Update README.md

README.md

@@ -44,11 +44,10 @@ a significant amount of discomfort when creating and utilizing physician notes,
 #### Model
 We leveraged Google's Pegasus abstractive text summarization to generate summaries of the discharged transcriptions included in the MTSamples dataset. 
 This was later utilized to prompt the Transformer's Masked Language Modeling(MLM) functionality to train the model to generate meaningful text with better structure and organization than the original.
-Additionally, Data Engineers that work with patient electronic records consistently spend an excessive amount of time parsing through the unstructured discharge notes format to accomplish their tasks.
-The solution will be instrumental for agents who are not directly facing patients but hold back-end roles that are also of immense importance.
 
 #### Use Cases
-This model allows for the efficient summarization of complexly documented doctor notes. It provides instant access to insight with proper semantic cues in place.
+This model allows for the efficient summarization of complexly documented doctor notes. It provides instant access to insight with proper semantic cues in place. Additionally, Data Engineers that work with patient electronic records consistently spend an excessive amount of time parsing through the unstructured discharge notes format to accomplish their tasks.
+The solution will be instrumental for agents who are not directly facing patients but hold back-end roles that are also of immense importance.
 
 #### Limitations & Future Aspirations
 With an increased amount of data, more deliberate results might be achieved through more training. Synthetic transcriptions could be created with GPT models to in turn train on. Also, further improvements on the model's summarization capabilities have been considered.