Predictive Analytics: Drive Affordability and Better Health
Predictive models are an amazingly powerful use of data. And we have so many new reasons to use these advanced approaches to offset rising costs of care and challenging health conditions: deferred care, the increased need for mental health care, and virtual care adoption are disrupting historical patterns. How do predictive models work? Where is it best to consider using predictive analytics? What should you be doing with the results?
Watch this webinar to learn about how predictive analytics can fit into an overall analytic strategy. Invest in capabilities that allow you to act upon results, instead of sitting in reports in your inbox. Predictive analytics, when put to purpose, can be an instrumental part of a broader strategy to drive down costs and improve health. Make sure you have the big picture so you get the most from these investments.
A framework to use to help discern where and how predictive capabilities are highly useful
Insights into how you can couple predictive capabilities and leading indicators
Approaches other health insurance providers are taking to act upon what their data is telling them
Webinar hosted by AHIP.
Rani Aravamudhan, MBBS Senior Clinical Consultant
Health Data & Management Solutions (HDMS)
Rani Aravamudhan joined HDMS as a Senior Clinical Consultant. She is a physician, specializing in General Medicine with extensive experience in the EMR/EHR and population-health industries with a focus on clinical transformation, workflow design and development, value-based care, risk management and clinical quality and performance reporting. Her strong background in clinical medicine and experience in the HIT industry make her successful in navigating payer, provider, and technology vendor landscapes.
Prior to joining HDMS, Rani worked for Philips Wellcentive as a Services Leader, where she led teams of program managers, clinical specialists and training staff along with heading their Data Governance committee. Before that, Rani was leading EMR/EHR implementations, workflow and process optimization consulting at McKesson Corporation and was their resident subject matter expert for all CMS quality reporting programs. Rani earned her medical degree at Grant Medical College in Mumbai University, India.
Keith Wilton Vice President of Product Management
Health Data & Management Solutions (HDMS)
Keith Wilton is the Vice President of Product Management, with more than 15 years’ experience in Product Management and an emphasis on creating and deploying complex software applications. Keith joined HDMS in March 2016 after serving as Vice President of Product Management at Backstop Solutions, a leading player in the Alternative Investment space. Prior to Backstop, Keith ran product management for an arm of Morgan Stanley, and for other organizations. Keith received his B.S. degree from the University of Illinois Urbana-Champaign.
Prepare for your health analytics implementation before you buy a thing!
Avoid buyer’s remorse.
Did you ever have a home improvement project that finished late and cost more than you expected? How about a technology implementation that finished late and cost more?
You are more likely to be on-time and on-budget if your plan is thoughtful and reflects your reality. Don’t you want to have confidence knowing what you’re really getting into?
So, here are three tips to set you up for implementation success when it comes to health analytics:
One-size does not fit all. It’s unlikely your implementation is the same as other organizations. Why? Because the culture of your organization is a huge factor. Dig in. What are the details behind YOUR implementation plan?
Discuss what will be problematic or painful based on your experience and what you are moving away from. Are those complexities appropriately addressed, cared for, or resourced? Think about metric definitions and consensus, data quality, data reconciliation, matching and integration across sources, and slowly changing history.
Identify what is- and is-not in your control. If something is beyond your direct control, is there a named resource and escalation path? What risk does that pose to the project timeline based its nature. For instance, your health analytics implementation is reliant on data from others. How are your relationships and service level agreements with those partners and vendors? How does that affect your plan and what’s the back-up plan?
Before your implementation starts, refresh your knowledge of the day-to-day contacts, authorities, and any contractual SLA’s you have in place. If there will be costs associated with establishing new feeds or data interfaces, identify those early.
Top down, bottom up, or an interesting mix? Think about the approach that will work better for your organization. What process works for you – here’s my data – what can I do with it? Or here are my objectives – what data do I need? There are pros and cons to each but thinking about this as you prioritize is invaluable for setting internal expectations and getting the right resources lined up.
Use phase 1 for quick wins. Standard sources generally seamlessly populate the most common views. Users feel like they get a lot out of the gate and that helps tremendously with adoption.
Remember, you’re better off with an implementation plan that’s realistic rather than one that sounds like a dream but doesn’t work well for you in the end.
Enlight is a flexible analytic platform that unlocks the power of data. It brings data to life and reveals connections and insights so you can make being healthy more affordable, convenient, engaging, and equitable.
Published in BenefitsPro Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS
While many employers are willing to invest in wellness programs, they aren’t always clear on the goals for these benefits.
Rather than jumping on the wellness bandwagon or adding a program just to expand the suite of benefits, employers would be better served to evaluate and make decisions based on data.
Read how HDMS recommends employers approach this, published in Benefits Pro. or just read below – we’ve copied the article to this page.
Wellness programs have become a staple of employer benefits offerings. According to one KFF trends report, nearly 9 in 10 employers with a workforce of 200 or more offered some sort of workplace wellness initiative in 2019.
While many employers are willing to invest in wellness programs—which often are offered through third-party vendors—they aren’t always clear on the goals for these benefits. Multiple surveys and studies across the industry attest to this. No clear goals mean no systematic approach to defining and consequently measuring ROIs.
Most employers rely on wellness vendors’ claims about the potential to improve health outcomes and reduce health care costs. They do not necessarily have the means and/ or expertise to independently verify the proposed advantages either prior to or after implementing them.
Metrics used by vendors to illustrate their successes are not always applicable to all populations or groups. For instance, let’s say a vendor’s “expected outcomes” include a 20% increase in smoking cessation rates. Is that 20% over three months or over three years since the last smoking incident? Or, is it based on a one-time pledge by the participant? What was the size of the overall smoker population in their sample data? Then there is always the question, “Is that the right metric for your population?”
Data analytics can provide objective insights to evaluate such partnerships before beginning, renewing or expanding a wellness program.
Prepare for the evaluation
Like any strategic endeavor, effective ROI measurement requires diligent groundwork before actual data analysis can begin. It is essential to ensure that the right metrics are chosen for measurement of “before” and “after” states.
Concrete objectives will vary by employer as well as by program—but beware of setting goals focused solely on short-term “dollars-in” vs. “dollars-out.” An effective wellness program aimed at promoting better rates of preventive care with active engagement may actually increase expenses in the immediate and short term. In such cases, the true long-term objective should be to shift health care services from unpredictable, high-cost settings like the emergency department (ED) to more predictable, lower-cost settings like primary care physicians’ offices.
Today’s wellness programs tend to be more holistic in their approach to employee health than the offerings of just a few years ago. Many employers are looking for more than isolated reductions in smoking rates or ED visits. They are starting to understand the overall health and financial benefits to their businesses possible through programs that integrate physical health with mental health and well-being. This adds obvious layers and complexity to the ROI conversation.
Preparing for any ROI measurement requires assessing all the data sources at your disposal. It’s critical to obtain as close to a 360-degree view of the entire employee population as possible, which typically requires melding multiple disparate data sources. Medical and pharmacy claims, lab values, biometric and clinical data from electronic health records (EHRs) are some examples. Data warehousing and analytics solutions can help this process by aggregating, integrating, enriching and normalizing data along with consultative services to provide the right insights.
Finally, a realistic timeframe for measuring program outcomes is a must, especially when claims are part of equation, to allow for the time lag between services rendered and paid out. Hence 12 to 18 months serves as an optimal window to gauge discernible changes to patterns of care experience and member behavior. That said, periodic measurements throughout this time are essential for tweaking and adjusting workflows and processes to ensure proper data aggregation (e.g. presence of required code sets, uniform cadence in receipt of various data types, etc.).
Data-driven ROI analysis
Be aware of employee engagement factors
Achieving maximum ROI from wellness programs comes from changing behaviors, especially of those who are most at risk for adverse health events and consequently would benefit the most from these initiatives. For example, employees with chronic conditions who struggle with medication adherence or with managing stress due to work and family obligations. Promoting and maintaining engagement in such groups is challenging, but key to the success of the program itself.
On the same token, initial engagement tends to be high among members who are healthier and would likely gain little from a wellness or similar program, especially when there are participation incentives involved. Engagement typically tends to decline once the incentive requirements are met or phased out.
Setting up cohorts of participants with these factors in mind is critical because the metrics chosen to measure success levels in each will vary. Leveraging the expertise of data analytics vendors and consultants to define and set up such study cohorts—with and without comparable controls—goes a long way in these endeavors.
For example, employees who are engaged in wellness programs tend to also take advantage of preventive services and have a primary care provider. Consequently, data typically will show that they have higher rates of primary care and in-network utilization—whereas those who don’t participate have more ED and out-of-network services.
Establish key metrics
t is vital to ask the question, “Are we measuring the right things for each cohort for this particular initiative?” Defining the right metrics for a cohort is therefore an important aspect of the study design. Example: Establishing new primary care provider relationships and closing care gaps would be good metrics for employees who have traditionally not sought regular primary care in the past. On the other hand, keeping pertinent lab or biometric values within normal ranges, or garnering low scores on health risk assessment tools may be better suited for healthier and more engaged populations. Establishing clear baselines for each metric on day 0 is imperative for apples-to-apples comparisons.
Many employers are using non-traditional data sources to track metrics like sick time, other leave utilization, and rates of disability claims to evaluate the effectiveness of a wellness program. Data analytics and warehousing vendors offer tremendous advantages in this area by integrating disparate data sources.
Consider a pilot program
Pilot programs for a carefully chosen group with comparative control groups is always recommended, especially for new wellness initiatives. In addition to ironing out administrative and process challenges, they provide a great means of gauging the operational effort and resources required. This is an often-overlooked expense not featured in ROI calculations.
Results from a pilot program can go a long way toward determining an effective roll-out strategy. It’s essential to compare these results against the total employee population for the same timeframe. Example: An increase in the rates of flu vaccine compliance among employees in a pilot group does not mean much if vaccine compliance also increased in the total employee population due to onsite flu clinics. With successful pilots that show a definite improvement in outcomes for the participants, odds of further success are better when the program is expanded to demographically similar employees.
Let measurable results drive strategic investments
The last few months have brought renewed focus on the overall well-being of the workforce. Employers recognize the importance of the physical, mental and emotional wellness of their employees and their families. It’s not surprising that wellness program vendors, especially those that provide integrated services, are popular.
But rather than jumping on the wellness bandwagon or adding a program just to expand the suite of benefits, employers would be better served to make data-driven decisions. They would do well to engage the many data analytics vendors who provide evaluation services to answer key questions. “Is this right for our company?” and “Will this save me money on health care costs?” are the types of questions that can be answered even before the program is implemented, based on existing statistics or sample data sets.
Rani Aravamudhan is senior clinical consultant at HDMS. She is a physician (specialty – General Medicine) with extensive experience in the EMR/EHR and population health industries with a focus on clinical transformation, workflow design and development, value-based care, risk management and clinical quality and performance reporting. Her strong background in clinical medicine and experience in the HIT industry make her successful in navigating payer, provider and technology vendor landscapes.
Keep Essential Workers Safe: Data Analytics Strategies to Guide Effective Benefits Design
Published in HR Executive
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS
HR executives have followed the time-tested adage of past behavior being the best predictor of future behavior – evaluating utilization patterns over time to make educated projections for the upcoming year. However, given the skewed healthcare consumption caused by COVID-19, these traditional means of assessing year over year trends fall predictably short.
Read the five strategies suggested to guide effective benefit design.
Benefits Design Decisions during The Great Resignation
Trying to keep employees happy and healthy? Trying to attract new talent?
With staggering resignation rates throughout the country, employers are naturally looking at benefits. What’s the right mix to both retain employees to prevent expensive losses, and attract replenishment talent?
Navigating the “Great Resignation” to an advantage means directly addressing these unknowns. It requires a holistic approach to health benefits. It’s no longer enough to have a handful of options that seem like they should fulfill employees’ specific needs.
Think about the relationship between a doctor and their patient. Providers consider the whole patient, including their demographics, medical history, and social determinants of health. Yes, they focus on health outcomes, but also fostering better patient experience and satisfaction levels to ensure their practice maintains a stellar reputation. If you too can take a holistic view of your workforce population, you’ll nail it. You’ll offer competitive and thoughtfully designed health benefits that really resonate with employees.
How do you do this?
With data you already have access to.
Derive insights from powerful data stories that exist about your workforce.
Your health benefits will not only address your employees’ needs but anticipate them. You won’t fear the sticker shock that comes with an expansive benefits package. Having analyzed health data you will have eliminated under-utilized and costly benefits that your employees don’t need or want. You’ll get better value for what you are spending.
Use data to design the right health and wellness programs for YOUR population.
No guess work or finger crossing.
Happy, healthy employees – the building blocks for success and long term loyalty.
Health data is key to successful transformation as a side effect of the “Great Resignation” trend. Use analytics to evaluate specific benefits and associated holisitic wellness. Do mental health apps reduce reliance on prescription pain medications or chiropractic visits? Get a better understanding of employees’ wants, needs, and what’s working. Unfortunately the individual reports you have today can’t always connect the dots for you.
Using data differently gives is broader understanding of people, wellness, and ultimately, productivity. It’s easy with connected health data and even fun (!) with a predictive analytics system. Equipped with data-driven insights, you’ll create a competitive advantage beyond just hiring, by offering benefits that really work for your employees. You’ll have happier, healthier employees bringing their best self to work everyday.
Cultivate a connected health view
A connected view of your health data makes it possible to spot emerging trends more quickly and evaluate employee behaviors as they evolve. You can monitor in real-time how your population uses their healthcare services to identify opportunities for improvement and increase employee retention.
For example, you can use prescription data to view trends in new medication for anxiety and depression as an indicator of your workforce’s overall wellbeing. This canary in a coal mine can help you implement wellness perks for your employees more quickly, such as mental health days or increased behavioral health services. Connected health data creates a birds-eye view of your population’s greatest commonalities and shared wants and needs. If many of your employees have dependents, childcare coverage might resonate more than the social benefits that young professionals may seek.
By integrating all types of employee benefits data — from traditional sources (such as medical, eligibility, and pharmacy) and non-traditional sources (such as wellness programs, disease or care management programs, biometrics, wearables, provider and lab data) — you have the power to create a benefits program specifically targeted to your employees.
More than ever, employees need to feel valued and employers need to improve retention rates with competitive and custom health benefits. Understanding the “Great Resignation,” particularly how to address it, is critical for your company’s success in the immediate and long-term future.
Won’t this pass soon?
You are not alone
Arm yourself with data visualizations, cohort analysis, and other tools. Easily evaluate your workforce and adjust health programs to meet their evolving expectations. We’ll help you deliver measured results and continued success… long after the Great Resignation.
Your friends at HDMS
Beyond Transparency in Coverage - Embrace plan sponsors with strategic analytics
What makes you trust someone?
Maybe they act upon facts and share these openly with you? Even when it’s not great news?
While everyone works to meet Transparency in Coverage regulations, we see the chance for you to leap ahead. Anticipate where the market is going and offer more than traditional plan sponsor reporting – bring your plan sponsors business transparency, strategic plan performance transparency. You’ll earn their trust; you’ll be rewarded with retention.
With major industry changes, new care options, and changes in care needs, people have lots of new questions. Be the health plan that easily gives plan sponsors answers, even to hard questions.
Employers benefit because health benefit satsifaction is a contributing factor to employee retention. With the right analytics, it can be easy to find opportunities to improve, maybe by introducing additional plan options. Yes, that’s right. Design analytics that introduce the potential value of your buy-ups. With better plan performance everyone wins as health care costs lower overall.
In an industry built upon trust,
Embrace it, lead with trust
Increase plan sponsor trust with an analytic strategy that delivers better plan transparency, too.
You’ll deepen relationships, earn loyalty, and retain your customers.
What could controlled plan sponsor plan performance transparency look like?
Self-service analytic front-ends are what people want, to explore data. But the secret is the data itself. If you want to focus on using data for plan performance improvements, your analytic views will naturally be very specific to your business.
Take a peek at HDMS Enlight™. Imagine plan sponsors with access to data and analytics you choose or design. See teams working side by side with accounts, helping them to optimize and get the most out of your thoughtfully designed plans and networks.
HDMS clients – have your team walk you through your dashboards. Where can you take this next? We’ll help tailor and expand this for you!
How Data is Driving 2021 Benefits Strategies
Published in Fierce Healthcare
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS
Health plans typically design benefit offerings by assessing patterns of utilization and cost data in previous years. Thanks to disruptor-in-chief, COVID-19, this traditional approach was rendered inadequate to say the least.
With the pandemic demanding agility, many health plans turned to flexible data analytics and infrastructures capable of generating original, actionable intelligence at a moment’s notice. Having the ability to respond rapidly to their clients’ immediate data needs, despite ongoing uncertainties, is much like having a fire truck ready to put out fires whenever and wherever they arise.
Health plans needed to quickly pivot business processes to align with evolving customer needs. For healthcare benefits administrator Meritain Health, the large-scale shift in the consumption of care that followed the onset of the pandemic required swift adjustments to accommodate changes in multiple areas: fluctuating sites of service and code sets and payment structures, to name a few. Hence Meritain implemented several strategic variations to their business processes.
Integrated Mental Health Strategies: A Right-Sized Approach to 2021
Published in HealthPayerIntelligence
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS
Member programs encouraging mental health and wellness have increased in popularity lately among health plans and sponsors. There is a growing consensus that a happy, healthy workforce can lead to better business results. Consequently, promoting mental health strategies is considered a “win-win” proposition.