Feb
4
Heavy Equipment and Medicine
Filed Under Published Articles
Heavy Equipment and Medicine
Offered for Publication in WCN Newsletter, July 2011
All Copyrights reserved
Just a few days ago I was performing a routine Commercial Driver’s License Examination for a patient, and had the luxury of time to get to know him better. We struck a conversation about his work. He and his colleagues are in the process of creating a method of using multiple sources of data about the heavy equipment they manage to predict the probability of failures. They visit sites, and acquire data on bull dozers, earth movers and other extremely valuable pieces of equipment.
The information acquired is analogous to that which we acquire in medical practices: vital engine component performance; frequency of oil changes and its chemical analysis; maintenance records; break down/system failure reports; age of equipment and other objective measures of equipment “health.”. These are similar to blood tests, cardiograms, etc. These managers of valuable equipment are challenged with technical obstacles, such as integration of information from multiple sources, and the variations of operators. They ultimately strive to accurately estimate the costs of maintenance, and the breakeven point for taking equipment off line and planning for replacement.
The advantage is that they have a less complex set of metrics, and are able to work in an ethical environment driven entirely by economics. In spite of these advantages their effort parallels the recent developments in healthcare. We are finally getting to a point that data acquisition is inherent in the work we perform in healthcare through electronic health records. The analysis of this data is the next wave of technological opportunity that will revolutionize not just health care but also heavy equipment management, and virtually every information intensive industry in the world.
The accumulation of data and its use to estimate future problems is in general called predictive analytics. An over simplified description of predictive analytics is the accumulation of data, the warehousing of data (collecting, organizing and “scrubbing” of multiple sources of data) and then the analysis of that data to predict the most likely future. It is intuitive to consider this in machines, where mechanical failure rates are well documented through lab testing of a limited number of systems. In healthcare this seems practical, but given the exponentially larger number of variables seems an impossible task.
In part an analogous process of predictive analytics is inherent to the intellectual processes of healthcare professionals. Professionals acquire data through examinations, diagnostics and laboratory tests and create a diagnosis, prognosis and treatment plan. The ability to accurately predict the future (prognosis) is one area where we as people could dramatically benefit from the experiences of heavy equipment managers. When we accept that the “peripheral brains” provided by predictive analytics can enhance our abilities, we can finally expect that we will get closer to the error rates currently enjoyed in the airline industry, or many others we have come to expect to perform as promised. Even to the point of accurately understanding the costs and expected benefits of aggressive management.
Understanding the progress made in managing expensive and mission critical equipment will lead us in Medicine to practice more effectively, and to aid pts in understanding the risks and benefits of a wide and growing array of options. Ultimately we will finally be able to understand the contributed value of preventive care, and the benefits of routine examinations, or oil changes. The most expensive and mission critical equipment in the world is in our hands, and it is time we used the same technological advantages that the mining industry currently and routinely incorporates.
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