On its own, healthcare is creating a data deluge, and analytics are evolving fast to bring value to many domains. Different solutions are required to be successful in the upcoming commercial health insurance exchanges (HIX) in which high-quality coding of diagnosis data becomes the currency for payment.
Analytical tools — if used properly and understood well — can bring meaning and context to “big data” by allowing users to create personalized reports and visualizations. In the context of HIX, analytical tools can identify health claims that are improperly coded. The result: Data can be improved before the final transfer payments between participating plans results in substantial financial losses.
But how can payers pry open the lid of their analytics and uncover the performance of their algorithms, associations, and business rules?
The answer is to take important steps toward real-time visualization and reporting of results to achieve the deepest insight into the behaviors, symptoms, and treatment of their population. Only with comprehensive member profiles can they achieve improved precision and lower the cost of interventions.
Here are five tactics for moving in that direction:
1. Tap non-traditional data sources. Typically, when payers stratify and capture member data, they look at traditional data collection methodologies such as HEDIS quality measures, chart reviews and batch claim processing. When assessing risk in the new exchanges, these traditional data sources are still valuable, relevant and should continue to be utilized. Incorporating data outside these traditional methodologies, however, can greatly improve accuracy in predictions about individual member risk factors, lower utilization, and ultimately help control costs.
Ninety percent of the data in the world today has been created in the last two years. It is a powerful figure, but not all of it is coming from traditional data sources — especially given the proliferation of powerful text-, photo-, and video-mining tools being developed. The HIX population might pay a lower premium and have higher utilization costs. For this reason, payers should not merely rely on traditional data sources alone to gain insight into this population to increase their risk factors, lower their utilization costs, and improve their overall health.
Non-traditional data sources might include behavior and symptom data that are not acceptable for submission as risk-adjustable diagnosis codes. But it holds powerful value and relevancy, so why wouldn’t health plans tap it? One reason might be that the data does not reside within the health plan’s current data infrastructure.
[See also: The great EHR market shakeout — it's pending, but when?]
For example, an average of 25 percent of patients do not fill their prescriptions. This sort of information offers payers a rich data source, and if payers can capture this type of data, their analytics can enhance precision, thus allow for the improvement of risk factor scores and help payers better manage and push interventions to their members. Another example refers to the growth of Electronic Health Records (EHRs) across the country, which is projected to be in the 80 percent range by 2016. As we move more deeply into EHRs, there are opportunities to capture traditional and non-traditional data offering real-time visibility.
2. Measure efficacy. Measuring individual algorithm efficacy against expected outcomes allows payers to adjust those outcomes to meet financial performance goals. Looking deeper into condition weights and confidence levels applied to each category offers insight into which members are targeted and for which intervention. Understanding individual clinical profiles and benchmarking a unique member’s behaviors helps payers evaluate patterns and sequences used to deploy the right intervention at the right time. Is there an opportunity to drive another intervention or a more powerful intervention type? Was there some other information or data source that made us suspect and target these members?
Benchmarking member profiles against others who have the same or similar pathways improve the confidence in the intervention deployed and can bring payers closer to the targeted risk factor — and that can both boost financial impact and reduce program costs by 8-12 percent.
3. Track and visualize results. Incorporating transparent and flexible business intelligence tools offers instant access to program performance. BI tools should demonstrate performance of algorithms over time and have the capability to display:
• Benchmarks within industry
• Network and provider profiling
• Member profiling
• Individual risk factors and Identification of method utilized to close gap(s)
• Financial and Payment Transfer Impacts
• Realization rates and associations
• Advanced analytics patterns to align, filter, rank, and compare
How else can payers demystify the black box? By making sure the dashboards are highly configurable, customizable and are pushed directly to web-based products viewable anywhere with Internet access or downloadable for offline viewing and sharing across individuals and departments.
4. Real-time visibility. Payers don’t have the luxury of operating in a holding pattern. They need tools and technology to gain instant access to member health data, and obtain real-time results, rather than waiting for days, weeks, or even months for a report.
There are two basic viewpoints on transaction processing: batch processing and real-time. Unmasking realization rates in real-time can provide insight into risk adjustment intervention effectiveness. A payer’s strategy should include connecting member surveys, chart reviews, and assessments to reporting tools without delay while interacting with condition confidence levels and program options so they can create customized deployment actions. This will reduce the time to calculate financial impact and program results. It means reducing the delay between interventions and reporting, improving confidence and increasing program performance.
The faster the information is delivered, the more valuable it is, and the more payers can do to drive value to their programs.
5. Harnessing emerging trends to bridge the data gap. New data sources and new methods will improve targeting, precision, and financial impact. New technologies will have a dramatic influence on communication and member engagement. Mobile health technologies are forecasted to reach 1.158 billion users by 2020, according to Strategy Analytics. Many individuals entering the health insurance marketplace will have little to no clinical history. Mobile applications help you improve confidence levels for suspected risk adjustment and care gaps. Mobile alerts can help prevent and treat health conditions. Among the myriad benefits waiting to be realized are better care coordination, greater control, a more comprehensive and measurable solution.
[Infographic: HIX race begins with Big Data.]
Many payers have deployed solutions to help members understand their benefit design, but these frequently seem to be one-way communication tools. Payers who use such apps to make members smarter and more engaged will have a competitive advantage by informing their members of interventions they can do right now. As such payers can amass more data in a shorter time than traditional data collection methodologies. This sort of interaction with members will improve confidence in risk scores, understand risk adjustment and care gaps, and enable payers to alert and prepare members to address certain care decisions.
Black box revealed
Success with risk adjustment, quality, and cost containment on the exchanges depends on a deep look inside the black box, and the performance of the programs deployed for revenue optimization.
Commercial exchanges require smarter, faster analytics to address concerns today versus waiting down the road. Real-time visibility provides risk adjustment solutions that will drive cost effectiveness and appropriate interventions. Be bold and seek out non-traditional data sources, utilize mobile technologies, measure your analytical algorithm performance, and insist upon transparent BI tools.
These strategies will help protect precious risk adjustment dollars.
Related HIX articles:
Are you making the most of your HIX analytics?
Are health plans using the right HIX tools?
