The McKinsey Global Institute suggests that predictive maintenance will decrease costs between 10% and 40%, saving organizations in the long term. Downtime from equipment maintenance scheduling will also be cut in half, providing even more benefit. In comparison, the US Department of Energy indicated that predictive maintenance is cost-effective and leads to roughly 25% to 30% reduction in maintenance costs and a 70% to 75% decrease in equipment breakdowns. Laboratory equipment failure can be both costly and devastating to operations. The high cost of repairing or replacing equipment is a major area of concern for laboratories and hospitals. There are also potential losses in downtime when medical professionals cannot use the equipment to complete the necessary research or medical diagnoses.
AI will allow your organization to determine which medical equipment has the highest probability of malfunctioning. Your organization will be able to use past equipment failure data to predict the likelihood of equipment malfunction in the future. Compared to existing equipment quality check procedures that use scheduled maintenance checks, AI will allow your organization to use likelihood of failure data to effectively manage all of your medical equipment. This will enable your maintenance staff to take proactive measures by developing an understanding of what may lead to equipment failure. Advancements in equipment failure predictions will not only save costs and reduce downtime, but also enable your organization to prevent any equipment failures during critical medical procedures, ensuring the safety of your patients.
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Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.