background and about section updates

pages/about.mdx

@@ -3,7 +3,7 @@
 This is the capstone project for the MIDS program at UC Berkeley. The goal of this project is to develop a machine learning model
 that can predict the likelihood of a patient having a certain disease based on various textual, tabular, and visual data.
 
-In medical diagnostics, healthcare professionals encounter significant challenges due to the fragmented and varied formats of patient data. These issues often lead to longer times for diagnosing, higher healthcare costs, and treatment delays, which collectively heighten risks to patients.
+In emergency care, healthcare workers face significant challenges due to the fragmented and varied formats of patient data. Specifically, physicians struggle with the limited access to the complex, interwoven relationships between prior doctors' notes, current vitals, and chest X-ray images. These data integration issues often result in extended wait times for diagnosis, increased healthcare costs, and delays in treatment, all of which collectively heighten patient risks.
 
 ### Untapped Potential of EHR Systems
 Despite the detailed patient profiles created by Electronic Health Record (EHR) systems, which include diagnostic results, radiology studies, and clinical notes, the full potential of these records for personalized patient care remains largely untapped. Current AI diagnostic tools are unable to fully leverage the diverse data types available, leading to significant gaps in the ability of healthcare providers to analyze and understand the complexities of healthcare data. This oversight impedes the advancement of personalized medicine and comprehensive patient care.
@@ -17,6 +17,11 @@ As a pioneer in multimodal medical diagnostics, MedFusion Analytics accurately p
 
 ## Team
 
-Steven Chang, Carolyn Dunlap, Lee Gary, Cinthya Rosales, Adam Saleh, Esteban Valenzuela
+If you are interested in learning more about this project, feel free to contact us at:
 
-If you are interested in learning more about this project, feel free to contact us at [email protected]
+- [Steven Chang](mailto:[email protected])
+- [Carolyn Dunlap](mailto:[email protected])
+- [Cynthia Rosales](mailto:[email protected])
+- [Lee Gary](mailto:[email protected])
+- [Adam Saleh](mailto:[email protected])
+- [Esteban Valenzuela](mailto:[email protected])

pages/docs/background.mdx

@@ -4,7 +4,7 @@ title: Background
 
 # Background
 
-In medical diagnostics, healthcare professionals encounter significant challenges due to the fragmented and varied formats of patient data. These issues often lead to longer times for diagnosing, higher healthcare costs, and treatment delays, all of which increase risks for patients.
+In emergency care, healthcare workers face significant challenges due to the fragmented and varied formats of patient data. Specifically, physicians struggle with the limited access to the complex, interwoven relationships between prior doctors' notes, current vitals, and chest X-ray images. These data integration issues often result in extended wait times for diagnosis, increased healthcare costs, and delays in treatment, all of which collectively heighten patient risks.
 
 ### Untapped Potential of EHR Systems