How we effectively tackled engagement and enrollment issues

How we effectively tackled engagement and enrollment issues


One of our corporate wellness providers provides a “high-touch” intervention model for complex cases. Without clear insights, uncovering the right members to target is an art more than a science. One of their clients has requested that the wellness provider increase program engagement for the interventions. The main issue that they are facing is difficulties in getting their members enrolled in wellness programs. Other major issues faced by the end customer are not being able to see the ROI of the programs and not able to identify members with future risks.

Approach and Outcome

We decided to stratify the population to get to the root of the problem. We took two years worth of data to analyze and uncover chronic conditions and co-morbidities in the member population. We identified the top seven chronic conditions for the employer as well as calculated individual risk scores for each member, plus a trajectory for future risk and costs. We identified 6-7 times the number of people with multiple chronic conditions in their population along with gaps in care and their future risk scores.

Armed with this information, we were able to filter a thousand members that were not enrolled and we identified prime candidates for the program. This data is being used to craft targeted campaigns to enroll new users into the wellness programs.

Here’s an example of how we identified the prevalence of top diseases in the population.


According to the chart above, there’s an increase in members under the mental health disorder category including affective psychosis, depression and eating disorders. Obesity has shown 46% increase – this is alarming, as it is anticipated to influence future growth in diseases of the vascular axis (like CAD, CHF) and should be targeted to positively impact long-term outcomes.

The outcome of understanding the prevalence of disease was helpful in tailoring wellness or coaching protocols.  Certainly, from a disease management perspective, it was relevant to understand what could be managed medically, and what could be managed by keeping members compliant with medications, screening, and other interventions that can help manage or reduced the incidence of the disease.
We were able to improve enrollment by 50% and increase outreach through telephonic conversations and messages. The higher outcome-based incentives worked for the client as they were able to enroll more members. We’ve calculated the projected impact if a member’s conditions are managed to meet median costs. We have also provided support to get as many members enrolled into wellness programs through our outreach program (text, email, and letter) and to drive change. We’ve suggested age-appropriate screenings, enrollment for diabetes program and finally to closely monitor inefficient use of healthcare services.


Member enrollment and communication were the key factors

Member enrollment and communication were the key factors


We presented our latest findings to our gloves manufacturing company who was interested in learning more about cost savings and see if their healthcare costs were in line with industry benchmarks. Our goal was to present the data in such a manner that would be of use to the client. Member engagement was also a top priority for this client.

Approach / Outcome

We ran this client’s data on z5 and were able to pinpoint the following findings that came out of the data:

  • ER visit was found to be two times higher than the benchmark (169/1000). This client’s population’s visit to the ER was 422/1000 in 2016 and this number is projected to increase by 200% in 2017. The main reason why this number is so high is because the nearest healthcare facility is a minimum of 30 miles away. Hence, it was easier to visit the ER.


We proposed an alternative that could work for these high unchecked ER visits – Telemedicine. An access to a doctor over the phone would help reduce ER visits.

  • We also discussed the population risk with the client. We found out the exact percentage of the population that are at high risk. We were also able to identify members that were at a medium risk and were in danger of moving to the high-risk category. According to the chart below, 39% of the members are at medium risk and 11% are at high risk in September. A point to not here is that members the medium risk members have increased since May, 2016. Basically, 50% of the population is high and medium risk, compared to typically 25% in other groups we have observed.


We proposed that the client should communicate the findings with the employees so that the member engagement process can be made easier. We also proposed working with third party solution providers or our partners that would help in member enrollment.

  • Another observation that we made with the existing data is that the pharmacy costs of this client have shot up significantly and are predicted to be even higher in the next 12 months.


We were able to recommend that at least 57% of this Rx spend could be reduced if the members switched to the mail order option.

  • Under chronic conditions, we found significant gaps in care across conditions such as diabetes and hypertension.


We proposed finalizing and deploying diabetes and hypertension management programs across the population. We suggested targeting and on-boarding as many of the 235 members with diabetes and/or hypertension. It would be important to review incentives at this stage to modify behavior across key cost drivers and risks.

In the end, the goal with this client was to develop a communication plan that would have to be shared with employees (division or individual level via mobile text) to promote alternatives and incentives: mail order Rx, preventative care, diabetes program and finally alternatives to ER.

Finding $590,000 in potential savings for a client

Finding $590,000 in potential savings for a client


We recently worked with a client who provides diversified engineering services. While the group comprised a relatively young population, healthcare costs had been steadily rising the last three years, and they could not pinpoint the cost drivers contributing to these increases to even begin understanding what to do to curb those increases. The client was not clear which of the specific common factors (overuse of ER, declining population health, unmanaged chronic conditions, high pharmacy spend) was the prime factor in order to focus resources.


Processing the client’s claims data through z5, we discovered a consistent prevalence of 5  chronic diseases that were much higher than the national benchmarks – hyperlipidemia, hypertension, diabetes, chronic pain and blood disorders. Commensurately, in terms of population risk, the high-risk cohort was more than twice what we expect in a similar group.  Our analysis also showed that preventive office visits much lower than average for this group.

A second finding was their pharmacy spending totaled 8% higher than it could have been, again based on benchmark comparisons. We saw too that average script costs were 10% higher than benchmarks.

The third and an intriguing find was that all the cases of ER overutilization were tied to members that had one or more of the 5 chronic conditions problem areas identified.



It was imperative to address the riskiest members first, and action was taken to accelerate coaching for the 155 members in emerging and intervenable high-risk categories. The coaches were given direction to pay particular attention to the 5 members exhibiting 6 (and  more) ER visits.

One of the prime goals of the coaching intervention was to address the severe gaps in care (especially preventive services), for the hypertensives, diabetics, and those with blood disorders.  The company also stepped incentives for biometric screening in order to filter pre-diabetic members with the goal of reaching them before they lapse into full condition.

The client has also implemented a more traditional disease management programs around diabetes  and hyperlipidemia specifically.

Projected Results

For members that were at a normal risk level, the average amount paid per member was $1,381 and there were 1,824 members in this category. For members in the emerging and intervenable high-risk categories, the average cost per member was $5,135 and $5,643 respectively and there were 200 and 155 members in these categories. If we were to engage these members with the right interventions, we are looking at a potential saving of at least $590,000.


How data analysis has helped this client save $$$

How data analysis has helped this client save $$$


We recently worked with one of our brokers to help their client save on healthcare costs since it rose in 2015. The rising healthcare costs was a huge concern for the client and they could not pinpoint the cost drivers and needed help to curb their healthcare costs.


We examined their data to uncover the main cost drivers

  • Conditions across the population were unchecked and out of control
  • Lots of gaps in preventive care visits, and trending over the last 2 years, possibly leading to higher higher costs
  • Significant care gaps across chronic conditions, including wellness and routine measures
  • Diabetes, a significant cost and health factor in many populations, runs 5 points higher than benchmark

The chart below depicts the high costs that’s trending across the board for two reasons:

  • Inpatient visits grew by 75%, outpatient by 34%
  • While office visits only by 7%



Working with the client, we recommended the following actionable opportunities to help them save on healthcare costs:

  • Implement novel PBM solution(s) to stem the growing pharmacy costs ($77k impact)
  • Put in programs with incentives to close preventative care gaps: office visits, screening
  • Control unmanaged chronic conditions to target $200k in potential savings
  • Can do in stages, e.g. diabetes then mental health
  • Diabetes management program can potential save $185,000
  • Deploy a telemedicine program and also review plan design to minimize the $125k of avoidable ER and Urgent Care utilization
  • Implement a tool to pro-actively identify cost saving opportunities, risks, and track programs across diabetes, preventative, telemedicine, and others

Since chronic health was a concern, we suggested the following to the client:

  • Large predicted costs tied to Diabetes, Hyperlipidemia, and mental health.
  • All of the prevalent chronic condition risks can be managed for future cost avoidance.
  • Target members for improved engagement and outcomes.

The image below depicts how the chronic conditions are driving future costs.


Lastly, we also helped the client understand the savings related to ER utilization and pharmacy utilization costs. For pharmacy utilization, we saw a potential of $77,000 in savings because:

  • Cost per prescription has increased drastically for brand drugs from $126 in 2014 to $158 in 2015
  • Opportunity for conversion to generic whenever a therapeutic equivalence drug is available
Unveiled hidden factors causing over $22 million spend for a pharmaceutical client

Unveiled hidden factors causing over $22 million spend for a pharmaceutical client


A pharmaceutical company with 3,500 employees was spending over $22 M on their healthcare spend, with their biggest issue being the lack of clear insights into their healthcare risks and cost drivers. This employer has a mainly young population and had assumed that they were healthy in general, but wanted to know of any issues or hidden factors that might impact the productivity and well-being of their employees.


The preliminary step in all such analysis is to load the client’s data onto our z5 Risk Management Platform. Immediately upon doing so, we discovered that while average age of the group was “young”, the number of individuals who were a higher than normal risk was relatively high for this group. Actually we observed 20% higher than average risk for members aged 30- 40 years old.  Moreover, while overall the health was good, and the most expensive members were consuming lower than typical costs, the mid-risk range population was driving higher than typical cost for this population. In fact, this year’s highest risk members were in the lower half in previous years.

Our clinical analysis supported these indicators, with fundamental shortcomings in care gaps.  We observed almost 45% non-compliance in areas such as fundamental preventative screenings including cervical cancer and PSA testing. This group of Emerging High Risk members created an opportunity for identifying those ~1,400 members, and targeting them with pro-active programs to mitigate their risk factors.

Other factors easily surfaced in the platform were certain high-costs related to an unusual number of high-risk deliveries, as well as much higher-than-typical ER utilization, presumably by a population with no time (or patience) to visit a PCP.


While the employer’s perception was that the population was healthy as a whole, using our platform, emerging risks that had the potential to spiral out of control without any intervention were uncovered, and we were able to make specific recommendations for mitigation:

  1. We targeted four areas for a focused program around reducing gaps-in-care for members with the three most significant chronic conditions, as well as general and preventative wellness measures:
    1. Asthma: improve use of medications
    2. Hyperlipidemia: drive compliance with annual lipid screenings and medication adherence
    3. General wellness: improve age-appropriate medical screenings as well as routine and preventive office visits
  2. This group had strong indication for prenatal therapy and pregnancy management. Utilization of these programs has been noteworthy.
  3. Telemedicine alternative were recommended.  We modeled almost 10% ER cost savings based on a modest channel redistribution.