By Eric Fischer, Senior Product Manager, Data Solutions
In part 5, the final post of our series on how Accruent Data Insights works and how it helps the healthcare technology management (HTM) profession, we focus on medical device life expectancy.
For healthcare technology management (HTM) professionals and other healthcare administrators, it is often difficult to determine the best time to replace a medical device. With limited capital to spend and limited people to accept delivery of, prepare, properly install, learn about and train staff on new equipment, it’s a complex process with many moving parts – and that doesn’t even include
decommissioning old equipment. Replacing equipment according to the AHA recommended life expectancy guidelines is often neither financially feasible nor clinically necessary.
Herein lies a huge dilemma: Life expectancy data cannot be ignored. Aging, outdated equipment may lead to your organization not meeting the current standard of care. You need good life expectancy data to help you gain credibility in your recommendations during capital planning as well as justify your plans to capital approval boards.
Accruent’s Approach Using Big Data and Statistical Probabilities
To address this issue, Accruent started with the assumption that AHA guidelines are useful as a baseline starting point. At a minimum, it is valuable to know that an exam table should expect a much greater life expectancy than an infusion pump. Accruent decided to merge that data with what we see happening in the real world for our customers, but first we had to perform a significant amount of data cleanup.
For example, Accruent sifted through the install and retired dates for each model, removing install dates that did not match up with work order history (e.g., install dates after the first work order date) and removing other outliers. We repeated a similar process for retired dates. Once we had reliable data, we could then use it to develop real-world life expectancy data for Accruent Data Insights.
Accruent then explored different methods of analyzing the data and landed upon survival analysis as a valuable solution for this use case. After experimenting with different models, we were able to determine the probability of life expectancy at various points in the lifecycle. Below is an example of a result for one device model, which we replicated for 16,000 medical device models:
The above chart shows that there is a 50% chance that the device will continue to be in use after 10 years. Furthermore, it shows that there is only a 90% chance that an organization will keep the device in use after 14 years. If this device were still in use at the hospital after 14 years, the hospital would likely consider replacing the device. Certainly, other factors need to be considered before replacing equipment, but this enriched life-expectancy perspective can add significant value to your decision-making process.
Accruent Data Insights and Device Life Expectancy Data
Life expectancy data was then compared side by side with the category averages. The results were generally consistent, but Accruent analyzed and addressed any significant variations. Accruent then placed the end results in the capital planning reports in Accruent Data Insights. The data was also evaluated in the customizable “Replacement Priority Scoring” calculation and applied to all assets in the hospital’s CMMS system.
How Accruent Data Insights Can Help You
We often hear customers talk about the pain points involved in capital planning process and how political and uncertain the outcomes are. Good life expectancy data found in Accruent Data Insights can help you gain credibility in your recommendations during capital planning as well as justify your plans to capital approval boards.
In addition, Accruent Data Insights is automated, so getting useful results is quick and painless. Even simply mapping your cost data is a simple and easy. If you’d like to learn more, please reach out! Ask your Account Executive for a free data cleaning report card so we can show your how this technology can help you. As always, we can be reached at firstname.lastname@example.org.
Looking for the full Accruent Data Insights Deep Dive weekly series? See:
- Accruent Data Insights Deep Dive Part 1 – Standardizing MDM and Model Names
- Accruent Data Insights Deep Dive Part 2 – Standardizing Medical Device Categories
- Accruent Data Insights Deep Dive Part 3 – Mining for End-of-Support Dates
- Accruent Data Insights Deep Dive Part 4 – Determining Medical Device Costs
- Accruent Data Insights Deep Dive Part 5 – Measuring Life Expectancy