Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Introduction: Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. Methods: We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. Results: We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Conclusion: Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

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.

MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objective: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Development of a Hypertension Electronic Phenotype for Chronic Disease Surveillance in Electronic Health Records: Key Analytic Decisions and Their Effects.

INTRODUCTION: Modernizing chronic disease surveillance with electronic health record (EHR) data may provide better data to improve hypertension prevention and control, but no consensus exists for an EHR-based surveillance definition for hypertension. The Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot surveillance system was used to develop and test an electronic phenotype for hypertension. METHODS: We used MENDS data from 1,671,279 patients in Louisiana to examine the effect of different analytic decisions on estimates of hypertension prevalence. Decisions included 1) whether to restrict surveillance to patients with recent blood pressure measurements, 2) varying the number and recency of encounters to define the population at risk of hypertension, 3) how to define hypertension (diagnosis codes, antihypertensive medication, blood pressure measurements, or combinations of these), and 4) how to handle multiple blood pressure measurements on the same day. Results were compared with independent estimates of hypertension prevalence in Louisiana from the Behavioral Risk Factor Surveillance System (BRFSS). RESULTS: Applying varying criteria resulted in hypertension prevalence estimates ranging from 19.7% to 59.3%. A hypertension surveillance strategy that includes a population with at least 1 clinical encounter with measured blood pressure in the previous 2 years and identifies hypertension using all available data (≥1 diagnosis code, ≥1 antihypertensive medication, and ≥2 elevated blood pressure values ≥140/90 mm Hg on separate days) generated estimates in line with population-based survey data. This definition estimated the crude 2019 hypertension prevalence in the state of Louisiana as 43.4% (age-adjusted, 41.0%), comparable with the crude BRFSS estimate of 39.7% (age adjusted, 37.1%). CONCLUSION: Applying different criteria to define hypertension using EHR data has a large effect on hypertension prevalence estimates. The proposed electronic phenotype generates hypertension prevalence estimates that align with independent estimates from BRFSS.

Syndromic Surveillance for COVID-19, Massachusetts, February 2020-November 2022: The Impact of Fever and Severity on Algorithm Performance.

OBJECTIVES: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record-based surveillance algorithm for COVID-19-like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases. MATERIALS AND METHODS: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases. RESULTS: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020-January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses. PRACTICE IMPLICATIONS: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care.

Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation.

BACKGROUND: Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy and precision may not carry over for smaller areas that are sparsely represented by non-random samples. METHODS: We developed small-area estimation models using a combination of EHR data drawn from MDPHnet, an EHR-based public health surveillance network in Massachusetts, the American Community Survey, and state hospitalization data. We estimated municipality-specific prevalence rates of asthma, diabetes, hypertension, obesity, and smoking in each of the 351 municipalities in Massachusetts in 2016. Models were compared against Behavioral Risk Factor Surveillance System (BRFSS) state and small area estimates for 2016. RESULTS: Integrating progressively more variables into prediction models generally reduced mean absolute error (MAE) relative to municipality-level BRFSS small area estimates: asthma (2.24% MAE crude, 1.02% MAE modeled), diabetes (3.13% MAE crude, 3.48% MAE modeled), hypertension (2.60% MAE crude, 1.48% MAE modeled), obesity (4.92% MAE crude, 4.07% MAE modeled), and smoking (5.33% MAE crude, 2.99% MAE modeled). Correlation between modeled estimates and BRFSS estimates for the 13 municipalities in Massachusetts covered by BRFSS's 500 Cities ranged from 81.9% (obesity) to 96.7% (diabetes). CONCLUSIONS: Small-area estimation using EHR data is feasible and generates estimates comparable to BRFSS state and small-area estimates. Integrating EHR data with survey data can provide timely and accurate disease monitoring tools for areas with sparse data coverage.

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases.

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1-digital public health workflows; Horizon 2-population health data and analytics; Horizon 3-precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

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.

Denominators Matter: Understanding Medical Encounter Frequency and Its Impact on Surveillance Estimates Using EHR Data.

BACKGROUND: There is scant guidance for defining what denominator to use when estimating disease prevalence via electronic health record (EHR) data. OBJECTIVES: Describe the intervals between medical encounters to inform the selection of denominators for population-level disease rates, and evaluate the impact of different denominators on the prevalence of chronic conditions. METHODS: We analyzed the EHRs of three practices in Massachusetts using the Electronic medical record Support for Public Health (ESP) system. We identified adult patients' first medical encounter per year (2011-2016) and counted days to next encounter. We estimated the prevalence of asthma, hypertension, obesity, and smoking using different denominators in 2016: ≥1 encounter in the past one year or two years and ≥2 encounters in the past one year or two years. RESULTS: In 2011-2016, 1,824,011 patients had 28,181,334 medical encounters. The median interval between encounters was 46, 56, and 66 days, depending on practice. Among patients with one visit in 2014, 82-84 percent had their next encounter within 1 year; 87-91 percent had their next encounter within two years. Increasing the encounter interval from one to two years increased the denominator by 23 percent. The prevalence of asthma, hypertension, and obesity increased with successively stricter denominators - e.g., the prevalence of obesity was 24.1 percent among those with ≥1 encounter in the past two years, 26.3 percent among those with ≥1 encounter in the last one year, and 28.5 percent among those with ≥2 encounters in the past one year. CONCLUSIONS: Prevalence estimates for chronic conditions can vary by >20 percent depending upon denominator. Understanding such differences will inform which denominator definition is best to be used for the need at hand.

Temporal Patterns in Chlamydia Repeat Testing in Massachusetts.

INTRODUCTION: National guidelines recommend test-of-cure for pregnant women and test-of-reinfection for all patients with chlamydia infections in order to interrupt transmission and prevent adverse sequelae for patients, partners, and newborns. Little is known about retesting and positivity rates, and whether they are changing over time, particularly in private sector practices. METHODS: Electronic health record data on patients with chlamydia tests were extracted from three independent clinical practice groups serving ≅20% of the Massachusetts population. Records were extracted using the Electronic medical record Support for Public Health platform (esphealth.org). These data were analyzed for temporal trends in annual repeat testing rates by using generalized estimating equations after index positive chlamydia tests between 2010 and 2015 and for differences in intervals to first repeat tests among pregnant females, non-pregnant females, and males. Data extraction and analysis were performed during calendar years 2017 and 2018. RESULTS: An index positive C. trachomatis result was identified for 972 pregnant female cases, 10,309 non-pregnant female cases, and 4,973 male cases. Test-of-cure 3-5 weeks after an index positive test occurred in 37% of pregnant females. Test-of-reinfection 8-16 weeks after an index positive test occurred in 39% of pregnant females, 18% of non-pregnant females, and 9% of males. There were no significant increases in test-of-cure or test-of-reinfection rates from 2010 to 2015. Among cases with repeat tests, 16% of pregnant females, 15% of non-pregnant females, and 16% of males had positive results. CONCLUSIONS: Chlamydia test-of-cure and test-of-reinfection rates are low, with no evidence of improvement over time. There are substantial opportunities to improve adherence to chlamydia repeat testing recommendations.

State and Local Chronic Disease Surveillance Using Electronic Health Record Systems.

OBJECTIVES: To assess the feasibility of chronic disease surveillance using distributed analysis of electronic health records and to compare results with Behavioral Risk Factor Surveillance System (BRFSS) state and small-area estimates. METHODS: We queried the electronic health records of 3 independent Massachusetts-based practice groups using a distributed analysis tool called MDPHnet to measure the prevalence of diabetes, asthma, smoking, hypertension, and obesity in adults for the state and 13 cities. We adjusted observed rates for age, gender, and race/ethnicity relative to census data and compared them with BRFSS state and small-area estimates. RESULTS: The MDPHnet population under surveillance included 1 073 545 adults (21.8% of the state adult population). MDPHnet and BRFSS state-level estimates were similar: 9.4% versus 9.7% for diabetes, 10.0% versus 12.0% for asthma, 13.5% versus 14.7% for smoking, 26.3% versus 29.6% for hypertension, and 22.8% versus 23.8% for obesity. Correlation coefficients for MDPHnet versus BRFSS small-area estimates ranged from 0.890 for diabetes to 0.646 for obesity. CONCLUSIONS: Chronic disease surveillance using electronic health record data is feasible and generates estimates comparable with BRFSS state and small-area estimates.

Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting.

BACKGROUND: Reporting of adverse events (AEs) following vaccination can help identify rare or unexpected complications of immunizations and aid in characterizing potential vaccine safety signals. We developed an open-source, generalizable clinical decision support system called Electronic Support for Public Health-Vaccine Adverse Event Reporting System (ESP-VAERS) to assist clinicians with AE detection and reporting. METHODS: ESP-VAERS monitors patients' electronic health records for new diagnoses, changes in laboratory values, and new allergies following vaccinations. When suggestive events are found, ESP-VAERS sends the patient's clinician a secure electronic message with an invitation to affirm or refute the message, add comments, and submit an automated, prepopulated electronic report to VAERS. High-probability AEs are reported automatically if the clinician does not respond. We implemented ESP-VAERS in December 2012 throughout the MetroHealth System, an integrated healthcare system in Ohio. We queried the VAERS database to determine MetroHealth's baseline reporting rates from January 2009 to March 2012 and then assessed changes in reporting rates with ESP-VAERS. RESULTS: In the 8 months following implementation, 91 622 vaccinations were given. ESP-VAERS sent 1385 messages to responsible clinicians describing potential AEs. Clinicians opened 1304 (94.2%) messages, responded to 209 (15.1%), and confirmed 16 for transmission to VAERS. An additional 16 high-probability AEs were sent automatically. Reported events included seizure, pleural effusion, and lymphocytopenia. The odds of a VAERS report submission during the implementation period were 30.2 (95% confidence interval, 9.52-95.5) times greater than the odds during the comparable preimplementation period. CONCLUSIONS: An open-source, electronic health record-based clinical decision support system can increase AE detection and reporting rates in VAERS.