knowledge matters volume 1 issue 2

IN S I D E TH I S I S S U E

1 Welcome to Knowledge

Matters

2 The Green line controversy

3 Unify 2 … Now at a

desktop near you!

4 A3: Ask an Analyst

5 Skills Builder

7 Focus On … Mental Health

8 Useful Links

8 News and Events

8 Coming soon…

8 Fun Fact

8 Quick Quiz

Since the last issue, analyst network meetings have been held in Kent and Surrey with the Sussex network meeting on 8th August. There has been support for the idea of holding regular analyst network meetings and also creating a virtual community for the sharing of tools, knowledge and best practice. A programme of practical measurement/analytical training modules is currently being developed which will start to be delivered in the autumn. Dates and details will be published in the next issue of Knowledge Matters. Over the coming months, the SHA will be leading some work with the Information Centre for Health and Social Care across South East Coast to develop new and more sophisticated metrics to describe the pathways of care for stroke, COPD and dementia. The programme will seek to combine existing data sets from primary care, provider Trusts, social services and community services with a view to providing comparative information which is meaningful to clinicians. At the end of June, the SHA was alerted to the fact that SUS will have a period of down time for a maximum of 6 weeks from 26th July for essential maintenance. The SHA has issued guidance regarding how to proceed over this period. For further details please contact Andrew Wilk (01293 778876). Finally, here’s a summary of the tools and products circulated by the team over the past 2 months: - mental health benchmarking, Annual Health Check scoring tool, programme budget analysis, British Association of Day Surgery (BADS) benchmarking tool, 18 week dashboard and AESOP (A&E System to Optimise Performance). For further information or to obtain your own copy, please e-mail: -

[email protected]

“Monitoring, helping,

creating and facilitating –

that’s what we’re about”

July 2007 Volume 1 Issue 2

Knowledge Management Team, South East Coast Strateg ic Health Authori ty

[email protected]

Welcome to Knowledge Matters By Samantha Riley

Samantha Riley

Do you have something you would like to contribute to Knowledge Matters? Please contact us!

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In the first issue of knowledge matters we posed the following Quick Quiz item:

Which line on the graph below represents a standardised normal distribution?

The answer; A standardised normal distribution has a mean of zero and a variance of one as represented by the green line, seems to have created a storm of controversy so we thought that we would explore the topic in a bit more detail in this issue…

In fact all of the lines showed a normal distribution, which is defined as a distribution equally distributed either side of its mean (or average). While they differ in how spread out they are, the area under each curve is the same. The shape of a normal distribution can be specified mathematically in terms of two measures: the mean (µ) which shows where the centre of the distribution rests along the axis (in the case in question, the x-axis) and the standard deviation (σ) which is a measure of the spread of data, and is defined as the square root of the total variation. The mean and standard deviation parameters are variable depending on the spread and clustering of the data, but they all have the same general shape some times referred to as bell curves. However… We asked about the standardised normal distribution A standardised normal distribution shows the same characteristics as any other normal distribution but has a specific mean of 0 and a standard deviation of 1. A standardised normal distribution can be created from unstandardised data using any of a number of transformational techniques. This is represented in the diagram by the green line (with the mean (µ) and standard deviation (σ) values shown in the legend). And how is all this useful? Well, plotting the distribution of any measure is crucial in the proper description and understanding of your data. For example, if you plot the frequency (number) of age in years of patients in a stroke recovery unit, your data is likely to be skewed, with the mean age at the higher end of the scale, and a relatively small standard deviation. Not very surprising given the circumstances, but skewed data like this can preclude the use of particular statistical tests (which have ‘normality’ as an underlying assumption). So, continuing the example, if you wished to see if there was a statistically significant impact of age on length of stay in your stroke recovery unit, you would first need to ‘normalise’ your data by applying transformational techniques to gain a standardised normal distribution. Then you could carry out your analysis. Further information is available at: http://en.wikipedia.org/wiki/Normal_distribution , http://davidmlane.com/hyperstat/normal_distribution.html or, for more interactive examples http://www-stat.stanford.edu/~naras/jsm/NormalDensity/NormalDensity.html

The Green Line Controversy …. By Katherine Cheema

Is there something that you wish you knew more about? To suggest future topics for knowledge matters contact the team

Page Page Page 333 Knowledge MattersKnowledge MattersKnowledge MattersKnowledge Matters

Unify 2, the new data collection tool developed by the Department of Health, went live on the 18th May 2007. The new system replaced its predecessor, Unify 1, for all returns due from this date, including RTT and GP referrals submissions, monthly monitoring returns and the weekly SITREP and delayed transfers data. There are several key changes to the system which have been developed with a view to improving access to historical data, smoothing out the submissions process and improving security. Whilst Unify 2 remains a web-based system, its front end has been redesigned and the navigation of the site changed. For people looking for specific data, there is now a glossary provided that returns details of available data for specified subjects and time periods. Unify 2 also includes more stringent security measures; instead of organisational passwords all users in all organisations must have individual accounts with specified security access. This ranges from ‘view only’ to full user-account managers. Dependent on security access, users are able to log-in and ‘impersonate’ other organisations to see (but not edit) data. The data within Unify 2 can be uploaded using Excel based templates or manually, directly onto the web-based form. The system contains many more validation checks than Unify 1 and therefore data quality should increase. Most returns are submitted directly to the Department of Health without sign-off from the SHA in an effort to reduce administrative burden.

The key change in Unify 2 is the method of extraction. Data added to the new system is stored in a data-warehouse which can be queried via an Oracle server with Discoverer as a querying tool. Two versions of Discoverer (or Disco) are available to users, although specific permissions may be required. The first is Disco Viewer, based in HTML only. This allows users to see the results of predefined queries that are shared. Users can either view or export the data in a number of formats for use outside of the system. The second is Disco Plus, which requires Java, and enables users to create their own queries with a view to sharing with others. Currently only Disco Viewer is available, but it is planned for Disco Plus to be available to specified users soon. Perhaps the most useful development in Unify 2 is the ability for users to directly query historical data from all returns (including those submitted via ‘pigeon-hole’ facilities) and extract in a way that is most useful to them.

Unify 2 has, as with any system, experienced some teething problems in its early weeks of implementation. If you have experienced any problems, please notify Katherine Cheema or Fiona Cantrell who are maintaining a log of issues to feed into the national Unify 2 Project Delivery Board, on which the SHA is represented. Once Unify 2 is fully live, the potential to utilise the largely untapped source of national and local standardised data will be enormous.

Unify 2 …. Now at a desktop near you! By Katherine Cheema

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Discoverer Services

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Discoverer

Plus

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Discoverer Services

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If you have a question for the team please e-mail: [email protected]

Q - I can't upload my return to Unify2 using the upload spreadsheet or via manual data entry, the page keeps timing out. I'm wasting loads of time trying again to see if the system is up and running again. What should I do to ensure DH have my return by the deadline?

A - Try to upload once or twice. If you still experience problems e-mail the completed returns to [email protected] and cc [email protected] or [email protected] outlining the problems you have experienced with the Unify2 website.

- Fiona Cantrell, Performance Analyst Q - We are updating all of our Occupation Codes can you confirm where HCAs in Social Care and those in Health Care should be coded. Are you able to help with this please?

A - I think there is some times confusion with occupation codes due to Clinical setting Vs non clinical setting, it is some times tempting to code HCA’s working in a clinical setting as "unqualified nursing" or “Nursing Assistants”(N9*) . It's the role that's important and if the role is "unqualified nursing" then use N9* else use H1*. Nursing support staff should only be coded as Healthcare Assistants (H1* codes) if they have undertaken, or are working towards, competencies or qualifications specifically related to their role.

These are usually NVQ Level 2 or Level 3 in Care, but might also be specific local qualifications in healthcare. Nursing Assistants (N9*) are not qualified in this way, and are not working towards competencies.

- Kiran Cheema, Workforce Analyst Q- I need some activity benchmarking data, help!

A. There are a number of options depending on what you want to benchmark. There is a fair amount of information available online via sites like: www.performance.doh.gov.uk – Various quarterly returns by trust and commissioner for waiting lists, elective admissions, outpatients. www.hesonline.org.uk – Not as useful but does provide annual breakdowns by HRG / diagnosis / procedure.

At the SHA we are provided on a regular basis with provider based record level inpatient data by all acute Trusts in South East Coast. In addition we have access to datasets for outpatients, A&E, inpatients, waiting lists, mental heath via SUS for all SEC Trusts. This enables us to provide benchmarking information across the whole patch on a wide range of measures. One key area that we are frequently contacted about is national average / top quartile / top decile length of stay. Unfortunately the online PIANO utility which provided this information free of charge has been withdrawn and replaced by Performance Monitor which is provided by Dr Foster’s. Due to the significant cost associated with Performance Monitor, the SHA has chosen to not subscribe to this product. Instead, we have recently obtained access to full national HES data and, once training has been completed in the next month or so, we will be able to provide this and a whole range of other national benchmarks on acute Trust activity quickly and easily.

- Simon Berry, Development Analyst

A3: ASK AN ANALYST



Welcome to the Skills Builder, a New Feature to Knowledge Matters. We aim to provide short articles covering topic areas that we get asked about, ranging from exploring mathematical techniques to demonstrating technological tips and tricks. This issue Percentages … we all know what this means … don’t we? Well you may be surprised at the amount of confusion people still have around the concept. So we are going back through the basics exploring percentages and some of the useful dos and less useful don’ts? Percent(%) means "out of 100" and is a way of expressing any number as a fraction of 100, or another way of think about it is dividing any number into 100 equal pieces or 100 percentages.

Have you ever thought about the Value of a percentage? The value of a percentage depends on the number you start with, that is one percent of 100 is 1 (100 divided by100 or 100/100), one percent of 150 is 1.5 (150 divided by100 or 150/100), one percent of 130 is… 1.3

Percentages are a useful way of comparing two quantities relative to each other. For example how does 1 compare to 3? Or 50 compare to 150? While the changes in values are very different, that is the difference between 1 and three is 2 and the difference between 50 and a 150 is 100, the percentage change in both cases is exactly the same, which is in both cases it will take 300 percentages of the first number and to add up to the second number.

So what does this mean in terms of comparison? Well from our example above we can say that the second number is 300 percent of the first number, but what does this mean in terms of increase? The second number represents an increase of 200% from the first number.

If 90 is a 12.5% increase from 80 does this mean that 80 is a 12.5% decrease from 90?

It is important to remember that the value of 1 percent changes with the value that you are comparing to, 1% of 90 is 0.9 and 1% 80 is 0.8, so when comparing 90 to 80, the difference is 12.5

percentages of 80 more, while when comparing 80 to 90, the difference is 11.2 percentages of 90 less

To calculate a percentage Increase: ((Second number/first number) – 1)*100 e.g. the increase from 50 to 150: ((150/50)-1)*100=

Step 1: 150/50 = 3 Step 2: 3-1 =2 Step 3: 2*100 = 200%

To calculate a percentage Decrease: (1-(Second number/first number))*100 e.g. the increase from 150 to 50: (1-(50/150)-1)*100=

Step 1: 50/150 = 0.33 Step 2: 1-0.33 =0.66 Step 3: 0.66*100 = 66%

Skills Builder: - Percentages

1st number

Value of 1 percentage of 1st number = 1st number/100

Number of percentages that make up the first

number? … 100

2nd number

Number of percentages that make up the second number = 2nd number/ Value of 1 percentage of

1st number

I.e:



Using percentages: We have shown how to use percentages to compare two numbers with each other, but what are the other uses of this technique?

Using percentages to compare sets of values with different units: Percentages can be used to compare data sets which have different units for example the number of people in post and the monthly paybill.

Month 1 Month 2

Staff in post 109 132

Paybill(£)/Activity 200,000 250,000

The first issue is that the two sets of data are measuring different things; we can use percentages to compare the changes in each data set between month 1 and month 2. Using the above formulae [e.g. ((132/109)-1)*100] there is an increase in staff 21%, however the increase in paybill is 25%. Thus a disparity that is not immediately obvious becomes apparent.

Using percentages to compare trends in sets of values e.g over time:

Month 1 Month 2 Month 3

Staff in post 109 132 125

-10%

-5%

0%

5%

10%

15%

20%

25%

Month 2 Month 3

Staff in Post

Compare the month on month change … what does this tell you? All it tells us is that there is a 21% increase between months 1 and 2, and a 5% decrease between months 2 and 3. Can you use this to trend? No. Each of the above percentages is a discrete unit showing the relationship between two months - the value of % changes each month, for the change between month 1 to 2 the value of % is 1.09, but for the change between month 2 and 3 the value of % is 1.32 . Therefore we have to use a “baseline” e.g. M1 so that the comparison is valid i.e. compare each data point change from M1 in order to produce a valid trend. Using Percentages to “standardise” Scaling


Staff in post 109 132 125

Paybill/Activity 200,000 250,000 225,000

As above and can be used to scale to fit and compare changes. (e.g. the change in staff from month 1 to month 2 is 21%, the change from month 1 to month 3 is 15%). As a trend this actually tells us little – however when compared to a related set of data (in this case paybill) it becomes a powerful tool.

0%

5%

10%

15%

20%

25%

30%


Staff in Post

Paybill

In future issues we will go on to explore Percentiles and Benchmarking.



Focus On – Mental Health ….. By Adam Cook

“There is lots of

information out there –

you’ve just got to know

where to look!”

“...with increasing care provided

in the community, the provision

of information to describe and

compare these services is

increasingly important”

“A key question is, what is

the data telling us and what

actions should be instigated

as a result?”

There’s hardly any information available, and what data is available is wrong.

That’s what I was told by the majority of people when I embarked on the task of providing benchmarking analysis for mental health Trusts within South East Coast.

Three months later, what I have discovered is that there is an in fact a huge amount of information available, and whilst there clearly are issues relating to data quality, what we have currently is certainly good enough to prompt significant discussion and debate regarding variation against a range of indicators.

So, what information is available ?

For many years all provider organisations have submitted Admitted Patient Care and Outpatient Care CDS records to NWCS/SUS. From this information it is possible to look at the numbers of admissions, admission rates, length of stay, first-to-follow-up ratios and DNA rates. In addition to this there is a Mental Health Minimum Data Set (MHMDS) which has been mandated for the past 3-4 years and provides much more detailed information relating to mental health spells – including data such as CPA level, contacts with Health Professionals, readmissions to hospital and community survival times. A useful guide is available at http://www.ic.nhs.uk/webfiles/Services/Datasets/MHMDS/usingmhmds.pdf which provides a whole host of ideas regarding how MHMDS data can be used locally:

There are a number of central returns which help contribute further to the cauldron of mental health data. Since last spring non-acute delayed transfers of care have been collected weekly on the SITREP, and there is data on bed stock and occupancy on the KH03.

Look further and you find information on caseloads for various teams and numbers of professional groups (formerly undertaken by Durham University http://www.amhmapping.org.uk and now by Mental Health Strategies http://mentalhealthstrategies.co.uk/home). Look further still and you find information on PCT spend on a range of services including mental health (contact Simon Berry for a copy of the Programme Budget benchmarking tool). Finally, Dr Fosters has recently made available a free tool (Key Indicators Graphical System) which contains the Performance Assessment Framework (PAF) Indicators and enables a comparison to be made between local authorities. Whilst not all indicators relate to mental health, KIGS provides an additional useful data source. http://www.drfoster.co.uk/localGovernment/kigs.asp

Where next?

Back in June, I produced and distributed a benchmarking pack which to be honest raised more questions than gave answers. Work is ongoing with providers and commissioners to further refine this analysis and answer a range of questions…..

• What does the data show?

• What actions should be instigated as a result of what the data tells us?

• What is the quality of the data and how can we improve it?

• What questions should be asked of the data?

• What data do we need that is currently unavailable & how do we obtain it?

Ultimately, our intention is to agree a standard set of comparative information which will be produced and shared on a regular basis. If you want to learn more or

would like to be involved, please do get in touch!



Performance

Free HES Data not quite detailed or in date enough to be really useful, but one of the best sources of free benchmarking data available: http://www.hesonline.org.uk/Ease/servlet/ContentServer?siteID=1937&categoryID=192

Workforce

The link below will take you to the Working Time Directive benchmarking tool, see your progress towards WTD 2009, compare yourself nationally or against your SHA patch http://www.healthcareworkforce.nhs.uk/working_time_directive/wtd_resources/wtd_benchmarking_tool.html General

NACS codes downloads - download & memorise all your favourite NHS organisation codes: http://nww.connectingforhealth.nhs.uk/nacs/downloads/

ONS Health Area cluster groupings This classification groups health areas into clusters based on similar characteristics. http://www.statistics.gov.uk/about/methodology_by_theme/area_classification/ha/cluster_summaries.asp

Knowledge matters is the newsletter of NHS South East Coast’s Knowledge Management Team, to discuss any items raised in this publication, for further information or to be added to our distribution list, please contact:

Knowledge Matters C/O Knowledge Management Team NHS South East Coast York House 18-20 Massetts Road Horley,Surrey, RH6 7DE

Phone: 01293 778899

E-mail: [email protected]

To contact a team member: [email protected]

July 07 Q4 Better Care Better Value indicators published: http://www.productivity.nhs.uk/

24th – GP patient survey published

27th – SUS goes off line for 6 weeks

August 07 3rd – UNIFY 2 release 4 goes live (will provide greater functionality – for further details see the UNIFY website) 8th - Sussex Analyst Network meeting Fit for Purpose Database launch (delayed from June) NHS Workforce Review Teams (WRT) Workforce Risk Assessments Published

September 07 7th – Kent analyst network meeting 11th – Surrey analyst network meeting

19th - SHA Analytical Fair (focus on 18 weeks)

This Issues Useful Links …

Fun Fact In 2006/07 only 1 person was admitted to South East Coast hospitals after being struck by lightning compared to 3 people being admitted as a victim of a volcanic eruption

News and Events …

In Development and coming soon …

What is the missing number in the sequence below?

1, 1, 2, 3, 5, 8, 13, ?, 34, 55, 89

Quick Quiz

Outpatient referrals analysis (August)

New to follow up tool (August)

Interactive Inpatient Patient Survey Tool (August)

QOF benchmarking tool (September)

Demand management monitoring tools(September)

Length of stay benchmarking tool (October)

Knowledge Management website (November)

Updated

knowledge matters volume 1 issue 2

Documents

issue of knowledge matters

s i s s u e

distribution res

knowledge management

mental health benchmarking

s i d e th

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