The topic of bundled payments, and how healthcare organizations and ACOs will respond, has been a vital point of discussion as of late. As a result, my favorite read last week was Bundled Payments: Challenges & Opportunities written by Sheldon Hamburger and Erica Jankelovitz, JD, on the HIMSS site. For the uninitiated, bundled payments are a flat price reimbursement for a bundle of services that comprise an episode of care. As we are all aware, services for the same episode of care can vary by patient. We also know that a small percentage of patients comprise the bulk of healthcare costs. However, the fixed price reimbursement for a single episode of care will not vary. By controlling the effort put forth to impact health, providers and payers work together towards “gainsharing” to control, and then reduce, the cost of care.
This leads us to an important conundrum: how can we predict the patient experience in a reliable way? How do we understand what causes some patients to require more (expensive) care and some patients to require less (expensive) care? If we can uncover these patterns, then we ultimately reduce the total cost of care.
How do we do this? I’m glad you asked.
World-renowned Harvard strategists Kaplan and Porter, best known for the Balanced Scorecard and Five Forces, respectively, have given much thought to the issue in their piece, “How to Solve the Cost Crisis in Health Care.“ They wrote:
“The biggest problem with health care…is that we’re measuring the wrong things the wrong way.”
Kaplan and Porter ultimately feel that there is an almost complete lack of understanding around the costs required to deliver care. No big shock experienced by this reader. Costs are currently tracked by specialty or department instead of tracking costs by patient with specific condition over the cycle of care. Under the idea of “what gets watched gets done” this can (and does) mean disastrous things in terms of escalating healthcare costs. Tracking costs using managerial accounting methods, which would be wise under bundled payments, would allow providers to correct systemic cost issues by linking healthcare costs directly to the patient and then directly to process improvement. Likewise, efficient providers can be rewarded for their behavior, causing a shift in underlying motivations and financial results we see in healthcare.
Using Analytics to Support Patient-Level Cost Data
Analyzing costs at this micro level requires strong use of analytics. Analyzing costs means nothing if you are using bad data. By using integrated advanced analytics, financial executives can turn data into information rapidly to not only understand the important components of their current key drivers but also predict the future through modeling with pro-forma statements. You can get to this point by integrating any current data source with business intelligence tools so that you can move away from the current Excel based world (I like to call them Excel spread-marts) and have all of your data in the same place so that you are making important financial decisions using a single version of the truth.
Further, any industry that is capital intensive, like healthcare, needs to use predictive analytics to win this fight. When high cost Patient Peter walks into an episode of care, we need to have a reliable prediction of costs. Will the low cost Patient Paul provide enough available margin to cover it? Advanced predictive analytics can help answer those questions system, and nation, wide so that we can finally control the cost of care.
Tune into my next post where I discuss how patient generated data can help a healthcare organization recognize a Patient Peter from a Patient Paul, and how patient engagement technologies are used to bring down the cost of care for both of these swell guys.
