Methods and systems for maximizing ophthalmic medical device uptime via predictive health monitoring and proactive preventative maintenance

A method for training a predictive model to predict failure events on an ophthalmic medical device, comprising:

• generating a training data set from a set of measurements of operational parameters associated with the ophthalmic medical device, wherein the training data set includes a plurality of records and each record of the plurality of records identifies:
• a measurement of an operational parameter,
• a time at which the operational parameter was measured, and
• a difference between the time at which the operational parameter was measured and a time at which a failure event occurred with respect to the ophthalmic medical device;
• training one or more machine learning models based on the training data set to generate one or more failure predictions in respect of the ophthalmic medical device;
• and
• deploying the trained one or more machine learning models to one or more computing systems.

A method for performing preventative maintenance on ophthalmic surgical devices based on preventative modeling, comprising:

• generating a training data set from a set of measurements of operational parameters associated with an ophthalmic medical device, wherein the training data set includes a plurality of records and each record of the plurality of records identifies:
• a measurement of an operational parameter,
• a time at which the operational parameter was measured, and
• a difference between the time at which the operational parameter was measured and a time at which a failure event occurred with respect to the ophthalmic medical device;
• training one or more machine learning models based on the training data set to generate one or more failure predictions in respect of the ophthalmic medical device;
• receiving, from the ophthalmic medical device, measurements of one or more operational parameters associated with the ophthalmic medical device;
• predicting, using the one or more trained machine learning models, a future failure of the ophthalmic medical device based, at least in part, on the received measurements of the one or more operational parameters;
• and
• taking one or more actions to perform preventative maintenance on the ophthalmic medical device based on the predicted future failure of the ophthalmic medical device.