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Too Long Don’t Wait (tl;dw) is an innovative application that leverages electronic health record (EHR) timestamp data to provide real-time waiting time predictions for emergency rooms. By entering a zip code, users can access estimated wait times for nearby hospitals, dynamically adjusted according to the current patient load. The app not only informs users but also empowers them with crucial insights that could improve outcomes in critical health situations. With ambitions to broaden its reach throughout the San Francisco Bay Area, tl;dw is fueled by University of California San Francisco (UCSF) students' dedication to enhancing the efficiency of healthcare access.

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The tl;dw app is designed for patients seeking urgent care, enabling them to make informed decisions about where to receive treatment based on waiting times. It can also aid healthcare professionals in managing patient expectations and streamlining emergency department operations. The application is especially useful for non-critical emergencies where patients may have the flexibility to choose between multiple ERs.

The accuracy of tl;dw's waiting time predictions depends on the quality and timeliness of EHR data. It currently serves only San Francisco and Oakland in California, and its effectiveness can vary during peak times or unforeseen emergencies. Additionally, it does not account for the severity of incoming cases, which can affect wait times significantly.

As of now, data supporting tl;dw’s effectiveness has not been collected. The application is in the early stages of deployment, and its predictive model is based on computational methodologies that show promise.

The creators of tl;dw, a group of dedicated University of California San Francisco students, believe in the power of data-driven solutions to enhance healthcare delivery. Their primary aim with this app is to minimize wait times in emergency rooms, thereby potentially reducing patient complications and improving survival rates. They uphold the highest standards of ethical practice, with a strong focus on safeguarding patient confidentiality in their use of electronic health record (EHR) data.


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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D David Day

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