersed with other activities required of their job. They may generate a daily/weekly/monthly report or only report when an event is detected that requires notification/response. There are similar triage, analysis and reporting workflow stages to the first meta-workflow type, but in contrast these analysts are focused on informing leadership and response in the form of policy modification. They are also subject to answering leadership-driven biosurveillance queries.
Conclusions
In these interviews, analysts described the shortcomings of various technologies that they use, or technology features that they wish were available. These can be grouped into the following feature categories:
Data: Analysts want rapid access to all relevant data sources, advisories for data that may be relevant to their interests, and availability of information at the appropriate level for their analysis (e.g., output of interpretations from expert analysts instead of raw data).
Enhanced search: Analysts would like customization of information based on relevance, selective filtering of sources, prioritization of search topics, and the ability to view other analysts searches.
Verification: Analysts want indications of information that has been verified or discarded by other analysts, a trail of information history and uses, and automatic verification (e.g., data quality editing) if possible.
Analytics: Analysts want access to forecasting models, services to suggest analysis methods, pointers to other analysts’ expertise, methods, and reports, and tools for “big data” exploitation.
Collaboration and communication: Analysts want assistance identifying people who may have needed information, real-time chat, the ability to compare analyses with colleagues, and the ability to shield data, results, or collaborations from selected others.
Archival: Analysts want automation to provide lessons learned, methods and outcomes for related events, the ability to automatically improve baselines with analyzed data, and assistance with reporting on interim analytic decisions.
The current understanding of the biosurveillance analyst’s functions and processes, based on the results of these interviews, will continue to evolve as further dialog with analysts are combined with results of evaluations during subsequent phases of the new BSVE program.
PMCID: PMC3692935
biosurveillance; workflow; collaboration; operations
15.  A Systematic Evaluation of Data Streams for Global Disease Surveillance 
Objective
The overall objective of this project is to provide a robust evaluation of data streams that can be leveraged from existing and developing national and international disease surveillance systems, to create a global disease monitoring system and provide decision makers with timely information to prepare for and mitigate the spread of disease.
Introduction
Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. Specifically, this project entails the evaluation of data streams either currently in use in surveillance systems or new data streams having the potential to enable early disease detection. An overview of this project will be presented, together with results of data stream evaluation. This project will help gain an understanding of data streams relevant to early warning/monitoring of disease outbreaks.
Methods
Three specific aims were identified to address the overall goal of determining the relevance of data streams for global disease surveillance. First, identify data streams as well as define metrics for the evaluation. Second, evaluate data streams using two different methodologies, decision analysis modeling using a support tool called Logical Decisions® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a Surveillance Window concept developed at LANL that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. This would obtain a ranked list of useful data streams. Additionally, evaluate data integration algorithms useful for a global disease surveillance system through a review of scientific literature. Finally, validate the top-ranked data streams by application of specific historical outbreaks to determine whether the data streams are capable of providing early warning or detection of the particular disease before it became a large outbreak.
Results
Seventeen categories of data streams were identified that ranged from traditional ones such as clinic/healthcare provider and laboratory records to newly emerging sources of information such as social media and internet search queries. The Logical Decisions® based evaluation of data streams identified 5 data streams that consistently showed utility regardless of the goal of biosurveillance. However, different data streams varied in rank, given different biosurveillance goals, and there is no one top ranked data stream. Surveillance window based evaluation of data streams during disease outbreaks identified data streams that had high utility for early detection and early warning regardless of disease, while others were more disease and operations specific. Additionally, we have built a searchable biosurveillance resource directory that houses information on global disease surveillance systems.
Conclusions
LANL has developed a robust evaluation framework to determine the relevance of various traditional and non-traditional data streams in integrated global disease surveillance. Through the use of defined surveillance goals, metrics and data stream categories, not only have we identified data streams currently in use that have high utility, but also new data streams that could be exploited for the early warning/monitoring of disease outbreaks. Our robust evaluation framework facilitates the identification of a defensible set of options for decision makers to use to prepare for and mitigate the spread of disease.
PMCID: PMC3692853
evaluation; disease surveillance; data streams
16.  Coverage and Timeliness of Combined Military and Veteran Surveillance Systems 
Objective
We determined the utility and effective methodology for combining patient record information from the Departments of Veterans Affairs (VA) and Defense (DoD) health surveillance systems.
Introduction
An objective of the Joint VA/DoD BioSurveillance System for Emerging Biological Threats project is to improve situational awareness of the health of combined VA and DoD populations. DoD and VA both use versions of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). With a retrospective outpatient data collection available, we analyzed relative coverage and timeliness of the two systems to understand potential benefits of a joint system.
Methods
We used the US Office of Management and Budget’s core-based statistical area (CBSA) to group data from the respective systems by megapolitan (>1 million), metropolitan (50,000-1 million) and micropolitan (10,000–50,000) areas. We performed frequency analyses and mapped coverage of the VA and DoD medical systems in these CBSAs. To determine comparability, we compared International Classification of Diseases, 9th Revision (ICD-9) code usage from 2007–2010 by age group in the respective systems and then formulated a working definition of influenza-like illness (ILI). We then compared CBSA-level temporal detection timeliness in the two systems for the H3N2 epidemic of 2007–9 and the H1N1 pandemic in 2009.
Results
We identified a total of 939 CBSAs, with generally diffuse geographic coverage by VA facilities and higher concentration in larger metro and mega areas for DoD facilities. Of the 51 mega CBSAs, all have at least one VA facility and 63% have a DoD facility. Coverage is sparser for the metro CBSAs and lighter still for the micro CBSAs (Table 1). Although the VA coverage is greater, in many CBSAs with dual coverage, the DoD visit volume is comparable or greater. Patient age distribution differs sharply, with >85% of the VA patients over 45 years of age compared to 22% of DoD patients. For all CBSAs, the overall VA/DoD visit ratio is 1.92, but the ratios for 0–17 years is 0.004, 18–44 years 0.33, 45–64 years 5.20 and >65 years 11.63.
Based on an analysis of ICD-9 codes used in the two systems, the DoD uses symptom-based ILI codes far more frequently than the VA, especially codes for diseases often seen in children (e.g., otitis media). Analysis of ILI-related codes assigned in both systems led to a common code set for comparative analysis. From applying alerting algorithms to visit counts based on this code set, detection was better in DoD data for 57% and 77% of CBSAs for seasonal and pandemic influenza, respectively, and better in VA data for 37% and 14% of CBSAs (Table 2). The VA system performed better during the typical H3N2 seasonal flu compared to the H1N1 outbreak. The DoD system performed better during the H1N1 pandemic, although outperformed the VA for both.
Conclusions
The coverage analysis demonstrates two complementary surveillance systems with evident benefits to a fused national health picture. The VA system patient volume roughly doubles the DoD system, and provides better geographic coverage in smaller CBSAs; however, the DoD includes younger populations, better coverage in strategic metro areas, and more pre-diagnostic ILI coding. From analysis of both outbreaks, relative timeliness could be improved in 92% of CBSAs with access to both systems, with more information provided in CBSAs where only one type of facility exists.
PMCID: PMC3692883
Syndromic surveillance; Merged systems; Government
17.  Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance 
PLoS ONE  2014;9(1):e83730.
In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.
doi:10.1371/journal.pone.0083730
PMCID: PMC3879288  PMID: 24392093
18.  Enhanced Disease Surveillance during the 2012 Republican National Convention, Tampa, FL 
Objective
To describe disease and illness surveillance utilized during the 2012 Republican National Convention (RNC) held August 26–30, 2012 in Tampa, FL.
Introduction
While the Tampa Bay Area has previously hosted other high profile events that required heightened disease surveillance (e.g., two Super Bowls), the 2012 RNC marked the first national special security event (NSSE) held in Florida. The Hillsborough County Health Department (HCHD), in conjunction with the Pinellas County Health Department (PinCHD) coordinated disease surveillance activities during this time frame. This presentation will focus of the disease surveillance efforts of the Hillsborough County Health Department during the 2012 RNC.
In addition to the surveillance systems that are used routinely, the HCHD Epidemiology Program implemented additional systems designed to rapidly detect individual cases and outbreaks of public health importance. The short duration of RNC, coupled with the large number of visitors to our area, provided additional surveillance challenges.
Tropical Storm Isaac, which threatened Tampa in the days leading up to RNC, and an overwhelming law enforcement presence likely dissuaded many protestors from coming to Tampa. As a result, a tiny fraction of the number of protestors that were expected actually showed up.
Methods
Our normal daily analysis of the emergency department (ED) data using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) was expanded to look in detail at ED volumes and chief complaints of those patients who live outside of a 5-county Tampa Bay area. This analysis used patient zip code to determine place of residence. Additionally, ESSENCE queries were utilized to look for heat, tear gas, and RNC related exposures. The ESSENCE system also receives Poison Control data every 15 minutes. Expanded analyses of the Poison Control data were conducted as well. Two Disaster Medical Assistance Teams (DMATs) were deployed in Tampa during the RNC. Data was collected electronically and transmitted through ESSENCE as well.
The HCHD also asked infection preventionists, health care providers, hotels, labs, and Mosquito Control to lower their reporting threshold to us during the RNC period. We provided updates to all our partners with respect to diseases and outbreaks of public health importance occurring in our county.
Results
No epidemiologic events linked to the RNC were detected through the HCHD’s enhanced surveillance that was conducted. Decreased patient volumes were seen during the RNC at our EDs closest to the convention site. No significant increases in ED visits from outside of our 5-county area were noted during the RNC. Urgent care centers reported seeing patients associated with the RNC for a variety of reasons including respiratory and gastrointestinal illness. DMAT surveillance showed mainly routine visits but four secret service agents did seek care for respiratory illness during the convention.
Conclusions
Substantial time and resources were devoted to disease surveillance in the 6 months leading up to the RNC and during the event. While no epidemiologic events were detected, the public health surveillance infrastructure has clearly been strengthened in our county. We are receiving our ED syndromic data, from many of our hospitals, every two hours as opposed to every day. We have established relationships with our urgent case centers and hope to begin receiving urgent care center data on a daily basis in the near future. Receiving DMAT data through ESSENCE could prove very useful in the future, especially in Florida where hurricanes are always a threat. Lastly the improved relationships with our health care providers should be beneficial as we move forward.
PMCID: PMC3692915
mass gathering; national special security event; convention
19.  Early Detection of Influenza Activity Using Syndromic Surveillance in Missouri 
Objective
To assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.
Introduction
Syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.
Methods
The Missouri ESSENCE system utilizes data from 84 hospitals, which represents up to 90 percent of all ED visits occurring in Missouri statewide each day. The influenza season is defined as starting during Centers for Disease Control and Prevention (CDC) week number 40 (around the first of October) and ending on CDC week 20 of the following year, which is usually at the end of May.
A confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. Laboratory results are reported on a weekly basis. To assess the severity of influenza activity, all flu seasons were compared with the 2008–09 season, which experienced the lowest influenza activity based on laboratory data. Analysis of variance (ANOVA) was applied for this analysis using Statistical Analysis Software (SAS) (version 9.2).
The standard ESSENCE ILI subsyndrome includes ED chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. The ESSENCE ILI weekly percent is the number of ILI visits divided by total ED visits.
Time series of weekly percent of ILI in ESSENCE were compared to weekly counts of laboratory confirmed influenza cases. Spearman correlation coefficients were calculated using SAS. The baseline refers to the mean of three flu seasons with low influenza activity (2006–07, 2008–09 and 2010–11 seasons). The threshold was calculated as this baseline plus three standard deviations.
The early warning potential of the ESSENCE weekly ILI percent was evaluated for five consecutive influenza seasons, beginning in 2006. This was accomplished by calculating the time lag between the first ESSENCE ILI warning versus the first lab confirmed influenza warning. A warning was identified if either lab confirmed case counts or weekly percent of ILI crossed over their respective baselines.
Results
For each influenza season evaluated, weekly ILI rates reported via ESSENCE were significantly correlated with weekly counts of laboratory-confirmed influenza cases (Table 1). The baseline of ILI activity in ESSENCE was 1.8 ILI /100 ED visits/week and the threshold was set at 4.1 ILI visits per 100 ED visits/week. The ESSENCE ILI baseline provided, on average, two weeks of advanced warning for seasonal influenza activity. Figure 1 shows that two influenza seasons (2007–08 and 2009–10) were more severe than others examined based on the ESSENCE percent ILI threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05).
Conclusions
The significant correlation between ILI surveillance in ESSENCE and laboratory confirmed influenza cases justifies the use of weekly ILI percent in ESSENCE to describe seasonal influenza activity. The ESSENCE ILI baseline and threshold provided advanced warning of influenza and allowed for the classification of influenza severity in the community.
PMCID: PMC3692881
ESSENCE; syndromic surveillance; influenza-like illness (ILI); baseline; threshold
20.  Localized Cluster Detection Applied to Joint and Separate Military and Veteran Subpopulations 
Objective
We examined the utility of combining surveillance data from the Departments of Defense (DoD) and Veterans Affairs (VA) for spatial cluster detection.
Introduction
The Joint VA/DoD BioSurveillance System for Emerging Biological Threats project seeks to improve situational awareness of the health of VA/DoD populations by combining their respective data. Each system uses a version of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE); a combined version is being tested.
The current effort investigated combining the datasets for disease cluster detection. We compared results of retrospective cluster detection studies using both separate and joined data. — Does combining datasets worsen the rate of background cluster determination?
— Does combining mask clusters detected on the separate datasets?
— Does combining find clusters that the separate datasets alone would miss?
Methods
Cluster determination runs were done with a spatial scan statistics implementation previously verified [1] by comparison with SaTScan software [2] using DoD data from the Biosense system.
Input data files were extracted from a repository of outpatient records from both DoD and VA facilities covering 4 years beginning Jan. 1, 2007. This repository includes over 37 million DoD records and over 86 million VA records. Input files were matrices of daily Influenza-like-Illness (ILI) or gastrointestinal (GI) visit counts. Matrix rows were consecutive days, columns were patient residence zip codes, and entry (i, j) was the number of visits on day i from with zip code j. These files were made for DoD data, VA data, and combined data.
For assessing the alerting burden from combining datasets, three sets of runs were executed using data from three regions, Baltimore/Washington D.C. (dominated by DoD data), Los Angeles (mainly VA data), and Tampa (representation of both). For each region, sets of 1672 daily runs were executed for ILI and GI syndrome data. Lastly, focused runs were done to investigate known outbreaks in New York (GI, Jan–Mar 2010), San Diego (ILI, Dec 2007–Apr 2008 and Fall 2009), and New Jersey (GI, Jan–Mar 2010).
Results
Combining the data sources increased the rate of significant cluster alerting by a manageable 1–10% across run sets. Some clusters found only when the data were combined persisted over several days and may have indicated small events not reported in either system; however, we were unable to validate minor events that may have occurred in past years.
Retrospective looks at known outbreaks were successful in that clustering evidence found in separate DoD and VA runs persisted when data sets were combined. For the New York run, a West Point outbreak was seen in repeated clusters of combined data, beginning days before the event report. However, clustering did not consistently produce alerts before outbreak report dates. In the New Jersey DoD runs, repeated clusters indicated a 10-week GI outbreak at Fort Dix; adding VA data that dominated the record counts gave the same clusters with no added cases, so the DoD event was probably self-contained. The San Diego runs were aimed at detecting unusually severe influenza epidemics in February 2008 and in the fall of 2009, and numerous clusters were found but did not enhance regional disease tracking.
Conclusions
From the analysis, combining DoD and VA data enhances cluster detection capability without loss of sensitivity to events isolated in either population and with manageable effect on the customary alert rate. For cluster detection, there may be many geographic regions where a health monitor in one of the systems would benefit from combined data. More detailed outbreak information is needed to quantify the timeliness/sensitivity advantages of combining datasets. In events examined, clustering itself yielded an occasional but not consistent timeliness advantage.
PMCID: PMC3692743
ESSENCE; Department of Defense; scan statistics; cluster detection; Veterans Administration
21.  Utility of System Generated Syndromic Surveillance Alerts to Detect Reportable Disease Outbreaks 
Objective
In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.
Introduction
Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.
Methods
Within the ESSENCE-FL query portal, the Merlin Reportable Diseases Data Source was selected with a weekly time resolution by event date. Case Classification included all confirmed, probable and suspect cases, reported and not yet reported, during the time period of week 35, 2011, to week 35, 2012. The ESSENCE Weighted Moving Average (EWMA 1.2) detector was used to classify weekly counts as either of normal, warning or alert status based on previous weeks’ counts, indicating the possibility of an emerging outbreak. These weekly statuses were then compared with outbreaks reported in Merlin’s fully integrated outbreak reporting system and with outbreak reports submitted to EpiCom, Florida’s EpiX or health alert network. An ESSENCE-FL generated warning or alert was considered valid if a corresponding outbreak of 2 or more epi-linked pertussis cases were reported in either Merlin’s outbreak module or in EpiCom. For the sake of brevity in this abstract, the analysis of pertussis is presented, while other reportable disease conditions of immediate interest will be presented at the conference.
Results
Examination of 494 pertussis cases reported from September 2011 to September 2012 showed that of 53 weeks, 38 weeks contained normal case counts, 11 weeks generated warnings, and 4 weeks produced alerts. The number of warnings that corresponded to actual outbreaks was 6 of 11, whereas 2 of the 4 alerts matched reported outbreaks. Of the remaining 38 weeks, 12 had outbreaks reported with no warning or alert generated by ESSENCE-FL. When comparing confirmed outbreak status with ESSENCE-FL weekly data count status, warning/alert versus normal, it was found that the sensitivity of ESSENCE-FL to detect a true outbreak was 40.0% while the specificity was 78.8%. This comparison generated a positive-predictive value of 53.3% and a negative predictive value of 68.4%.
Conclusions
The ability of ESSENCE-FL to act as a first alert system for emerging disease events using Merlin reportable disease data should be considered with constraint. While warnings or alerts about potential pertussis outbreaks were generated correctly about half the time, the nearly one-third of reported outbreaks with no warning or alert makes the utility of the alerts questionable as far as initiating immediate action without prior verification of the alert. Florida does not currently have a requirement for centrally documenting all outbreaks, so it is likely that outbreaks occurred but were not recorded, precluding verification of all outbreaks.
PMCID: PMC3692865
Syndromic; Surveillance; Outbreaks
22.  Characteristics of Veterans Accessing the Veterans Affairs Telephone Triage Who Have Depression or Suicidal Ideation: Opportunities for Intervention 
Objective
To characterize Veterans who call telephone triage because of suicidal ideation (SI) or depression and to identify opportunities for suicide prevention efforts among these telephone triage users using a biosurveillance application.
Introduction
Veterans accessing Veterans Affairs (VA) health care have higher suicide rates and more characteristics associated with suicide risk, including being male, having multiple medical and psychiatric comorbidities, and being an older age, compared with the general U.S. population. The Veterans Crisis Line is a telephone hotline available to Veterans with urgent mental health concerns; however, not all Veterans are aware of this resource. By contrast, telephone triage is a national telephone-based triage system used by the VA to assess and triage all Veterans with acute medical or mental health complaints.
Methods
The VA Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) was queried for telephone triage calls during January 1–June 30, 2012. Calls were classified as SI or depression when the triage nurse selected SI or depression as the Veteran’s chief complaint from a set of fixed options. Demographic and recommended follow-up time and location information was reviewed. A random sample of 20 SI calls and 50 depression calls were selected for chart review to determine whether Veterans were examined in a clinic or followed up by a clinician by telephone within 2 weeks of the veteran’s call.
Results
During January 1–June 30, 2012, 253,573 total calls were placed to telephone triage. Among these calls, 2,460 unique Veterans placed 417 calls for SI and 2,290 calls for depression. This represents 1% (2,707/253,573) of all calls placed during the period. All encounter information is available in the surveillance application within 24 hours of the call being placed. Median age of callers was 55 years (range: 19–94); 86% were male; and 6% placed repeat calls. The median number of repeat calls was 2 (range: 2–10). Among the 2,707 calls for SI or depression, 1,286 (48%) were made after routine business hours (5:00 p.m.–8:00 a.m.), and 646 (24%) were made on weekends. The greatest proportion of calls were from Wisconsin and Northern Illinois (17%) and the Southeastern United States (14%). Among the 2,290 calls for depression, 1,401 callers (61%) were recommended for urgent follow up or within 24 hours. 771 (34%) were assigned a follow up location of an emergency department; 117 (5%) an urgent care; 1,332 (58%) a physician’s office or clinic; 52 (2%) self-care at home; and 18 (1%) were unspecified. Among the 417 calls for SI, callers 410 (98%) were recommended for urgent follow-up or within 24 hours. 330 (79%) were assigned a follow-up location of an emergency department; 38 (9%) an urgent care; 43 (10%) a physician’s office or clinic; 3 (1%) self-care at home; and 3 (1%) unspecified. Among the 20 SI and 50 depression calls for which the charts were reviewed, 1 (5%) SI call and 6 (12%) depression calls had no documented follow-up by telephone or in person with a clinician within 2 weeks of initial call.
Conclusions
Telephone triage represents an additional data source available to surveillance applications. Although telephone triage is not the traditional method provided by the VA for triage of urgent mental health concerns, >2,000 Veterans called it with acute symptoms of SI or depression during January–June 2012. Training for suicide prevention should be prioritized for operators working during the high-volume periods of off-hours and weekends when approximately half and one-quarter of calls were received, respectively. We recommend standard notification of suicide prevention coordinators regarding calls to telephone triage for SI or depression to prevent loss to follow-up among Veterans at risk for suicide. Further investigation into reasons for increased call burden in identified geographic areas also is recommended.
PMCID: PMC3692783
Surveillance; Veterans; Suicide Risk
23.  The Epidemiologic Vocabulary of the West and the Former Soviet Union: Different Sides of the Same Science 
Objective
The purpose of this project was to develop an English-Russian Epidemiology Dictionary, which is needed for improved international collaboration in public health surveillance.
Introduction
As part of the US Department of Defense strategy to counter biological threats, the Defense Threat Reduction Agency’s Cooperative Biological Engagement Program is enhancing the capabilities of countries in the former Soviet Union (FSU) to detect, diagnose, and report endemic and epidemic, man-made or natural cases of especially dangerous pathogens. During these engagements, it was noted that Western-trained and Soviet-trained epidemiologists have difficulty, beyond that of simple translation, in exchanging ideas.
The Soviet public health system and epidemiology developed independently of that of other nations. Whereas epidemiology in the West is thought of in terms of disease determinants in populations and relies on statistics to make inferences, classical Soviet epidemiology is founded on a more ecological view with the main focus on infectious diseases’ spread theory. Consequently many fundamental Soviet terms and concepts lack simple correlates in English and other languages outside the Soviet sphere; the same is true when attempting to translate from English to Russian and other languages of the FSU. Systematic review of the differences in FSU and Western epidemiologic concepts and terminology is therefore needed for strengthening understanding and collaboration in disease surveillance, pandemic preparedness, response to biological terrorism, etc.
Methods
Following an extensive search of the Russian and English literature by a working group of Western and FSU epidemiologists, we created a matrix containing English and Russian definitions of key epidemiologic terms found in FSU and Western epidemiology manuals and dictionaries, such as A Dictionary of Epidemiology (1), Epidemiology Manual (2) and many other sources. Particular emphasis was placed on terms relating to infectious disease surveillance, analysis of surveillance data, and outbreak investigation. In order to compare the definitions of each term and to elucidate differences in usage and existing gaps, all definitions were translated into English and Russian so that the definitions could be compared side by side and discussed by the working group.
Results
Six hundred and thirty one terms from 27 English and 51 Russian sources were chosen for inclusion based on their importance in applied epidemiology in either the West or the FSU. Review of the definitions showed that many terms within biosurveillance and infectious disease public health practice are used differently, and some concepts are lacking altogether in the Russian or English literature. Significant gaps in FSU epidemiology are in the areas of biostatistics and epidemiologic study designs. There are distinctive differences in FSU and Western epidemiology in the conceptualization and classification of disease transmission, surveillance practices, and control measures.
Conclusions
Epidemiologic concepts and definitions significantly differed in the FSU and Western literature. To improve biosurveillance and international collaboration, recognition of these differences must occur. Detailed analysis of epidemiology terminology differences will be discussed in the presentation and paper. Major limitations of the work were scarcity of prior research on the subject and lack of bilingual epidemiologists with the good understanding of FSU and Western approaches. A bilingual reference in the form of a dictionary will greatly improve mutual comprehension and collaboration in the areas of biosurveillance and public health practice.
PMCID: PMC3692890
Surveillance; Dictionary; Collaboration
24.  Comparing Syndromic Surveillance and Poison Center Data for Snake Bites in Missouri 
Objective
This study intends to use two different surveillance systems available in Missouri to explore snake bite frequency and geographic distribution.
Introduction
In 2010, there were 4,796 snake bite exposures reported to Poison Centers nationwide (1). Health care providers frequently request help from poison centers regarding snake envenomations due to the unpredictability and complexity of prognosis and treatment. The Missouri Poison Center (MoPC) maintains a surveillance database keeping track of every phone call received. ESSENCE, a syndromic surveillance system used in Missouri, enables surveillance by chief complaint of 84 different emergency departments (ED) in Missouri (accounting for approximately 90% of all ED visits statewide). Since calling a poison center is voluntary for health care providers, poison center data is most likely an underestimation of the true frequency of snake envenomations. Comparing MoPC and ESSENCE data for snake envenomations would enable the MoPC to have a more accurate depiction of snake bite frequency in Missouri and to see where future outreach of poison center awareness should be focused.
Methods
Archived data from Toxicall®, the MoPC surveillance system, was used to query the total number of snake bite cases from 01/01/2007 until 12/31/2011 called into the MoPC center by hospitals that also participate ESSENCE. Next, ESSENCE data was used to estimate the total number of snake envenomations presenting to EDs in Missouri. This was accomplished using the same date range as well as searching for key terms in the chief complaints that would signify a snake bite. The results of each datasearch were compared and contrasted by Missouri region.
Results
The Toxicall® search showed a total of 324 snake bite cases. The initial ESSENCE data query showed a total of 1983 snake bite cases. After certain data exclusions, there was a total of 1763 ESSENCE snake bite visits. This suggests that approximately 18% of all snake bite visits reported in Missouri ESSENCE were called into the MoPC. The results are demonstrated by Missouri region in Figure 1. This figure also shows that the greatest number of ESSENCE visits for snake bites were reported by Southwest region hospitals whereas the Eastern region hospitals placed the greatest number of calls to MoPC regarding snake bites.
Conclusions
The total number of snake bite cases from Missouri ESSENCE ED visits is much greater than the number of snake bites cases called into the MoPC by ESSENCE participating hospitals. This underutilization of the poison center demonstrates the increased need for awareness of the MoPC’s free services. In Missouri, the MoPC should target hospitals in the Southwest region for outreach in particular based on these findings. Poison centers are staffed by individuals trained in all types of poisonings and maintain a list of consulting physicians throughout the United States experienced in management and treatment of venomous snake bites (2). Any healthcare facility would benefit from MoPC assistance. Finally, syndromic surveillance allows for quick and easy data compilation, however there are some difficulties when attempting to search for a particular condition. Communication and partnership between two different public health organizations will be beneficial toward future public health studies.
PMCID: PMC3692746
ESSENCE; surveillance; Missouri; Poison Center; snake
25.  The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis 
PLoS Medicine  2013;10(1):e1001363.
Caroline Free and colleagues systematically review controlled trials of mobile technology interventions to improve health care delivery processes and show that current interventions give only modest benefits and that high-quality trials measuring clinical outcomes are needed.
Background
Mobile health interventions could have beneficial effects on health care delivery processes. We aimed to conduct a systematic review of controlled trials of mobile technology interventions to improve health care delivery processes.
Methods and Findings
We searched for all controlled trials of mobile technology based health interventions using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990–Sept 2010). Two authors independently extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and we used random effects meta-analysis to give pooled estimates.
We identified 42 trials. None of the trials had low risk of bias. Seven trials of health care provider support reported 25 outcomes regarding appropriate disease management, of which 11 showed statistically significant benefits. One trial reported a statistically significant improvement in nurse/surgeon communication using mobile phones. Two trials reported statistically significant reductions in correct diagnoses using mobile technology photos compared to gold standard. The pooled effect on appointment attendance using text message (short message service or SMS) reminders versus no reminder was increased, with a relative risk (RR) of 1.06 (95% CI 1.05–1.07, I2 = 6%). The pooled effects on the number of cancelled appointments was not significantly increased RR 1.08 (95% CI 0.89–1.30). There was no difference in attendance using SMS reminders versus other reminders (RR 0.98, 95% CI 0.94–1.02, respectively). To address the limitation of the older search, we also reviewed more recent literature.
Conclusions
The results for health care provider support interventions on diagnosis and management outcomes are generally consistent with modest benefits. Trials using mobile technology-based photos reported reductions in correct diagnoses when compared to the gold standard. SMS appointment reminders have modest benefits and may be appropriate for implementation. High quality trials measuring clinical outcomes are needed.
Please see later in the article for the Editors' Summary
Editors’ Summary
Background
Over the past few decades, computing and communication technologies have changed dramatically. Bulky, slow computers have been replaced by portable devices that can complete increasingly complex tasks in less and less time. Similarly, landlines have been replaced by mobile phones and other mobile communication technologies that can connect people anytime and anywhere, and that can transmit text messages (short message service; SMS), photographs, and data at the touch of a button. These advances have led to the development of mobile-health (mHealth)—the use of mobile computing and communication technologies in health care and public health. mHealth has many applications. It can be used to facilitate data collection and to encourage health-care consumers to adopt healthy lifestyles or to self-manage chronic conditions. It can also be used to improve health-care service delivery processes by targeting health-care providers or communication between these providers and their patients. So, for example, mobile technologies can be used to provide clinical management support in settings where there are no specialist clinicians, and they can be used to send patients test results and timely reminders of appointments.
Why Was This Study Done?
Many experts believe that mHealth interventions could greatly improve health-care delivery processes, particularly in resource-poor settings. The results of several controlled trials (studies that compare the outcomes of people who do or do not receive an intervention) of mHealth interventions designed to improve health-care delivery processes have been published. However, these data have not been comprehensively reviewed, and the effectiveness of this type of mHealth intervention has not been quantified. Here, the researchers rectify this situation by undertaking a systematic review and meta-analysis of controlled trials of mobile technology-based interventions designed to improve health-care service delivery processes. A systematic review is a study that uses predefined criteria to identify all the research on a given topic; a meta-analysis is a statistical approach that is used to pool the results of several independent studies.
What Did the Researchers Do and Find?
The researchers identified 42 controlled trials that investigated mobile technology-based interventions designed to improve health-care service delivery processes. None of the trials were of high quality—many had methodological problems likely to affect the accuracy of their findings—and nearly all were undertaken in high-income countries. Thirty-two of the trials tested interventions directed at health-care providers. Of these trials, seven investigated interventions providing health-care provider education, 18 investigated interventions supporting clinical diagnosis and treatment, and seven investigated interventions to facilitate communication between health-care providers. Several of the trials reported that the tested intervention led to statistically significant improvements (improvements unlikely to have happened by chance) in outcomes related to disease management. However, two trials that used mobile phones to transmit photos to off-site clinicians for diagnosis reported significant reductions in correct diagnoses compared to diagnosis by an on-site specialist. Ten of the 42 trials investigated interventions targeting communication between health-care providers and patients. Eight of these trials investigated SMS-based appointment reminders. Meta-analyses of the results of these trials indicated that using SMS appointment reminders significantly but modestly increased patient attendance compared to no reminders. However, SMS reminders were no more effective than postal or phone call reminders, and texting reminders to patients who persistently missed appointments did not significantly change the number of cancelled appointments.
What Do These Findings Mean?
These findings indicate that some mHealth interventions designed to improve health-care service delivery processes are modestly effective, but they also highlight the need for more trials of these interventions. Specifically, these findings show that although some interventions designed to provide support for health-care providers modestly improved some aspects of clinical diagnosis and management, other interventions had deleterious effects—most notably, the use of mobile technology–based photos for diagnosis. In terms of mHealth interventions targeting communication between health-care providers and patients, the finding that SMS appointment reminders have modest benefits suggests that implementation of this intervention should be considered, at least in high-income settings. However, the researchers stress that more trials are needed to robustly establish the ability of mobile technology-based interventions to improve health-care delivery processes. These trials need to be of high quality, they should be undertaken in resource-limited settings as well as in high-income countries, and, ideally, they should consider interventions that combine mHealth and conventional approaches.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001363.
A related PLOS Medicine Research Article by Free et al. investigates the effectiveness of mHealth technology-based health behavior change and disease management interventions for health-care consumers
Wikipedia has a page on mHealth (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
mHealth: New horizons for health through mobile technologies is a global survey of mHealth prepared by the World Health Organization’s Global Observatory for eHealth (eHealth is health-care practice supported by electronic processes and communication)
The mHealth in Low-Resource Settings website, which is maintained by the Netherlands Royal Tropical Institute, provides information on the current use, potential, and limitations of mHealth in low-resource settings
The US National Institutes of Health Fogarty International Center provides links to resources and information about mHealth
doi:10.1371/journal.pmed.1001363
PMCID: PMC3566926  PMID: 23458994

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