HEALTHCARE ANALYTICS COURSE

Meet Your Instructor & Course Overview

• Instructor introduction
• Course objectives & flow
• Q&A

Explore the growing significance of advanced analytics within the healthcare sector by placing data-driven insights at the core of healthcare innovation.

• The Triple Aim
• NHS data flows and the importance of data integration
• The importance of analytics in population health
• Important UK healthcare terminology
• Descriptive, diagnostic, predictive and prescriptive analytics

(Optional) Assignment #1: Excel Reflection

Reflect on your experience utilising Excel, considering its potential role in evaluating diverse healthcare data and analyses.

Gain insights into safeguarding sensitive health information by exploring the protective mechanisms of data governance, and understanding how healthcare fraud detection contributes to fostering a secure data environment.

• NHS data flows
• Privacy laws for health data: Appropriate applications
• Protective measures implemented by a data governance framework for healthcare data
• Detection mechanisms for healthcare fraud
• Interactive discussion: The importance of ethics and advanced analytics

(Optional) Assignment #2: Navigating Healthcare Practices

Explore the nuanced aspects of data flows within healthcare systems, the detection of healthcare fraud, and the ethical considerations that shape decision-making in healthcare through a quiz presented by your instructor.

Master the art of navigating diverse sources of healthcare data, while gaining a comprehensive understanding of their significance in informing critical decisions within the healthcare sector.

• Sources of healthcare data and lookups in the UK
• Popular NHS data sets and collections
• Incorporating relevant data into decision-making processes to drive informed choices
• Various tools used in data analytics
• Working with Excel

Assignment #3: Data transformation with Excel

Explore Excel functionalities for a deeper understanding of effective data set utilisation.

This workshop enables you to improve your data analysis skills by recognizing patterns, describing distributions, and engaging with diverse graphical representations, providing you with a comprehensive toolkit for effective analysis and decision-making.

• Common data types
• Patterns or distributions in statistics
• Describing distributions using numerical measures such as mean, median and standard deviation
• The role of confidence intervals and percentiles
• Considerations for graphical representations of data 

Assignment #4: Data Distribution Analysis

Embark on a revealing journey into UK health expectancy, unravelling trends, variations, and gender comparisons through a comprehensive data analysis. Delve into the significance of confidence intervals, offering a unique perspective on understanding health expectancies.

Master advanced Excel charting techniques and data presentation skills to visually communicate complex healthcare analytics.

• Optimising data for analyses: Cleaning data and managing data
• Considerations in using different chart types
• Merging and joining various sources of data
• Demo: Using formulas to calculate descriptive statistics and direct standardisation of metrics

Empower yourself with the knowledge and skills to determine the practical and significant distinctions between two means, strengthening your analytical capabilities for real-world applications.

• t-test
• Assumptions for the t-test
• t-test for two sample means
• t-test for paired mains
• Longitudinal data: t-test

Assignment #5: T-test Analysis

Embark on a dynamic exploration of regional disparities, statistical analysis, and hypothesis testing within the realm of public health data.

Acquire the ability to meaningfully determine variations between three or more means by building a strong foundation for sophisticated statistical comparisons in healthcare contexts.

• ANOVA
• How to create categories
• Stating hypotheses
• ANOVA for three or more groups
• Longitudinal data: ANOVA
• Demo: How to set up and execute ANOVA in Excel

Investigate the importance and meaningful connection between multiple variables to unravel underlying relationships.

• Relationships and patterns
• Scatterplots and correlations
• Introduction to Regression
• Demo: Tableau

Assignment #6: Data Patterns

Engage in a dynamic exploration of data patterns and relationships, unveiling insights into the intricate interplay between health factors. Delve into correlation analysis by visually inspecting the chosen variables for a profound understanding.

Implement regression theory on a dataset, showcasing the practical application of analytical techniques to derive meaningful insights.

• Single linear regression
• Multiple regression
• Discussion: Examine the applications of regression models, the strategic incorporation of independent variables, and the effective presentation of results

Assignment #7: Applying regression

Extend previous insights by conducting a simple linear regression analysis, quantifying and interpreting the relationship between two variables. Assess the statistical significance and compare findings with prior correlation analysis, enhancing comprehension of data dynamics.

Gain insight into the most effective sampling design for different contexts, emphasising its role within the broader framework of research design.

• Research driven by questions
• Sampling
• Different types of sampling designs
• Research design
• Demo: Engage in a simulated research scenario by providing different research questions and choosing an appropriate sampling design for each

In this class, you’ll gain expertise in navigating methods that are used to measure the quality of healthcare, thereby enhancing your understanding about their role in assessing and maintaining healthcare standards.

• Healthcare quality and value
• Measures, metrics and indicators
• The purpose and importance of Key Performance Indicators (KPIs)
• Achieving performance goals: Highlighting how healthcare organisations reach their objectives

Assignment #8: Quality improvements through data

Engage in a dynamic exploration of the importance of KPIs to uncover valuable insights into real-term growth data.

Enhance your ability to effectively narrate and communicate stories using data.

• Structuring effective data displays for improved comprehension
• Communicating information with specific stakeholders
• Case study: How Power BI is used to visualise healthcare data for the NHS
• Demo: PowerBI

(Optional) Assignment #9: Visualising Data

Discover real-life data visualisations, decipher their effectiveness, and elucidate the value it delivers to both the business and the stakeholders.

Gain insight into the role and significance of AI in healthcare.

• Defining artificial intelligence
• The benefits and limitations of AI solutions
• The role of generative AI in healthcare analytics
• Interactive discussion: What are some of the ethical considerations around the use of AI for data analytics in healthcare?

Navigate the healthcare analytics landscape, and gain career insights from industry experts.

• Career guidance
• Adapting to evolving job roles
• Industry insights from the guest speaker

Assignment #10: Final Project

Employ visualisation tools like Excel, Tableau, or Power BI to craft an impactful presentation centred on healthcare data. Offer a succinct interpretation of the insights from the visualisation, and showcase your prowess in healthcare analytics by revealing actionable insights that contribute to informed decision-making.