Capital Replacement Planning
Here’s an understatement for you: Evidence-based capital replacement planning isn’t easy. As we’ve discussed before, developing a replacement forecast requires hospitals to assess thousands of devices across as many performance metrics and replacement criteria—lots of data crunching. Furthermore, trying to combine those criteria to determine the overall performance of a piece of equipment is exponentially more difficult.
Understanding Equipment Cost
Catholic Health Initiatives (CHI) is a health system comprised of 105 hospitals. According to a recent article by HFMA, “Over a three-year period, three technology advisers (clinical engineers at CHI) reviewed approximately 425,000 pieces of equipment and technology, valued at $3.3 billion at CHI facilities, with each piece of equipment getting reviewed once every three years.”
I feel like that number bears repeating: four hundred twenty-five thousand pieces of equipment and technology. CHI is actively engaged in developing assessments on age, reliability, end-of-life status and regulatory compliance on all that equipment. That’s a lot of number crunching. Some hospitals don’t have the manpower for an initiative like this, so they are only using evidence-based replacement forecasting for a subset of assets, or nothing at all. That’s scary when you consider this “fun fact” that all hospitals have to face: The average number of medical devices per staffed bed increased 62% between 1995 and 2010, while annual service and maintenance cost increased 90% to $3,144 per bed, per year.
Effective capital planning saves money. Without it, hospitals are prone to excessive service costs on underperforming equipment, or prematurely replacing equipment that’s still performing reliably, along with high unit costs due to last minute, small lot purchases. This represents hundreds of millions of dollars leaking out of hospitals every year.
When you think about the healthcare system, it’s quite a dynamic space. Sure there’s a fair share of bureaucracy and inefficiency, but in the pursuit of constantly striving to cure illness and deliver better care, it’s an industry perpetually growing with new, sometimes groundbreaking, technology. It’s an industry that continues to need the implementation of lean methodologies applied to processes like replacement planning.
The goal of data-driven capital planning is all about finding value. Discovering “what”, “where” and “why” with regards to equipment in the hospital is what it’s all about. Then there’s the “how.” How does an HTM department go about the processes of collecting all the information and crunching all the numbers? If you relish the idea of Exceling a health system like CHI into real cost saving, I’ve got a toothbrush you can also clean their hallways with. It’s hard to deliver any real value if you are doing everything manually.
Automation saves time and money right when it shows up to the party. When it comes to replacing technology, assets are given scores based on those previously mentioned performance criteria (age, reliability, end-of-life status, regulatory compliance, etc.) Getting to those scores requires mining raw data from the maintenance management system; normalizing and assessing each performance criteria; and applying weightings to each criteria to account for risk, cost and patient impact. All of that comes together in the form a single CERF (capital equipment replacement forecast) score that can be tracked over time to indicate when replacement of an asset should be considered. You can get those scores the hard way, or you can do it the easy way. Can you guess which category automation falls under?
Beyond hard numbers, clinical engineering teams can round up the usual suspects; (I can already hear the Law and Order music) interviewing department heads, physicians and nurses in the pursuit of collecting qualitative feedback on technology, but this too can be streamlined through automated surveys. Qualitative performance criteria can be weighted and rolled into the overall CERF score.
Back to the real, hard numbers—the green kind. Oregon Health & Science University is a public university in Oregon with a main campus, including two hospitals, in Portland. OHSU needed to stretch a $40 million replacement budget in order to fund other strategic purchases. With the help of Mainspring Healthcare Solutions, OHSU used operational data, such as, mean time between failure, downtime, and service cost trends to guide replacement spending. The result was a 50% reduction in replacement spending, or $20 million falling out of the sky, which they were able to reallocate toward new strategic initiatives. This is a story with a happy ending, so I won’t go into detail about how much it costs a hospital to make that $20 million through delivering care.