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
- 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
Module 1: Introduction: Personalized Treatment Plans and Course Overview
Highlights the Course Overview & Objectives
What is a personalized treatment plan
Gives a description of a personalized treatment plan. Introduces the 4-P's (Predictive, Preventative, Personalized, Participatory) for Personalized Treatment Plans
How to build a personalized treatment plan
Introduces the 4-Step process necessary to build a Personalized Patient Treatment Plan
How to Evaluate the effectiveness of a personalized treatment plan
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
Module 2: What is Predictive Analytics
What is Predictive Analytics?
A description of what is predictive analytics
Benefits of using Predictive Analytics for Personalized Treatment Plans?
Lists the benefits of using Predictive Analytics
How Personalized Treatment Plans can Leverage on Predictive Analytics
Describes 5 Ways (Risk Stratification, Workflow Automation, Readmission Prevention, Provider Attribution & Risk Adjustment, Financial Risk Calculation) Personalized Treatment Plans can leverage on Predictive Analytics
Goal of bringing Predictive Analytics to Healthcare
Description of 7 goals for bringing Predictive Analytics to Healthcare
Module 3: Predictive Analytics: Applications used for Personalized Treatment Plans
New Data Streams for Personalized Treatment Plans
The impact of new data streams for Personalized Treatment Plans
Module 4: Clinical Decision Support Systems
Decision Support Systems for Personalized Treatment Plans
Describes the important features of a Decision Support System
Types Of Decision Support Systems
Describes the 3 types of decision support systems
Risk Factors of Decision Support Systems
Describes 3 Risk Factors of Decision Support Systems
Module 5: Predictive Analytics becoming a Routine Part of Patient Care
Recommendation Engines for Personalized Treatment Plans
Introduces how Recommendation Engines may be used for Personalized Treatment Plans
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
Electronic Medical Records for Personalized Treatment Plans
Introduces how Electronic Medical Records may be used for Personalized Treatment Plans
What are the Benefits of Electronic Medical Records?
Highlights the Benefits of Electronic Medical Records for Personalized Treatment Plans
Introduces how Text Mining may be used for Personalized Treatment Plans
Module 7: Challenges facing the Healthcare Industry & Personalized Treatments
Health Information Exchange
Introduces how a Health Information Exchange may be used for Personalized Treatment Plans
Health Information Privacy & Security
Introduces the 8 Fundamental Principles of Fair Information Practice under the Health Information Privacy & Security Requirements
Predictive Analytics for Personalized Treatment Plans Closing Remarks
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.
A list of the sources of information for this course, Predictive Analytics for Personalized Treatment Plans.