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.     

Utility of Syndromic Surveillance Using Novel Clinical Data Sources

Objective

To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data.

Introduction

Historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (EDs) and urgent care (UC) clinics to public health agencies. The use of data from inpatient and ambulatory settings is now gaining interest and support throughout the United States, largely as a result of the Stage 2 and 3 Meaningful Use regulations [1]. Questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. Solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions.

Methods

We conducted a literature search in the published and grey literature that scanned for relevant articles in the Google Scholar, Pub Med, and EBSCO Information Services databases. Search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “EHR syndromic surveillance”. Information gleaned from each article included data use, data elements extracted, and data quality indicators. In addition, several stakeholders who provided input on the September 2012 ISDS Recommendations [2] also provided articles that were incorporated into the literature review.ISDS also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources.

Results

The number of public health agencies (PHAs) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ED and UC data. Some health departments, private medical organizations (including HMOs), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2].In inpatient settings, many of the necessary infrastructure and analytic tools are already in place. Syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 Joplin, Missouri tornado [3].In contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. The existing systems vary in how they address data volume and what types of encounters they capture. Ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4].

Conclusions

The existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. However, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. Though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment.     

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.     

Towards Interoperability for Public Health Surveillance: Experiences from Two States

Objective

To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance.

Introduction

The use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary.Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test.Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice.

Methods

To characterize the use of standardized vocabularies in electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, we analyzed ELR messages from two states: Indiana and Wisconsin. We examined the data in the ELR messages where tests and results are reported (3). For each field, the proportion of field values that used either LOINC or SNOMED CT codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields.

Results

Results are summarized in

Nontraumatic Oral Health Classification for Alternative Use of Syndromic Data

Objective

To develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in North Carolina (NC) Emergency Departments (EDs) using syndromic surveillance system data.

Introduction

Lack of access to regular dental care often results in costly, oral health visits to EDs that could otherwise have been prevented or managed by a dentist (1). Most studies on oral health-related visits to EDs have used a wide range of classifications from different databases, but none have used syndromic surveillance data. The volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for NC EDs.

Methods

Literature review, input by subject matter experts (SMEs), and analysis of syndromic data was used to create the nontraumatic oral health classification. BioSense, a near real-time, national-level, electronic health surveillance system was the source of the NC ED syndromic data. Visits with at least one oral health-related ICD-9-CM code were extracted for NC fiscal years 2008–2010. Univariate analyses of chief complaint (CC) and final diagnosis data along with SME consultation were used to determine the CC substrings and ‘white list’ of ICD-9-CM codes used as inclusion criteria to classify visits as oral health-related. These analyses and consultations also determined the trauma-related codes and substrings used to exclude visits.

Results

Oral Health-Related ICD-9-CM CodesWhite List ICD-9-CM CodesOral Health-Related CC Substrings521.x780.60388.70Tooth and ache522.x305.1682.0Tooth and abscess523.x401.9786.2Tooth and pain525.x784.0478.19Tooth and abcess^**528.x^*784.2780.6Dental526.9Open in a separate windowx = includes all numbers under this ICD-9-CM subheading^*Except 528.3 and 528.5^**Most common misspelling of abscessIn summary, an ED visit had a nontraumatic oral health classification if it contained 1) an oral health-related CC substring with no trauma-related ICD-9-CM codes or CC substrings or 2) an oral health-related ICD-9 code accompanied by no oral health-related or trauma-related CC substrings and with no other diagnosis codes except for those on the whitelist.

Conclusions

There is increasing demand to determine ways to use syndromic surveillance data in an alternative way for population health surveillance. This use of BioSense data provided a practical classification of patient records for the tracking of nontraumatic oral health-related visits to NC EDs. Visit estimates created using this classification in combination with other pertinent information could prove useful to policymakers when deciding upon resource allocation aimed at reducing this unnecessary burden on the NC ED system. The large volume of records in syndromic surveillance systems offers substantial weight of evidence for alternative use in epidemiological studies; however, accurate classification of records is required to select cases of interest. While data volume precludes validation of every included record, a combination of human expertise and data analysis can provide credible classification criteria.     

Recommendations for Syndromic Surveillance Using Inpatient and Ambulatory EHR Data

Objective

To develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data.

Introduction

MUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1).Open in a separate windowFigure 1:Syndromic surveillance data can inform public health functions.To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.

Methods

ISDS, in collaboration with the Division of Informatics Solutions and Operations at the Centers for Disease Control and Prevention (CDC), and HLN Consulting, convened a multi-stakeholder Work-group of clinicians, technologists, epidemiologists, and public health officials with expertise in syndromic surveillance. Recommended MUse guidelines were developed by performing an environmental scan of current practice and by using an iterative, expert and community input-driven process. The Workgroup developed initial guidelines and then solicited and received feedback from the stakeholder community via interview, e-mail, and structured surveys. Stakeholder feedback was analyzed using quantitative and qualitative methods and used to revise the recommendations.

Results

The MUse Workgroup defined electronic syndromic surveillance (ESS) characteristics. Specifically, data are characterized by their timeliness, sensitivity rather than specificity, population focus, limited personally identifiable information, and inclusion of all patient encounters within a specific healthcare setting (e.g., emergency department, inpatient, outpatient). Based on stakeholder input (n=125) and Workgroup expertise, the guidelines identify priority syndromic surveillance uses that can assist with:

1. Monitoring population health;Informing public health services; andInforming interventions, health education, and policy by characterizing the burden of chronic disease and health disparities.

Similarly, the Workgroup identified data elements to support these uses in the hospital inpatient setting and possibly in the ambulatory care setting. They were aligned to previously identified emergency department and urgent care center data elements and Stage 1–2 clinical MUse objectives. Core data elements (required for certification) cover treating facility; patient demographics; subjective and objective clinical findings, including chief complaint, body mass index, smoking history, diagnoses; and outcomes. Other data elements were designated as extended (not required for certification) or future (for future consideration). The data elements and their specifications are subject to change based on applicable state and local laws and practices.Based on their findings and recommended guidelines detailed in the report, the Workgroup also identified community activities and additional investments that would best support public health agencies in using EHR technology with syndromic surveillance methodologies.

Conclusions

The widespread adoption of EHRs, catalyzed by MUse, has the potential to improve population health. By identifying and describing potential ESS uses of new sources of EHR data and associated data elements with the greatest utility for public health, the recommendations set forth by the ISDS MUse Workgroup will serve to facilitate the adoption of MUse policy by both healthcare and public health agencies.     

症状监测在汶川地震救援部队中的应用

目的探索症状监测在地震救援部队中及早发现和控制重要传染病和其他疾病流行的作用。方法2008—05．15／06．14以某部参加救灾全体人员为监测对象,确立发热、腹泻、咽痛、皮疹、皮肤外伤、眼结膜红肿等6种症状为监测指标。结果共监测2726人,在31d监测期内,6种症状的罹患率分别为1．28％～16．62％,皮疹和外伤在救援初期分别有1个发病高峰,发热在第3周有1个发病高峰。荨麻疹、湿疹、急性上呼吸道感染、急性扁桃体炎、外伤等为主要疾病。结论在地震救援部队执行任务期间进行症状监测是可行的,监测的6种主要症状代表性强,可起到预警作用。     

Evaluation of Electronic Ambulatory Care Data for Use in the Influenza-like Illness Surveillance Network (ILINet)

Objective

To determine if a syndromic influenza-like illness (ILI) definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Introduction

During summer 2012, Washington State Department of Health (WA DOH) surveyed ILINet providers and found that more than half either utilize their electronic medical record system (EMRS) to gather and report weekly ILINet data, or intend to implement queries to do so in the future. There are a variety of EMRS being used state-wide, and providers that currently utilize these systems to report ILINet data apply a wide range of methods to query their data. There exists great interest in the evaluation of ambulatory care data within the context of Meaningful Use and little research is published in this area. WA DOH sought to evaluate electronic data from WA outpatient clinic networks in order to determine if a syndromic ILI definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Methods

Public Health Seattle King County (PHSKC) receives electronic health data from the University of Washington Physicians Network (UWPN), comprised of ten outpatient clinics, on an automated basis. Data are sent daily for all outpatient visits that occurred the previous day and include clinic name, visit date and time, patient age, sex, zip code, chief complaint and diagnoses, and both a visit and patient key. Outpatient data from August 2007 through August 2012 were queried for ILI visits using the syndromic category for ILI previously validated for ED syndromic surveillance data: (1) ICD codes for influenza or mention of “flu” in chief complaint or diagnosis, or (2) a chief complaint or diagnosis of fever plus cough, or (3) a chief complaint or diagnosis of fever plus sore throat.Using this definition, we assessed the correlation between the proportion of visits for ILI in the UWPN data and number and percentage of positive influenza laboratory tests reported by the University of Washington (UW) Virology Laboratory. We plan to apply this methodology to evaluate outpatient data from an additional clinic network, with statewide locations, and present these findings.

Results

The median number of weekly visits captured in the data was 6,622. Three clinics were excluded from further analyses due to insufficient data, leaving seven clinics remaining in the dataset (median number of weekly visits: 6,167). Overall, the proportion of ILI visits in the UWPN data strongly correlated with the number and percentage of positive influenza tests reported by the UW Laboratory during August 2007 through August 2012 (correlation coefficients 0.85 and 0.77, respectively). The correlation between proportion of ILI visits and number positive influenza tests among individual clinics ranged from 0.62 — 0.83. Overall, the proportion of ILI visits among the age category 5 to 24 years most strongly correlated with number positive influenza tests (correlation coefficient: 0.86).

Conclusions

During August 2007 through August 2012, the percentage of ILI visits detected in UWPN data using a previously validated definition for ILI in ED syndromic surveillance data strongly correlated with influenza activity in the community. Based on these findings, data from the UWPN network will be incorporated into ILINet during the 2012–2013 Influenza season. Findings from our analysis support the validity of using syndromic ambulatory data for ILI surveillance. Furthermore, we plan to use these results to formulate guidance for ILINet providers who want to utilize EMRS for weekly ILINet reporting.Open in a separate windowProportion of ILI visits within electronic clinic network data and number positive influenza tests, August 2007 – August 2012, Washington State