MedTrace Logo

Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits

NCT06245694 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 50000

Last updated 2024-11-22

No results posted yet for this study

Summary

Future predictive modeling in emergency medicine will likely combine the use of a wide range of data points such as continuous documentation, monitoring using wearables, imaging, biomarkers, and real-time administrative data from all health care providers involved. Subsequent extensive data sets could feed advanced deep learning and neural network algorithms to accurately predict the risk of specific health conditions. Moreover, predictive analytics steers towards the development of clinical pathways that are adaptive and continuously updated, and in which healthcare decision-making is supported by sophisticated algorithms to provide the best course of action effectively and safely. The potential for predictive analytics to revolutionize many aspects of healthcare seems clear in the horizon. Information on the use in emergency medicine is scarce.

Aim of the study is to evaluate the performance of using routine-data to predict resource usage in emergency medicine using the commonly encountered symptom of acute neurologic deficit. As an outlook, this might serve as a prototype for other, similar projects using routine medical data for predictive analytics in emergency medicine.

Conditions

  • Artificial Intelligence
  • Resource Allocation
  • Neurologic Manifestations

Sponsors & Collaborators

  • Medical University of Vienna

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
120 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-01-01
Primary Completion
2025-01-01
Completion
2030-01-01

Countries

  • Austria

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06245694 on ClinicalTrials.gov