Why is Evidence-Based Capital Replacement Planning getting so much attention?
The Affordable Care Act has significantly increased pressure on hospitals to reduce costs without sacrificing quality of care. And U.S. Hospitals spend about $40 billion in capital purchases every year and billions more to maintain those assets. CFOs have taken notice that this is an area of substantial cost saving opportunity and have started asking for justification on all capital spending, including replacement of IT, medical and facilities equipment. Expected life, depreciation schedules and departmental wish lists are no longer enough to get a capital request approved. Evidence-based replacement forecasting gives organizations the ability to use actual operating data, such as downtime and total cost of ownership trends to make smart capital investment decisions.
For example, OHSU a 530-bed Level 1 trauma center was able to reduce their medical equipment replacement budget by nearly $20MM by using evidence-based decision making. That was a 45% reduction. They did this by extending the life of equipment that would have normally been replaced due to age and anecdotal stories. They also reduced clinical delays and operating expenses by replacing equipment with escalating service costs and downtime. This level of savings is pretty typical and that is driving a lot of interest.
Sounds great – why doesn’t everyone do it?
Well, frankly, because it isn’t easy to do. Developing a replacement forecast requires hospitals to assess thousands of devices across as many performance metrics and replacement criteria. That alone is a tremendous amount of data crunching, but trying to combine those criteria to determine the overall performance of a piece of equipment is exponentially more difficult. I don’t want to bore you with the mathematics, but basically you need to take all of the criteria measurements and normalize them to a standard scale, such as 0-100. Then you can apply weightings and combine them to get an overall replacement score for each asset, which we call the CERFscore™. Once you have taken those steps, you can look at your assets from multiple dimensions such as modality, model, location, department, age, etc. This is where things get really interesting.
Then there is the data issue… Many hospitals have serious data issues that need to be addressed prior to taking the steps above. The analysis won’t work if there isn’t a standard nomenclature that allows you to easily look across a fleet of similar devices, for example infusion pumps. You also need the operating data for the equipment, which is typically available in the equipment maintenance database.
Doing this manually is very time consuming, so most hospitals only use evidence-based replacement forecasting for a small subset of assets…or they don’t do it at all.
And Mainspring’s CERF™ automates that process?
Yes. Capital Equipment Replacement Forecasting was designed with help from our customers to automate the most cumbersome pieces of the capital replacement process. What we discovered was that building the automation was also fairly difficult. We had to find the right balance between structure and flexibility since our customers wanted to have the ability to use their own criteria, weightings and groupings. Another important design criteria was the ability to create powerful reporting tools that could communicate the “so what” of that analysis to multiple audiences like department administrators, clinicians, the capital committee and finance.
The product makes it easy to categorize your assets and apply custom criteria schemes and weightings. Once that step is complete CERFscores™ are automatically calculated and updated for every asset. It can also automate the aggregation of asset information from multiple databases, even if the databases use a different asset naming convention.
Another great feature is that it tracks your outliers and automatically flags them. So even if your fleet of infusion pumps looks fine from an aggregated CERF Score perspective, you may find that there are several individual assets that are underperforming for one or more of the criteria. Outlier flags identify potential issues and allow you to dig into why that’s the case. Are these specific pumps utilized more than others? Are they just lemons? And depending on the results of your analysis, you can either redeploy the pumps among high and low utilization departments, or replace your outliers, possibly with less expensive refurbished equipment. Either way, you extend the life of your entire fleet and free up capital dollars. CERF™ makes all of this easy.
Don’t most CMMS and Asset Management Systems do this with reporting?
No, not at all. Sure, any really good Enterprise Asset Management system can provide information like “percentage of expected life remaining”, “total cost of ownership” (TCO) and even “mean time between failure” (MTBF). But those are static reports looking at a single criterion at a point in time. Using evidence-based forecasting to make smarter replacement decisions, requires the ability to compare multiple performance criteria across multiple dimensions such as modality, model, location, department, risk category and more. So if you want to assess overall performance of your MRIs…across all of your facilities…for the last twelve months…relative to how they have performed over the last three or four years, you couldn’t get that from your CMMS or EAM reporting.
I will give you a real world example of this. We’re working with a 300-bed medical center that is a Level-1 trauma center and teaching hospital. As part of the process of assessing their current data, they ran a snapshot TCO report for their assets. That single report was over 3,000 pages long. Now imagine having to pull that data for prior years to get a trend; doing the same thing for five or six more criteria; then taking all of that data and trying to consolidate it into a single assessment for each of their 8,000 pieces of medical equipment. Doing all of that work to get to the point where you can start doing the real analysis just isn’t practical. That’s why we built CERF™.
What’s next for Capital Equipment Replacement Forecasting?
We’re really excited about how our clients are pushing CERF™ to the next level. For one, they’re seeing Capital Equipment Replacement Forecasting as the platform for decentralizing the capital planning process. By giving their department heads visibility into results for their departments’ equipment, the replacement planning discussion starts with everyone reading from the same playbook. This is completely unique in the industry and allows the department heads to take ownership of their piece of the process in a way that wasn’t possible without CERF™. We’re also incorporating qualitative criteria such as “usability” and “technical obsolesce” into the CERF to add another layer of richness to the CERFscore™.
How can a hospital get started with CERF™?
Mainspring makes it easy to get started. We can take a snapshot of a hospitals data and run a free evaluation to determine the current status of your database. From there, we can interface into your current CMMS product and pull the data out on a regular basis, crunch it in the CERF™ engine and serve it up in executive dashboards. Since CERF™ is cloud-based, we can typically have people up and running in just a few weeks.
CERF™ and CERF Score™ are registered trademarks of Mainspring Healthcare Solutions