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What should health benefits look like now?

Health care is changing quickly.

Read about the top 5 trends in health care and the analytics you can use to navigate plan design with confidence.

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Top 5 Health Care Trends

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Preventive Care Starts with the Annual Wellness Exam

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:

  • An individual who does not proactively seek care (often referred to as a “non-utilizer”) gets monitored for any new and/or existing physical and emotional problems, assessed for various risks and guided to close relevant care gaps (e.g. BMI, Mammogram, Blood Pressure check)
  • Establishes a relationship between the member (including his/ her family) and the Primary Care Provider (PCP)’s office making it more likely for the PCP to be the first point of contact for any health issue - rather than an Urgent Care or ER

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.

What does the data show?

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:

  • Those that HAD received an Annual Wellness Exam during the reporting year
  • Those that had NOT had an Annual Wellness Exam during the reporting year

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:

  • Members who have NOT had an Annual Wellness Exam within the last reporting year, consistently incurred higher overall Cost AND higher number of Visits to the ER for complaints (conditions) that are classified as: “Non-Emergent”, “Primary Care Appropriate” and “Preventable/ Avoidable.”
  • There were a higher number of members WITHOUT an Annual Wellness Exam within the last reporting year, with one or more visits to the ER for diagnoses, that qualify as “Ambulatory Care Sensitive Conditions”

What does this mean?

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.

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A Data-Smart Approach to Employee Benefits Management and Preventative Care

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

How Data Insight Strengthens Preventive Cancer Screening Outcomes

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:

  • Risk factors such as high proportions of members who are overweight, have high cholesterol or high blood sugar levels, or smoke.
  • Regional health trends that may point to potential socioeconomic-based risks for members, like higher-than-average prevalence rates of lung cancer or heart disease. For example, 6.2 percent of Ohio’s population has heart disease, even as rates across the nation dropped.5
  • Evidence of possible “hot spots” within a plan sponsor membership. For instance, analytics show certain locations where employers and health plans should focus eff orts on encouraging preventative cancer screenings (member education, onsite clinic involvement, etc.).

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.

Case Study: Measure the Impact of Preventive Cancer Screenings

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:

  • While the rate of newly diagnosed cases of breast cancer remained steady, it was higher than the state average.
  • The number of new cases of colorectal and cervical cancer among state employees increased; however, the rate of occurrences was lower than the state average.

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:

  • Preventive screenings were associated with 80% of new cases of breast cancer among plan members.
  • Among members who received preventive screenings, 11% received additional treatments – and not just for cancer (e.g., removal of benign tumors or polyps).
  • Cervical cancer screenings helped identify women who need additional testing to detect or rule out uterine or ovarian cancer.

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

Optimize Value Through Claims Analysis

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.


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  1. “Top 10 Health Conditions Costing Employers the Most,” Employee Benefit News, Feb. 9, 2018,
  2. Desai, P., “Why Health Screening Programs Fail and What Employers Can Do About It,” Corporate Wellness Magazine,
  4. Kim, J.J., Burger, E.A., Regan, C., et al., “Screening for Cervical Cancer in Primary Care: A Decision Analysis for the U.S. Preventive Services Task Force,” JAMA, Aug. 21, 2018,
  5. “Heart Disease Hotspots: 14 States with Highest Rates,” CBS News,
  6. HDMS proprietary data
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Population Health Management for Employers: Reducing Employee Risk for Metabolic Syndrome

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:

  • Excess body fat around the waist
  • Triglyceride levels of 150 or higher
  • A level of high-density lipoprotein (HDL), or “good” cholesterol, that is lower than 40 in men and 50 in women
  • A blood pressure rate of 130/85 or higher
  • A fasting blood sugar level of 110 or more (levels of 110- 125 indicate prediabetes)

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.

Why Claims Data Analysis Is Critical to Addressing Metabolic Syndrome

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:

  • Which populations to target.
  • The right approaches for intervention (e.g., wellness initiatives designed to help employees maintain a healthier weight, lower their cholesterol or blood pressure, or bring blood sugar levels in line).
  • The right incentives to encourage behavior change for improved health.

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.

How Employers Are Using Data to Find Solutions

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:

  • 22% of employees were diagnosed with high blood pressure
  • 13% were obese
  • 10% had high cholesterol
  • 8% had diabetes

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:

  • Employees with both high blood pressure and cholesterol recorded $1,367 in PMPM costs.
  • PMPM costs for those who were diagnosed with all four conditions totaled $2,033.

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

Lessons Learned: Improving Health and Wellness Through Data

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.


Metablic Syndrome Perspective Paper


  5. HDMS proprietary data
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Predictive Analytics in Healthcare: Actionable Insights that Deliver Results

For stakeholders across the health care system, much of the knowledge and insight needed to make better value-based care decisions remains locked away within vast amounts of raw data. Here’s how one third-party administrator (TPA) used proactive analytics to unlock this knowledge, reduce costs and improve outcomes for clients.

There is no shortage of data in health care. Industry stakeholders— employers, plan sponsors, payers, TPAs, health systems and provider organizations—are sitting on vast amounts of raw data, and more is generated and collected every day from a growing number of sources.

Industry estimates indicate only about 20 percent of this data is structured, meaning it is quantitative and objective, including vital signs and health markers like blood sugar and cholesterol levels. Up to 80 percent of health care data is unstructured, or qualitative and subjective, such as patient assessments of pain and level of discomfort gathered during patient encounters.1

Structured data can reside in digital silos and in differing formats that may present barriers to sharing and analysis. The sheer volume and nature of unstructured data presents even more of a challenge; qualitative data is frequently stored in system text fields, making it difficult to retrieve, interpret and analyze.

The result: despite the large amount of data available, health care organizations don’t always have the right data they need to make effective decisions— especially because system transformation toward value-based care and population health requires different datasets for optimal decision-making.

Proactive Analytics Unlocks Value

Proactive analytics is the key to unlocking the value hidden away in mountains of raw structured and unstructured data. The spectrum of analytical capabilities—from descriptive and diagnostic to predictive and prescriptive analytics—is about processing raw data into useable information and turning that information into knowledge and actionable insight. Proactive analytics is about taking action—knowing where and how to act, and measuring the results of those actions.

For health care organizations currently under or transitioning to value-based contracts, proactive analytics offers a tremendous opportunity to optimize performance and gain a competitive edge by addressing affordability and cost concerns, delivering better value to stakeholders throughout the system, and managing through market uncertainty

HDMS And Meritain Health: A Powerful Strategic Partnership

HDMS enables health care organizations to seize this opportunity through a powerful analytics platform that securely aggregates and integrates data from any source and performs value-added analytics and reporting that transforms raw data into meaningful information, robust knowledge and actionable insights.

HDMS partners with stakeholders across the health care system that want to move from a reactive reporting model (common in fee-for-service environments) to a proactive, analytically driven solutions model to deliver greater value and better results to their clients and members. Meritain Health is one such stakeholder.

Meritain Health, a leading national TPA, is known for providing its clients with flexible, actionable data solutions, extensive network strategies, and integrated best-in-class partner support. The following use cases illustrate how Meritain’s strong partnership with HDMS has enabled them to deliver best-in-class proactive analytical intelligence and decision support to clients.

Trend Analysis

Standard health plan reporting shows comparisons of current versus prior periods. This helps identify trends but leads to questions of why there are differences and what is causing the changes. One of the most important ways proactive analytics unlocks value in data is by enabling a deeper understanding of what, exactly, is driving trends. HDMS’ Components of Trend methodology enables clients to drill into and deconstruct data patterns across a variety of components in order to pinpoint why trends are occurring and what is causing them—without undue extrapolation or guesswork.

Use Case 1: Improving Cost Trends

Meritain’s client, a large education system with 30,000 member lives, wanted to understand cost drivers behind a year-over-year increase in plan expenditures in order to reduce risk and lower expenses. Using HDMS’ analytics platform, a Components of Trend assessment revealed the emergency department (ED) service category was significantly affecting overall plan expenses due to inappropriate utilization.

Based on this analysis, Meritain made plan modifications and developed strategies to steer members to more appropriate care, including increased contributions for preventive care and the addition of a telemedicine provider. The changes resulted in a 17.4 percent reduction in ED visits, a 20.1 percent increase in utilization of preventative care, and a 4.2 percent decrease in overall plan spending.

High-Cost Claimants

High-cost claimants (HCCs) concern most payers and plan sponsors because although they typically represent about 1 percent of members, they account for 33 percent of spending. Early identification and mitigation strategies can be helpful, but plans are challenged in identifying which members will become HCCs since prior HCC status only predicts future status in 25 percent of cases.2

HDMS’ platform helps plans identify members at risk of becoming HCCs in the next 12 months through use of predictive models based on chronic and comorbid conditions and compliance history. The platform can also predict a program’s effect on members’ health status, enabling clients to offer appropriate services before the member becomes a HCC.

Use Case 2: Reducing HCC Expenses While Improving Health

Meritain’s client, a construction company with 500 member lives, wanted to decrease plan expenses while maintaining the best level of care and improving health outcomes, consistent with the company’s firm belief in investing in their people to drive success. Meritain used the HDMS platform to identify people with a chronic or comorbid condition at risk of becoming HCCs and compare HCC activity with medical and disease management program participation.

The analysis enabled the company to identify at-risk employees, develop early intervention and engagement strategies, and validate the positive effect of medical/disease management programs, leading the employer to provide greater incentives for participation. These strategies led to a 35 percent increase in program participation, a 6.2 percent reduction in HCCs, and overall plan savings of 23.7 percent due to the decrease in HCCs.

Specialty Drug Costs

According to HDMS client data, specialty drugs cost 10 to 15 times more than traditional drugs and account for about one-third of plan pharmaceutical spending. These costs are projected to grow about 20 percent annually. Managing this spending involves more than focusing on the drugs themselves. Cost must be considered in the context of the member’s medical condition, medication compliance and treatment efficacy.

By linking medical, pharmacy and other data sources, HDMS’ platform captures this holistic view and enables plans to zero in on the practical effect of specialty drug spending and developing strategies for reducing that spending while ensuring quality member care.

Use Case 3: Decreasing Specialty Drug Costs

Meritain’s client, a large education system with 30,000 member lives, wanted to gain a deeper understanding of pharmaceutical utilization and determine opportunities to decrease specialty drug expenses while ensuring quality care for members and improving health outcomes.

Meritain used HDMS’ platform to integrate medical and prescription data for high-cost and high-risk patients, then drilled down to ensure participation in a medical-management program focused on adherence and closing care gaps. When possible, members were moved to a lower dosage and frequency. The results included a 12.2 percent reduction in year-over-year medical expenses for members filling specialty-drug prescriptions and a decrease in specialty drug costs of 19.5 percent.

Network Leakage

Strategies to keep members in network provide an effective way to help control plan spending and ensure quality care and better care coordination— particularly important in the era of value-based care. HDMS’ analytics platform features built-in research capabilities for exploring network leakage and identifying members and conditions associated with inappropriate or ineffective out-of-network care, especially in high-cost service areas. These insights inform proactive interventions on both the member and provider side (for example, a member’s assigned primary care physician) to keep care where it is most cost effective.

Use Case 4: Understanding and Stopping Network Leakage

A Meritain hospital system client with 9,000 member lives wanted to gain a deeper understanding of how care was delivered outside their network by analyzing referral patterns, member demographics and treated conditions, as well as address challenges related to domestic providers referring members to out-of-network care.

Using analytical data from the HDMS platform, Meritain was able to recommend interventions, including education and outreach to referring providers, that resulted in 38 percent fewer out-of-network referrals, 14.4 percent greater network use, and an overall reduction of 10.8 percent in the hospital’s medical plan spending.

About Meritain Health

National leader in third-party plan administration, business process outsourcing, self-funded plan designs, network management solutions and health management strategies

  • Over 30 years of experience
  • Over 1 million member lives across the U.S.
  • Independent subsidiary of Aetna
  • 70% increase in member population since 2011

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  1. Smithwick, J. (2015) Unlocking the value of unstructured patient data. Becker’s Health IT & CIO Review. Retrieved from unlocking-the-value-of-unstructured-patient-data.html
  2. Wilson, D., Troy, T., Jones, K. (2016) High Cost Claimants: Private versus Public Sector Approaches. American Health Policy Institute and Leavitt Partners.
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The Power of Prescriptive Analytics in Healthcare

Imagine two health plan sponsors that each employ ~10,000 mostly blue-collar workers. These plans decide to launch care management programs designed to reduce unnecessary emergency department (ED) visits and costs. The initiatives make sense, based on reams of data suggesting that a number of employees at both companies are receiving care in the ED that could be treated more appropriately — and less expensively — in an office or retail clinic setting, or possibly through a telemedicine visit.

Both programs include care managers and access to a triage service designed to divert patients from the ED to a primary care clinician. For those who end up in the ED despite these efforts, the member’s primary care clinician is notified of the patient’s status in the ED, increasing the likelihood of better care coordination. To reduce the chance of an ED revisit, the patient is sent home with educational materials about appropriate settings for health care.

One year after the programs were launched, ED visits and spending are down for both firms, but one realizes significantly larger savings. It turns out that when setting up the program, this employer had the foresight to act on an insight the other did not — the fact that a small number of its workers were habitual visitors to the ED. Armed with that knowledge, this employer rolled out a “right-sized” program that hit the bullseye. Meanwhile, the other company must scramble to play catch-up. In hindsight, the second company now sees that it could have succeeded with fewer care managers and a more streamlined telemedicine program focused mainly on habitual ED users.

Prescriptive analytics is the difference between having foresight and relying on hindsight. It is the difference between getting it right the first time, rather than doing so months or years — and potentially millions of dollars — later.

Prescriptive Analytics Tell You What to Do

The answer for employers like those profiled above is to invest in a platform that taps into the power of predictive and prescriptive analytics.

More often than not, reports that employers and payers rely on to make decisions are based on descriptive analytics — a summary of historical data that explains what happened. It’s great information, but provides only half the equation. Sometimes reports include predictive analytics, where information is extracted from existing data to explain what might happen in the future. This is the other half of the equation, which outlines multiple potential futures based on many possible actions. While predictive analytics is an improvement over descriptive analytics, using it alone can lead to confusion, given the many potential scenarios it produces.

What’s missing is a clear path to the best course of action. That is where prescriptive analytics comes in. It utilizes specialized software that pores over the many potential solutions and helps select the best one. The bottom line: Prescriptive analytics tell you what to do.

Payer Account Reporting “Must-Haves”

Payer account reporting is only as good as the data that comprises it. Insurers and employers whose prescriptive analyses are hitting the mark are likely using an analytics platform that offers the following:

  • Airtight security and data-integrity processes. Health care ranks near the bottom of major US industries in this regard. Legacy systems are particularly vulnerable in an era when attacks are more frequent and sophisticated. Best practices include:
    • Annual SOC II audits
    • Internal pen testing and static code scanning
    • Third-party pen testing and vulnerability scanning
    • Internal security auditing
    • Third-party daily review
    • Compliance with the National Institute of Standards and Technology’s security and     privacy controls
    • Role security
    • Secure passwords
  • Ability to link claims to biometric and other non-claims data. Allow plans and sponsors to go beyond diagnostic codes and adjudicated medical claims to identify illnesses earlier, in some cases before the recording of a diagnosis code on a claim. As an example, elevated BMI, cholesterol and blood pressure readings coupled with low current use of health services can unmask metabolic syndrome and potential high utilization in the future.
  • An online portal with key performance indicators and insights. 24/7 access to an easy-to-read dashboard of KPIs, along with an alert system that sends messages when a measure prompts a predetermined alert.
  • Proven methods to analyze trends. After spotting a troubling trend, use trusted tools from respected third parties to quickly get to the bottom of what’s causing it. For example, the Total Cost of Care model from HealthPartners of Minnesota can help determine whether cost, volume or intensity of service is the key driver. An analytics platform that offers a suite of such tools saves time and avoids aggravation.
  • Detailed insights into specialty pharmacy. This is the fastest growing component of medical spending, accounting for 36% of total drug spending in 2015. The keys are to:
    • have a common definition of specialty pharmacy and KPIs
    • design and refine cost containment strategies such as preauthorization programs, closed formularies and limited networks
  • Relevant benchmarks. Plan sponsors want to know how they compare to others. It’s important to have benchmarks that are up to date; large enough to be statistically credible and valid; and able to be broken out by region, plan design and industry type. They also need to be adjusted for age, gender and illness burden.
  • Network analysis. This is particularly important for employers who have moved to narrow or tiered networks. They will want to know why employees are going outside the network — specifically, what services are they seeking, from which providers and at what price? They’ll want to know whether to include a favored provider and at what price point. More importantly, they will want to know if providers in the network are the most efficient and/or high-quality in the area — or are there better choices?
  • Indicators of value-based care. As plans move from fee-for-service to prospective payment or value-based contracting, employers will want to know if their employees are actually receiving more high-value care and less low-value care. Just as important, are members receiving enough care? This is another instance where respected third-party tools come into play, making it easier for plan sponsors to monitor care and communicate findings with local providers.

Liberating the Power of Data with Storytelling

Finally, payer account reporting requires more than just a good analytics platform. Analytics can be little more than statistical noise unless they tell a story. Bridging the gap between data and a relevant story requires specialized skills from subject matter experts. They are the people who understand a plan sponsor’s goals and objectives, and they can tell the story to liberate the power of the data.


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Overcoming Patient Leakage

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