Knowledge Matters Vol10 Issue 3

Volume 10 Issue 3 August 2016 Welcome to Knowledge Matters Welcome to this triumphant Olympic edition of Knowledge Matters!!

Jam packed again with interesting, informative and useful stuff and a great read on your tea break.

In this issue we have an update on the changes to the Quality Observatory team and structure following Kate’s promotion to Head of Transformation Analytics - don’t worry, it’s all good news!

We have our usual update from Samantha Riley, this time an informative article on the Private Healthcare Information Network; an article by Pauline Smith on the work of KSS Patient Safety Collaborative around measurement; an exciting new app developed by the team for enabling easier collection of National Safety Thermometer data and its successful implementation at Mid Yorkshire Hospitals NHS Trust—contact us if you have an interesting idea for an app!

Nikki Lawford has written a handy guide to the new world of STPs and just what they are, a couple of handy skills builders from our shiny new analysts, recently moved over from supporting FFT, Dani and Becki, a piece by Trishna on averages and an in depth look by Liam on the Central Alerting System.

There’s a couple of job opportunities within our Programmes team on page 15 and finally, an Olympic poem from our resident analytical poet, Adam Cook.

Inside This Issue : The Private Healthcare Information Network

2

Quality Observatory Restructure

6

The Safety Thermometer App

10

Sepsis Showcase

3

Sustainability and Transformational Plans (STPs)

7

Ask An Analyst—Averages

12

The Kent Surrey & Sussex Patient Safety Collaborative

4

Skills Builder—Avoiding Nested iFs & Overcoming chart ouliers

9

CAS—An Expanded look

14

News

15

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2

The Private Healthcare Information Network

By Samantha Riley, Director Elmcroft Associates Ltd. Hello Knowledge Matters readers – long time no see! Well, it has been over a year now since I took my first step outside of the NHS world for very many years. My eyes have been opened to a whole new and exciting world – which has at times provided surprises.

I recently came across The Private Healthcare Information Network (PHIN) - an independent not-for profit organisation. I had not previously encountered this organisation and was interested to learn about what they do. PHIN’s vision is that all patients considering private healthcare will have access to trustworthy, comprehensive information on both quality and price to help them make their decisions.

One of the things that has surprised me over recent months is to learn that virtually no information is currently available to patients considering private healthcare treatment. In 2014, an investigation by the CMA (Competition and Markets Authority) found that the information available to people considering private healthcare was inadequate. The CMA took the view that patients did not have sufficient information available to understand and compare their options to help them make informed choices.

As a response to this, the CMA published the Private Healthcare Market Investigation Order 2014. This imposed a legal duty on private hospitals to submit data to PHIN (which by the way was established in 2012) for private healthcare. PHIN has a duty to publish this data as performance measures to help patients make informed decisions.

Over the next five years, PHIN aim to become the first service that patients and doctors turn to when they want to understand and choose private healthcare. They aim to raise standards and expectations for the availability, transparency and usability of health information, and act as a model for information governance.

The CMA has specified 11 performance measures for PHIN to publish at both hospital and consultant level.

These are as follows: a) b) c) d) e) f) g) h) i) j) k)

volumes of procedures undertaken; average lengths of stay for each procedure; infection rates (with separate figures for surgical-acquired and facility-acquired infection rates); readmission rates; revision surgery rates; mortality rates; unplanned patient transfers; a measure of patient feedback and/or satisfaction; relevant information from the clinical registries and audits; procedure-specific measures of patient reported health outcome; and frequency of adverse events.

This year, the focus is on working with hospitals and consultants to develop a robust process for recording, submitting and validating their data.

As required by the CMA, PHIN will publish this data in full – as performance measures, from April 2017. So, for the first time it will then be possible to compare a range of indicators between NHS and private healthcare providers. Exciting times!

If you would like to learn more about the work of PHIN have a look at their website : https://www.phin.org.uk

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Sepsis Showcase

By Dani Collier, Information Analyst Over the past year there has been a huge focus on Sepsis awareness. On the 5th May 2016, I had the opportunity to witness some of the amazing work organisations have done at the Kent, Surrey and Sussex Patient Safety Collaborative (KSS PSC) Sepsis Showcase event.

Sepsis can be a life threatening or life changing illness if not treated quickly. It is estimated 44,000 people will die from severe sepsis in a year. This is more than bowel, breast and prostate cancer combined, yet very few people are aware of sepsis*. With more awareness and shared knowledge, the number appears to be increasing, not necessarily because more people are developing sepsis, severe sepsis or going into septic shock but due to increased recognition and improved data collection. Hospitals are able to recognise and treat suspected sepsis much quicker and therefore improve the outcome for the patient in terms of mortality and length of stay in hospital There has been ongoing work around sepsis awareness and the urgency of treatment. There are many innovative ideas from around Kent, Surrey and Sussex on raising the profile of sepsis; ranging from ‘Sock it to Sepsis’, apps and even songs on YouTube!

Many different tools have been created and used within trusts to help diagnose and treat the patient within a very short time frame. Some tools/apps can alert the emergency department if the patient is receiving treatment for another illness. The specialist teams can advise of any medications and help avoid medication errors and further complications.

When all working together it appears sepsis is being spotted and treated earlier, leading to less deaths and complications. This can only continue to improve.

Find out more at: http://www.kssahsn.net/what-we-do/KSSPatientSafetyCollaborative/sepsis *Source: Sepsis Trust - http://sepsistrust.org/

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4

Kent Surrey & Sussex Patient Safety Collaborative

By Pauline Smith , Senior Improvement Manager The Kent Surrey and Sussex Patient Safety Collaborative has a robust measurement work stream with the aim of developing and promoting the intelligent and in depth use of data and analytics to drive improvement and evidence change. Each of our six work streams of Sepsis, Acute Kidney Injury, Medication errors, Pressure Damage and Safety Discharge and Transfer and Mental Health, all have a strategy for developing better information from which to plan and track improvement across Kent Surrey and Sussex.

Some of our challenges have been the lack of standardised approaches to measurement across KSS, and provider organisations have worked together to begin to standardised data sets, and to develop dashboards which allow them to see where changes to practice have delivered improvement. Other challenges in measuring for improvement are where organisations already have small numbers, for example pressure damage in acute settings – this has meant thinking about how we measure that more carefully, and some organisations are using a “time between events” approach, seeking to lengthen that, i.e. 100 days pressure damage free – which will eventually impact on the incidence of pressure damage.

KSS PSC measurement work, supported by the QO, is having a national impact on the production of data for improvement at a national level in Acute Kidney Injury We have worked with the Kate Cheema at the QO and clinicians across KSS to develop the decision tool to enable clinicians to ensure they were complying with the national cquin and including the required info on the discharge summaries, supporting primary care to deliver high quality care.

The PSC has agreed 7 key process measures of care that should be delivered to patients once AKI has been identified, designed to help improve the quality and management of AKI, supported by the KSS AKI data dictionary. Trusts are submitting audit data on these measures completed with all their AKIN 3 patients and receive reports. o As part of our role as national lead for the AKI cluster, KSS held a national AKI measurement for improvement event in Birmingham with all the Patient Safety Collaborative’s, UK Renal Registry, Think kidneys and NICE, to establish a reliable and sustainable way of measuring outcomes for AKI. This event started the development of a common understanding of the potential data sets available from UK Renal Registry to support a long term measurement strategy for PSCs, Strategic Clinical Networks and others working on AKI Improvement, including: •

Establishing broad agreement on a common set of metrics that are deliverable from that data set.

•

Discussing limitations and assumptions associated with the data.

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5 •

Establishing broad agreement on data access and availability of reports, their format, frequency etc.

•

Enabling better engagement with and understanding of the benefits of reporting data to the UK RR and how it can be used in an effective way which will encourage submission and drive up data quality.

•

Consider process measures that can be used to support work locally

•

Scoping a data sharing project with the UK Renal Registry to develop so that the process measures and outcome measures can be aligned and used more effectively for improvement work.

Measurement highlights from other work streams include:

Pressure Damage • All provider organisations sharing clinical incident data to create an alternative to the national safety thermometer data and get closer to an idea of incidence of PD in KSS • Standardising and sharing SSKIN bundle data to identify best practice and spread Sepsis Developed Clinician and Coder Partnership for Sepsis to reduce the significant variation in local coding practices, starting off with a coding audit • Reviewing whether it is possible to use CQUIN data to support improvement •

Safe Discharge and Transfer A high number requests for discharge transport are made on the day – obviously making the smooth and efficient discharge and transport of patients a very difficult logistical challenge. This work stream brings together providers and South East Coast Ambulance to review the data they collect to support organisations in better discharge planning, giving a better, safer experience for patients.

•

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6

Quality Observatory All Change!

By Simon Berry, Head of QO Analytical Services So it’s all change in the Quality Observatory; the old structure had the team divided into three broad areas that seemed ideal post 2013 NHS reorganisation, namely, Provider Services, Commissioner Services and Development Services headed up by myself, Kate and Kiran respectively.

Now over 3 years in with this structure, it seems the distinction between Commissioner and Provider services doesn't seem as necessary so we have taken the opportunity, following Kate Cheema’s promotion to higher things, to reorganise the team to more reflect the type of work we do and our customer relationships.

Development Services is staying as is with Kiran and his team of four. The Commissioner and Provider Services are being merged and reorganised into three streams under myself as Head of QO Analytical Services. These three streams are as follows •

Projects - this is being headed up by Adam Cook, supported by Charlene Black. The role of the team is to provide a reactive fast turnaround service to short term projects and ad hoc requests as well as providing up front support in the initial setup phase of longer term projects before handing off to other members of the QO team.

•

Analytics - this is being headed up by Nikki Lawford, supported by Dani Collier and Becki Ehren. Their role is to primarily handle the QO’s longer term analytical contracts requiring regular updates of dashboards / products but also to provide a reactive service for ad hoc requests for customers associated with those contracts.

•

Programmes - this is being headed up by Rebecca Matthews, supported by Trishna Julha, Liam Blaney and two band 4 administrators (currently out for recruitment). Their role is to primarily support the QO’s large contracts requiring helpdesk / enquiry support as well as provide analytics relating to those projects, an example being the national Friends and Family Test for NHS England.

Although these three teams have distinct areas of interest and responsibilities, with the cross-skilling we encourage and develop in the team, there is the flexibility to pool resource for bigger projects and provide backup for other team members for things like holidays. On top of that, we also have within our team two people qualified to PRINCE2 practitioner level and a Microsoft Certified DBA. What will it mean to you? Well you will continue to receive the dedicated, customer focussed quick response you’ve always had from the team, but there will also be greater capacity for your projects going forward as well as clear points of contact if you are interested in the Quality Observatory working for you.

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7

What are STPs?

By Nikki Lawford, Specialist Information Manager - Analytics I’m sure many of you will have heard the term ‘STP’ being thrown around a lot lately but might not know quite what it is, or indeed where us data people might be involved.

STP actually refers to Sustainability and Transformational Plans, which are part of the NHS England Five Year Forward View and were launched in December 2015. The aim is to transform the way that healthcare is planned by focussing on a defined geographical area and its population, rather than individual organisations. To achieve this, organisations were asked to move away from working in isolation and instead come together to form STP ‘footprints’, in order to challenge the status quo and transform the way health and care services are currently planned, making sure they will be sustainable for the future.

Before planning can start, each STP footprint has needed firstly to understand in some detail how services are currently delivered and the impact on different healthcare settings. The first step has therefore been to pull together a baseline view and develop a ‘do nothing’ scenario - something which demonstrates to NHS England the state of play over the next five years if no changes are made to current ways of working. Importantly, they also need the ability to demonstrate the impact of any potential changes to each different care setting in both activity and spend terms.

The Quality Observatory, together with some of our South, Central & West CSU colleagues, have been quite heavily involved in developing STP modelling tools for some of our customers to assist in this process.

Working alongside Rubicon Health Consulting and using a fairly complex model, we have helped several STP footprints to deliver their initial plans to NHS England. Detailed activity and cost data is entered into the model for pre-defined CCGs or localities, age groups, admission methods plus groupings by number of long term conditions.

As much detail as possible has been included for all local providers including acute, community hospitals, community teams, mental health, ambulance services etc. We have also been required to include specialist commissioned activity plus, for the major local providers, a total view which includes activity originating from outside of the STP footprint as well.

Once all this data has gone into the model, we have then been able to summarise it in a variety of ways to show the current state of play and create detailed forecasts for the next five years.

Different growth assumptions have been incorporated based on things like inflation, demographics and new housing. Admission avoidance assumptions are applied by reallocating activity, for example, away from urgent care into a community setting, reducing length of stay and so on.

We have applied different ‘currencies’ to each activity type in order to model the effect on providing alternative care to patients, for example translating acute bed days into community nursing hours or maybe converting A&E attendances into GP contacts, and then calculating the potential savings to be made. Our partners at Rubicon have then been able to develop a complete financial model which calculates where the gaps are, from a commissioner, provider and taxpayer perspective.

The submitted plans are now informing discussions between NHS England and each of the STP footprints about where they are now and where they need to be by 2020/21 and, crucially, how to develop a sustainable programme of transformation which will close the gaps identified around health, quality and finance.

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8

Avoiding Nested Ifs with a Vlookup

By Becki Ehren, Information Analyst I am very new to my Information Analyst role, and a lot of Excel tricks still amaze me on a daily basis, so I thought I would share my lesson on avoiding a very large nested formula, and using a Vlookup with a True statement instead!

I am working on Stroke SSNAP data as provided by the Royal College of Physicians in a series of reports that need processing into one of our dashboards for South East Clinical Networks. As part of that, overall scores for the various elements of the KSS region need to be calculated as they aren't provided. In this example I’m working with data from the first domain, specifically, the proportion of patients scanned within 12 hours of clock start. As I mentioned before, I am a bit of an amateur when it comes to the wonders of A s core of 100 i s obtained i f 95% or more of patients are scanned within 12 hours Excel, so try not to wince too much at the sight of this formula!

A s core of 90 i s obtained i f 90% to <95% of pa tients are s canned within 12 hours Anyway, the scores are as follows: A s core of 80 i s obtained i f 85% to <90% of pa tients are s canned within 12 hours A s core of 70 i s obtained i f 80% to <85% of pa tients are s canned within 12 hours A s core of 60 i s obtained i f 75% to <80% of pa tients are s canned within 12 hours A s core of 50 i s obtained i f 70% to <75% of pa tients are s canned within 12 hours A s core of 40 i s obtained i f 65% to <70% of pa tients are s canned within 12 hours A s core of 30 i s obtained i f 60% to <65% of pa tients are s canned within 12 hours A s core of 20 i s obtained i f 55% to <60% of pa tients are s canned within 12 hours

‘=IF($E$60>=95,100,IF(AND($E$60>=90,$E$60<95),90,IF (AND($E$60>=85,$E$60<90),80,IF(AND ($E$60>=80,$E$60<85),70,IF(AND ($E$60>=75,$E$60<80),60,IF(AND ($E$60>=70,$E$60<75),50,IF(AND ($E$60>=65,$E$60<70),40,IF(AND ($E$60>=60,$E$60<65),30,IF(AND ($E$60>=55,$E$60<60),20,IF(AND ($E$60>=50,$E$60<55),10,0))))))))))’

A s core of 10 i s obtained i f 50% to <55% of pa tients are s canned within 12 hours A s core of 0 i s obtained i f l ess than 50% of patients are scanned within 12 hours

It turns out that this formula didn’t fit in Excel 2003 as it will only accept up to 7 nested IF statements, so I firstly had to change to using Excel 2010, but even though I was able to create the formula it still proved difficult to understand and modify for use with other metrics in the data with different limits - a different approach was TIME SCORE needed!! It turns out all that complicated formula can be completely avoided by using a simple 0 0 0 50 50 10 vlookup.

55 60 65 70 75 80 85 90 95

55 60 65 70 75 80 85 90 95

20 30 40 50 60 70 80 90 100

I created a lookup table, with the scores and percentages, to pull my results from. I then used the formula ‘=VLOOKUP(A2,$D$2:$F$12,3,TRUE). This basically means find the figure in ‘A2’ (my data), in the percentages and scores table in $D$2:$F$12, and pull out the score in the 3rd column. The reason the ‘True’ statement is so crucial to this, is because it finds an approximate value within the lookup column in the table. For specific values that you know are going to appear you would use FALSE. In this case make sure you have put the lookup values in the correct order, otherwise you’ll find you’ll end up with some quite unusual answers!!

Here’s another example using times. Excel is often a bit funny with times that go over 24 hours; you can either custom format the cells with [hh]:mm:ss which will show times over 24 hours (not necessary for this case, but useful to know!), or you can convert the times into a value, which makes the lookup process much more straightforward.

As you can see from the table, 5 hours equates to 0.20833333 of one day in Excel terms, so as long as the value that I am looking up doesn’t go higher than this, it will fall under the score of 20. If the score is 3 hours or less but no less than 2 hours, it will have a score of 40 (you get the gist). Make sure that the table starts with 0 and gets bigger; otherwise your lookup won’t be able to reference the correct cells.

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TIME (Value) 0 0.03125 0.04166667 0.05208333 0.0625 0.08333333 0.125 0.16666667 0.20833333 0.25 0.33333333

TIME [hh]:mm:ss 00:00 00:45:00 1:00:00 01:15:00 01:30:00 02:00:00 03:00:00 04:00:00 05:00:00 06:00:00 08:00:00

SCORE 100 90 80 70 60 50 40 30 20 10 0

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9

Overcoming Outliers on Charts

By Danielle Collier, Information Analyst Occasionally on charts you find that one organisation that has submitted data way out of range of similar organisations. If the chart is dynamic and based on a fixed list of organisations and different indicators, you shouldn’t just delete the organisation, but annoyingly the chart does not really show anything other than one large column plus a couple of bumps along the x-axis.

From the table below, let’s say we are mainly interested in Trust A, which is showing us that for every 1,000 patient contacts 17.95 patient incidents are reported, however Trust B has 893. All other similar organisations within the country have between 3 and 39 patient incidents reported per 1,000 mental health contacts. A sense check of this data will indicate that for Trust B either the data is incorrect, the organisation differs significantly from your own or there could be other reasons which only Trust B may be aware of.

Indicator data - unsorted Rank Org Rate 11 A 17.95 1B 893.00 12 C 17.79 7D 20.96 10 E 18.31 5F 24.35 4G 27.25 15 H 4.20 13 I 17.23 3J 31.02 2K 39.42 8L 20.27 6M 21.72 9N 20.05 16 O 3.00 14 P 9.37

1000

If you are not confident that the data has been submitted correctly and you are unable to make any sense of what the chart is showing you, you can hide the culprit and make a note somewhere as to why you have done this. Without the chart being dynamic (where your chart data is using flagged named ranges), you can just hit delete, but in this case we cannot as we would still like to view Trust A amongst other indicators where the data makes sense! The image on the left does not really tell us much at all about Trust A! 500

0

B

K

J

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F

M D

L

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Formula to search raw data: =SUMIF($H$5:$H$20,$B$1&$B5,$K$5:$K$20)

The way in which we can get around this is by using the formulas ‘ISNA’, ‘VLOOKUP’ and ‘IF’. ISNA will return either a ‘True’ or ‘False’. VLOOKUP will search for the value in the table, if the value cannot be found Excel will return #N/A. The IF statement will tell Excel what you want it to do depending on whether a condition is met.

Create a table in which you can reference the organisation you want to hide and the indicator it is for. The code in the ‘lookup’ column here is the indicator Z2 and the organisation B.

E F Erroneous data lookup Org Z2B B

Now within the table and cells in which you are summing the raw data (column C in this instance), add in an IF statement, VLOOKUP and ISNA to search the Erroneous data table. The below gives an example:

=IF(ISNA(VLOOKUP($B$1&$B5,$E$5:$F$20,1,0))=TRUE,SUMIF($H$5:$H$20,$B$1&$B5,$K$5:$K$20),"") Let’s break down this formula: =IF(ISNA(VLOOKUP($B$1&$B5,$E$5:$F$20,1,0))=TRUE, SUMIF($H$5:$H$20,$B$1&$B5,$K$5:$K$20), “”) This says, if the Vlookup is not found (i.e. “is #N/A”) then perform the SumIf part of the formula as normal. If the value is found by the vlookup in our ‘Erroneous data’ table, then return a blank.

The chart has now automatically removed Trust B since it was found in the ‘Erroneous data’ table. Any future organisations added to that table will also be removed (as long as your vlookup extends to include the full table as it increases).

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10 Implementation of the Safety Thermometer App at Mid Yorks Hospitals NHS Trust.

HAELO

The Safety Thermometer App, built by the NHS Quality Observatory and supported by Haelo, has been introduced to Mid Yorkshire Hospitals NHS Trust to revolutionise the monthly audit of data. The audit, a national requirement, aims to monitor harm-free care across the nation.

The Safety Thermometer, contrary to popular belief, has nothing to do with temperature! It is a monthly audit that is routinely undertaken on the same day each month, trust-wide including community services, to provide a snapshot of harm-free care.

Standard Procedure On a set day, each ward collects data on a wide range of health issues such as; the old and new pressure ulcers, falls, VTEs (Venous Thrombo-Embolisms), catheters and UTIs (Urinary Tract Infections) to measure the number of patient harms for a specified time period. The information was then inputted into an Excel template, which was in turn emailed to the Nursing Directorate to transfer into a central spreadsheet for submission to HSCIC (Health and Social Care Information Centre). Dashboards were then produced, both

by HSCIC and the Trust, to show local and national trends of harm-free care, as an indicator of how safe the ward or hospital is.

The process was extremely time-consuming and inefficient for both the ward staff and the Nursing Directorate, and there was a general sense that the time spent on collecting and submitting the data was disproportionate to its value, leaving very little time to analyse the results and focus on improvements. As a result, there was a higher risk of inputting incorrect data when transcribing the information and the cumbersome nature of the process meant that ward staff were restricted to who was able to conduct the audit. On average it was estimated that 76.25 hours were spent in total per month on collecting and processing the information for submission in the acute setting alone (excluding paediatrics and maternity)

The Digitalisation of Data After initial research, mobile applications were found to be available which have the capability of allowing users to input the data directly into an internet connected iOS or Android device to streamline the collection process. After reviewing several applications, Mid Yorkshire Hospitals NHS Trust trialled an app developed by the NHS Quality Observatory supported by Haelo. The app has many benefits including; simple to use and helpful built in guidance, can be easily installed on existing VitalPAC iPads, has offline functionality â&#x20AC;&#x201C; allowing collection of the data in cases where wifi is unavailable, it does not collect any patient-identifiable data so confidentiality is not compromised and no data is stored on the device once the audit has been uploaded. Best of all, the app is free!

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11

With the new process, the registered nurse uses the Safety Thermometer app to collect the data and then uploads it directly to the Safety Thermometer website. The Nursing Directorate can then export all the audits and merge them with the HSCIC template for submission to HSCIC. It is estimated that, in the acute setting, the new process will save on average 58 hours per monthâ&#x20AC;&#x201C; thatâ&#x20AC;&#x2122;s696 hours per year! Of this, 73% (507 hours) are clinical hours. That equates to approximately ÂŁ11.5k per annum and, although the savings are time releasing and not cash releasing, the figures are significant. Due to the nature of the app and its integrated guidance, the audit can be easily carried out by other registered nurses on the ward, enabling the ward managers to use their time more effectively.

Successful Feedback Feedback from Mid Yorkshire Hospitals NHS Trust wards has been very positive, even from those who are reluctant to embrace technology. Comments include how much time has been saved, how easy it is to use and understand and how refreshing it is to be able to come on shift to do the audit to find it has already been done by another member of staff with little to no supervision.

Bright Future The Safety Thermometer app has been a great success and is now been rolled out to all adult inpatient areas in Mid Yorkshire Hospitals NHS Trust. The Nursing Directorate is using this as an opportunity to relaunch the Safety Thermometer process and clarify guidance. IT Services will continue to support the process as it embeds and will then look at how the process can be rolled out to Community services.

Following that, Maternity, Paediatrics and Medication Safety Thermometer collections will be reviewed to assess the possibilities for rolling out a similar process.

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12

Better Than Average

By Trishna Julha One question that frequently arises is whatâ&#x20AC;&#x2122;s the difference between a mean and a median. Trishna Julha, Programme Support Analyst, freshly returned from maternity leave, provides a quick refresher!

Solution: Complexity 2/5 â&#x20AC;&#x201D; Do you know the difference between a mean and a median?

We probably see reference to means and medians all over the place. A mean is the same as the average, the median is the middle and none of this is to be confused with the mode. Still confused? Letâ&#x20AC;&#x2122;s do a little refresher.

In any research, enormous data is collected. To describe it meaningfully, one needs to summarise it. One of the ways to summarise it is using a single index/value.

Both mean and median are measures of central tendency, that is, a statistical measure that identifies a single value as representative of an entire distribution. It aims to provide an accurate description of the entire data.

The most common of the two and easiest to explain is the Mean, commonly called, average.

For example, the number of bed days recorded in a Maternity ward: 2, 2, 3, 3, 2, 3, 20 Arithmetic mean= =

Sum (Total of the numbers) How many numbers there are in the dataset (2+2+3+3+2+3+20) / 7

=5

The arithmetic mean implies that a maternity patient spends 5 days on average in hospital.

The mean uses every value in the data and hence can be a good representative of the data. Repeated samples drawn from the same population tend to have similar means. The mean is therefore the measure of central tendency that best resists the fluctuation between different samples.

What is the Median?

The median is the value in the middle of the data set that has been arranged in order.

Arranged in order, the bed days recorded in a Maternity ward, as per our example, are: 2, 2, 2, 3, 3, 3, 20

Median = 3 This means that half of patients spent less than 3 days and half spent more than three days in hospital.

In this case, does the mean or the median best explain the central position of the dataset? Would we be right to simply conclude that a patient has an average of 5 bed days?

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13 With a closer look at the data, we notice that most patients spent 2 or 3 days only in hospital and there was one patient who had a long stay. Here, the median is a robust meaningful single value to describe the data.

Bottom line â&#x20AC;&#x201C; The mean is used where the data is symmetrically distributed as it is sensitive to extreme values or outliers (an outlier is a data value that falls outside the shape of the distribution for that variable. It indicates a special case that might be worth further investigation), especially if the sample is small.

In a dataset with outliers or a skewed distribution, the mean is pulled in the direction of extreme scores or tail. If a distribution is skewed, the median is a more robust measure.

In a perfectly symmetrically distributed data set, the mean and median will actually be the same.

Mean Medi-

For data skewed to the right, the median is in the middle but the mean is higher than the median.

Median Mean

For data skewed to the left, the median is in the middle but the mean is lower than the median.

Median Mean

We should also bear in mind that averaging rates is not a good idea.

To conclude: The choice about which measure of central tendency to use, should be deliberate. It should be appropriate to the size and/or variability of the data set.

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14

CAS—An Expanded Look

By Liam Blaney, Programme Support Analyst In the last issue we gave a brief introduction to a new piece of work we have taken on, the Central Alerting System (CAS). Since then our involvement with this service has grown, so let’s take a more in depth look at the wonderful world of medical alerts!

To recap, the Central Alerting System (CAS) is a web-based cascading system for issuing patient safety alerts, important public health messages and other safety critical information and guidance to the NHS and others, including independent providers of health and social care.

We provide the service on behalf of NHS England and the Department of Health. Originally we were providing the service for the South West regional team, however as of April 2016 we have expanded our reach and now also serve the South East, South Central and Wessex areas.

The most common types of medical alert we receive are missing/stolen prescriptions and patient alerts (for example where a patient may be trying to claim medication inappropriately). We also cascade notifications of medication recalls, warnings about potentially faulty medical devices, safety awareness resources and more!

Due to their urgent nature the aim is to get the information to the service providers as soon as we can - the majority of alerts will be distributed by ourselves within minutes of initial receipt. Currently GPs and pharmacists make up the largest proportion of alert recipients, however we also distribute to dentists, optometrists and NHS estates contacts where appropriate.

An example of a Patient Safety Alert:

Medical alerts primarily come in two formats – a direct email from the CAS central team containing a PDF version of the alert along with important information for the recipient, or via a template completed by the person issuing the alert (whether this be a practice manager, a member of the regional team, a Local Counter Fraud Officer etc.). Once we receive the alert we review and seek authorisation from the CAS liaison officer of the respective region and then distribute the alert accordingly. Care is taken to ensure any alerts containing patient identifiable information are only distributed to secure email addresses (i.e. @nhs.net).

We also have the capacity to distribute alerts via post where requested, although due to the urgent nature we much prefer to distribute via email to ensure the information is received promptly.

One of the biggest challenges we face is ensuring our distribution lists are kept up to date. Generally when we send an alert we will receive a few (sometimes hundreds!) “out of office” and “undeliverable” replies. Where this happens we will contact the provider directly and request an alternative/updated contact to ensure the information is still being received. We also receive updates from the regional teams (and also the providers themselves) which we use to maintain our contact lists.

Any queries can be directed to our mailbox alerts.scwcsu@nhs.net. Further information on CAS can be found here - https://www.cas.dh.gov.uk/Home.aspx.

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NEWS Birthdays since the last issue include: Kiran Cheema - here you can see him enjoying one of his gifts, a bar of Instant Regret Chocolate containing chilli extract rated at 6.4 million Scoville units! For reference, a Jalapeno comes in at up to 20k Scoville units, a Scotch Bonnet 100k to 350k

Scoville units and the current worldâ&#x20AC;&#x2122;s hottest, the Carolian Reaper, at 1.57 million Scoville units. If you look closely in the last picture you might be able to see the sweat breaking out and steam beginning to pour from his ears!

Rebecca Matthews and I also had birthdays celebrated with our favourite alcoholic beverages, mmm wine and gin.........

New Starter We have a new addition to the team, Iâ&#x20AC;&#x2122;ll let him introduce himself!

My Name is Paul White, I have been working in the NHS for 10 years specialising in Business Intelligence. I operate through my Business Intelligence Consultancy business, which supports Healthcare organisations in getting the best out of their Business Intelligence service. I have joined the Quality Observatory team to support a couple of key projects, which will provide further benefits to their customers and will enable them to make better decisions about the provisions of healthcare that they may need to provide to their local health economies. My Journey into Business Intelligence started very differently to most Business intelligence employees. ..

www.QualityObservatory.nhs.uk

I had started studying fine art and found my way into the NHS as a temporary data entry analyst, which I used as a platform to build my knowledge and skills needed for the Business Intelligence sector and progressed into management positions.

In 2015, I had been nominated by a member of the senior management team, in my previous organisation, to enrol on the NHS Leadership Academy - Mary Seacole programme, which I had completed and passed and earned a post graduate certificate in Healthcare leadership.

I am very passionate about art and have been commissioned in the past to do work for clients. I have also completed a Charity Night walk covering a full marathon distance. I also like to travel, with my most recent destination taking me to South Asia and have planned a trip to the Bahamas and Mexico at the end of the year.

Job Opportunities at the QO There are a number of exciting opportunities coming up at the QO, so if you want to come and work with our fun and lively team why donâ&#x20AC;&#x2122;t you take the time out and apply.

We have two Band 4 programme support assistant jobs available. Largely working in the Friends and Family team, these posts will provide data management and administrative support to the Quality Observatory. You can apply online at NHS Jobs by clicking here. The person specification and job description are also available from the same link. If you have any queries around these roles please contact Rebecca.matthews@qualityobservatory.nhs.uk In addition to those, one of our team is going on maternity leave in November and we will be looking for a band 7 analyst for maternity cover. So this will be an ideal position for some who wants to dip their toes into the waters of the QO, or someone who is looking for a secondment opportunity. We need someone with solid analytical skills and a good working knowledge of at least MS Excel and SQL, although knowledge and experience of other data interrogation and presentation packages, and also web development skills would be advantageous.

This position will be going on to NHS jobs in the very near future, so keep an eye out for it. If you want to know more details please contact adam.cook@qualityobservatory.nhs.uk

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Fascinating Facts

Olympic Poem It's Olympic time once again, they're handing out the medals

Olympic Facts this time!

To people using oars and horses, diving boards and pedals.

The 1900 Olympics bizarrely featured both pigeon racing and live pigeon shooting as sports! I suspect not both at the same time.....

See them hare around the track with a burst of speed, See them score one more point to go into the lead.

Watch the boxers duck and dive and land another blow, And the other fighters twist and grapple, going for a throw.

There's the mighty weightlifter with a perfect snatch, Or pop over to the tennis courts and view a doubles match.

There are sports upon the water and sports upon the land,

Bonus fact, the 1896 Olympics featured the 100m Freestyle for Sailors; this was only open to competitors from the Greek Navy. The Olympics that year were held in.... Athens.

Coincidence??

Some where they sit down and others where they stand.

There are balls of different shapes and many different sizes Batted, tossed, passed and bounced; all eyes upon the prizes.

Weapons there are brandished - sword and bow and gun, Every eager sportsman keen not to be outdone.

Medals for the victors, Golden, Silver and Bronze, With all the winning athletes now heroes and icons.

Simon saysJJ. Apparently, according to the internet, banging your head against a brick wall burns 150 calories per hour! Good to know when you are dealing with information flows in the NHS!

Knowledge matters is the newsletter of NHS Quality Observatory. To discuss any items raised in this publication, for further information or to be added to our distribution list, please contact us.

Hosted by: South Central & West Commissioning Support Unit

E-mail: info@qualityobservatory.nhs.uk To contact a team member: firstname.surname@qualityobservatory.nhs.uk