Explore

PAWHM2020

Predictive Analytics World Healthcare Munich - Virtual Edition 2020

Virtual Event, Germany
11 - 12 May 2020
The conference ended on 12 May 2020

Important Dates

Abstract Submission Deadline
10th May 2020

About PAWHM2020

Improve patient care with predictive analytics at Predictive Analytics World Healthcare - Virtual Edition!

Topics

Health and safety, Engineering and technology

Call for Papers

The PAW Healthcare program will feature sessions and case studies across Healthcare Business Operations and Clinical applications so you can witness how data science and machine learning are employed at leading enterprises and resulting in improved outcomes, lower costs, and higher patient satisfaction.

Predictive analytics addresses today's pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions:

Personalized medicine. 

Per-patient prediction and analytically enhanced diagnosis drives individual clinical treatment decisions

Insurance. 

Predictively guided decisioning combats risk and renders insurance more equitable and profitable

Hospital administration. 

Analytics detects and recoups loss due to fraud and waste

Healthcare marketing.  

From medical suppliers to healthcare screening service providers, the performance of industry enterprises hinges on analytically targeted marketing

Drug development.  

Analytics advances pharmaceutical engineering, testing, and other processes

Much more. 

Other applications include predicting per-patient disease progression, mortality risk, availability of clinical trial participants, consumer prescription adherence, and more

Prices: 

Predictive Analytics World for Healthcare Ticket (Livestream ONLY): EUR 500.00, 

Predictive Analytics World for Healthcare Ticket (Livestream AND Recordings): EUR 750.00

Time: 09:00 - 18:00

Keep Up to Date with PaperCrowd

Sign up and follow your favorite conferences.

We are no longer accepting conference submissions on PaperCrowd. We apologise for any inconvenience.