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Posted by Sarah Ramsey

  • Sep 2, 2020

Cost Metrics Series - #5 Unit Account Based Margin Comparisons


Identifying comparative benchmarks for key KPIs can help your organization find outliers to identify the highest impact areas operators can focus on to fix. Margin per shift analysis is a simple way to point operators in the right direction.

Health systems, hospitals and medical groups have a lot of data at their finger tips but it can be hard to find the time to analyze and figure out:
where there are opportunities, 
their relative importance and  
how they can react quickly 

One great tool is to set up benchmarks by different business drivers to identify who is furthest from goal AND with the greatest volume impact.


The margin per shift concept takes into account both expected revenue and cost to identify where you may have the highest impact across the follow drivers:


Revenue drivers: Patient Volume, Patient Acuity


Cost Drivers: Productivity, Rates, Shift Distribution (are you filling your schedule with w2, 1099 or 3rd party)


What are unit accounts and why do they matter? 

Most groups we talk to only have backward looking financials based on P&L statement. These statements can lag up to 30 days. Unit accounts are statisical measures that can be used to predict financial. Unit accounts we use in our forecasting include:


1. Volume: Encounters (CPT Codes)


2. Volume: Work RVUs (based on the CPT Code)


3. Hours/Shifts


By using averages we can predict revenue based on a mix of forecast and actuals by taking expected encounters x avg acuity x avg collections rate. And via Kimedics because our rates are directly connected with the schedule for automated pay calculations we can very accurately forecast costs. Most forecasts are lagged or do not update with best data possible.


One group used margin/shift benchmarking analysis to identify $155k in savings for one physician who missed his EMR training

In a recent study, we found over $155k in annual opportunities for a 6 doctor ICU. And our analytics highlighted one single outlier that helped Operators instantly know where to focus. After a quick review they were able to see that the provider was billing 4% of encounter as critical care vs. his peers at 96% critical care. He missed his EMR training and a simple training brought his billing back in line with his peers.


Inaccurate forecasts do not provide value, in our article we share how our unit account driven forecasts can point operators to problems as quickly as possible.


You can download the full case study here.


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