Engaging in Improving Patient Experience Through Analytics
Master data-driven strategies to enhance patient experience in healthcare. Learn predictive analytics and decision support systems.
Master data-driven strategies to enhance patient experience in healthcare. Learn predictive analytics and decision support systems.
This course, the second part of a two-part series, focuses on improving patient experience and reducing pain points in healthcare processes through analytic and decision support frameworks. Students learn to use data for effective decision support, make predictions for individual patient outcomes, and develop recommendations based on these predictions. The course covers experience-oriented predictions, outreach strategies, operational considerations for implementing decision support innovations, and building business cases for experience management solutions. Throughout the course, participants apply concepts to operational improvements in their own organizations, gaining practical skills in healthcare analytics and decision support.
Instructors:
English
Tiếng Việt
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What you'll learn
Master techniques for predicting individual patient outcomes in healthcare
Understand and analyze CAHPS survey data for patient satisfaction insights
Develop strategies for optimizing outreach campaigns in healthcare
Learn to integrate decision support innovations into existing healthcare workflows
Understand operational challenges in implementing analytics-driven solutions
Develop skills in building business cases for experience management initiatives
Skills you'll gain
This course includes:
46 Minutes PreRecorded video
5 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive exploration of using analytics to improve patient experience in healthcare settings. It is structured into four modules, each focusing on critical aspects of data-driven decision-making in healthcare. The first module covers predicting outcomes, including methods for making predictions about individual patient outcomes and analyzing CAHPS survey data. The second module focuses on making recommendations based on predictions, exploring outreach strategies and resource allocation for interventions. The third module addresses operational considerations for implementing decision support innovations, including workflow and ecosystem integration. The final module covers building a business case for experience management decision support, examining stakeholder impact and financial considerations. Throughout the course, students engage with real-world healthcare scenarios and apply concepts to their own organizational contexts.
Predicting Outcomes
Module 1 · 2 Hours to complete
Whole Picture Recommendations
Module 2 · 3 Hours to complete
Operational Considerations
Module 3 · 5 Hours to complete
Business Case Considerations
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Innovating Healthcare Through Machine Learning
Craig is a thought leader, innovator, and entrepreneur who serves as part-time faculty in the Healthcare Informatics graduate program, concentrating on the business applications of machine learning and decision support in healthcare. His career began as a data scientist at NASA’s Jet Propulsion Laboratory, where he developed a communication planning application for deep-space missions, including Pioneer, Voyager, and Galileo. This expertise has allowed him to effectively translate complex technological solutions to healthcare challenges, focusing on prediction, scheduling, planning, resource allocation, and campaign management. Currently, he is the co-founder and Chief Science/Technology Officer at Decision Point Healthcare and also the founder and principal consultant at Synthesis Health Systems.
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Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.