On-Demand Webinar Details:
Today, population health solutions are defined as the health outcomes and indicators of a community. In reality, the full story can only be told if we include social influences, economic situations, physical environments and mental behavioral outcomes. Gaining a more complete view of a population through a variety of non-traditional data sources will have a significant positive impact on the Triple Aim.
The Timmaron Group, using advanced health data analytics powered by HDMS, has developed a successful approach for bridging the gap between an environment of incomplete and disparate data to a transformative action playbook. The result is a 360-degree community view of members, providers and populations that identifies opportunities to improve the quality of care, provider effectiveness and overall lower health care costs.
Attendees will learn how the vision of a 360-degree community view can be developed and implemented based on lessons from real-life examples and use-cases including both financial and health outcome results.
Speaker: Barbara D. Stinnett, Technology & Healthcare Operating Executive, Timmaron Group
For most people, the term “Preventive Care” suggests age appropriate cancer screenings, flu shots and childhood vaccinations. What about appropriate regular monitoring of symptoms that prevents the worsening of a chronic illness? Or timely interventions that reduce or prevent complications due to a medication or a stressful life event?
Preventive care is a much broader concept that includes (but not limited to) activities that lead to the overall reduction of adverse events (e.g. fewer life-threatening complications due to a chronic illness) and the promotion of overall health in the entire population.
The “Annual Wellness Exam” (aka Annual Physical, Annual check-up, Health Maintenance Visit, Preventive Care Visit, etc.) is perhaps one of the most underutilized benefits in a health plan even though it is available at no out-of-pocket cost to the covered individual (with most federal, state and commercial plans).
Getting an Annual Wellness Exam regularly offers two main advantages:
From a Payer (Employer, Health Plan, other) and Provider (individual Physician, Group Practice, Health System, other) standpoint, there is also a financial advantage in ensuring all members get a Wellness Exam every year as described below.
In looking at the Professional component of medical claims data for the last 3 years, HDMS saw an overwhelming trend among our customers. We classified members into two groups:
Adult members in both groups were then compared for ER Utilization, particularly for Avoidable ER usage using the NYU Emergent Status & AHRQ Prevention Quality Indicator (PQI) methodologies.
The results showed:
These reports show a clear pattern. Members who get an Annual Wellness Exam are less likely to use the ER for conditions that can be treated and/ or managed at a less expensive site of care. Hence, it is in the best interest of the organization to encourage and incent all their members to establish a relationship with a PCP and get regular Wellness exams.
As healthcare costs continue to increase, more employers and health plans are evaluating the impact of their health and wellness benefits – including the effectiveness of preventive screenings.
Three out of five U.S. employers use health screenings and risk assessments to screen for expensive chronic conditions, such as cancer.1 Yet, 79 percent of large U.S. employers and 44 percent of mid-sized employers do not measure the effectiveness of employee wellness programs, including preventive screenings.2
With the cost of employee health benefits expected to rise 5 percent in 2019, it is critical that employers and health plans develop a data-centric approach to measuring the effectiveness of preventive screenings.3
Analytics inform a high-value approach for health benefits design by providing employers and health plans insights into opportunities for targeted interventions that reduce costs and improve health. Data analytics also help avoid “one-size-fits-most” solutions that may not be a good fi t given member and provider characteristics.
Increasingly, analytics are used to track outcomes of preventive care. For example, a recent study examined the impact of preventive cervical cancer screenings and showed these eff orts resulted in substantially lower deaths and increased lifespans.4
Analytics can also help employers and health plans prioritize preventive cancer screening offerings. Criteria might include:
The analysis of claims data – as well as socioeconomic data that might be available from state and regional health organizations – can provide powerful insights in developing a high-value approach to preventative cancer screening health benefits for members that improves outcomes.
Employers and health plans can demonstrate success through data analytics by determining the impact of preventative cancer screenings on access to treatment, risk and costs of care.
For example, a state health plan covering around 205,000 employees and dependents set out to identify the rate at which members were diagnosed with cancer after undergoing preventive screenings for breast, colorectal and cervical cancers.
For the overall state population, new cases of colorectal and cervical cancer have been decreasing while new cases of breast cancer are increasing. However, analysis of claims data for the state health plan differs for state employees:
By collaborating with HDMS experts, the state health plan created episode-based analysis groups, or cohorts, to assess compliance with preventive screenings compared to national guidelines and measure the impact of such screenings on early cancer detection and treatment.
Members in the episode-based analysis group included those who were newly diagnosed with breast, colorectal and cervical cancers as well as those who had been identified as having a recurring cancer diagnosis within two years of initial detection of the cancers. The results were enlightening:
Increased early diagnosis. The majority of new cases of breast, colorectal and cervical cancer were initially diagnosed following preventive screenings:
Decreased risk. The study showed early diagnosis of cancer through preventive screenings was associated with significantly reduced members’ risk scores. Members who were diagnosed earlier through preventive screening had significantly lower concurrent risk scores compared to other members with the same type of cancer. Higher risk scores are typically associated with members with later stages of cancer that require more complex treatment.
Specifically, members diagnosed with breast cancer through preventive screenings had an average risk score of less than 1.00 while members diagnosed outside of preventive screenings had average risk scores from 5.88 to 6.53. Similarly, members diagnosed with cervical cancer through preventive screenings had average risk scores of 1.00 while those diagnosed later exhibited risk scores of 3.31 to 4.22.
Reduced costs of care. Analysis also revealed the impact of preventive screenings in lower costs of care. The cost of treating breast and cervical cancer for women identified by preventive screening was lower on average.6
The results showcase the power of using data to measure the effectiveness of preventive screenings. When employers and health plans leverage claims and socioeconomic data analysis to refine their approach to benefits design, they are more empowered to reduce costs and improve outcomes.
For many employers, the rise in metabolic syndrome among employees and their dependents is making a deep impact on health care costs.
Thirty-five percent of U.S. adults suffer from metabolic syndrome, a cluster of conditions that puts them at greater risk for heart disease, diabetes and stroke.1 Individuals with metabolic syndrome have three of the following five characteristics:
When these risk factors increase among employees and their dependents, so do employers’ health care costs. Nationally, obesity alone has more than a $2 trillion impact on health care costs.2 Medical costs for obese individuals are at least 36 percent higher than for Americans of healthy weight.3 The more risk factors an employee has, the greater the impact on health care costs. Employees who have metabolic syndrome also typically have lower productivity and higher rates of absenteeism.
For employers, using claims data to gauge employees’ risk of metabolic syndrome can inform approaches that improve employee health, increase productivity, lower absenteeism rates and reduce health care costs.
Measuring the rate of metabolic syndrome among employees can help employers develop targeted interventions that improve employee health and reduce costs. Employers also can use claims data to determine the extent to which employees have health conditions that could lead to metabolic syndrome. With this information, employers can proactively halt the spread of metabolic syndrome by investing in programs and incentives that encourage changes toward healthier behaviors.
Metabolic syndrome is one of the few health conditions that can be reversed with changes in lifestyle or pharmacologic treatment, such as exercise, cholesterol medication and/or a healthier diet. By using claims data to measure the number of employees who have high blood pressure, obesity, high cholesterol and diabetes – as well as the percentage of those who have two or more of these risk factors – employers can better assess risk for metabolic syndrome. A deeper dive into claims data for this population also can reveal the extent to which these risk factors increase employers’ health care costs.
For example, one study found people with high blood pressure who are also obese spend about $1,000 a year more on health care than individuals who don’t have these risk factors.4 Meanwhile, those who have high blood pressure, obesity, low HDL cholesterol and triglyceride levels of 150 or higher spend about $1,600 more on care per year, according to the study. In fact, the presence of even one of these health factors increases health care costs, researchers found.
Drilling down into employee claims data gives employers a powerful tool for reducing the proportion of employees who suffer from metabolic syndrome. Employers can use the data to determine:
With actionable insight, the potential to prevent metabolic syndrome for at-risk employees rises. The likelihood of reversing metabolic syndrome among employees who already have been diagnosed with this syndrome also increases.
Employers can learn a great deal about their employees’ risk for metabolic syndrome through claims data analysis. Let’s take a look at how one national employer’s review of medical claims provided the insight needed for action.
A national car retailer partnered with HDMS to evaluate ways to reduce its employee health care costs. Short-term disability claims and employee absences were trending upward, and the company sought to gain a greater understanding of the health conditions and risk factors employees faced.
A claims data analysis showed incidence of metabolic syndrome and risk factors for this syndrome were prevalent among employees:
In addition, a deeper dive into the data showed the greater the risk factors for metabolic syndrome among employees, the more employee health care costs increased. While the average per member per month (PMPM) costs per employee totaled $348, the average PMPM cost per obese employee was $987. Among employees with high cholesterol, average PMPM costs totaled $1,188.
Costs were even higher for employees with two or more conditions:
Medical costs for employees on short-term disability were 9 percent higher when employees exhibited signs of metabolic syndrome, the analysis showed. Incidence of short-term disability and the duration of short-term disability also rose when claimants had metabolic syndrome, according to the analysis.
With this information in hand, the company was better positioned to proactively address these health conditions among its employee population. Based on the analysis, the company revamped its health and wellness program to focus on reducing high cholesterol, high blood pressure, body mass index and more.
Within one year, the number of employees at risk for metabolic syndrome dropped 4 percent. Employee absenteeism decreased, reducing labor expenses (such as the expense of replacement labor to cover absences) by $140,000. Today, the company continues to use claims data analysis to improve employee health and wellness, reduce health care costs and limit the impact of metabolic syndrome on productivity.5
A data-driven approach to lowering the risk of developing metabolic syndrome can help employers, employees and their dependents find success in better managing their health.
Access to claims data and analysis empowers employers to actively address the health of their employee population. For example, a data-driven approach to improving employee health can be the starting point for creating a healthier companywide culture. It can help establish company-specific health and wellness goals as well as determine incentives that could drive the behavior change needed to lower metabolic risk. It also empowers employers to reduce health care costs by better managing employees’ health conditions before they become high-cost conditions.
One of the lessons learned from a data-driven approach to addressing metabolic syndrome is that managing health conditions while they are at the early stage of the “disease pathway” can have a deeper, longer-term impact on employers’ health care costs. Many times, employers look to the 5 percent of conditions that comprise 20 percent of health care costs nationwide in determining where to focus health and wellness initiatives. However, by using claims data to dig deeper, such as by determining the conditions most prevalent among employees and developing targeted interventions based on population-specific insights, our experience shows employers are better able to make a more meaningful impact on employee health, productivity, absenteeism rates and health care costs.
Using claims data analysis to address potentially costly conditions before they reach the high-cost stage can set the foundation for a healthier workplace for years to come.
Read how Lowe's works with HDMS to understand trends within its population and identify patterns that may indicate the need for proactive
intervention to improve its population’s overall health.
A case study for employers and health plans
The Affordable Care Act (ACA) requires employers to fully cover preventive screenings for breast, cervical/uterine and colorectal cancers.
For one state agency, declining member utilization of these preventive screenings was a cause for concern. Why were utilization rates dropping? Moreover, what impact was the reduction having on the agency’s costs and its members’ health outcomes?
The state agency, which administers health benefits for 205,000 employees and dependents, set out to identify the cost and outcomes of the ACA-required preventive cancer screenings. What the agency really wanted to know was whether the screenings were resulting in earlier cancer detection, which in turn required less invasive and less costly treatment.
For quite some time, the agency simply assumed that the screenings were cost effective. The challenge was to accurately quantify their impact at a time when:
The state agency’s population health manager (PHM) uses HDMS’ analytics and reporting solution on a quarterly basis to analyze trends in cost and utilization of employee benefits. With HDMS’ data management expertise, the PHM trusted the credibility of the analysis. To further evaluate the cancer screenings, the PHM took advantage of the solution’s built-in evidence-based guidelines to create episode-based analysis groups (cohorts) from claims and enrollment data to measure whether members:
Analysis clearly showed the value of preventive cancer screenings for members and for the state agency:
Today, the state agency reviews a preventive screening dashboard every quarter to monitor outcome metrics. Furthermore, working together with HDMS to perform proactive data analysis may open up new insights into opportunities to reduce costs and improve member health. It’s just one powerful illustration of how robust data analysis can help employers and health plans measure and enhance the effectiveness of preventive health benefits.
The ACS’ updated preventive screening guidelines are now focused on smaller populations. However, they target age and gender groups that account for 82 to 92 percent of breast, cervical, uterine and colorectal cancer diagnoses. Screenings identify 68 percent of new breast cancer cases and more than 89 percent of other new cancer cases earlier. So, although the number of eligible members who received preventive cancer screenings declined, compliance with Healthcare Effectiveness Data and Information Set (HEDIS) guidelines, which measures individual clinical care influenced by health plan programs, generally improved. (The exception was compliance for breast cancer screenings.)
¹Grady, D., “American Cancer Society, in a Shift, Recommends Fewer Mammograms,” The New York Times, Oct. 20, 2015, https://hms.harvard.edu/news/american-cancer-society-shift-recommends-fewer-mammograms
HDMS proprietary data
Developed by Yale New Haven Health Services Corporation/ Center for Outcomes Research and Evaluation (YNHHSC/ CORE), CMS readmission logic provides hospital-wide, all-condition readmission metrics that can help measure and assess the overall quality of care at a given inpatient facility.
CMS readmission methodology classifies all eligible admissions and sources of readmission into five clinical cohorts: surgical/gynecological, medical, cardiovascular, cardiorespiratory and neurology.
Excluded from CMS readmission logic are admission and readmission events for patients who leave against medical advice and admissions for conditions that are not traditionally addressed in short-stay, acute-care hospitals. The measure also excludes possible planned readmissions, as these events are not correlated with poor quality of care.
In their public reporting, CMS risk standardizes the readmission rate for each hospital to adjust for underlying readmission risk, by condition, among hospitals. As these risk adjustment factors were developed with the Medicare population in mind, the HDMS adaptation does not currently include this specific form of risk-adjustment.
CMS readmission logic is claims-based and requires complete enrollment files and medical claims with well-populated diagnosis codes to ensure accuracy and applicability. No additional data sources are required.
Timeline: The algorithm is integrated into our standard data processing and could be turned on as quickly as your next data refresh.
Readmission events are costly, and often preventable through discharge instructions or coordinating care plans with the patient’s primary care provider(s). Under the 2010 Hospital Readmissions Reduction Program (HRRP), CMS restructured their hospital payment system to reduce readmission rates. In support of this program, CMS contracted YNHHSC to measure causes of unplanned readmission rates. It is anticipated that other health plans will restructure their hospital payment with a dependency on hospital readmission rates.