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.
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.
Countering COVID-19: Using phased data analytics to shape benefits plans
You need to add a widget, row, or prebuilt layout before you’ll see anything here. 🙂
Infographic: Diseases of Despair
What is an Analytics Agenda?
Hoping to get something done?
Every organization is different. Some are understaffed and trying to cover the basics; some are well invested and applying cutting edge techniques to shape the future. Some organizations handle everything in-house; some work with multiple trusted partners. At HDMS we base our work around a collaborative Analytics Agenda. It’s our way of making sure we understand your organization’s goals, culture, available resources and partners to create and use healthcare analytics for the benefit of your business and employee health. An Analytics Agenda is a documented plan that gives you clarity on how HDMS will make you successful.
We use Analytics Agendas to generate results momentum. That means not only do the reports and KPI dashboards you implement in phase 1 continue to deliver value, but we use the Analytics Agenda to find new ways and deliver new incremental value over time.
Help your organization grow – step by step, constantly moving the ball forward. An HDMS Analytics Agenda serves as a documented understanding of what your organization is trying to achieve, how healthcare analytics support priorities, and then digs into the tactical and technical aspects of what and how a collaborative team will perform work to deliver value in these spaces. Analytic efforts often focus across
Analytic projects – rapid responses
Long term studies
Identifying where time is spent, what analytics are needed, and how data supports these efforts are all part of creating an Analytics Agenda.
Clients find creating this deliverable energizing because through the process we are able to formulate a plan that was otherwise difficult to progress internally. We guide you through this process so that you can lead within your organization. As creative problem solvers we are used to overcoming internal obstacles and we bring these strategies and ideas to your experience when you need help.
As the path and plan forward solidify, it’s time to get to work! Our Data Operations team handles the administration and execution of gathering data from your various data vendors. They carefully define data integration and data quality processes to deliver things like whole health views, total cost of care analysis, and high impact opportunities – based on priorities set in the Analytics Agenda.
We pull in early insights to refine the Analytics Agenda, considering both your analytic results as well as our collective intelligence of other organizations like yours. For instance, we might accelerate some tasks if we see an opportunity to reveal cost trend drivers that could be positively impacted if deeper analysis were made available sooner.
Executing the Analytics Agenda varies across organizations, since resources and partner collaborations are considerably different. The HDMS team fills in where needed and supports you on your terms. You might be almost self-sufficient, or you may want day to day help with general and deep-dive expertise for complex data analysis. Regardless of the resourcing model, through our technology your internal stakeholders and trusted business advisors will have relevant and progressive data analytics that enable intelligent decision-making.
A successful implementation is a given. Yes of course you’ll see value in phase 1. The Analytics Agenda reveals its “varsity” status as time moves on and you and your team just keep bringing the answers. Once the basics are taken care of, you will be able to move in so many directions, and your ability to serve your organization will create an acceleration effect.
The needs of employees and their families will change and so will the health care services they use, the programs offered, and the data produced and available to augment analysis. Bringing in new data sources and adjusting and adapting at the speed of change is a reality with HDMS. We work with you to understand your needs, create a plan, and execute at an accelerated pace. Our team and technology make it easy, and your Analytics Agenda means you know how and when you’ll deliver results – again and again and again.
Watch an example of how we engage with our Clients in support of the Analytic Agendas that we create for them.
COVID-19: Healthcare data analytics for planning in our changed world
March 11th saw the World Health Organization (WHO) declare a pandemic. This marked a change that immediately impacted our daily lives – our world has a new normal. In it, we need to rapidly adapt to change and healthcare data can show us what to tackle first.
Our ability to understand risk and anticipate spread of the novel coronavirus is inextricably linked to rapidly evolving data. What we know changes swiftly and yet our lives cannot completely stop; businesses still need to function. Now more than ever, we need to harness health analytics to guide companies through ongoing data-driven decisions as we react, recover, re-open, and plan for 2022.
A COVID-19 Analytics Agenda
HDMS recognized the opportunity to leverage our clinical and analytic experts to offer content to support companies that extend even beyond our own client base. We created an Analytics Agenda and shared supporting resources to best leverage health analytics to support your organization.
We recommended a framework of COVID-19 analytics that included these key focus areas in three phases:
Acute: quantify your population vulnerabilities for strategic planning and resource management
Short term: understand COVID costs, quantify cost offsets due to delayed or reduced care. Use this to re-plan the current benefit year, track wellness issues, and perform cost modeling for 2021
New normal: use COVID insights to tune benefit plans for 2022, manage wellness in our new world, accelerate good health and prevention
HDMS clients should reach out to their Customer Experience team members for analytic templates that produce the results and metrics shared here.
Since we’re in Phase 3 – Adjusting to a new normal, our clinical and analytic expert resources have been digging into the following areas. We’re building new client-specific reports, and rolling out new content in Enlight. What’s hot?
Mental Health: Leading indicators, trend stabilization, Mental health impact on chronic condition costs
Deferred Care: Risk assessment and engagement strategies to offset foregone care
Virtual Care: Billing inconsistencies (virtual care and telemedicine billing practices)