Systems and methods to triage and assess solution steps to empower a user in resolving a reported issue
A computer-implemented system assists resolution of service cases for medical imaging devices by retrieving device and case data, estimating likelihoods and timeframes for multiple resolution pathways, and presenting results in a user interface. Input may include images of device error messages captured by a camera; optical character recognition extracts text, and language detection and translation normalize messages for analysis. Models leverage historical repair data, technician profiles, and part replacement indicators to score pathways and forecast durations. A cost metric can combine likelihood and timeframe with service and scheduling costs to recommend actions and trigger service calls. The approach reduces downtime and improves dispatch and repair efficiency across customer and provider personnel.
1 . A non-transitory computer readable medium, storing:
data related to medical imaging devices; and
instructions readable and executable by at least one electronic processor to:
retrieve, based on a query received for the current service case, data related to a current medical imaging device for which the current service case to be performed;
generate, from the retrieved data and for each of a plurality of available service case resolution pathways by applying predefined rules or executing a trained machine learning model using inputs derived from the retrieved data including complexity analysis (C) from technical documents, number of escalated cases (E), and reoccurrence (R) of the problem on the current medical imaging device, (i) a resolution likelihood score indicative of a likelihood of the resolution pathway resolving the current service case, and, using inputs including the complexity analysis (C) and repair time of historical service records (R) for the same medical device type, and (ii) a timeframe for the resolution pathway to resolve the current service case; and
provide, on a display device of an electronic processing device, a user interface (UI) the generated resolution likelihood score and the generated timeframe for the resolution pathways.
2 . The non-transitory computer readable medium of claim 1 , further comprising:
receiving the query including receiving, from a digital camera or an electronic device that includes a digital camera, an image of an error message displayed by the current medical imaging device;
wherein the retrieving of the data related to the current medical imaging device includes performing optical character recognition (OCR) to extract the error message from the image.
3 . The non-transitory computer readable medium of claim 2 , wherein the instructions further include:
detecting a natural language of the error message;
determining whether the detected language of the extracted text matches a language used by the current medical imaging device; and
translating the detected language into the language used by the current medical imaging device if the detected language does not match the language used by the current medical imaging device.
4 . The non-transitory computer readable medium of claim 1 , wherein the generating the resolution likelihood score includes:
generating the resolution likelihood score as a grade indicative of a pathway to resolve the issue with the current medical imaging device.
5 . The non-transitory computer readable medium of claim 1 , wherein the resolution pathway comprises a customer employee of the customer of the current medical image device, and the generation, from the retrieved data, of (i) a resolution likelihood score indicative of a likelihood of the customer employee resolving the current service case, and (ii) a timeframe for the customer employee to resolve the current service case further includes:
generating the resolution likelihood score indicative of the likelihood of the customer employee resolving the current service case based on a historical effectiveness of the customer employee in resolving historical service cases within a predetermined timeframe.
6 . The non-transitory computer readable medium of claim 1 , wherein the resolution pathway comprises a customer employee of the customer of the current medical image device, and the generation, from the retrieved data, of (i) a resolution likelihood score indicative of a likelihood of the customer employee resolving the current service case, and (ii) a timeframe for the customer employee to resolve the current service case further includes:
generating the resolution likelihood score indicative of the likelihood of the customer employee resolving the current service case based at least on a metric indicative of likelihood that resolution of the current service case includes performing a non-customer-serviceable part replacement.
7 . The non-transitory computer readable medium of claim 1 , wherein the resolution pathway comprises a maintenance provider employee, and the generation, from the retrieved data, of (i) a resolution likelihood score indicative of a likelihood of the maintenance provider employee resolving the current service case, and (ii) a timeframe for the maintenance provider employee to resolve the current service case further includes:
generating the resolution likelihood score based on a historical effectiveness of the maintenance provider employee in resolving historical service cases within a predetermined timeframe.
8 . The non-transitory computer readable medium of claim 1 , wherein the instructions further include:
generating the resolution likelihood score and the timeframe for a resolution pathway comprising a customer employee of the customer of the current medical image device;
generating the resolution likelihood score and the timeframe for a resolution pathway comprising a maintenance provider employee; and
generating a service call to the customer employee or the maintenance provider based on a higher resolution likelihood score within the timeframe.
9 . The non-transitory computer readable medium of claim 1 , wherein the instructions further include:
applying a predetermined cost metric to the generated resolution likelihood score and the generated timeframe to determine a cost of resolution for the current service case; and
providing the cost of resolution on the display device.
10 . The non-transitory computer readable medium of claim 9 , wherein determining a cost of resolution for the current service case further includes:
determining a cost of a service call for the current service case and/or costs related to scheduling data related to the current medical imaging device.
11 . A servicing support system, comprising:
a database storing data related to medical imaging devices; and
at least one electronic processor programmed to:
retrieve, based on a query received for a current service case, data related to a current medical imaging device for which the current service case to be performed;
generate, from the retrieved data and for each of a plurality of available service case resolution pathways by applying predefined rules or executing a trained machine learning model using inputs derived from the retrieved data including complexity analysis (C) from technical documents, number of escalated cases (E), and reoccurrence (R) of the problem on the current medical imaging device, (i) a resolution likelihood score indicative of a likelihood of the resolution pathway resolving the current service case, and, using inputs including the complexity analysis (C) and repair time of historical service records (R) for the same medical device type (ii) a timeframe for the resolution pathway to resolve the current service case, and (iii) a cost of resolution for the current service case; and
provide, on a display device of an electronic processing device, a user interface (UI) displaying the generated resolution likelihood score, the generated timeframe for the resolution pathways, and then cost of resolution.
12 . The servicing support system of claim 11 , wherein the resolution pathway comprises a customer employee of the customer of the current medical image device, and generate, from the retrieved data, (i) a resolution likelihood score indicative of a likelihood of the customer employee resolving the current service case, and (ii) a timeframe for the customer employee to resolve the current service case further includes:
generating the resolution likelihood score based on a historical effectiveness of the customer employee in resolving historical service cases within a predetermined timeframe.