Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.

BACKGROUND: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments' community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements-such as vitals measurements and medications-to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives. OBJECTIVE: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations. METHODS: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race. RESULTS: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements. CONCLUSIONS: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence.

Real world performance of the 21st Century Cures Act population-level application programming interface.

OBJECTIVE: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Developing public health surveillance dashboards: a scoping review on the design principles.

BACKGROUND: Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. METHODOLOGY: This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. RESULTS: Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. CONCLUSION: Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

A messaging standard for environmental inspections: is it time?

Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central organization to drive modernization of EH data. We summarize the status of EH information systems; argue for greater interoperability, including use cases for a messaging standard for environmental inspections; and present recommendations to better align EH services and data modernization efforts currently underway in other areas of public health.

Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.

OBJECTIVE: Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. METHODS: A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources. RESULTS: Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability. CONCLUSION: This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.

Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy.

Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.

Building a More Diverse Public Health Informatics Workforce: Preliminary Results.

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State.

OBJECTIVE: The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. MATERIAL AND METHODS: We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. RESULTS: Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. DISCUSSION: Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. CONCLUSION: Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.

How Easy Is It to Read Websites About Schizophrenia?

Unlike legibility, which is determined according to the formal characteristics of a text, such as typeface and page shape, 'readability' defines whether the text is easy to follow and understand by the reader. In addition, readability refers to the level of education necessary for comprehending a given text. The readability of a text can be objectively measured using mathematical formulas based on the relationships between the number of syllables, words and sentences it contains. In order for schizophrenia patients and their relatives to understand their current situation and what kind of process awaits them and to adapt to treatment, the informative texts on websites should be easy to read. Our study aimed to examine the texts about schizophrenia on websites in terms of Turkish readability levels. For the study, 'schizophrenia' was typed into the Google search engine, and the first 50 eligible websites were included. Web pages were analysed in three groups: hospitals, websites created by physicians specializing in psychiatry and other websites. In our study, formulas developed by Atesman and Bezirci-Yilmaz were used to evaluate readability levels. Statistical analyses used analysis of variance (ANOVA) to compare normally distributed groups of three. There was no statistically significant difference between the readability of the three groups (p>0.05). Our research discovered that the initial 50 websites inspected concerning schizophrenia contained texts that were challenging to read, requiring a minimum of 12 years of education. The readability level of schizophrenia-related websites was observed to be much higher than the average education level in Turkey. This situation may pose difficulties for individuals with schizophrenia and their relatives to get information about schizophrenia online. In addition, if the general population has more accurate information about schizophrenia and can understand it correctly, it may reduce the wrong attitudes and stigmatization towards individuals with schizophrenia.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

BACKGROUND: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). OBJECTIVE: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. METHODS: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. RESULTS: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. CONCLUSIONS: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

Identification of Hypertension in Electronic Health Records Through Computable Phenotype Development and Validation for Use in Public Health Surveillance: Retrospective Study.

BACKGROUND: Electronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level. OBJECTIVE: We aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network. METHODS: Using existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F1-score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population. RESULTS: Among a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F1-score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F1-score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F1-score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015). CONCLUSIONS: We identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease.

Completion of the Transfer of the Online Journal of Public Health Informatics (OJPHI) to JMIR Publications.

Founded in 2009, the Online Journal of Public Health Informatics (OJPHI) strives to provide an unparalleled experience as the platform of choice to advance public and population health informatics. As a premier peer-reviewed journal in this field, OJPHI's mission is to serve as an advocate for the discipline through the dissemination of public health informatics research results and best practices among practitioners, researchers, policymakers, and educators. However, in the current environment, running an independent open access journal has not been without challenges. Judging from the low geographic spread of our current stakeholders, the overreliance on a small volunteer management staff, the limited scope of topics published by the journal, and the long article turnaround time, it is obvious that OJPHI requires a change in direction in order to fully achieve its mission. Fortunately, our new publisher JMIR Publications is the leading brand in this field, with a portfolio of top peer-reviewed journals covering innovation, technology, digital medicine and health services research in the internet age. Under the leadership of JMIR Publications, OJPHI plans to expand its scope to include new topics such as precision public health informatics, the use of artificial intelligence and machine learning in public health research and practice, and infodemiology in public health informatics.

Marketing yourself in social media as a global health organization: lessons to be learned.

PURPOSE: To evaluate whether X, formerly known as Twitter, is being used effectively to advance the goals of International Volunteers in Urology (IVUmed). How is X activity associated with end-user engagement? METHODS: Monthly analytics of the X account @IVUmed were reviewed between September 2014 and November 2022 using https://analytics.twitter.com/ . Outcomes included tweets, mentions, impressions, engagements, interactions, followers, and profile visits. Statistical analysis using Mann-Whitney U test and Spearman's rank-order correlation was performed. Top tweet content between December 2020 and November 2022 was also analyzed and assigned one of seven different categories: research, workshops, mission statement, educational materials, fundraising, individual spotlight, and other. RESULTS: Of @IVUmed's 1668 followers, 1334 (80.0%) were individuals. One thousand one hundred twenty-six (84.4%) individuals listed their locations with the majority (79.8%) residing in high-income countries. Tweet impressions have increased over time; they were significantly higher (p < 0.01) on average after the onset of COVID-19 in March 2020. From December 2020 to November 2022, new followers were positively correlated with tweet impressions (p < 0.01), total mentions (p < 0.01), and profile visits (p < 0.01). Profile visits were positively correlated with total tweets (p < 0.01). The content categories for monthly top tweets that proportionally garnered the most engagements were workshops (50%) and individual spotlight (29%), despite not being the most tweeted about content categories. CONCLUSION: Non-profit organizations wishing to increase their web-based outreach can benefit from increased primary X activity. While not evaluated in this study, it may also improve fundraising capabilities. Nevertheless, periodic review of account activity is important to ensure engagement of the targeted audience.

Time trend and seasonality in medically attended respiratory syncytial virus (RSV) infections in US children aged 0-5 years, January 2010-January 2023.

OBJECTIVE: The long-term time trend and seasonality variations of first-time medically attended respiratory syncytial virus (RSV) infections among young children are unknown. We aim to examine the time trend of medically attended first-time RSV infections among young children in the USA from January 2010 through January 2023. DESIGN: This is a population-based cohort study using electronic health records (EHRs). Monthly incidence rate of medically attended first-time RSV infection (cases per 10 000 000 person-days). A time-series regression model was used to model and predict time trends and seasonality. SETTING: Multicenter and nationwide TriNetX Network in the USA. PARTICIPANTS: The study population comprised children aged 0-5 years who had medical visits during the period of January 2010 to January 2023. RESULTS: The data included 29 013 937 medical visits for children aged 0-5 years (46.5% girls and 53.5% boys) from January 2010 through January 2023. From 2010 through 2019, the monthly incidence rate of first-time medically attended RSV infection in children aged 0-5 years followed a consistent seasonal pattern. Seasonal patterns of medically attended RSV infections were significantly disrupted during the COVID-19 pandemic. In 2020, the seasonal variation disappeared with a peak incidence rate of 20 cases per 1 000 000 person-days, a decrease of 97.4% from the expected peak rate (rate ratio or RR: 0.026, 95% CI 0.017 to 0.040). In 2021, the seasonality returned but started 4 months earlier, lasted for 9 months, and peaked in August at a rate of 753 cases per 1 000 000 person-days, a decrease of 9.6% from the expected peak rate (RR: 0.90, 95% CI 0.82 to 0.99). In 2022, the seasonal pattern is similar to prepandemic years but reached a historically high rate of 2182 cases per 10 000 000 person-days in November, an increase of 143% from the expected peak rate (RR: 2.43, 95% CI 2.25 to 2.63). The time trend and seasonality of the EHR-based medically attended RSV infections are consistent with those of RSV-associated hospitalisations from the Centers for Disease Control and Prevention (CDC) survey-based surveillance system. CONCLUSION: The findings show the disrupted seasonality during the COVID-19 pandemic and a historically high surge of paediatric RSV cases that required medical attention in 2022. Our study demonstrates the potential of EHRs as a cost-effective alternative for real-time pathogen and syndromic surveillance of unexpected disease patterns including RSV infection.

Digital health and care: emerging from pandemic times.

In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems. We conclude that the field of digital health has rapidly matured during the pandemic, but there are still major sociotechnical, evaluation and trust challenges in the development and deployment of new digital services.

Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map.

OBJECTIVES: Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis. SETTINGS AND DESIGN: This paper presents a proof of concept for the global loneliness map, using data collected in October 2022. Twitter posts containing keywords such as 'lonely', 'loneliness', 'alone', 'solitude' and 'isolation' were gathered, resulting in 841 796 tweets from the USA. City-specific data were extracted from these tweets to construct a loneliness map for the country. Sentiment analysis using the valence aware dictionary for sentiment reasoning tool was employed to differentiate metaphorical expressions from meaningful correlations between loneliness and socioeconomic and emotional factors. MEASURES AND RESULTS: The sentiment analysis encompassed the USA dataset and city-wise subsets, identifying negative sentiment tweets. Psychosocial linguistic features of these negative tweets were analysed to reveal significant connections between loneliness, socioeconomic aspects and emotional themes. Word clouds depicted topic variations between positively and negatively toned tweets. A frequency list of correlated topics within broader socioeconomic and emotional categories was generated from negative sentiment tweets. Additionally, a comprehensive table displayed top correlated topics for each city. CONCLUSIONS: Leveraging social media data provide insights into the multifaceted nature of loneliness. Given its subjectivity, loneliness experiences exhibit variability. This study serves as a proof of concept for an extensive global loneliness map, holding implications for global public health strategies and policy development. Understanding loneliness dynamics on a larger scale can facilitate targeted interventions and support.

Hepatitis C Cascades: Data to Inform Hepatitis C Elimination in the United States.

The United States has a goal to eliminate hepatitis C as a public health threat by 2030. To accomplish this goal, hepatitis C virus (HCV) care cascades (hereinafter, HCV cascades) can be used to measure progress toward HCV elimination and identify disparities in HCV testing and care. In this topical review of HCV cascades, we describe common definitions of cascade steps, review the application of HCV cascades in health care and public health settings, and discuss the strengths and limitations of data sources used. We use examples from the Massachusetts Department of Public Health as a case study to illustrate how multiple data sources can be leveraged to produce HCV cascades for public health purposes. HCV cascades in health care settings provide actionable data to improve health care quality and delivery of services in a single health system. In public health settings at jurisdictional and national levels, HCV cascades describe HCV diagnosis and treatment for populations, which can be challenging in the absence of a single data source containing complete, comprehensive, and timely data representing all steps of a cascade. Use of multiple data sources and strategies to improve interoperability of health care and public health data systems can advance the use of HCV cascades and speed progress toward HCV elimination.

[Germina: tool for automatic detection of clusters of COVID-19 cases]. / Germina: herramienta para la detección automática de agrupaciones de casos de COVID-19.

The COVID-19 pandemic showed that epidemiological surveillance was under-resourced to respond to increases in cases and outbreaks. The high community transmissibility among the school population in the city of Barcelona at the beginning of the sixth wave strained the local COVID-19 surveillance unit. Using SCRUM methodology, Germina was developed and implemented as a software tool capable of capturing, harmonizing, integrating, storing, analysing and visualizing data from multiple information sources on a daily basis. Germina identifies clusters of three or more school cases and calculates epidemiological indicators. The implementation of Germina facilitated the epidemiological response to the sixth wave of COVID-19 in the school setting in the city of Barcelona. This tool is transferable to other exposure settings and communicable diseases. The use of automated informatics tools such, as Germina, improves epidemiological surveillance systems and supports evidence-based decision making.

Development and implementation of an interoperability tool across state public health agency's disease surveillance and immunization information systems.

Public health information systems have historically been siloed with limited interoperability. The State of Minnesota's disease surveillance system (Minnesota Electronic Disease Surveillance System: MEDSS, ∼12 million total reportable events) and immunization information system (Minnesota Immunization Information Connection: MIIC, ∼130 million total immunizations) lacked interoperability between them and data exchange was fully manual. An interoperability tool based on national standards (HL7 and SOAP/web services) for query and response was developed for electronic vaccination data exchange from MIIC into MEDSS by soliciting stakeholder requirements (n = 39) and mapping MIIC vaccine codes (n = 294) to corresponding MEDSS product codes (n = 48). The tool was implemented in March 2022 and incorporates MIIC data into a new vaccination form in MEDSS with mapping of 30 data elements including MIIC demographics, vaccination history, and vaccine forecast. The tool was evaluated using mixed methods (quantitative analysis of user time, clicks, queries; qualitative review with users). Comparison of key tasks demonstrated efficiencies including vaccination data access (before: 50 clicks, >2 min; after: 4 clicks, 8 s) which translated directly to staff effort (before: 5 h/week; after: ∼17 min/week). This case study demonstrates the contribution of improving public health systems interoperability, ultimately with the goal of enhanced data-driven decision-making and public health surveillance.