Distributed Data Networks That Support Public Health Information Needs

Fostering Governance and Information Partnerships for Chronic Disease Surveillance: The Multi-State EHR-Based Network for Disease Surveillance

CONTEXT

Electronic health records (EHRs) are an emerging chronic disease surveillance data source and facilitating this data sharing is complex.

PROGRAM

Using the experience of the Multi-State EHR-Based Network for Disease Surveillance (MENDS), this article describes implementation of a governance framework that aligns technical, statutory, and organizational requirements to facilitate EHR data sharing for chronic disease surveillance.

IMPLEMENTATION

MENDS governance was cocreated with data contributors and health departments representing Texas, New Orleans, Louisiana, Chicago, Washington, and Indiana through engagement from 2020 to 2022. MENDS convened a governance body, executed data-sharing agreements, and developed a master governance document to codify policies and procedures.

RESULTS

The MENDS governance committee meets regularly to develop policies and procedures on data use and access, timeliness and quality, validation, representativeness, analytics, security, small cell suppression, software implementation and maintenance, and privacy. Resultant policies are codified in a master governance document.

DISCUSSION

The MENDS governance approach resulted in a transparent governance framework that cultivates trust across the network. MENDS's experience highlights the time and resources needed by EHR-based public health surveillance networks to establish effective governance.

Common data model for sickle cell disease surveillance: considerations and implications

Objective

Population-level data on sickle cell disease (SCD) are sparse in the United States. The Centers for Disease Control and Prevention (CDC) is addressing the need for SCD surveillance through state-level Sickle Cell Data Collection Programs (SCDC). The SCDC developed a pilot common informatics infrastructure to standardize processes across states.

Materials and Methods

We describe the process for establishing and maintaining the proposed common informatics infrastructure for a rare disease, starting with a common data model and identify key data elements for public health SCD reporting.

Results

The proposed model is constructed to allow pooling of table shells across states for comparison. Core Surveillance Data reports are compiled based on aggregate data provided by states to CDC annually.

Discussion and Conclusion

We successfully implemented a pilot SCDC common informatics infrastructure to strengthen our distributed data network and provide a blueprint for similar initiatives in other rare diseases.

Recommendations to Improve the Nature and Extent of Relationships Among Organizations Within a Network to Enhance Supports for Transgender Survivors of Sexual Assault

CONTEXT

To enhance the provision of comprehensive supports to transgender (trans) survivors of sexual assault, a structurally marginalized group with complex care needs, we developed an intersectoral network of trans-positive health care and community organizations in Ontario, Canada.

OBJECTIVE

As a baseline evaluation of the network, we conducted a social network analysis to determine the extent and nature of collaboration, communication, and connection among members.

DESIGN

Relational data (eg, activities of collaboration) were collected from June to July 2021, and analyzed using a validated survey tool, Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER). We shared findings in a virtual consultation session with key stakeholders and facilitated discussion to generate action items. Consultation data were synthesized into 12 themes through conventional content analysis.

SETTING

An intersectoral network in Ontario, Canada.

PARTICIPANTS

Of the 119 representatives of trans-positive health care and community organizations invited to participate in this study, 78 (65.5%) completed the survey.

MAIN OUTCOME MEASURES

Proportion/count of organizations collaborating with other organizations. Network scores for value and trust.

RESULTS

Almost all (97.5%) invited organizations were listed as collaborators, representing 378 unique relationships. The network achieved a value score of 70.4% and trust score of 83.4%. The most prominent themes were "Communication and knowledge exchange channels," "Clearer roles and contributions," "Indicators of success," and "Client voices at the centre."

CONCLUSION

As key antecedents of network success, high value and trust indicate that network member organizations are well positioned to further foster knowledge sharing, define their roles and contributions, prioritize the integration of trans voices in all activities, and, ultimately, achieve common goals with clearly defined outcomes. There is great potential to optimize network functioning and advance the network's mission to improve services for trans survivors by mobilizing these findings into recommendations.

Identifying requisite learning health system competencies: a scoping review

OBJECTIVES

Learning health systems (LHS) integrate knowledge and practice through cycles of continuous quality improvement and learning to increase healthcare quality. LHS have been conceptualised through multiple frameworks and models. Our aim is to identify and describe the requisite individual competencies (knowledge, skills and attitudes) and system competencies (capacities, characteristics and capabilities) described in existing literature in relation to operationalising LHS.

METHODS

A scoping review was conducted with descriptive and thematic analysis to identify and map competencies of LHS for individuals/patients, health system workers and systems. Articles until April 2020 were included based on a systematic literature search and selection process. Themes were developed using a consensus process until agreement was reached among team members.

RESULTS

Eighty-nine articles were included with most studies conducted in the USA (68 articles). The largest number of publications represented competencies at the system level, followed by health system worker competencies. Themes identified at the individual/patient level were knowledge and skills to understand and share information with an established system and the ability to interact with the technology used to collect data. Themes at the health system worker level were skills in evidence-based practice, leadership and teamwork skills, analytical and technological skills required to use a 'digital ecosystem', data-science knowledge and skill and self-reflective capacity. Researchers embedded within LHS require a specific set of competencies. Themes identified at the system level were data, infrastructure and standardisation; integration of data and workflow; and culture and climate supporting ongoing learning.

CONCLUSION

The identified individual stakeholder competencies within LHS and the system capabilities of LHS provide a solid base for the further development and evaluation of LHS. International collaboration for stimulating LHS will assist in further establishing the knowledge base for LHS.

Online two-way estimation and inference via linear mixed-effects models

In this article, we tackle the estimation and inference problem of analyzing distributed streaming data that is collected continuously over multiple data sites. We propose an online two-way approach via linear mixed-effects models. We explicitly model the site-specific effects as random-effect terms, and tackle both between-site heterogeneity and within-site correlation. We develop an online updating procedure that does not need to re-access the previous data and can efficiently update the parameter estimate, when either new data sites, or new streams of sample observations of the existing data sites, become available. We derive the non-asymptotic error bound for our proposed online estimator, and show that it is asymptotically equivalent to the offline counterpart based on all the raw data. We compare with some key alternative solutions both analytically and numerically, and demonstrate the advantages of our proposal. We further illustrate our method with two data applications.

Aligning healthcare, public health and social services: A scoping review of the role of purpose, governance, finance and data

Organisations spanning social services, public health and healthcare have increasingly experimented with collaboration as a tool for improving population health and reducing health disparities. While there has been progress, the results have fallen short of expectations. Reflecting on these shortcomings, the Robert Wood Johnson Foundation (RWJF) recently proposed a new framework for cross-sector alignment intended to move the field towards improved outcomes. A central idea in this framework is that collaboratives will be more effective and sustainable if they develop collaborative systems in four core areas: shared purpose, governance, finance and shared data. The goal of this paper is to provide a foundation for research on the four core areas of the cross-sector alignment framework. Accordingly, this study is based on two guiding questions: (1) how are collaboratives currently implementing systems in the four core areas identified in the framework, and (2) what strategies does the literature offer for creating sustainable systems in these four areas? Given the emergent nature of research on health-oriented cross-sector collaboration and the broad research questions, we conducted a systematic scoping review including 179 relevant research papers and reports published internationally from the years 2010-2020. We identified the main contributions and coded each based on its relevance to the cross-sector alignment framework. We found that most papers focused on programme evaluations rather than theory testing, and while many strategies were offered, they tended to reflect a focus on short-term collaboration. The results also demonstrate that starting points and resource levels vary widely across individuals and organisations involved in collaborations. Accordingly, identifying and comparing distinct pathways by which different parties might pursue cross-sector alignment is an imperative for future work. More broadly, the literature is ripe with observations that could be assessed systematically to produce a firm foundation for research and practice.

Deep-learning-based automated terminology mapping in OMOP-CDM

OBJECTIVE

Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

MATERIALS AND METHODS

To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings.

RESULTS

The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements.

DISCUSSION

The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system.

CONCLUSION

Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.

Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service

Electronic health records (EHRs) provide an alternative to traditional public health surveillance surveys and administrative data for measuring the prevalence and impact of chronic health conditions in populations. As the infrastructure for secondary use of EHR data improves, many stakeholders are poised to benefit from data partnerships for regional access to information. Electronic health records can be transformed into a common data model that facilitates data sharing across multiple organizations and allows data to be used for surveillance. The Colorado Health Observation Regional Data Service, a regional distributed data network, has assembled diverse data partnerships, flexible infrastructure, and transparent governance practices to better understand the health of communities through EHR-based, public health surveillance. This article describes attributes of regional distributed data networks using EHR data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. Colorado Health Observation Regional Data Service and our experience may serve as a model for other regions interested in similar surveillance efforts. While benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.

Geospatial Monitoring of Body Mass Index: Use of Electronic Health Record Data Across Health Care Systems

OBJECTIVES

The Colorado BMI Monitoring System was developed to assess geographic (ie, census tract) patterns of obesity prevalence rates among children and adults in the Denver-metropolitan region. This project also sought to assess the feasibility of a surveillance system that integrates data across multiple health care and governmental organizations.

MATERIALS AND METHODS

We extracted data on height and weight measures, obtained through routine clinical care, from electronic health records (EHRs) at multiple health care sites. We selected sites from 5 Denver health care systems and collected data from visits that occurred between January 1, 2013, and December 31, 2015. We produced shaded maps showing observed obesity prevalence rates by census tract for various geographic regions across the Denver-metropolitan region.

RESULTS

We identified clearly distinguishable areas by higher rates of obesity among children than among adults, with several pockets of lower body mass index. Patterns for adults were similar to patterns for children: the highest obesity prevalence rates were concentrated around the central part of the metropolitan region. Obesity prevalence rates were moderately higher along the western and northern areas than in other parts of the study region.

PRACTICE IMPLICATIONS

The Colorado BMI Monitoring System demonstrates the feasibility of combining EHRs across multiple systems for public health and research. Challenges include ensuring de-duplication across organizations and ensuring that geocoding is performed in a consistent way that does not pose a risk for patient privacy.

Applying a Commercialization-Readiness Framework to Optimize Value for Achieving Sustainability of an Electronic Health Data Research Network and Its Data Capabilities: The SAFTINet Experience

CONTEXT

Sustaining electronic health data networks and maximizing return on federal investment in their development is essential for achieving national data insight goals for transforming health care. However, crossing the business model chasm from grant funding to self-sustaining viability is challenging.

CASE DESCRIPTION

This paper presents lessons learned in seeking the sustainability of the Scalable Architecture for Federated Translational Inquiries Network (SAFTINet), and electronic health data network involving over 50 primary care practices in three states. SAFTINet was developed with funding from the Agency for Healthcare Research and Quality to create a multi-state network for comparative effectiveness research (CER) involving safety-net patients.

METHODS

Three analyses were performed: (1) a product gap analysis of alternative data sources; (2) a Strengths-Weaknesses-Opportunities-Threat (SWOT) analysis of SAFTINet in the context of competing alternatives; and (3) a customer discovery process involving approximately 150 SAFTINet stakeholders to identify SAFTINet's sustaining value proposition for health services researchers, clinical data partners, and policy makers.

FINDINGS

The results of this business model analysis informed SAFTINet's sustainability strategy. The fundamental high-level product needs were similar between the three primary customer segments: credible data, efficient and easy to use, and relevance to their daily work or 'jobs to be done'. However, how these benefits needed to be minimally demonstrated varied by customer such that different supporting evidence was required.

MAJOR THEMES

The SAFTINet experience illustrates that commercialization-readiness and business model methods can be used to identify multi-sided value propositions for sustaining electronic health data networks and their data capabilities as drivers of health care transformation.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.

The Spatial Distribution of Adult Obesity Prevalence in Denver County, Colorado: An Empirical Bayes Approach to Adjust EHR-Derived Small Area Estimates

Objectives

Measuring obesity prevalence across geographic areas should account for environmental and socioeconomic factors that contribute to spatial autocorrelation, the dependency of values in estimates across neighboring areas, to mitigate the bias in measures and risk of type I errors in hypothesis testing. Dependency among observations across geographic areas violates statistical independence assumptions and may result in biased estimates. Empirical Bayes (EB) estimators reduce the variability of estimates with spatial autocorrelation, which limits the overall mean square-error and controls for sample bias.

Methods

Using the Colorado Body Mass Index (BMI) Monitoring System, we modeled the spatial autocorrelation of adult (≥ 18 years old) obesity (BMI ≥ 30 kg m²) measurements using patient-level electronic health record data from encounters between January 1, 2009, and December 31, 2011. Obesity prevalence was estimated among census tracts with >=10 observations in Denver County census tracts during the study period. We calculated the Moran's I statistic to test for spatial autocorrelation across census tracts, and mapped crude and EB obesity prevalence across geographic areas.

Results

In Denver County, there were 143 census tracts with 10 or more observations, representing a total of 97,710 adults with a valid BMI. The crude obesity prevalence for adults in Denver County was 29.8 percent (95% CI 28.4-31.1%) and ranged from 12.8 to 45.2 percent across individual census tracts. EB obesity prevalence was 30.2 percent (95% CI 28.9-31.5%) and ranged from 15.3 to 44.3 percent across census tracts. Statistical tests using the Moran's I statistic suggest adult obesity prevalence in Denver County was distributed in a non-random pattern. Clusters of EB obesity estimates were highly significant (alpha=0.05) in neighboring census tracts. Concentrations of obesity estimates were primarily in the west and north in Denver County.

Conclusions

Statistical tests reveal adult obesity prevalence exhibit spatial autocorrelation in Denver County at the census tract level. EB estimates for obesity prevalence can be used to control for spatial autocorrelation between neighboring census tracts and may produce less biased estimates of obesity prevalence.