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.

Dentists' Information Needs and Opinions on Accessing Patient Information via Health Information Exchange: Survey Study.

BACKGROUND: The integration of medical and dental records is gaining significance over the past 2 decades. However, few studies have evaluated the opinions of practicing dentists on patient medical histories. Questions remain on dentists' information needs; their perception of the reliability of patient-reported medical history; satisfaction with the available information and the methods to gather this information; and their attitudes to other options, such as a health information exchange (HIE) network, to collect patient medical history. OBJECTIVE: This study aims to determine Indiana dentists' information needs regarding patients' medical information and their opinions about accessing it via an HIE. METHODS: We administered a web-based survey to Indiana Dental Association members to assess their current medical information-retrieval approaches, the information critical for dental care, and their willingness to access or share information via an HIE. We used descriptive statistics to summarize survey results and multivariable regression to examine the associations between survey respondents' characteristics and responses. RESULTS: Of the 161 respondents (161/2148, 7.5% response rate), 99.5% (n=160) respondents considered patients' medical histories essential to confirm no contraindications, including allergies or the need for antibiotic prophylaxis during dental care and other adverse drug events. The critical information required were medical conditions or diagnosis, current medications, and allergies, which were gathered from patient reports. Furthermore, 88.2% (n=142) of respondents considered patient-reported histories reliable; however, they experienced challenges obtaining information from patients and physicians. Additionally, 70.2% (n=113) of respondents, especially those who currently access an HIE or electronic health record, were willing to use an HIE to access or share their patient's information, and 91.3% (n=147) shared varying interests in such a service. However, usability, data accuracy, data safety, and cost are the driving factors in adopting an HIE. CONCLUSIONS: Patients' medical histories are essential for dentists to optimize dental care, especially for those with chronic conditions. In addition, most dentists are interested in using an HIE to access patient medical histories. The findings from this study can provide an alternative option for improving communications between dental and medical professionals and help the health information technology system or tool developers identify critical requirements for more user-friendly designs.

Preconception health status and associated disparities among rural, Midwestern women in the United States.

BACKGROUND: Little is known about the preconception/interconception health and behaviors of reproductive-age women in the rural Midwest of the United States. The purpose of this study was to quantify preconception/interconception health status and to identify disparities compared with statewide estimates. METHODS: In this cross-sectional study, we collected data on 12 health conditions and behaviors that are risk factors for adverse maternal and infant health outcomes from nonpregnant women ages 18-45 years in rural northwestern Ohio. Statistical tests were used to identify associations between selected demographic characteristics and a subset of eight high-priority health measures (smoking, diabetes, heavy alcohol use, folic acid intake, normal weight, sufficient physical activity, and effective contraception use); all but physical activity could be compared with Ohio estimates derived from the Behavioral Risk Factor Surveillance System and Ohio Pregnancy Assessment Survey. RESULTS: Three hundred-fifteen women participated, with 98.4% reporting at least one high-priority risk factor. Statistically significant differences were identified among subpopulations related to smoking, folic acid, normal weight, sufficient physical activity, and effective contraception use. In addition, the proportion of participants reporting hypertension (P < 0.001), smoking (P < 0.001), abnormal weight (P = 0.002), and lack of daily folic acid intake (P = 0.006) were statistically significantly higher than expected based on statewide estimates. CONCLUSIONS: Women in the rural Midwest of the United States are at risk for poor health and pregnancy outcomes. Statewide estimates tracking preconception/interconception health status may obscure variation for at-risk groups, particularly in rural or underserved areas. These findings illustrate the need for interventions to advance preconception/interconception health and improve methods to capture and analyze data for rural women.

Special Section on Inclusive Digital Health: Notable Papers on Addressing Bias, Equity, and Literacy to Strengthen Health Systems.

OBJECTIVE: To summarize significant research contributions on addressing bias, equity, and literacy in health delivery systems published in 2021. METHODS: An extensive search using PubMed and Scopus was conducted to identify peer-reviewed articles published in 2021 that examined ways that informatics methods, approaches, and tools could address bias, equity, and literacy in health systems and care delivery processes. The selection process comprised three steps: (1) 15 candidate best papers were first selected by the two section editors; (2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (3) the final selection of three best papers was conducted by the editorial committee of the Yearbook. RESULTS: Selected best papers represent studies that characterized significant challenges facing biomedical informatics with respect to equity and practices that support equity and literacy in the design of health information systems. Selected papers represent the full spectrum of this year's yearbook theme. In general, papers identified in the search fell into one of the following categories: (1) descriptive accounts of algorithmic bias in medical software or machine learning approaches; (2) enabling health information systems to appropriately encode for gender identity and sex; (3) approaches to support health literacy among individuals who interact with information systems and mobile applications; and (4) approaches to engage diverse populations in the use of health information systems and the biomedical informatics workforce CONCLUSIONS: : Although the selected papers are notable, our collective efforts as a biomedical informatics community to address equity, literacy, and bias remain nascent. More work is needed to ensure health information systems are just in their use of advanced computing approaches and all persons have equal access to health care and informatics tools.

Supporting Health Equity Through Data-Driven Decision-Making: A Local Health Department Response to COVID-19.

COVID-19 highlights preexisting inequities that affect health outcomes and access to care for Black and Brown Americans. The Marion County Public Health Department in Indiana sought to address inequities in COVID-19 testing by using surveillance data to place community testing sites in areas with the highest incidence of disease. Testing site demographic data indicated that targeted testing reached populations with the highest disease burden, suggesting that local health departments can effectively use surveillance data as a tool to address inequities. (Am J Public Health. 2021;111(S3):S197-S200. https://doi.org/10.2105/AJPH.2021.306421).

The synchronicity of COVID-19 disparities: Statewide epidemiologic trends in SARS-CoV-2 morbidity, hospitalization, and mortality among racial minorities and in rural America.

BACKGROUND: Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity, hospitalization, and mortality over time for minority and rural populations, especially during the U.S. fall surge. METHODS: Data were extracted from a statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Comparisons across time within groups were compared using ANOVA. RESULTS: Morbidity and mortality increased over time with notable differences among sub-populations. Initially, hospitalization rates among racial minorities were 3-4 times higher than whites, and mortality rates among urban residents were twice those of rural residents. By fall 2020, hospitalization and mortality rates in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Changes across time among demographic groups was significant for morbidity and hospitalization. Cumulative morbidity and mortality were highest among minority groups and in rural communities. CONCLUSIONS: The synchronicity of disparities in COVID-19 by race and geography suggests that health officials should explicitly measure disparities and adjust mitigation as well as vaccination strategies to protect those sub-populations with greater disease burden.

Practice and market factors associated with provider volume of health information exchange.

OBJECTIVE: To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use. MATERIALS AND METHODS: Provider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016. RESULTS: Providers used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use. DISCUSSION: HIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors. CONCLUSION: This furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.

Routine Healthcare Utilization Among Reproductive-Age Women Residing in a Rural Maternity Care Desert.

It is important that women of reproductive age have access to and use routine health services to improve birth outcomes. While it is estimated that more than 5 million women in over 1000 counties across the United States live in maternity care deserts, to date there have been no published studies characterizing access and barriers to routine healthcare utilization in these areas. Therefore, a cross-sectional study was conducted in a rural county in northwest Ohio with 315 women ages 18-45 years. Health insurance coverage, usual source of care, length of time since routine check-up, and barriers to receipt of health services were assessed via a self-reported, anonymous survey. Over one-tenth (11.3%) of participants reported having no health insurance coverage. A total of 14.4% reported having no usual source of care and 22.8% reported not having a routine check-up in the past year. Just over one-half (53.0%) of participants reported having at least one barrier to accessing health care. In a logistic regression analysis, having a routine check-up in the past year was inversely associated with number of barriers (OR 0.73, 95% CI 0.56-0.95; p = 0.019); women who reported more barriers were less likely to report receipt of preventive care in the past year. The results of this study reveal that many reproductive-age women living in a maternity care desert face challenges in accessing health services. Policies and programs need to be developed and implemented to close these gaps and maximize opportunities for optimal health.

Content analysis of behavior change techniques in maternal and infant health apps.

Maternal and infant health (MIH) mobile applications (apps) are increasingly popular and frequently used for health education and decision making. Interventions grounded in theory-based behavior change techniques (BCTs) are shown to be effective in promoting healthy behavior changes. MIH apps have the potential to be useful tools, yet the extent to which they incorporate BCTs is still unknown. The objective of this study was to assess the presence of BCTs in popular MIH apps available in the Apple App and Google Play stores. Twenty-nine popular MIH apps were coded for the presence of 16 BCTs using the mHealth app taxonomy. Popular MIH apps whose purpose was to provide health education or decision-making support to pregnant women or parents/caregivers of infants were included in the final sample. On an average, the reviewed apps included seven BCTs (range 2-16). Techniques such as personalization, review of general or specific goals, macro tailoring, self-monitoring of goals, and health behavior linkages were most frequently present. No differences in the presence of BCTs between paid and free apps were observed. Popular MIH apps typically included only a minority of BCTs found to be useful for health promotion. However, apps developed by healthcare developers incorporated a higher number of BCTs within the app content. Therefore, app developers and policymakers may consider strategies to increase health expert involvement in app design and content delivery.

Current Approaches and Trends in Graduate Public Health Informatics Education in the United States: Four Case Studies from the Field.

Background:Public Health Informatics (PHI) has taken on new importance in recent years as health and well-being face a number of challenges, including environmental disasters, emerging infectious diseases, such as Zika, Ebola and SARS-CoV-2, the growing impact of the Influenza virus, the opioid epidemic, and social determinants of health. Understanding the relationship between climate change and the health of populations adds further complexity to global health issues. Objectives: To describe four examples of curricula that exist in U.S. based graduate-level public and population health informatics training programs. Methods: Biomedical informatics educators are challenged to provide learners with relevant, interesting, and meaningful educational experiences in working with and learning from the many data sources that comprise the domain of PHI. Programs at four institutions were reviewed to examine common teaching practices that stimulate learners to explore the field of public health informatics. Results: Four case studies represent a range of pedagogical approaches to meeting the requirements of three established accreditation/certification agencies relevant to PHI education. Despite their differences, each program achieved the established learning objectives along with a substantive record of student learning achievements. Conclusion: The overarching goal of empowering learners to serve an active and dynamic role in enhancing preventive measures, informing policy, improving personal health behaviors, and clarifying issues such as quality, cost of care, and the social determinants of health, are essential components of PHI education and training, and must receive additional consideration now and in the future by educators, policy makers, administrators, and government officials.

Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems.

BACKGROUND: Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases. OBJECTIVES: To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. METHODS: Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study. RESULTS: Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). CONCLUSION: The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.

Public Health Informatics in Local and State Health Agencies: An Update From the Public Health Workforce Interests and Needs Survey.

OBJECTIVE: To characterize public health informatics (PHI) specialists and identify the informatics needs of the public health workforce. DESIGN: Cross-sectional study. SETTING: US local and state health agencies. PARTICIPANTS: Employees from state health agencies central office (SHA-COs) and local health departments (LHDs) participating in the 2017 Public Health Workforce Interests and Needs Survey (PH WINS). We characterized and compared the job roles for self-reported PHI, "information technology specialist or information system manager" (IT/IS), "public health science" (PHS), and "clinical and laboratory" workers. MAIN OUTCOME MEASURE: Descriptive statistics for demographics, income, education, public health experience, program area, job satisfaction, and workplace environment, as well as data and informatics skills and needs. RESULTS: A total of 17 136 SHA-CO and 26 533 LHD employees participated in the survey. PHI specialist was self-reported as a job role among 1.1% and 0.3% of SHA-CO and LHD employees. The PHI segment most closely resembled PHS employees but had less public health experience and had lower salaries. Overall, fewer than one-third of PHI specialists reported working in an informatics program area, often supporting epidemiology and surveillance, vital records, and communicable disease. Compared with PH WINS 2014, current PHI respondents' satisfaction with their job and workplace environment moved toward more neutral and negative responses, while the IT/IS, PHS, and clinical and laboratory subgroups shifted toward more positive responses. The PHI specialists were less likely than those in IT/IS, PHS, or clinical and laboratory roles to report gaps in needed data and informatics skills. CONCLUSIONS: The informatics specialists' role continues to be rare in public health agencies, and those filling that role tend to have less public health experience and be less well compensated than staff in other technically focused positions. Significant data and informatics skills gaps persist among the broader public health workforce.

Electronic Health Record (EHR)-Based Community Health Measures: An Exploratory Assessment of Perceived Usefulness by Local Health Departments.

BACKGROUND: Given the widespread adoption of electronic health record (EHR) systems in health care organizations, public health agencies are interested in accessing EHR data to improve health assessment and surveillance. Yet there exist few examples in the U.S. of governmental health agencies using EHR data routinely to examine disease prevalence and other measures of community health. The objective of this study was to explore local health department (LHD) professionals' perceptions of the usefulness of EHR-based community health measures, and to examine these perceptions in the context of LHDs' current access and use of sub-county data, data aggregated at geographic levels smaller than county. METHODS: To explore perceived usefulness, we conducted an online survey of LHD professionals in Indiana. One hundred and thirty-three (133) individuals from thirty-one (31) LHDs participated. The survey asked about usefulness of specific community health measures as well as current access to and uses of sub-county population health data. Descriptive statistics were calculated to examine respondents' perceptions, access, and use. A one-way ANOVA (with pairwise comparisons) test was used to compare average scores by LHD size. RESULTS: Respondents overall indicated moderate agreement on which community health measures might be useful. Perceived usefulness of specific EHR-based community health measures varied by size of respondent's LHD [F(3, 88) = 3.56, p = 0.017]. Over 70% of survey respondents reported using community health data, but of those < 30% indicated they had access to sub-county level data. CONCLUSION: Respondents generally preferred familiar community health measures versus novel, EHR-based measures that are not in widespread use within health departments. Access to sub-county data is limited but strongly desired. Future research and development is needed as LHD staff gain access to EHR data and apply these data to support the core function of health assessment.

Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process.

BACKGROUND: Cancer registries systematically collect cancer-related data to support cancer surveillance activities. However, cancer data are often unavailable for months to years after diagnosis, limiting its utility. OBJECTIVE: The objective of this study was to identify the barriers to rapid cancer reporting and identify ways to shorten the turnaround time. METHODS: Certified cancer registrars reporting to the Indiana State Department of Health cancer registry participated in a semistructured interview. Registrars were asked to describe the reporting process, estimate the duration of each step, and identify any barriers that may impact the reporting speed. Qualitative data analysis was performed with the intent of generating recommendations for workflow redesign. The existing and redesigned workflows were simulated for comparison. RESULTS: Barriers to rapid reporting included access to medical records from multiple facilities and the waiting period from diagnosis to treatment. The redesigned workflow focused on facilitating data sharing between registrars and applying a more efficient queuing technique while registrars await the delivery of treatment. The simulation results demonstrated that our recommendations to reduce the waiting period and share information could potentially improve the average reporting speed by 87 days. CONCLUSIONS: Knowing the time elapsing at each step within the reporting process helps in prioritizing the needs and estimating the impact of future interventions. Where some previous studies focused on automating some of the cancer reporting activities, we anticipate much shorter reporting by leveraging health information technologies to target this waiting period.

Monitoring public health reporting: data tracking in cancer registries.

INTRODUCTION: Timeliness of data availability is a key performance measure in cancer reporting. Previous studies evaluated timeliness of cancer reporting using a single metric, yet this metric obscures the details within each step of the reporting process. To enhance understanding of cancer reporting processes, we measured the timeliness of discrete cancer reporting steps and examined changes in timeliness across a decade. METHODS: We analyzed 76,259 cases of breast, colorectal and lung cancer reported to the Indiana State Cancer Registry between 2001 and 2011. We measured timeliness for three fundamental reporting steps: report completion time, report submission time, and report processing time. Timeliness was measured as the difference, in days, between timestamps recorded in the cancer registry at each step. We further examined the variation in reporting time among facilities. RESULTS: Identifying and gathering details about cases (report completion) accounts for the largest proportion of time during the cancer reporting process. Although submission time accounts for a lesser proportion of time, there is wide variation among facilities. One-seventh (7 out of 49) facilities accounted for 28.4% of the total cases reported, all of which took more than 100 days to submit the completed cases to the registry. CONCLUSIONS: Measuring timeliness of the individual steps in reporting processes can enable cancer registry programs to target individual facilities as well as tasks that could be improved to reduce overall case reporting times. Process improvement could strengthen cancer control programs and enable more rapid discovery in cancer research.

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

OBJECTIVES: Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. MATERIALS AND METHODS: We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. RESULTS: Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. CONCLUSION: Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.

Getting from here to there: health IT needs for population health.

The United States' decade-long transition from a paper- to technology-based information infrastructure has always been recognized as an initial step-a laying of the foundation-for future changes to the delivery of care. An increasingly important focal area for improvement is population health. Numerous policies and programs now require healthcare organizations to manage the risks, outcomes, utilization, and health of entire groups of individuals. Nonetheless, current health information technology (IT) systems are not ready to support population health improvements effectively and efficiently. Existing health IT systems were designed for organizations that are structurally, operationally, and culturally focused on individual care delivery, rather than improving health for a population. Opportunities exist to align health IT resources and population health management strategies to fill the gaps among technological capabilities, use and the emerging demands of population health. To realize this alignment, healthcare leaders must think differently about the types of data their organizations need, the types of partners with whom they share information, and how they can leverage new information and partnerships for evidence-based action.

Short-Term Medical Costs of a VHA Health Information Exchange: A CHEERS-Compliant Article.

The Virtual Lifetime Electronic Record (VLER) Health program provides the Veterans Health Administration (VHA) a framework whereby VHA providers can access the veterans' electronic health record information to coordinate healthcare across multiple sites of care. As an early adopter of VLER, the Indianapolis VHA and Regenstrief Institute implemented a regional demonstration program involving bi-directional health information exchange (HIE) between VHA and non-VHA providers.The aim of the study is to determine whether implementation of VLER HIE reduces 1 year VHA medical costs.A cohort evaluation with a concurrent control group compared VHA healthcare costs using propensity score adjustment. A CHEERs compliant checklist was used to conduct the cost evaluation.Patients were enrolled in the VLER program onsite at the Indianapolis VHA in outpatient clinics or through the release-of-information office.VHA cost data (in 2014 dollars) were obtained for both enrolled and nonenrolled (control) patients for 1 year prior to, and 1 year after, the index date of patient enrollment.There were 6104 patients enrolled in VLER and 45,700 patients in the control group. The annual adjusted total cost difference per patient was associated with a higher cost for VLER enrollees $1152 (95% CI: $807-1433) (P < 0.01) (in 2014 dollars) than VLER nonenrollees.Short-term evaluation of this demonstration project did not show immediate reductions in healthcare cost as might be expected if HIE decreased redundant medical tests and treatments. Cost reductions from shared health information may be realized with longer time horizons.

Characterizing Informatics Roles and Needs of Public Health Workers: Results From the Public Health Workforce Interests and Needs Survey.

OBJECTIVE: To characterize public health workers who specialize in informatics and to assess informatics-related aspects of the work performed by the public health workforce. METHODS (DESIGN, SETTING, PARTICIPANTS): Using the nationally representative Public Health Workforce Interests and Needs Survey (PH WINS), we characterized and compared responses from informatics, information technology (IT), clinical and laboratory, and other public health science specialists working in state health agencies. MAIN OUTCOME MEASURES: Demographics, income, education, and agency size were analyzed using descriptive statistics. Weighted medians and interquartile ranges were calculated for responses pertaining to job satisfaction, workplace environment, training needs, and informatics-related competencies. RESULTS: Of 10,246 state health workers, we identified 137 (1.3%) informatics specialists and 419 (4.1%) IT specialists. Overall, informatics specialists are younger, but share many common traits with other public health science roles, including positive attitudes toward their contributions to the mission of public health as well as job satisfaction. Informatics specialists differ demographically from IT specialists, and the 2 groups also differ with respect to salary as well as their distribution across agencies of varying size. All groups identified unmet public health and informatics competency needs, particularly limited training necessary to fully utilize technology for their work. Moreover, all groups indicated a need for greater future emphasis on leveraging electronic health information for public health functions. CONCLUSIONS: Findings from the PH WINS establish a framework and baseline measurements that can be leveraged to routinely monitor and evaluate the ineludible expansion and maturation of the public health informatics workforce and can also support assessment of the growth and evolution of informatics training needs for the broader field. Ultimately, such routine evaluations have the potential to guide local and national informatics workforce development policy.