Introduction to Healthcare Analytics PDF

Introduction to Healthcare Analytics Agenda Introduction Syllabus Walk-through Introduction to Healthcare Analytics Terminology & Concepts 2 Introduction – About You Would love to learn about you!! Please complete the survey on Canvas by next week as it will help us provide you help, and guidance ta...

Introduction to Healthcare Analytics Agenda Introduction Syllabus Walk-through Introduction to Healthcare Analytics Terminology & Concepts 2 Introduction – About You Would love to learn about you!! Please complete the survey on Canvas by next week as it will help us provide you help, and guidance tailored with regards to your skills and experience. 3 Introduction – Shakeel Khan Adjunct Faculty - Mercer University Healthcare Analytics Visual Reporting & Communication Marketing & Social Media Analytics - Emory University, Continuing Education Business Intelligence - MS Excel & Power BI 5+ yrs. in teaching data analysis & visualization Sr. Director, Product Management - Health Catalyst 15 + yrs. in Healthcare IT Product & Marketing Management 4 Syllabus – Key Topics Introduction to healthcare data analytics o Terminology & Concepts o Healthcare data sources o Basic analytics o Advanced analytics o Applications and practical systems Applications, current and future state o Provider Analytics o Payer Analytics o Life Sciences Analytics o Patient Analytics Business value of healthcare analytics o Business Challenges o Value Life Cycle o Healthcare Analytics Value Framework o Key Performance Indicators (KPIs) o BI Performance Benchmarks o BI Competency Centers o Business Performance-Based Approach Security, privacy, and risk analytics o Security and privacy of data o Risk analytics o Acceptable use o Best practices 5 Syllabus – Key Topics Health & healthcare data visualization o Importance o Best practices o Visualization Frameworks Compelling data displays o Dashboards o Reports o Multidimensional Exploratory Displays (MEDs ) Tools o Excel o Power BI o Tableau 6 Learning Objectives Introduction to healthcare data analytics o Terminology & Concepts: Transforming Healthcare through Data Analytics o Healthcare data sources o Basic analytics o Advanced analytics o Applications and practical systems 7 "We have to use technology to open up access to health care. We have to use it to empower patients, and we have to use it to improve the quality of care." 8 "We have to use technology to open up access to health care. We have to use it to empower patients, and we have to use it to improve the quality of care." President Barack Obama 9 Terminology & Concepts Transforming Healthcare through Data Analytics References: Healthcare Data Analytics, Chandan K. Reddy, Charu C. Aggarwal 10 What is Healthcare Analytics? Healthcare data analytics is the process of using data to improve healthcare outcomes and efficiency. In this session, we will discuss the benefits of healthcare data analytics, common data sources, and the tools and techniques used for analysis. Source: Science Soft 11 Benefits of Healthcare Analytics Improve Reduce Increase Improve patient outcomes Reduce costs Increase efficiency Analytics can identify inefficiencies in healthcare processes and help reduce costs Analytics can help healthcare organizations optimize their processes and increase efficiency Analytics can identify patterns in patient data that can help improve patient care and outcomes 12 Common Data Sources Electronic Health Records (EHRs): EHRs contain a patient's medical history, diagnoses, and treatments. Claims Data: Claims data contains information about the services a patient has received and how much the services cost. Patient-generated data: Data collected by patients themselves, such as wearable device data, can be used to monitor health status. 13 Tools and Techniques Descriptive & Diagnostics Analytics: Descriptive analytics is used to understand what has happened in the past. Diagnostics is to gain insights. Predictive Analytics: Predictive analytics is used to forecast what might happen in the future. Prescriptive Analytics: Prescriptive analytics is used to recommend actions to achieve specific outcomes. Machine Learning/Cognitive Analytics: Machine learning is a subset of artificial intelligence that uses algorithms to identify patterns in data. 14 Examples of Healthcare Data Analytics Applications Identifying Predicting Evaluating Identifying high-risk patients Predicting readmissions Analytics can be used to identify patients who are at risk of developing certain conditions Analytics can be used to predict which patients are at risk of being readmitted to the hospital Evaluating treatment effectiveness Analytics can be used to evaluate the effectiveness of different treatments for specific conditions 15 Conclusion Healthcare data analytics is a powerful tool for improving patient outcomes, reducing costs, and increasing efficiency. With the right tools and techniques, healthcare organizations can use data to make more informed decisions and improve the quality of care they provide. 16 Assignments 17 Exercises Surveys In Quizzes section: Due: as indicated in Canvas 18 Readings Textbook Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal Chapter: 1.1 1.2 Pages : 1-6 19 Software installation Install Microsoft Excel using Office 365 (64-bit) app setup Install Tableau Desktop and Prep 20