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

Electronic Health Records Versus Survey Small Area Estimates for Public Health Surveillance

INTRODUCTION

Electronic health records (EHRs) are increasingly being leveraged for public health surveillance. EHR-based small area estimates (SAEs) are often validated by comparison to survey data such as the Behavioral Risk Factor Surveillance System (BRFSS). However, survey and EHR-based SAEs are expected to differ. In this cross-sectional study, SAEs were generated using MDPHnet, a distributed EHR-based surveillance network, for all Massachusetts municipalities and zip code tabulation areas (ZCTAs), compared to BRFSS PLACES SAEs, and reasons for differences explored.

METHODS

This study delineated reasons a priori for how SAEs derived using EHRs may differ from surveys by comparing each strategy's case classification criteria and reviewing the literature. Hypertension, diabetes, obesity, asthma, and smoking EHR-based SAEs for 2021 in all ZCTAs and municipalities in Massachusetts were estimated with Bayesian mixed effects modeling and poststratification in the summer/fall of 2023. These SAEs were compared to BRFSS PLACES SAEs published by the U.S. Centers for Disease Control and Prevention.

RESULTS

Mean prevalence was higher in EHR data versus BRFSS in both municipalities and ZCTAs for all outcomes except asthma. ZCTA and municipal symmetric mean absolute percentages ranged from 12.0 to 38.2% and 13.1 to 39.8%, respectively. There was greater variability in EHR-based SAEs versus BRFSS PLACES in both municipalities and ZCTAs.

CONCLUSIONS

EHR-based SAEs tended to be higher than BRFSS and more variable. Possible explanations include detection of undiagnosed cases and over-classification using EHR data, and under-reporting within BRFSS. Both EHR and survey-based surveillance have strengths and limitations that should inform their preferred uses in public health surveillance.

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.

A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records

Introduction

Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates.

Methods

We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates.

Results

We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute.

Conclusions

We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

Associations between extreme precipitation, drinking water, and protozoan acute gastrointestinal illnesses in four North American Great Lakes cities (2009-2014)

Climate change is already impacting the North American Great Lakes ecosystem and understanding the relationship between climate events and public health, such as waterborne acute gastrointestinal illnesses (AGIs), can help inform needed adaptive capacity for drinking water systems (DWSs). In this study, we assessed a harmonized binational dataset for the effects of extreme precipitation events (≥90th percentile) and preceding dry periods, source water turbidity, total coliforms, and protozoan AGIs - cryptosporidiosis and giardiasis - in the populations served by four DWSs that source surface water from Lake Ontario (Hamilton and Toronto, Ontario, Canada) and Lake Michigan (Green Bay and Milwaukee, Wisconsin, USA) from January 2009 through August 2014. We used distributed lag non-linear Poisson regression models adjusted for seasonality and found extreme precipitation weeks preceded by dry periods increased the relative risk of protozoan AGI after 1 and 3-5 weeks in three of the four cities, although only statistically significant in two. Our results suggest that the risk of protozoan AGI increases with extreme precipitation preceded by a dry period. As extreme precipitation patterns become more frequent with climate change, the ability to detect changes in water quality and effectively treat source water of varying quality is increasingly important for adaptive capacity and protection of public health.

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

The Childhood Obesity Data Initiative: A Case Study in Implementing Clinical-Community Infrastructure Enhancements to Support Health Services Research and Public Health

CONTEXT

We describe a participatory framework that enhanced and implemented innovative changes to an existing distributed health data network (DHDN) infrastructure to support linkage across sectors and systems. Our processes and lessons learned provide a potential framework for other multidisciplinary infrastructure development projects that engage in a participatory decision-making process.

PROGRAM

The Childhood Obesity Data Initiative (CODI) provides a potential framework for local and national stakeholders with public health, clinical, health services research, community intervention, and information technology expertise to collaboratively develop a DHDN infrastructure that enhances data capacity for patient-centered outcomes research and public health surveillance. CODI utilizes a participatory approach to guide decision making among clinical and community partners.

IMPLEMENTATION

CODI's multidisciplinary group of public health and clinical scientists and information technology experts collectively defined key components of CODI's infrastructure and selected and enhanced existing tools and data models. We conducted a pilot implementation with 3 health care systems and 2 community partners in the greater Denver Metro Area during 2018-2020.

EVALUATION

We developed an evaluation plan based primarily on the Good Evaluation Practice in Health Informatics guideline. An independent third party implemented the evaluation plan for the CODI development phase by conducting interviews to identify lessons learned from the participatory decision-making processes.

DISCUSSION

We demonstrate the feasibility of rapid innovation based upon an iterative and collaborative process and existing infrastructure. Collaborative engagement of stakeholders early and iteratively was critical to ensure a common understanding of the research and project objectives, current state of technological capacity, intended use, and the desired future state of CODI architecture. Integration of community partners' data with clinical data may require the use of a trusted third party's infrastructure. Lessons learned from our process may help others develop or improve similar DHDNs.

A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative

CONTEXT

Integrating longitudinal data from community-based organizations (eg, physical activity programs) with electronic health record information can improve capacity for childhood obesity research.

OBJECTIVE

A governance framework that protects individual privacy, accommodates organizational data stewardship requirements, and complies with laws and regulations was developed and implemented to support the harmonization of data from disparate clinical and community information systems.

PARTICIPANTS AND SETTING

Through the Childhood Obesity Data Initiative (CODI), 5 Colorado-based organizations collaborated to expand an existing distributed health data network (DHDN) to include community-generated data and assemble longitudinal patient records for research.

DESIGN

A governance work group expanded an existing DHDN governance infrastructure with CODI-specific data use and exchange policies and procedures that were codified in a governance plan and a delegated-authority, multiparty, reciprocal agreement.

RESULTS

A CODI governance work group met from January 2019 to March 2020 to conceive an approach, develop documentation, and coordinate activities. Governance requirements were synthesized from the CODI use case, and a customized governance approach was constructed to address governance gaps in record linkage, a procedure to request data, and harmonizing community and clinical data. A Master Sharing and Use Agreement (MSUA) and Memorandum of Understanding were drafted and executed to support creation of linked longitudinal records of clinical- and community-derived childhood obesity data. Furthermore, a multiparty infrastructure protocol was approved by the local institutional review board (IRB) to expedite future CODI research by simplifying IRB research applications.

CONCLUSION

CODI implemented a clinical-community governance strategy that built trust between organizations and allowed efficient data exchange within a DHDN. A thorough discovery process allowed CODI stakeholders to assess governance capacity and reveal regulatory and organizational obstacles so that the governance infrastructure could effectively leverage existing knowledge and address challenges. The MSUA and complementary governance documents can inform similar efforts.

Trends in Outpatient Medical-Care Seeking for Acute Gastroenteritis During the COVID-19 Pandemic, 2020

The rate of enteric infections reported to public health surveillance decreased during 2020 amid the coronavirus disease 2019 (COVID-19) pandemic. Changes in medical care-seeking behaviors may have impacted the diagnosis of enteric infections contributing to these declines. We examined trends in outpatient medical care-seeking behavior for acute gastroenteritis (AGE) in Colorado during 2020 compared with the that of previous 3 years using electronic health record data from the Colorado Health Observation Regional Data Service (CHORDS). Outpatient medical encounters for AGE were identified using diagnoses codes from the International Classification of Diseases 10th Revision and aggregated by year, quarter, age group, and encounter type. The rate of encounters was calculated by dividing the number of AGE encounters by the corresponding total number of encounters. There were 9064 AGE encounters in 2020 compared with an annual average of 18,784 from 2017 to 2019 (p < 0.01), representing a 52% decrease. The rate of AGE encounters declined after the first quarter of 2020 and remained significantly lower for the rest of the year. Moreover, previously observed trends, including seasonal patterns and the preponderance of pediatric encounters, were no longer evident. Telemedicine modalities accounted for 23% of all AGE encounters in 2020. AGE outpatient encounters in Colorado in 2020 were substantially lower than during the previous 3 years. Decreases remained stable over the second, third, and fourth quarters of 2020 (April-December) and were especially pronounced for children <18 years of age. Changes in medical care-seeking behavior likely contributed to declines in the number of enteric disease cases and outbreaks reported to public health. It is unclear to what extent people were ill with AGE and did not seek medical care because of concerns about the infection risk during a health care visit or to what extent there were reductions in certain exposures and opportunities for disease transmission resulting in less illness.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation

Background

Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations.

Methods

Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings.

Results

By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis.

Conclusion

While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07393-1.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data

Automated analysis of electronic health record (EHR) data is a complementary tool for public health surveillance. Analyzing and presenting these data, however, demands new methods of data communication optimized to the detail, flexibility, and timeliness of EHR data.RiskScape is an open-source, interactive, Web-based, user-friendly data aggregation and visualization platform for public health surveillance using EHR data. RiskScape displays near-real-time surveillance data and enables clinical practices and health departments to review, analyze, map, and trend aggregate data on chronic conditions and infectious diseases. Data presentations include heat maps of prevalence by zip code, time series with statistics for trends, and care cascades for conditions such as HIV and HCV. The platform's flexibility enables it to be modified to incorporate new conditions quickly-such as COVID-19.The Massachusetts Department of Public Health (MDPH) uses RiskScape to monitor conditions of interest using data that are updated monthly from clinical practice groups that cover approximately 20% of the state population. RiskScape serves an essential role in demonstrating need and burden for MDPH's applications for funding, particularly through the identification of inequitably burdened populations.

A Case Study of Early-Onset Colorectal Cancer: Using Electronic Health Records to Support Public Health Surveillance on an Emerging Cancer Control Topic

Electronic health records (EHRs) are increasingly being used to support public health surveillance, including in cancer, where many population-based registries can now accept electronic case reporting. Using EHRs to supplement cancer registry data provides the opportunity to examine in more detail emerging issues in cancer control, such as the increasing incidence rates of early onset colorectal cancer (CRC). The purpose of this study was to evaluate the feasibility of a public health organization partnering with a health system to examine risk factors for early-onset CRC in a community cancer setting, and to further understand challenges with using EHRs to address emerging topics in cancer control. We conducted a mixed-methods evaluation using key informant interviews with public health practitioners, researchers, and registry staff to generate insights on how using EHRs and partnering with health systems can improve chronic disease surveillance and cancer control. A data quality assessment of variables representing risk factors for CRC and other clinical characteristics was conducted on all CRC patients diagnosed in 2016 at the participating cancer center. The quantitative assessment of the EHR data revealed that, while most chronic health conditions were well documented, around 25% of CRC patients were missing information on body mass index, alcohol, and tobacco use. Key informants offered ideas and ways to overcome challenges with using EHR data to support chronic disease surveillance. Their recommendations included the following activities: engaging EHR vendors in the development of standards, taking leadership roles on workgroups to address emerging technological issues, participating in pilot studies and task forces, and negotiating with EHR vendors so that clinical decision support tools built to support public health initiatives are freely available to all users of those EHRs. Although using EHR data to support public health efforts is not without its challenges, it soon could be an important part of chronic disease surveillance and cancer control.

Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance

OBJECTIVE

Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys.

METHODS

Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness.

RESULTS

The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys.

CONCLUSION

The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.

Postpartum Contraceptive Use Among Denver-Based Adolescents and Young Adults: Association with Subsequent Repeat Delivery

STUDY OBJECTIVE

To determine the association of postpartum contraceptive use with repeat deliveries among adolescents and youth.

DESIGN

Retrospective, observational analysis of electronic health record data.

SETTING

Single, urban facility in Denver, Colorado, United States.

PARTICIPANTS

Women aged 10-24 years who gave birth between January 1, 2011 and December 31, 2015.

INTERVENTIONS AND MAIN OUTCOME MEASURES

Postpartum contraceptive use and time to subsequent delivery.

RESULTS

Among 4068 women, 1735 (43%) used postpartum contraception. In adjusted analyses, characteristics associated with contraceptive use included Hispanic ethnicity (relative risk [RR], 1.1; P = .03), incremental prenatal visits (RR, 1.01; P = .047), and attendance at postpartum care (RR, 1.60; P < .001). Long-acting reversible contraceptive (LARC) use was higher among women younger than 15 years (reference: 20-24 years; RR, 1.12; P < .001) and lower among women aged 18-19 years (RR, 0.93; P = .009). Hispanic women had higher rates of LARC use than non-Hispanic women (RR, 1.07; P = .02). Compared with inpatient LARC placement, outpatient placement (1-4 weeks and 5 or more weeks) rates were lower (RR, 0.77 and RR, 0.89, respectively; P < .001). Time to subsequent delivery was shorter in non-LARC users (median, 659 days) and contraception nonusers (median, 624 days) compared with LARC users (median, 790 days; P < .001); non-LARC postpartum contraceptive use did not significantly alter time to repeat delivery compared with that in women who used no method (P = .24).

CONCLUSION

Postpartum LARC use reduced the risk of repeat pregnancy with a significant increase in time to the next delivery. Non-LARC use was not different from no contraceptive use in terms of time to repeat delivery.

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.

Monitoring opioid addiction and treatment: Do you know if your population is engaged?

BACKGROUND

Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution.

METHODS

Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP.

RESULTS

In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year.

CONCLUSIONS

A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.