Analytics Mistakes You Probably Make
Learn the common mistakes to avoid when growing your healthcare organization from data cub to data lion and key steps to take in order to get there faster.
Are You a Data Lion?
Healthcare organizations managing risk-based alternative payment models fall into two main categories. There are health systems with financial capital to make IT investments, industry-leading data expertise and operational discipline that I call the “data lions,” which are few and far between. And then there’s everyone else, where capital is scarce, IT resources aren’t fully leveraged, and data teams are so thin they’re one round of the flu away from not being able to submit their monthly reports.
Everyone wants to be a data lion, but getting there isn’t easy. It requires cultural and organizational changes, the right partnerships and financial investments. As organizations grow from cub to lion, there are common mistakes to avoid and key steps to take in order to get there faster.
Trust the Cloud
Server-mania is no longer prudent. Considering the mountains of data that health systems will be expected to manage, from population health outcomes, to community and social indicators, all the way to personalized genomic information, there’s no way to keep pace using one-off internal data centers. The same idea applies for analytics resources. Rather than waste focus on a server proliferation race that can’t possibly be won, the data lions have focused teams to leverage their unique relationships, insights and skills, leaving infrastructure and maintenance work to a cloud-based provider of shared resources. Have a good disaster recovery plan for when the gardener cuts your fiber, but learn to trust the cloud.
Benchmark the World
The healthcare landscape has linked providers together so that comparisons are not just drawn from markets, states or regions – increasingly they are national, and in some cases international. Take readmission or infection prevention programs, which set national benchmarks and impose penalties on anyone, anywhere, that doesn’t make the grade. Health systems are even measured beyond their industry, expected to provide the service of a Ritz Carlton, loyalty that rivals Apple with pricing as affordable as Sam’s Club. The point being, measurement has gone bigger and broader, often requiring benchmarking against a large, national pool in order to be effective. Data lions draw comparison groups as broadly as possible and make transparent reporting an organizational priority in order to prepare for a future where payment is tied to performance compared to the best of the best.
Too Many Process Measures, Too Few Total Cost Insights
Even though organizations are increasingly being paid based on value of care delivered, almost no one has real insights into total costs. Current measures are inadequate to assessing true value; they are the equivalent of the weight listed on our driver’s license – related to, but not based on, reality. Data lions have systems that pinpoint the true cost of every aspect of care delivery, married with data that can isolate how those costs affect outcomes. As a result, they know whether the next bundled payment contract will add margin or lead to layoffs. Moreover, data lions understand these insights are vital to drive organizational efficiencies, as well as continuous improvement. So while it’s great to know the flu shot rate for employees, the future is predicated on analytics that can show you whether those shots improved long-term health outcomes and lowered total medical costs for those that got them.
In The Godfather, Vito Corleone says “lawyers steal more money with a briefcase than a thousand men with guns and masks.” The same can be said of many HIT vendors. HITECH incentives and value-based care unleased a gold rush among tech companies, many of whom have no incentive to align with providers and no plans beyond the transaction at hand. And in the Wild West of HIT, too many are falling into traps. In the first, they allow EMR companies into business and financial areas where they have no competency. This is pure Stockholm Syndrome, where providers allow a deeper foothold because they are hostage to a single platform that prohibits EMR data extraction for use in another, better application. In the second, they trust data analytics to the technology arm of a payer, meaning data resides within the same adversarial organization that sits across the table to negotiate rate reductions and service contracts. Data lions reject both in favor of partners that have a long-term, mutually beneficial partnership in mind, choosing partners that don’t just install technologies, but also prioritize and systematize a total organizational improvement agenda. This assistance does not replace the relationships or organization knowledge, but the right partners should bring in best practices from the entire healthcare landscape to show where the “juice is worth the squeeze” to accelerate value generation, free of a sideline agenda.