The long road to semantic interoperability in support of public health: experiences from two states

Automating Case Reporting of Chlamydia and Gonorrhea to Public Health Authorities in Illinois Clinics: Implementation and Evaluation of Findings

BACKGROUND

Chlamydia and gonorrhea cases continue to rise in Illinois, increasing by 16.4% and 70.9% in 2019, respectively, compared with 2015. Providers are required to report both chlamydia and gonorrhea, as mandated by public health laws. Manual reporting remains a huge burden; 90%-93% of cases were reported to Illinois Department of Public Health (IDPH) via electronic laboratory reporting (ELR), and the remaining were reported through web-based data entry platforms, faxes, and phone calls. However, cases reported via ELRs only contain information available to a laboratory facility and do not contain additional data needed for public health. Such data are typically found in an electronic health record (EHR). Electronic case reports (eCRs) were developed and automated the generation of case reports from EHRs to be reported to public health agencies.

OBJECTIVE

Prior studies consolidated trigger criteria for eCRs, and compared with manual reporting, found it to be more complete. The goal of this project is to pilot standards-based eCR for chlamydia and gonorrhea. We evaluated the throughput, completeness, and timeliness of eCR compared to ELR, as well as the implementation experience at a large health center-controlled network in Illinois.

METHODS

For this study, we selected 8 clinics located on the north, west, and south sides of Chicago to implement the eCRs; these cases were reported to IDPH. The study period was 52 days. The centralized EHR used by these clinics leveraged 2 of the 3 case detection scenarios, which were previously defined as the trigger, to generate an eCR. These messages were successfully transmitted via Health Level 7 electronic initial case report standard. Upon receipt by IDPH, these eCRs were parsed and housed in a staging database.

RESULTS

During the study period, 183 eCRs representing 135 unique patients were received by IDPH. eCR reported 95% (n=113 cases) of all the chlamydia cases and 97% (n=70 cases) of all the gonorrhea cases reported from the participating clinical sites. eCR found an additional 14 (19%) cases of gonorrhea that were not reported via ELR. However, ELR reported an additional 6 cases of chlamydia and 2 cases of gonorrhea, which were not reported via eCR. ELR reported 100% of chlamydia cases but only 81% of gonorrhea cases. While key elements such as patient and provider names were complete in both eCR and ELR, eCR was found to report additional clinical data, including history of present illness, reason for visit, symptoms, diagnosis, and medications.

CONCLUSIONS

eCR successfully identified and created automated reports for chlamydia and gonorrhea cases in the implementing clinics in Illinois. eCR demonstrated a more complete case report and represents a promising future of reducing provider burden for reporting cases while achieving greater semantic interoperability between health care systems and public health.

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

Objective

We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors.

Materials and Methods

We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities.

Results

Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media.

Discussion

Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19.

Conclusions

The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic

BACKGROUND

Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.

MATERIALS AND METHODS

First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.

RESULTS

A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.

CONCLUSION

The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

Association between 30-day readmission rates and health information technology capabilities in US hospitals

ABSTRACT

Health information technology (IT) is often proposed as a solution to fragmentation of care, and has been hypothesized to reduce readmission risk through better information flow. However, there are numerous distinct health IT capabilities, and it is unclear which, if any, are associated with lower readmission risk.To identify the specific health IT capabilities adopted by hospitals that are associated with hospital-level risk-standardized readmission rates (RSRRs) through path analyses using structural equation modeling.This STROBE-compliant retrospective cross-sectional study included non-federal U.S. acute care hospitals, based on their adoption of specific types of health IT capabilities self-reported in a 2013 American Hospital Association IT survey as independent variables. The outcome measure included the 2014 RSRRs reported on Hospital Compare website.A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis, and corroborated by confirmatory factor analysis. Subsequent path analysis using Structural equation modeling revealed that a one-point increase in the hospital adoption of patient engagement capability latent scores (median path coefficient ß = -0.086; 95% Confidence Interval, -0.162 to -0.008), including functionalities like direct access to the electronic health records, would generally lead to a decrease in RSRRs by 0.086%. However, computerized hospital discharge and information exchange capabilities with other inpatient and outpatient providers were not associated with readmission rates.These findings suggest that improving patient access to and use of their electronic health records may be helpful in improving hospital performance on readmission; however, computerized hospital discharge and information exchange among clinicians did not seem as beneficial - perhaps because of the quality or timeliness of information transmitted. Future research should use more recent data to study, not just adoption of health IT capabilities, but also whether their usage is associated with lower readmission risk. Understanding which capabilities impact readmission risk can help policymakers and clinical stakeholders better focus their scarce resources as they invest in health IT to improve care delivery.

Data integration in cardiac electrophysiology ablation toward achieving proper interoperability in health information systems

INTRODUCTION

Providing information exchange and collaboration between isolated information systems (ISs) is essential in the health-care environments. In this context, we aimed to develop a communication protocol to facilitate better interoperability among electrophysiology study (EPS)-related ISs in order to allow exchange unified reporting in EPS ablation.

MATERIALS AND METHODS

This study was an applied-descriptive research that was conducted in 2019. To determine the information content of agreed cardiac EPS Minimum Data Set (MDS) in Iran, the medical record of patients undergoing EPS ablation procedure in the Tehran Heart Center (THC) hospital was reviewed by a checklist. Then, an information model based on Health Level Seven, Clinical Document Architecture (HL7 CDA) standard framework for structural interoperability has been developed. In this framework, using NPEX online browser and MindMaple software, a set of terminology mapping rules was used for consistent transfer of data between various ISs.

RESULTS

The information content of each data field was introduced into the heading and body sections of HL7 CDA document using Systematized Nomenclature of Medicine - Clinical Terminology names and codes. Then, the ontology alignment was designed in the form of thesaurus mapping routes.

CONCLUSION

The sensitive, complex, and multidimensional nature of cardiovascular conditions requires special attention to the interoperability of ISs. Designing customized communication protocols plays an important role in improving the interoperability, and they are compatible with the needs of future Iranian health information exchange.

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

BACKGROUND

The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible for data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures are required in order to maintain objective oversight and control. Therefore, the development of practice oriented and evidence-based policies is crucial. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally.

METHODS

We performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies.

RESULTS

Twenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations.

CONCLUSIONS

The description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability. CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated governance structures and policies to increase transparency on data access. The results of this review may contribute to the development of practice-oriented minimal standards for the governance of data access, which could also result in a stronger harmonization, efficiency, and effectiveness of CDW.

Glossary for public health surveillance in the age of data science

Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from healthcare providers to user-generated online content, the field of surveillance and health monitoring is changing rapidly. It is, therefore, the right time for a short glossary of key terms in public health surveillance, with an emphasis on new data-science developments in the field.

Reliability of administrative data to identify sexually transmitted infections for population health: a systematic review

Introduction

International Classification of Diseases (ICD) codes in administrative health data are used to identify cases of disease, including sexually transmitted infections (STIs), for population health research. The purpose of this review is to examine the extant literature on the reliability of ICD codes to correctly identify STIs.

Methods

We conducted a systematic review of empirical articles in which ICD codes were validated with respect to their ability to identify cases of chlamydia, gonorrhoea, syphilis or pelvic inflammatory disease (PID). Articles that included sensitivity, specificity and positive predictive value of ICD codes were the target. In addition to keyword searches in PubMed and Scopus databases, we further examined bibliographies of articles selected for full review to maximise yield.

Results

From a total of 1779 articles identified, only two studies measured the reliability of ICD codes to identify cases of STIs. Both articles targeted PID, a serious complication of chlamydia and gonorrhoea. Neither article directly assessed the validity of ICD codes to identify cases of chlamydia, gonorrhoea or syphilis independent of PID. Using ICD codes alone, the positive predictive value for PID was mixed (range: 18%–79%).

Discussion and conclusion

While existing studies have used ICD codes to identify STI cases, their reliability is unclear. Further, available evidence from studies of PID suggests potentially large variation in the accuracy of ICD codes indicating the need for primary studies to evaluate ICD codes for use in STI-related public health research.

Determinants of access to eHealth services in regional Australia

BACKGROUND

Australia has a universal public healthcare system, but access to eHealth services (i.e. use of the Internet and related technologies for healthcare services) remains a remarkable challenge, particularly in regional, rural and remote communities. Similar to many other countries, Australia faces the challenges of an ageing population and chronic disease management as well as balancing the supply of and the demand for quality healthcare and advanced medical procedures. The prima facie case for inequality in accessing eHealth services across geographical settings is widely acknowledged. However, regional residents' perceptions on access to eHealth services lack empirical evidence. Therefore, this study empirically investigates the current state and predictors of eHealth service access in regional Australia.

METHODS

A cross-sectional questionnaire-based household survey was conducted within a total of 390 randomly selected adults from the Western Downs Region in Southeast Queensland, Australia. Bivariate analysis was conducted to examine the relationship between eHealth access and respondents' characteristics. A multivariate logistic regression model was also performed to identify the significant predictors of eHealth service access in regional Australia.

RESULTS

Approximately 78% of the households have access to eHealth services. However, access to eHealth services in socioeconomically disadvantaged households was lower (19%) than that of their advantaged counterparts (25%). Factors that significantly increased the likelihood of accessing eHealth services included middle age (odds ratio [OR] = 2.75, 95% confidence interval [CI]: 1.84, 8.66), household size (three to four members) (OR = 2.29, 95% CI: 1.19, 4.73), broadband Internet access (OR = 1.67, 95% CI: 1.15, 2.90) and digital literacy (OR = 2.39, 95% CI: 1.23, 4.59). Factors that negatively influenced access to eHealth services were low educational levels (OR = 0.28, 95% CI: 0.09, 0.61), low socioeconomic status (OR = 0.65, 95% CI: 0.28, 0.83) and remote locations (OR = 0.66, 95% CI: 0.23, 0.80).

CONCLUSION

Emerging universal eHealth access provides immense societal benefits in regional settings. The findings of this study could assist policy makers and healthcare practitioners in identifying factors that influence eHealth access and thereby formulate effective health policies to optimise healthcare utilisation in regional Australia.

The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

OBJECTIVE

The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII).

METHOD

We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance.

RESULTS

The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners.

CONCLUSION

Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

Designing a communication protocol for acquired immunodeficiency syndrome information exchange

INTRODUCTION

Interoperability will provide similar understanding on the meaning of communicated messages to intelligent systems and their users. This feature is essential for controlling and managing contagious diseases which threaten public health, such as acquired immunodeficiency syndrome (AIDS). The aim of this study was also designing communication protocols for normalizing the content and structure of intelligent messages in order to optimize the interoperability.

MATERIALS AND METHODS

This study used a checklist to extract information content compatible with minimum data set (MDS) of AIDS. After coding information content through selected classification and nomenclature systems, the reliability and validity of codes were evaluated by external agreement method. The MindMaple software was used for mapping the information content to Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED-CT) integrated codes. Finally, the Clinical Document Architecture (CDA) format was used for standard structuring of information content.

RESULTS

The information content standard format, compatible selected classification, or nomenclature system and their codes were determined for all information contents. Their corresponding codes in SNOMED-CT were structured in the form of CDA body and title.

CONCLUSION

The complex and multidimensional nature of AIDS requires the participation of multidisciplinary teams from different organizations, complex analyzes, multidimensional and complex information modeling, and maximum interoperability. In this study, the use of CDA structure along with SNOMED-CT codes is completely compatible with optimal interoperability needs for AIDS control and management.

Drivers for the development of an Animal Health Surveillance Ontology (AHSO)

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency

Background

Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.

Objectives

The study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.

Methods

The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.

Results

All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals.

Conclusion

With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

The Rise of Big Data in Oncology

OBJECTIVES

To describe big data and data science in the context of oncology nursing care.

DATA SOURCES

Peer-reviewed and lay publications.

CONCLUSION

The rapid expansion of real-world evidence from sources such as the electronic health record, genomic sequencing, administrative claims and other data sources has outstripped the ability of clinicians and researchers to manually review and analyze it. To promote high-quality, high-value cancer care, big data platforms must be constructed from standardized data sources to support extraction of meaningful, comparable insights.

IMPLICATIONS FOR NURSING PRACTICE

Nurses must advocate for the use of standardized vocabularies and common data elements that represent terms and concepts that are meaningful to patient care.

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

Monitoring Depression Rates in an Urban Community: Use of Electronic Health Records

Objectives:

Depression is the most common mental health disorder and mediates outcomes for many chronic diseases. Ability to accurately identify and monitor this condition, at the local level, is often limited to estimates from national surveys. This study sought to compare and validate electronic health record (EHR)-based depression surveillance with multiple data sources for more granular demographic subgroup and subcounty measurements.

Design/Setting:

A survey compared data sources for the ability to provide subcounty (eg, census tract [CT]) depression prevalence estimates. Using 2011-2012 EHR data from 2 large health care providers, and American Community Survey data, depression rates were estimated by CT for Denver County, Colorado. Sociodemographic and geographic (residence) attributes were analyzed and described. Spatial analysis assessed for clusters of higher or lower depression prevalence.

Main Outcome Measure(s):

Depression prevalence estimates by CT.

Results:

National and local survey-based depression prevalence estimates ranged from 7% to 17% but were limited to county level. Electronic health record data provided subcounty depression prevalence estimates by sociodemographic and geographic groups (CT range: 5%-20%). Overall depression prevalence was 13%; rates were higher for women (16% vs men 9%), whites (16%), and increased with age and homeless patients (18%). Areas of higher and lower EHR-based, depression prevalence were identified.

Conclusions:

Electronic health record–based depression prevalence varied by CT, gender, race/ethnicity, age, and living status. Electronic health record–based surveillance complements traditional methods with greater timeliness and granularity. Validation through subcounty-level qualitative or survey approaches should assess accuracy and address concerns about EHR selection bias. Public health agencies should consider the opportunity and evaluate EHR system data as a surveillance tool to estimate subcounty chronic disease prevalence.

Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents

BACKGROUND

Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information.

MATERIALS AND METHODS

To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications.

RESULTS

Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries.

CONCLUSION

This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future.

Learning From the Crowd in Terminology Mapping: The LOINC Experience

National policies in the United States require the use of standard terminology for data exchange between clinical information systems. However, most electronic health record systems continue to use local and idiosyncratic ways of representing clinical observations. To improve mappings between local terms and standard vocabularies, we sought to make existing mappings (wisdom) from healt care organizations (the Crowd) available to individuals engaged in mapping processes. We developed new functionality to display counts of local terms and organizations that had previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) code. Further, we enabled users to view the details of those mappings, including local term names and the organizations that create the mappings. Users also would have the capacity to contribute their local mappings to a shared mapping repository. In this article, we describe the new functionality and its availability to implementers who desire resources to make mapping more efficient and effective.

Which veterans enroll in a VA health information exchange program?

OBJECTIVE

To characterize patients who voluntarily enrolled in an electronic health information exchange (HIE) program designed to share data between Veterans Health Administration (VHA) and non-VHA institutions.

MATERIALS AND METHODS

Patients who agreed to participate in the HIE program were compared to those who did not. Patient characteristics associated with HIE enrollment were examined using a multivariable logistic regression model. Variables selected for inclusion were guided by a health care utilization model adapted to explain HIE enrollment. Data about patients' sociodemographics (age, gender), comorbidity (Charlson index score), utilization (primary and specialty care visits), and access (distance to VHA medical center, insurance, VHA benefits) were obtained from VHA and HIE electronic health records.

RESULTS

Among 57 072 patients, 6627 (12%) enrolled in the HIE program during its first year. The likelihood of HIE enrollment increased among patients ages 50-64, of female gender, with higher comorbidity, and with increasing utilization. Living in a rural area and being unmarried were associated with decreased likelihood of enrollment.

DISCUSSION AND CONCLUSION

Enrollment in HIE is complex, with several factors involved in a patient's decision to enroll. To broaden HIE participation, populations less likely to enroll should be targeted with tailored recruitment and educational strategies. Moreover, inclusion of special populations, such as patients with higher comorbidity or high utilizers, may help refine the definition of success with respect to HIE implementation.

Shared Longitudinal Health Records for Clinical and Population Health

The ability of a health information exchange to consolidate information, collected in multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual’s medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter will describe the clinical benefits imparted by the shared health record (SHR) component of the OpenHIE infrastructure. It will also characterize the potential population health benefits of the aggregate level data contained and distributed by the Health Management Information System component of OpenHIE. The chapter will further discuss the implementation of these systems.

By the end of the chapter, the reader should be able to:●

Identify and describe the differences among an electronic medical record, electronic health record, and a shared heath record.

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Explain the role of a shared health record in a health information exchange.

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List and describe the components of a shared health record.

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Discuss the role and benefits of a health management information system within a health information exchange.

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Define a population health indicator.

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Identify and describe application domains for a health management information system.

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Define a database management system.

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Compare the implications of implementing a shared health record using an electronic health record system versus a database management system.

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Discuss emerging trends likely to shape the evolution of shared health records and health management information systems.

Utilizing an integrated infrastructure for outcomes research: a systematic review

OBJECTIVE

To explore the ability of an integrated health information infrastructure to support outcomes research.

METHODS

A systematic review of articles published from 1983 to 2012 by Regenstrief Institute investigators using data from an integrated electronic health record infrastructure involving multiple provider organisations was performed. Articles were independently assessed and classified by study design, disease and other metadata including bibliometrics.

RESULTS

A total of 190 articles were identified. Diseases included cognitive, (16) cardiovascular, (16) infectious, (15) chronic illness (14) and cancer (12). Publications grew steadily (26 in the first decade vs. 100 in the last) as did the number of investigators (from 15 in 1983 to 62 in 2012). The proportion of articles involving non-Regenstrief authors also expanded from 54% in the first decade to 72% in the last decade. During this period, the infrastructure grew from a single health system into a health information exchange network covering more than 6 million patients. Analysis of journal and article metrics reveals high impact for clinical trials and comparative effectiveness research studies that utilised data available in the integrated infrastructure.

DISCUSSION

Integrated information infrastructures support growth in high quality observational studies and diverse collaboration consistent with the goals for the learning health system. More recent publications demonstrate growing external collaborations facilitated by greater access to the infrastructure and improved opportunities to study broader disease and health outcomes.

CONCLUSIONS

Integrated information infrastructures can stimulate learning from electronic data captured during routine clinical care but require time and collaboration to reach full potential.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research

OBJECTIVE

Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research.

METHODS

We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio.

RESULTS

The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse.

CONCLUSION

Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

Public Health and Epidemiology Informatics: Recent Research and Trends in the United States

OBJECTIVES

To survey advances in public health and epidemiology informatics over the past three years.

METHODS

We conducted a review of English-language research works conducted in the domain of public health informatics (PHI), and published in MEDLINE between January 2012 and December 2014, where information and communication technology (ICT) was a primary subject, or a main component of the study methodology. Selected articles were synthesized using a thematic analysis using the Essential Services of Public Health as a typology.

RESULTS

Based on themes that emerged, we organized the advances into a model where applications that support the Essential Services are, in turn, supported by a socio-technical infrastructure that relies on government policies and ethical principles. That infrastructure, in turn, depends upon education and training of the public health workforce, development that creates novel or adapts existing infrastructure, and research that evaluates the success of the infrastructure. Finally, the persistence and growth of infrastructure depends on financial sustainability.

CONCLUSIONS

Public health informatics is a field that is growing in breadth, depth, and complexity. Several Essential Services have benefited from informatics, notably, "Monitor Health," "Diagnose & Investigate," and "Evaluate." Yet many Essential Services still have not yet benefited from advances such as maturing electronic health record systems, interoperability amongst health information systems, analytics for population health management, use of social media among consumers, and educational certification in clinical informatics. There is much work to be done to further advance the science of PHI as well as its impact on public health practice.

Learning from the crowd while mapping to LOINC

OBJECTIVE

To describe the perspectives of Regenstrief LOINC Mapping Assistant (RELMA) users before and after the deployment of Community Mapping features, characterize the usage of these new features, and analyze the quality of mappings submitted to the community mapping repository.

METHODS

We evaluated Logical Observation Identifiers Names and Codes (LOINC) community members' perceptions about new "wisdom of the crowd" information and how they used the new RELMA features. We conducted a pre-launch survey to capture users' perceptions of the proposed functionality of these new features; monitored how the new features and data available via those features were accessed; conducted a follow-up survey about the use of RELMA with the Community Mapping features; and analyzed community mappings using automated methods to detect potential errors.

RESULTS

Despite general satisfaction with RELMA, nearly 80% of 155 respondents to our pre-launch survey indicated that having information on how often other users had mapped to a particular LOINC term would be helpful. During the study period, 200 participants logged into the RELMA Community Mapping features an average of 610 times per month and viewed the mapping detail pages a total of 6686 times. Fifty respondents (25%) completed our post-launch survey, and those who accessed the Community Mapping features unanimously indicated that they were useful. Overall, 95.3% of the submitted mappings passed our automated validation checks.

CONCLUSION

When information about other institutions' mappings was made available, study participants who accessed it agreed that it was useful and informed their mapping choices. Our findings suggest that a crowd-sourced repository of mappings is valuable to users who are mapping local terms to LOINC terms.

Consolidating CCDs from multiple data sources: a modular approach

Background Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient’s various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data.

Materials and Methods The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system’s performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed.

Results All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49% in Problems, 60.6% in Medications, and 79% in Allergies) and reduced the size of the documents (57.5%) as well as the number of lines in each document (58%). The system executed at a rate of approximately 0.009–0.03 s per rule depending on the complexity of the rule.

Discussion and Conclusion Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.

Implications for informatics given expanding access to care for Veterans and other populations

Recent investigations into appointment scheduling within facilities operated by the US Department of Veterans Affairs (VA) illuminate systemic challenges in meeting its goal of providing timely access to care for all Veterans. In the wake of these investigations, new policies have been enacted to expand access to care at VA facilities as well as non-VA facilities if the VA is unable to provide access within a reasonable timeframe or a Veteran lives more than 40 miles from a VA medical facility. These policies are similar to broader health reform efforts that seek to expand access to care for other vulnerable populations. In this perspective, we discuss the informatics implications of expanded access within the VA and its wider applicability across the US health system. Health systems will require robust health information exchange, to maintain coordination while access to care is expanded. Existing informatics research can guide short-term implementation; furthermore, new research is needed to generate evidence about how best to achieve the long-term aim of expanded access to care.

Personalized medicine for chronic, complex diseases: chronic obstructive pulmonary disease as an example

Chronic, complex diseases represent the majority of healthcare utilization and spending in the USA today. Despite this, therapeutics that account for the heterogeneity of these diseases are lacking, begging for more personalized approaches. Improving our understanding of disease phenotypes through retrospective trials of electronic health record data will enable us to better categorize patients. Increased usage of next-generation sequencing will further our understanding of the genetic variants involved in chronic disease. Utilization of data warehousing will be necessary in order to securely handle, integrate and analyze the large sets of data produced with these methods. Finally, increased use of clinical decision support will enable the return of clinically actionable results that physicians can use to apply these personalized approaches.

Creating value: unifying silos into public health business intelligence

Introduction:

Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible United States hospitals and 60 percent of eligible providers have used certified electronic health record (EHR) technology and received Meaningful Use incentive funds (HITECH Act1).

Technology:

Certified EHR technology could create new public health (PH) value through novel and rapidly evolving data-use opportunities, never before experienced by PH. The long-standing “silo” approach to funding has fragmented PH programs and departments,2 but the components for integrated business intelligence (i.e., tools and applications to help users make informed decisions) and maximally reuse data are available now.

Systems:

Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and services research (PHSSR) may identify gaps and solutions for the PH community to address.

Conclusion:

Technology and system approaches to leverage this information explosion to support a transformed health care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the learning health system.