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Mental Health and Disability insights

Read through some astounding insights uncovered in a recent employer analytics project.

This work digs into how mental health affects the bottom line. It investigates what we can learn not only from medical and Rx claims data, but also point solution data and disability data.

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Case Study

Mind over matter

GLP-1 Analytics

Three clients use Smart GLP-1 Analytics for 3 different strategies.

There is no single right answer, but these analytics give organizations the insights they need to find the best approach for them.

 

Read these case studies for a sense of how these analytics can be put to work.

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Case Study

GLP-1 Analytics – 3 case studies

$400,000. Overnight.

Ever wonder why it helps to have an analytics team at your fingertips?

This HDMS client got the full benefit of quick response times from trusted analytic partners.

Read how HDMS saved this large retailer $400,000 with a quick overnight analysis.

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Case Study

The value of quick response times from trusted analysis

Employees for life?

 

We’re lifetime partners, aren’t we?

Leadership drives health with dashboards.

What does long-term mean to your member health?

Do employees stay with you for years?

Read how this company lowers costs and improves health across a lifetime.

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Case Study

Employees for life? Leadership drives health with dashboards.

Measuring Point Solutions

HDMS analyzes the effectiveness of third-party diabetic management solutions.

How can you tell if a point solution is delivering on it’s value promise?  Is it reaching and helping the parts of your population that need it most?

 

Read how HDMS helped a client determine whether they should renew their contract with their Diabetes Management point solution.

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Case Study

Analyzing the Effectiveness of Point Solutions (Third-Party Diabetic Management program)

Connecting mental and physical health to productivity

Improving employee mental health solutions through the power of data analytics

Read how a national retail employer partnered with HDMS to better understand the mental health needs of their population and how it impacted their productivity.

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Case Study

Improving Employee Mental Health Solutions through the Power of Data Analytics

Measure the Impact of Preventative Cancer Screenings with Patient Outcome Analytics

A case study for employers and health plans

A look at measuring preventative screenings

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 Analytic Challenge

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 American Cancer Society (ACS) released new, more targeted guidelines that lowered the number of people it recommended for the preventive screenings.1 (The ACS believed the change would result in higher prevention rates even with fewer people screened.)
  • Screening utilization was declining.
  • Only 6 to 8 percent of members who were screened were actually diagnosed with cancer or a related condition as a result.

The Solution

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:

  • Were diagnosed with any cancer within the three years prior to being diagnosed with breast, cervical, uterine or colorectal cancer. (This helped to identify new cancer cases as opposed to recurring cancer cases.)
  • Received medical services for a cancer diagnosis within 60 days of a preventive cancer screening.

The Results

Analysis clearly showed the value of preventive cancer screenings for members and for the state agency:

  • The majority of new cases of breast, colorectal and cervical cancer among the agency’s members were initially diagnosed as a result of preventive screenings.
    • 80% of new cases of breast cancer were associated with preventive screenings¹
    • 11% of members who received screenings received additional treatments – not just for cancer
    • Cervical cancer screenings led many members to additional uterine or ovarian testing
  • Members diagnosed with breast, cervical, uterine or colorectal cancer through the preventive screenings experienced fewer medical complications, as shown through lower relative health risk scores.
    • Breast Cancer
      • 00 Average risk score of members diagnosed with breast cancer
      • 88-6.53 Average risk score of members diagnosed with breast cancer
    • Cervical Cancer
      • 00 Average risk score of members diagnosed with breast cancer
      • 31-4.22 Average risk score of members diagnosed with breast cancer
    • Those diagnosed through preventive screenings recorded lower total costs of cancer care on a risk-adjusted cost basis, as well as relative to expected cancer treatment costs.
      • 9% Decrease in the cost of treatment for breast cancer
      • 6% Decrease in the cost of treatment for colon cancer
    • Overall, paid claims for all three types of cancer screenings was 3.6 percent lower than in previous years.

Data-informed insight improves health

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.

In the Know

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.)

Download the case study

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

Case Study

Measure the Impact of Preventative Cancer Screenings with Patient Outcome Analytics