There’s an old saying that says numbers don’t lie. I say they’re capable of telling half-truths. Gather up all the numbers in one place and you start getting some straight talk out of them. Gather up only a few and now (knowingly or unknowing) you’re making some “educated” guesses. It’s the basic fundamentals of card games like Poker and Black Jack—trying to figure out where the ace and where the deuce is. That’s fine and dandy if hospitals were into riverboat gambling; not so much when it comes to capital planning.
As part of their due diligence with regards to capital planning, many hospitals are implementing cost containment initiatives through evidence-based capital equipment replacement forecasting (CERF), with the goal of providing hospital leaders with accurate and objective insights into equipment—figuring out things like remaining life, service costs, reliability and market value. Implemented effectively, CERF isn’t a sporadic exercise but ongoing processes, and as such, their credibility relies on constant accuracy.
Knowledge is power, but that knowledge is gleaned from data. So, are all the numbers present and accounted for in CERF, or are we still playing Texas Hold’em, because all those big budget projections, fancy capital replacement matrixes and expert recommendations stand a bit more wobbly when the fundamental data supporting them isn’t telling the whole truth.
Hospitals cannot make fully informed investment decisions, or remain in compliance if they don’t know which assets they have and what condition they are in. When it comes to making those informed decisions, it’s all about data quality, or to put it another way, how clean your data is.
Data Management Solutions
Here’s another old saying: Do it right the first time. Mainspring Healthcare Solutions has found that in a typical hospital, the inventory recorded in operational and financial systems rarely match each other or the equipment that is actually in the facility. Many hospitals haven’t implemented a standard nomenclature, data master or data management plan. The result is poor data with inaccuracies as high as 60%. Inaccurate data means inaccurate safety inspection records, inaccurate inventory valuation... the list goes on.
Imagine embarking on a search in your database for your inventory of infusion pumps and after expending the time and energy, the results were as varied as: infusion pump; infusion pumps; pump, infusion—or the naming conventions used for defibrillators were as fragmented as: defibrillators; defib, defibrilator; defibrilater, etc.
What you thought was a simple query has quickly turned into a full-fledge interrogation of your database. Worse off, you start asking yourself bigger questions as your throat tightens and you sink in your chair. If the data is this scattered for the naming conventions of a pump or defibrillator, what about all the different model numbers, and every other piece of inventory for that matter. What else don’t you know?
Take a deep breath. If you’ve been dealt a bad hand of data on the clinical engineering side, you can still beat the odds. It starts with implementing accurate, clean and standardized data. Through automation, controls and accountability you can keep that data clean and organized, giving you a solid foundation for evidence-based replacement forecasting and meeting regulatory requirements.