courses - kurtosis courses for analysts 2013-14.pdf · format bar charts (horizontal and vertical)...

Courses Training solutions for NHS information analysts in 2013-14

information training for the NHS

Kurtosis provides training courses, seminars and events for clinicians, managers and information professionals in the NHS. We specialise in the field of information analysis and management. We believe that the quality of NHS decision-making can be improved by removing the barriers to effective communication in the dialogue between information specialists and decision-makers. Kurtosis: 99 Giles Street Edinburgh EH6 6BZ Tel 0131 555 5300 Email [email protected] | Website www.kurtosis.co.uk Course content © Kurtosis 2013 All cartoons © New Yorker and Cartoonbank.com. All Rights Reserved.

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Contents Training courses for information analysts

3 Introduction

4 Booking Information

5 The Main Stream

6 Visualizing Data

7 The Principles of Information Design

8 Arguing With Numbers

9 The Work Stream

10 Will there be beds for me and all who seek..?

11 Flow_ology

12 How to read a waiting list

13 The Stats Stream

14 Health Service SPC

15 Demystifying Statistics

16 Demystifying Confidence Intervals

17 About Kurtosis

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Courses A suite of nine training courses that help NHS analysts turn data into useful information

More than ever, managers and clinicians in the NHS need data to help them understand and solve their problems.

But it’s not as simple as just handing over the keys of a data warehouse to a team of analysts and then letting them loose. There are specific skills needed if you want to analyse and present NHS data effectively.

It is these specific skills that are addressed by the Kurtosis course portfolio.

The analyst portfolio divides into three streams:

1. The Main Stream courses deal with the basic skills: how to analyse data in general, how to design tables and charts effectively, and how to write and talk about data.

2. The Work Stream courses deal with specific area of data analysis: hospital beds, unscheduled care flows and elective waiting times.

3. The Stats Stream courses show how to usefully apply basic statistical techniques to NHS data: statistical process control,

mainstream statistics and confidence intervals.

The course portfolio presented here is a continually developing entity. The course content is the product of more than ten years of working with teams of NHS data analysts in England, Scotland and Wales.

All of the courses offered here are informal, participative and hands-on. And they all use real NHS examples.

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Booking Information This is what you need to know if you want to book one of these courses

Where are these courses delivered? Any of the courses in this brochure can be delivered on-site, on your premises, at a time convenient to you. All you need to provide in terms of space is a meeting room environment (as opposed to an IT training room, although we can also use those) and enough space for each participant and a laptop computer.

When? You can schedule on-site courses at a time to suit you and your team. Email [email protected] or phone 0131 555 5300 to arrange suitable dates.

How many? On-site courses work best for groups of between 6-12 people. We can bring eight laptops with us, so if you want to train more than eight people in one session, you will need to supply laptops for the extra participants.

What software skills are needed? All of the courses use Microsoft Office. As long as you know your way around Excel to just below intermediate level, you will be fine.

What is the style of the training? The courses are informal. And the emphasis is on discussion and participation. All of the courses involve a lot of hands-on exercises (either done together as a group, individually or in pairs).

Who delivers the training? All the training courses are delivered by Neil Pettinger. He was an NHS information manager for twenty years who reinvented himself ten years ago as a freelance training consultant.

How much do courses cost? Each one-day course costs £1,100+VAT. All expenses included. Discounts are offered for block bookings. Contact us for details.

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The Main Stream Three one-day training courses that address the basic data analyst skillset

The Main Stream is a set of three courses for analysts that address the challenge of (a) being able to analyse and visualize data in imaginative and relevant ways; (b) being able to design information so that it gets its message across effectively and (c) be able to describe verbally and in writing the findings and meaning of the data to a lay audience.

Visualizing Data Connect basic statistical techniques to the NHS workplace

The Principles of Information Design How to create tables and charts that communicate with clarity

Arguing With Numbers Describing data, constructing narratives, building an argument

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Visualizing Data Connect basic statistical techniques to the NHS workplace

Visualizing Data is a one-day course that shows analysts how to steal techniques and tools from the basic statistics syllabus that will help them present their data in ways that have meaning, relevance and resonance for NHS managers and clinicians.

Session 1

Distributions

A lot of things we measure are not very well described or explained if we just quote averages or maximums. So it often makes sense to use distributions as a way of visualizing them. Length of stay and waiting times are good examples of data that are suited to being visualized as distributions.

The Distributions session shows you:

how to draw histograms using pivot tables and the FREQUENCY array function,

how to draw boxplots,

how to draw cumulative frequency polygons

how to superimpose quantiles over the top of a distribution

Session 2

Trends

We can be lazy about the way we report the way things change over time. For example, we tend to plot time series data without worrying too much about whether daily, weekly or monthly is the most appropriate way to do it.

The Trends session shows you:

how to think systematically about whether to plot data hourly, daily, weekly, monthly

how to plot quantiles over time

how to think creatively about showing how a process moves through time

how to draw run charts and control charts

In this session we also look at ways other than line graphs of visualizing data that tracks movement over time.

Session 3

Margins of Error

We sometimes have to demonstrate the uncertainty of our data. We need to be able to visualize uncertainty in ways that make the understanding of it easier not harder

The Margins of Error session shows you:

how to draw confidence interval charts (comparing variable over time and over space)

how to draw confidence interval charts that demonstrate significant difference

how to draw funnel plots

Because of the greater complexity (and the risk of jargon), we pay a great deal of attention in this session to the way you go about explaining your visualizations.

Session 4

Relationships

Being ability to visualize relationships between variables is a first step on the way to being able to do cause-and-effect analysis. You need to be able to draw scatterplots and bubbleplots.

The Relationships session shows you:

how to draw basic scatterplots with two data series

how to draw scatterplots with multiple series

how to draw bubbleplots

Correlation is not causation but unless you can visualize what correlation looks like you will find it difficult to explore possible cause-and-effect relationships.

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The Principles of Information Design How to create tables and charts that communicate with clarity

This course shows how to align the way you present your data with the reasons for presenting the data. It teaches evidence-based information design principles and provides you with a toolkit of practical techniques that can be employed straightaway to communicate data more effectively and professionally.

Session 1

Theory and principles

Information design isn’t about formatting preferences; instead it’s about applying evidence-based principles to the way we present data.

The Theory and Principles session introduces the main sources of principles on the course:

Andrew Ehrenberg’s rules from The Problem of Numeracy

Edward Tufte’s grand principles of information design

Robin Williams’s principles of graphic design

Tim Brown’s approach to design thinking

These theoretical sources underpin the rest of the course’s content.

Session 2

Tables

Summary tables are the most basic form we use to output our data. We explore the principles behind good table design.

The Tables session shows you how to:

Order your data into rows and columns

Format numbers appropriately

Decide on what should be columns and what should be rows

Summarize into subtotals and totals

Use layout formatting principles (font size, colour, gridlines etc.) optimally

Session 3

Charts

The first thing we address in this session is the question of how to choose between a table and chart as your method of presenting your data.

Then the Charts session shows you how to:

Format line charts appropriately

Format bar charts (horizontal and vertical) appropriately

Format pie charts appropriately

Format scatterplots and bubbleplots appropriately

We spend time at the end of this session discussing other “non-standard” chart types that can also be used in NHS contexts.

Session 4

PowerPoint

Presenting charts as a part of a slideshow brings with it a special set of design principles.

In the PowerPoint session we show you how to:

Use design principles to enable clear step-by-step explanations of processes

Use juxtaposition to make your point clearer

Use superimposition to create multiple layers of charts on slides

Use PowerPoint’s sequence functionality to explore data

This is a session on PowerPoint that is a bullet-point free zone. It’s all about using PowerPoint to draw attention to visual effects that enhance the clarity of the data message.

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Arguing With Numbers Describing data; constructing narratives; building an argument

Arguing With Numbers is a one-day course that teaches how to write and speak about data. It covers the techniques needed for short, informal, objective presentations and documents as well as the skills needed to present data supporting lengthier, more complex arguments.

Session 1

Micro Theory and Examples

The first thing we teach is the “micro nitty-gritty” of how to “top and tail” data. Even when you’re just presenting one table or one chart, you need to know how to present it—using the written or spoken word—so that everybody knows why you’re presenting it and understands what the data’s saying.

This session shows you:

how to title data exhibits properly

how to set the context before you reveal your evidence

how to help the reader make sense of the data

how to summarise the data

This session is about integrating words and numbers seamlessly, about understanding when you do or do not need data exhibits.

Session 2

Shape, Structure and Narrative

Even short data reports and presentations need to contain an element of structure. But longer ones need it absolutely.


how to give narrative shape to the order in which you present your data

how to generate flow within a presentation

how to apply a pyramid structure to your argument

how to write introductions

We also use this session to introduce you to the narrative spectrum for data reports and presentations.

Session 3

Presenting Exploratory Findings

These are scenarios where we are exploring an issue with the data, without necessarily having a clear idea of where it is leading us. And yet we still have to apply order and logic to the way we present it.

We cover three scenarios:

Exploring a problematic issue by setting out some relevant data

Setting out a deductive argument that allows others to criticise and amend your logic

Examining a conventional explanation (or explanations) for something and critiquing that explanation (or explanations).

Session 4

Presenting Settled Findings

In this session we cover arguments at the other, tighter, more "settled findings", end of the spectrum. We cover two scenarios:

Setting out several options, any one of which will solve the problem

Setting out the one-and-only solution to a problem

Finally, the “action” problem. The reason you “argue with numbers” is that you want to influence your audience. You are seeking to prompt a new course of action, a change of behaviour. This means understanding how people react to data when it challenges their normal way of doing things. We show you how to answer the common criticisms made by audiences when confronted by data-driven arguments.

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The Work Stream Three one-day training courses that address specific NHS issues and problems

The Work Stream is a set of three courses for analysts that address the data requirements of three important issues facing the NHS: bed numbers and bed occupancy, unscheduled care pathways and elective waiting times. The emphasis with all three of these courses is on (a) identifying the data that is needed to help managers and clinicians make sense of the problems, and (b) identifying ways of analysing and presenting the data to make it an effective problem-solving tool.

Will there be beds for me and all who seek? Making acute hospital bed use visible

Flow_ology Visualizing and interpreting unscheduled care data

How to read a waiting list Using Microsoft Excel to measure, monitor and reduce NHS waiting times

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Will there be beds for me and all who seek? Making acute hospital bed use visible

This one-day course shows what data is needed to inform decisions about bed allocations. It teaches ways of analysing and presenting the data so that everybody gets a clear and accurate picture of how beds are being used. It shows how to connect bed data to wider, whole-system indicators; it shows how to investigate the cause-and-effect relationships that affect bed usage; and it shows how to model changes in activity levels and length of stay using techniques that have resonance for the managers and clinicians who need to see the results of such modelling.

Session 1

First, take your data

We start by teaching how to construct occupancy snapshot summary tables that show how many beds were occupied each hour and each day. We show ways of doing this on large datasets, combining both Microsoft Access and Excel.

This session shows you how to calculate:

Bed occupancy by day

Bed occupancy at different times of day

Bed occupancy for individual wards and specialties

The technical skills and knowledge from this session will allow you to describe bed usage accurately and meaningfully to managers and clinicians.

Session 2

Making the connections

It is vital when monitoring hospital bed usage that we communicate to managers the relationship between three key indicators: activity, length of stay and occupancy.


How to make clear the way the indicators are linked together

The best ways of monitoring each key indicator individually

This sessions also introduces the need to be able to analyse and present your data in ways that show the connections in the patient journey (e.g. between bed occupancy in Assessment and bed occupancy in specialty wards).

Session 3

Occupancy case studies

Analysts need to become familiar with what different levels of bed occupancy look like, and why they differ according to size, specialty etc.

The Occupancy case studies session shows you:

How does length of stay affect bed occupancy

How does the size of a bed complement affect bed occupancy?

How does the variability of demand affect bed occupancy?

Having an inventory of case studies will help you understand your own local bed allocations.

Session 4

What if..?

One of the most important objectives of data analysis in relation to hospital beds is that we have to be able to model different possible scenarios.

The What if..? session shows you three examples:

What if day case rates for selected surgical procedures?

What if more patients were discharged earlier in the day?

What if length of stay were reduced by x days?

We show you how to carry out these modelling exercises in ways that managers and clinicians will find clear and transparent.

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Flow_ology Using data to make sense of unscheduled care

Flow_ology proposes ways of identifying the right data for measuring, describing and monitoring unscheduled care processes. It encourages you to adopt a whole system approach to the problem by presenting data in ways that enable managers and clinicians to “see the connections” between the different parts of the system.

Session 1

The geography and arithmetic of unscheduled care

One of the biggest obstacles preventing managers and clinicians from making sense of unscheduled care data is that they can’t situate each piece of data into a framework.


How to create a meaningful template within which to situate your data

How to identify meaningful staging posts in the unscheduled care pathway

How to work out what indicators you need for each staging post

We finish this session by discussing how to match data definitions to the elements of the grid.

Session 2

Populating the unscheduled care system grid

We can be lazy about the way we report the way things change over time. We plot an average figure, we don’t worry too much about whether it’s a week, a month, whatever.


How to populate the grid using static numbers

How to populate the grid using time series data

How to populate the grid using distributions

In this session we also discuss the different media you can use in order to make clear these ways of visualizing the core indicators.

Session 3

Identifying and measuring symptoms of dysfunction

As well as being able to measure and describe the unscheduled care process using the core indicators, you also have to be able to describe what it looks like when things go wrong.


How to calculate outlier (boarding) levels accurately

How to relate bed occupancy to A&E four-hour target performance

How to relate bed occupancy to delayed discharge data

We also discuss how you might go about identifying the specific symptoms of dysfunction that exist within your own local unscheduled care system.

Session 4

Exploring the relationships within the system

The important thing about displaying data depicting unscheduled flow is that you do it in such a way that it encourages managers and clinicians to understand the relationships between the different staging-posts in the system.


How to use scatterplots to communicate these relationships

How to explore the vertical relationships in the grid

How to explore the horizontal relationships in the grid

Finally, we spend more time exploring the different ways you can use to present and publish unscheduled data in ways that engage the stakeholders.

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How to read a waiting list Using Microsoft Excel to measure, monitor and reduce NHS waiting times

How to read a waiting list shows how to visualize elective services using Microsoft Excel. It shows how to use the spreadsheets to plan ahead for future operational realities and how to use them to model "what-if" scenarios: you will learn how to quantify what is needed in order to meet and maintain waiting times targets.

Session 1

The Numbers

Measuring and describing an elective service means you have to be able to analyse and present data on (a) queue length; (b) demand; (c) activity; (d) urgency and (e) capacity.

The Numbers session shows you:

How these numbers inter-relate with each other

How to create simple spreadsheets that demonstrate these relationships effectively to others

In this session we also discuss the different ways of measuring and monitoring waiting time performance.

Session 2

Focus on urgency

It is vitally important that we understand the impact that urgency (and its counterpart: “in-turn-ness”) has on waiting time performance


A variety of urgency scenarios taken from real NHS situations

Reasons why urgency and “in-turn-ness” can be the way they are

How to measure urgency and “in-turn-ness” using numbers

Although it is important to understand the barriers to perfect scheduling, it is equally important that we understand how some organisations manage to overcome these barriers.

Session 3

Gathering the data

It’s one thing understanding how it all works; it’s quite another to be able to take your own local data and populate Excel spreadsheets with them.


How to specify the data extracts you need

How to create an Excel spreadsheet that the data can be dropped into

How to teach others how to use these spreadsheets

We also cover some technical issues to do with availability and definitional issues to do with what counts and what doesn’t count as an elective service

Session 4

Bottom-up modelling

As well as being useful as a way of demonstrating the way that waiting lists currently work, these spreadsheets are also effective as means of helping managers work out what their activity and capacity levels ought to be.

The Bottom-up modelling session shows you:

How to do "What-if?" scenarios using actual 'historical' data as your starting-point

How to add in sophistication to your modelling

How to get your modelling up-to-date

By doing modelling such as this in Excel, it can be easier for managers to see the workings of the model.

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The Stats Stream Three one-day training courses that teach the statistical skills needed by NHS analysts

The Stats Stream is a set of three courses for analysts that show how we can use key elements of statistics to lend resonance and sophistication to our analytical technique.

Health Service SPC Applying and explaining statistical process control to the NHS

Demystifying Statistics Connecting key elements of statistics to the NHS workplace

Demystifying Confidence Intervals p ± (1.96 × SE) and all that

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Health Service SPC Applying and explaining statistical process control to the NHS

A one-day course that shows NHS analysts how to apply statistical process control (SPC) to NHS performance management and improvement situations. The course emphasises the need to be able explain SPC as well as teaching the know-how to enable you to calculate and draw the run charts and control charts.

Session 1

Variation and process

The first session of the course covers much of the theoretical grounding you need in order to understand how SPC measures and describes variation.


why variation matters and why we need to understand it

different types of variation,

different ways of measuring variation

why and where do we “draw the line”

We also explain the importance of understanding the meaning of the word “process” when applying SPC to healthcare settings.

Session 2

Run charts

We use run charts to track trends and time series. We use them when we want to know if a shift in a process has occurred, or if we are monitoring whether the demand for a service has changed over time.

The Run charts session shows you:

how to draw run charts

how to interpret run charts according to the rules and tests

how to understand the meaning of the run chart tests in relation to probability

how to explain run charts to lay audiences

Session 3

Control charts

The first session on control charts covers the techniques you need in order to plot continuous data (e.g. waiting times, length of stay).

The Control charts session shows you:

how to draw XmR charts

how to draw x-bar and s charts

As well as the specific examples used in the teaching, a wide range of examples from NHS scenarios are also introduced so that participants can see the relevance and application of SPC in a variety of health service contexts.

Session 4

More control charts

The second session on control charts covers the techniques you need in order to plot discrete data (e.g. counts of referrals, counts of incidents, compliance percentages).


how to draw p charts

how to draw funnel plots

how to draw c charts

how to draw u charts

The final sessions closes with a summary of the day’s learning.

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Demystifying Statistics Connecting key elements of the basic statistics syllabus to the NHS workplace

A one-day course that shows analysts how to apply basic statistical techniques to their work. Demystifying Statistics will suit information and performance analysts who want to learn—or who want to refresh their learning—about such knowledge can help shine a light on NHS problems and situations.

Session 1

Standard deviation and standard error

We begin with standard deviation, and what it means, and how you go about explaining it, and what its properties are, and why it is relevant to NHS data analysis.


How to explain standard deviation to a layperson

Calculating standard deviation the long and short ways in Microsoft Excel

Understanding the properties of standard deviation in relation to the normal distribution

We then move onto the more complicated concept of standard error, which underpins most of the techniques that we’ll need in Sessions 2 and 3 of this course.

Session 2

Confidence intervals for means and proportions

The second session begins with an explanation of how we calculate confidence intervals for sample estimates.

We then move onto the methods needed for calculating confidence intervals that describe differences between samples.


How to calculate confidence intervals for sample estimates with both parametric and non-parametric data

How to calculate confidence intervals that test for significant difference between samples

Throughout, we show how confidence intervals are relevant to everyday NHS performance management scenarios.

Session 3

Calculating P values

In session three we examine the convention of quoting P values for findings. What do P values mean?


How to understand the null hypothesis

How to choose a hypothesis test

How to calculate P values using the large sample normal test

How to quote and interpret P-values

Session 4

Correlation and regression

Being able to visualize relationships between variables is a first step on the way to being able to do cause-and-effect analysis. You need to be able to draw scatterplots and bubbleplots


How to draw basic scatterplots

How to calculate the coefficient of correlation (r)

How to interpret and explain the coefficient of correlation

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Demystifying Confidence Intervals p ± (1.96 × SE) and all that

A one-day course that shows NHS information analysts how to understand, calculate, apply, interpret and explain confidence intervals. Demystifying Confidence Intervals will suit those information and performance analysts who want to learn—or who want to refresh their learning—about how confidence intervals can be applied to NHS problems and situations.

Session 1

Theory and Basics

Most of the first session is devoted to building an understanding of some theoretical basics.

This session explains:

The difference between parametric and non-parametric data

Standard deviation and the Normal distribution

The t distribution and the binomial distribution

How to understand and explain standard error

This knowledge provides us with the grounding to move on to the next three sessions.

Session 2

Estimating from samples

One of the basic jobs that confidence intervals do is help us describe the precision of an estimated value obtained from a sample.


How to calculate confidence intervals around mean values from a sample

How to calculate confidence intervals around proportions from a sample

How to use newer, more accurate methods for calculating confidence intervals for proportions

Session 3

Statistically significant difference

The real power of confidence intervals can be said to reside in their ability to describe whether two averages or two proportions are significantly different.


How to draw confidence interval charts (comparing variable over time and over space)

How to draw confidence interval charts that demonstrate significant difference

Session 4

Small samples, Normality and logarithmic transformations

The final session deals with the complications caused by small samples and data that isn’t Normally distributed.


How to use the t distribution for small samples

How to test for the Normality of distributions

How to transform non-Normal data using a logarithmic transformation

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About Kurtosis “We have lots of information technology. We just don’t have any information.”

Four corporate types are meeting round a laptop-covered table. A fifth—the Head of Information, presumably—is standing up. He looks a bit stressed, a bit dishevelled, a bit guilty.

“We have lots of information technology. We just don’t have any

information.”

© Sydney Harris at CartoonBank.com 2002. All Rights Reserved.

You might say that Sydney Harris' New Yorker cartoon holds resonance for the NHS. Choked by cumbersome IT systems. Brim-full of managers complaining of 'information overload'. Awash with data. Yet somehow conspicuously failing to turn all that data into useful information.

"Too much data; not enough information" is a symptom of a disconnect between the decision-makers and the data-providers. The data-providers—developers, analysts, systems managers, eHealth people—understand about data but have a limited grasp of the decision-making world. The decision-makers, by contrast, understand the operational and strategic problems of the NHS all too well, but have a limited grasp of how data can help solve those problems.

Kurtosis addresses this disconnect between the grey suits and the geeks. We help managers and clinicians achieve a better understanding of what data is available and what can be done with it. And we help data analysts achieve a better understanding of the clinical and managerial agenda by proposing new and better ways of analysing the data.

Kurtosis is Neil Pettinger. After twenty years as an NHS information manager, Neil reinvented himself as a training consultant and he now drives up and down the motorways of the UK delivering courses and workshops for health service staff.

information training for the NHS

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