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.

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.

Increased Risk of Stroke Among Young Adults With Serious Traumatic Brain Injury.

OBJECTIVE: To quantify the risk of acute ischemic stroke (AIS) following traumatic brain injury (TBI) according to severity. SETTING: Indiana Network for Patient Care, including medical records from more than 100 Indiana hospitals and affiliated practices. PARTICIPANTS: Individuals 18 years and older with TBI from 2005 to 2014. DESIGN: Retrospective cohort. MAIN MEASURES: AIS incidence in the first 30, 31 to 180, and 181 days after TBI. Time to AIS using a stratified Cox proportional hazards model. RESULTS: Among 58 294 patients with TBI, AIS risk was greatest in the first 30 days (incidence rate = 23.3 per 1000 person-months), declining to 3.1 and 1.3 per 1000 person-months after 31 to 180 and 181 days or more, respectively. Cervical artery dissection increased the risk of AIS in the first 30 days (incidence rate = 170.9 per 1000 person-months). In the first 30 days, serious TBI increased risk for all age groups, with the largest effect observed among those aged 18 to 24 years. Over time, serious TBI modified the effect of age on AIS only for those aged 18 to 24 years. CONCLUSIONS: These findings add to a growing body of work demonstrating that the acute and postacute stages of TBI play an accelerative role in AIS risk, particularly among younger patients, cervical artery dissection, and serious TBI.

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.

Leveraging Health Information Exchange to Construct a Registry for Traumatic Brain Injury, Spinal Cord Injury and Stroke in Indiana.

Traumatic brain injury (TBI), spinal cord injury (SCI) and stroke are conditions of interest to public health as they can result in long-term outcomes and disabilities. Specialized registries can facilitate public health surveillance, however only 4% of hospitals in the United States actively engage in electronic reporting to these registries. We leveraged electronic claims and clinical data from a health information exchange to create a statewide TBI/SCI/Stroke registry to facilitate the study of long-term outcomes and health services utilization. The registry contains 109,943 TBI patients, 9,027 SCI patients and 117,084 stroke patients with a mean of 3 years of follow-up data after injury. Additionally, the registry contains data on individual patient encounters, prescriptions and clinical variables. The high-dimensional data with large sample sizes may present a valuable informatics resource for injury research as well as public health surveillance.

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.