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Instructor

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Dr. Carol Hargreaves

Dr. Carol Hargreaves, is the founder of Business Data Analytics Solutions Pvt. Ltd. Born leader with a passion for solving business problems using analytics & machine learning techniques, she has built several data driven systems that deliver organic revenue growth and enable decision making within businesses. Her solutions allow business processes to become smarter and faster while keeping customers engaged & delighted Dr. Hargreaves holds a PhD in Statistics and an MBA from the University of Wales (Cardiff). She was awarded the Foundation of Research and Development Scholarship in 1988. As an Analytics and Business Intelligence professional with over 27 years of work experience, she has held leading roles in the pharmaceutical, healthcare, fast moving consumer goods industry & the education Industry. An excellent Analytics Instructor for solving hands-on real world business problems. Dr. Hargreaves has worked with a variety of leading companies like Pfizer, Novartis, Merck Sharp & Dohme, Nestle, MasterFoods, Goodman Fielder, Foxtel, Aztec (IRI), Cegedim Strategic Data (Quintiles-IMS), National Health & Medical Research Council, University of Sydney and National University of Singapore.

Predictive Analytics for Personalized Treatment Plans

Instructor: Dr. Carol Hargreaves

'Prescribing the Right Treatment to the Right Patient at the Right Time'

  • Learn benefits and challenges of Personalized Treatment Plans.
  • It is also introduces how 'Predictive Analytics', 'Recommendation Engines' and 'Text Mining' may be used for building effective Personalized Treatment Plans.
  • Instructor is an Analytics and Business Intelligence Professional with over 27 years of experience and has worked with a variety of leading companies like Pfizer, Novartis and Merck to make businesses more intelligent.

Course Description

The key objective of this course is to consider new ways for the diagnosis of patient treatment since not all patients respond to a drug in the same way and a one-size-fits-all approach to patient treatment may have very little effect or serious side-effects for patients. With new and advanced technology there is now a move towards using a data driven approach for diagnosing patient treatment. This course covers the benefits and challenges of Personalized Treatment Plans. It is also introduces how 'Predictive Analytics', 'Recommendation Engines' and 'Text Mining' may be used for building effective Personalized Treatment Plans. This course concludes with the Healthcare Information Privacy and Security Requirements for Personalized Patient Treatments.

What am I going to get from this course?
  • Build a Personalized Treatment Plan using a 4-Step Process
  • Evaluate the Effectiveness of a Personalized Treatment Plan by computing the percent effectiveness for Personalized Treatments versus Standard Treatments
  • Understand the Use and Benefits of:
    • Electronic Medical Records
    • Decision Support Systems
    • Predictive Analytics
    • Recommendation Engines
    • Text Mining
          for Personalized Treatment Plans

Prerequisites and Target Audience

What will students need to know or do before starting this course?
This course is an introductory level course for all levels of students (beginner, intermediate, expert). The aim of the course is to introduce different concepts and theory that can enhance the diagnosis of patient treatment from standard treatment to personalized patient treatments. There are no pre-requisites for this course.
Who should take this course? Who should not?
This course is for:
 
  •  Healthcare Professionals 
    • Medical Practitioners
    • Doctors
    • Surgeons
    • Oncologists
    • Nurses
    • Pharmacists
    • Paediatricians, etc
  • Government Healthcare Policy Makers
  • Clinic/Hospital Operations Management Staff
who want to understand how different techniques ( e.g. Predictive Analytics, Recommendation Engines, Text Mining, etc) can be used for Personalized Treatment Plans

Curriculum

Module 1: Introduction: Personalized Treatment Plans and Course Overview
Lecture 1 Course Overview
01:34

Highlights the Course Overview & Objectives

Lecture 2 What is a personalized treatment plan
07:41

Gives a description of a personalized treatment plan. Introduces the 4-P's (Predictive, Preventative, Personalized, Participatory) for Personalized Treatment Plans

Lecture 3 How to build a personalized treatment plan
03:34

Introduces the 4-Step process necessary to build a Personalized Patient Treatment Plan

Lecture 4 How to Evaluate the effectiveness of a personalized treatment plan
02:17

Lists the 3-Steps for evaluating the effectiveness of a personalized treatment plan. Gives the formula to compute 'Percent Effectiveness' for Standard Treatment versus Personalized Treatment

Quiz 1 Module 1 Quiz

Module 2: What is Predictive Analytics
Lecture 5 What is Predictive Analytics?
04:34

A description of what is predictive analytics

Lecture 6 Benefits of using Predictive Analytics for Personalized Treatment Plans?
02:26

Lists the benefits of using Predictive Analytics

Lecture 7 How Personalized Treatment Plans can Leverage on Predictive Analytics
03:45

Describes 5 Ways (Risk Stratification, Workflow Automation, Readmission Prevention, Provider Attribution & Risk Adjustment, Financial Risk Calculation) Personalized Treatment Plans can leverage on Predictive Analytics

Lecture 8 Goal of bringing Predictive Analytics to Healthcare
01:46

Description of 7 goals for bringing Predictive Analytics to Healthcare

Quiz 2 Module 2 Quiz

Module 3: Predictive Analytics: Applications used for Personalized Treatment Plans
Lecture 9 New Data Streams for Personalized Treatment Plans
02:20

The impact of new data streams for Personalized Treatment Plans

Quiz 3 Module 3 Quiz
Module 4: Clinical Decision Support Systems
Lecture 10 Decision Support Systems for Personalized Treatment Plans
02:25

Describes the important features of a Decision Support System

Lecture 11 Types Of Decision Support Systems
04:29

Describes the 3 types of decision support systems

Lecture 12 Risk Factors of Decision Support Systems
02:31

Describes 3 Risk Factors of Decision Support Systems

Quiz 4 Module 4 Quiz
Module 5: Predictive Analytics becoming a Routine Part of Patient Care
Lecture 13 Recommendation Engines for Personalized Treatment Plans
05:23

Introduces how Recommendation Engines may be used for Personalized Treatment Plans

Quiz 5 Module 5 Quiz

Question 2: Is the statement below TRUE or FALSE Physicians use the recommended treatment to reaffirm their own approach so that the patient is given the treatment that is the most likely effective treatment for him. (TRUE) Question 3: Is the statement below TRUE or FALSE The Recommender System helps both the patient and physician by reducing the number of physician visits and independently managing the patient’s diabetes with the correct diet (TRUE) Question 4: Is the statement below TRUE or FALSE Recommendation Engines cannot be built using Predictive Modelling. (FALSE)

Module 6: Electronic Medical Records for Personalized Treatment Plans
Lecture 14 Electronic Medical Records for Personalized Treatment Plans
02:59

Introduces how Electronic Medical Records may be used for Personalized Treatment Plans

Lecture 15 What are the Benefits of Electronic Medical Records?
01:43

Highlights the Benefits of Electronic Medical Records for Personalized Treatment Plans

Lecture 16 Text Mining
03:00

Introduces how Text Mining may be used for Personalized Treatment Plans

Quiz 6 Module 6 Quiz
Module 7: Challenges facing the Healthcare Industry & Personalized Treatments
Lecture 17 Health Information Exchange
04:04

Introduces how a Health Information Exchange may be used for Personalized Treatment Plans

Lecture 18 Health Information Privacy & Security
03:32

Introduces the 8 Fundamental Principles of Fair Information Practice under the Health Information Privacy & Security Requirements

Lecture 19 Predictive Analytics for Personalized Treatment Plans Closing Remarks
02:17

A summary on the benefits and challenges of Personalized Treatment Plans. Highlights the 7 key requirements (Electronic Medical Records, Decision Support System, Predictive Analytics, Recommendation Engine, Text Mining, Health Information Exchange, Health Information Privacy & Security) for an effective Personalized Treatment Plan.

Lecture 20 References
00:34

A list of the sources of information for this course, Predictive Analytics for Personalized Treatment Plans.

Quiz 7 Module 7 Quiz

Reviews

4 Reviews

Empty user
neha M

August, 2017

Great learning experience and great course for any analytics professional looking to understand recommendation engines and text mining and use them for building effective personalized treatment plans.

Empty user
julian W

August, 2017

A very good course for healthcare professionals. As a working healthcare professional I hardly get time to update myself outside of work. But his course provided me a good opportunity to learn predictive analytics.

Empty user
Chandana D

August, 2017

The instructor has masterfully communicated the data and thoroughly crafted the course materials, and the course is both easy to follow and enjoyable. You learn benefits and challenges of personalized treatment plans. The content is also very relevant to practical industry use.

Empty user
rob C

August, 2017

An intense course but you will be confident in using predictive analytics for the diagnosis of patient treatment than ever before. The professor has made the course interesting and knowledge rich. The course shows lots of practical advice and tips for how to carry a predictive analytics project from beginning to end!

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