Understanding the value of data analytics in HTM

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HTMs lay out uses and advantages
of data analytics for purchasing
and maintaining equipment

Understanding the value of data analytics in HTM

por John R. Fischer , Staff Reporter
From internal training to deciding which devices to continue supporting and which to retire, HTMs face a variety of tasks in their line of work that depend on access to the appropriate data. Finding, organizing and managing this information can be a challenge. Data analytics, a tool for extracting useful insights from data, continues to grow in popularity among providers who are seeking to complete these tasks with greater efficiency, while minimizing cost, time and risks to patients.

It is this rising interest which prompted the formation of a three-person panel this weekend at the 2019 AAMI Exchange in Cleveland. The three took the time to explain how HTM programs can properly set up, use and benefit from data analytics in their daily routine, with all agreeing that the first step was to establish benchmarks as a basis for data analysis, such as service cost, end-of-life, date of purchase, and risk. Doing so requires communication from all hospital programs.

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“Build a relationship with the departments — your lab, OR, oncology, any area where you may feel challenged. Build that trust, get that information, and start putting the information into your database,” said Makidah Mahdi, director of clinical engineering at Henry Ford Health System. “You need to understand the total cost of ownership for every single asset you have in your inventory. After you start capturing that information, you have to come up with a benchmark and a service cost ratio for every single asset you have.”

Once metrics are established, it is important to prioritize them to identify and communicate issues and matters that require the most urgency or hold great significance. One example is decisions around purchasing new pieces of equipment, which require relaying information such as utilization rates, age and lifespan data with leaders and teams in their healthcare system, as well as other stakeholders.

“The data that we keep now is excessive and needs to be used. We should be the drivers of a lot of these equipment purchasing decisions,” said Joseph A. Haduch, senior director of BioTronics for University of Pittsburgh Medical Center. “Are analytics the be all and end all of equipment you purchase? No, but they ought to be. You use that to meet with the clinical teams, your finance people, your facility people, to show quantitatively what the equipment should be replacing.”

Data analytics can be used for a variety of tasks, including determining the best engineers to receive training for work on certain devices, or assessing utilization, abuse and misuse of equipment to figure out if a piece of equipment should be retired early.
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