The client manually entered patient data, assessments, doctors’ orders, and notes. This was a time consuming process subject to human error. In order to create a more efficient process, the organization sought to optimize the process and eliminate the risk of error. The process was automated by allowing doctors to speak patient summary forms right into the system.
Text from doctor-patient conversations was analyzed, recorded, and organized into 8 categories: Strength, Range of Motion, Palpation, Reflexes, Special Test, Sensation, examHeader, and Inspection. For instance, in text with 20-30 sentences, 1 or 2 might be categorized as “Reflexes” and others as “Inspection.”
Confidentiality and lack of data were initial roadblocks, and the organization thought the project was undoable due to lack of doctor-patient conversation audio training data.
Many organizations face similar challenges:
1. Uncertainty about how to leverage data:
Organizations have patient records and follow up data but are unsure how to use it to their advantage.
2. Limited knowledge of artificial intelligence:
Organizations looking to automate business processes may understand they need to use AI but don’t have the expertise on their team equipped to identify which problems are best suited to AI solutions.
3. Dealing with private data:
Hospitals may be open to providing companies with private data while others are not, and conversations between doctors and patients are confidential.