Are there patients in your practice with undiagnosed hypertension who may be "hiding in plain sight"?
Of the 75 million Americans who have hypertension, almost half do not have the condition under control. About 11 million of them don’t know their blood pressure is too high and are not receiving treatment to control it, even though most of these individuals have health insurance and visit a health care provider each year.
That means potentially millions of people with uncontrolled hypertension are seen by clinicians but remain undiagnosed. While following best practices and providing the highest levels of care, providers can still have patients “hiding in plain sight” who are at risk for or have undiagnosed hypertension. Finding these patients and spreading the word about how other health care professionals can find them may help save lives.
Watch this video to learn more about the four steps to finding patients “hiding in plain sight” with undiagnosed hypertension:
Start with the steps that make the most sense for your practice or system. The most important action to take is beginning the search for these patients. Treating uncontrolled hypertension dramatically reduces patients’ risk for heart attack and stroke. Bringing individuals “hiding in plain sight” into clear view will help protect millions from unnecessary and preventable events.
Million Hearts® has made blood pressure control a national priority to help save lives and improve Americans’ cardiovascular health. Comparing a health system’s or practice’s calculated hypertension prevalence to the expected hypertension prevalence, generated by the Hypertension Prevalence Estimator Tool, will help health systems and practices identify potential patients with undiagnosed hypertension.
This interactive Million Hearts® tool is a great starting point for health systems or practices to better understand hypertension prevalence among their patient population. The tool generates an expected percentage of patients with hypertension based on the specific characteristics of a health system’s or practice’s patient population. Providers can then compare the expected prevalence to their calculated prevalence—if the values are quite different, there may be patients “hiding in plain sight” with undiagnosed hypertension.
Millions of people with uncontrolled hypertension are seen by health care professionals each year, but their hypertension remains undetected or undiagnosed. Use of the Estimator Tool allows health systems or practices to generate an expected hypertension prevalence among their ambulatory patient population.
Health systems and practices can use this tool to start or build on their existing hypertension management quality improvement process by comparing the expected hypertension prevalence generated from the tool with their calculated prevalence; if the tool generates a hypertension prevalence larger than the calculated prevalence, more rigorous quality improvement activities could be carried out to understand why the difference exists and identify potential strategies to detect and treat the undiagnosed patients within the health system or practice.
A health system’s or practice’s calculated hypertension prevalence is the percentage of their patient population they report having hypertension according to medical record documentation and administrative data collected for their patients.
To calculate your hypertension prevalence, divide the number of adult patients ages 18–85 with a hypertension diagnosis (ICD-10-CM-I10) by the total number of adult patients ages 18–85, excluding pregnant women and those with end-stage renal disease. Multiply that value by 100 to calculate the final prevalence.
Additional strategies to determine the calculated hypertension prevalence are included in the Estimator Tool glossary.
Through our own analyses and work with multiple groups throughout the country, we have identified an issue where many people currently receiving care within health systems have multiple abnormal blood pressure readings but have not been diagnosed with hypertension and, therefore, remain untreated.
Use of this tool is one of the first steps a health system or practice can take to understand if they might have patients with undiagnosed hypertension “hiding in plain sight” that warrant further exploration. Systems and practices can then use other available materials to detect the potentially undiagnosed hypertensive patients.
We think the importance of working through this process is best summed up by one of our partners, the Mountain Comprehensive Health Corporation: “We thought we were doing a pretty good job with detecting high blood pressure. When we joined the Million Hearts® initiative, we were amazed at how many of our patients have had repeated episodes of high blood pressure that were never diagnosed or addressed. Our patients are receiving better care, and our quality measure performance is improving.”
The expected prevalence is generated using stratified national hypertension prevalence estimates, based on data from the National Health and Nutrition Examination Survey (NHANES), that are then applied to the specific characteristics of the health system’s or practice’s patient population.
NHANES is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. Learn more about NHANES.
The tool offers flexibility in the data used to generate a health system’s or practice’s hypertension prevalence estimates. Visit the tool to see what data are requested and determine what information you can provide. Although the Estimator Tool includes the strongest predictors of hypertension prevalence according to our NHANES model, these and other factors not included in the tool may affect hypertension prevalence differently across health systems and practices. Therefore, the tool’s accuracy in generating hypertension prevalence will likely vary across users.
Presentation depends on the type of data provided. If you provide all of the data requested, you will receive hypertension prevalence estimates stratified by age, sex, race/ethnicity, and co-morbidity status. These estimates will have a specific point estimate (e.g., 35.5%) and 95% confidence intervals (e.g., 35.1% to 35.9%).
The 95% confidence intervals are based on how well the tool is able to leverage the NHANES data, which is representative of the U.S. population, to generate the hypertension prevalence of the health system’s or practice’s population based on that population’s characteristics. Therefore, the size of the confidence intervals is driven mostly by sample size (i.e., the number of people seen within the health system) and not as much by the type or level of detail of the data provided.
Performance of the main tool (i.e., user reports data stratified by age, sex, race/ethnicity, and co-morbidity status) was assessed by applying samples from cohorts with known hypertension prevalence; small differences in expected versus calculated prevalence were identified (range: −3.3% to 0.6%).
In general, the Estimator Tool tended to somewhat underestimate hypertension prevalence during validation testing. Therefore, it likely provides a conservative estimate, especially among populations with a hypertension prevalence greater than the U.S. prevalence.
The tool has not been validated for how it generates the expected hypertension prevalence among health systems or practices with a high prevalence of patients with low socioeconomic status or when limited co-morbidity data are available for use in the tool. Although the results obtained using these modified data entry options are likely valid, interpret them with caution.
Yes, “Development and Validation of a Hypertension Prevalence Estimator Tool for Use in Clinical Settings” was published in the Journal of Clinical Hypertension in January 2016.
Yes, public health practitioners can use this tool to generate the expected hypertension prevalence among the people currently receiving health care within their community.
This data snapshot shows the differences between hypertension prevalence estimates of AMGA data calculated using three distinctive criteria and the CDC Hypertension Prevalence Estimator Tool for the period of July 2013–June 2014.
Article explaining the “hiding in plain sight” phenomenon and summarizing what large health systems have done to find patients with undiagnosed hypertension. (JAMA, November 2014)
Hour-long interview with experts from the Centers for Disease Control and Prevention and the Health Center Network of New York exploring the science and implementation of finding patients with potentially undiagnosed hypertension. (Public Health Live, February 2016)
Compilation of materials to help clinicians map and identify enhancements to clinical workflows that improve detection and diagnosis of hypertension. (National Association of Community Health Centers, January 2016)
Study describing the development and validation of a tool that health systems can use to compare their reported hypertension prevalence with expected prevalence. (Journal of Clinical Hypertension, January 2016)
Study demonstrating the move from patient identification to diagnosis using a technology-based strategy and illustrating how finding undiagnosed hypertensive patients is not a documentation issue. (Annals of Family Medicine, July 2014)
Study comparing the rates of new hypertension diagnosis for different age groups and identifying delay predictors in the initial diagnosis among young adults who regularly use primary care. (Journal of Hypertension, January 2014)
Study examining electronic health record data application to find potentially undiagnosed hypertensive patients and the variability in the magnitude of the “hiding in plain sight” problem across 11 community health centers. (Perspectives in Health Information Management, April 2012)
Study showing how diagnosis leads to treatment by examining and identifying the diagnosis rates of prevalent and incident hypertension cases in a large outpatient health care system. (American Journal of Hypertension, January 2012)