Dynamic Data in Action: better patient care, improved public health

PRIMIS

Fifth Annual Conference 11 – 12 May 2005

Piecing Together the Future

Dynamic Data in Action: better patient care, improved public health

Sheila Teasdale

Julie Richardson

Dr Michael Soljak

PRIMIS



Overview of session

• What do we mean: ‘dynamic data’?– Sheila Teasdale, PRIMIS Service Director

• Using Dynamic Data: Case Study– Julie Richardson, Greenwich PCT

• Why ‘Choosing Health’ needs you– Dr Michael Soljak, Health Improvement Directorate, DoH

• Current activities, future plans– Sheila Teasdale

PRIMIS



‘Dynamic Data’• Recorded at the point of care

– High quality• Complete, accurate, relevant, accessible and timely

– Supports• direct patient care• avoidance of error• medicolegal aspects• cohort care (chronic disease management)• preventive care and health promotion• clinical audit and clinical governance• GMS quality indicators• PCT data requirements• data for other clinicians and other health sectors• national data requirements

PRIMIS



A case study

Julie Richardson

Greenwich PCT

PRIMIS



Why ‘Choosing Health’needs you

Dr Michael Soljak

Health Improvement Directorate

Department of Health

PRIMIS



Current activities,future plans

Sheila Teasdale

PRIMIS



Current activities, future plans

• Supporting public health campaigns– NCASP National Diabetes Audit– Flu– Smoking and obesity– Pneumococcal vaccine– Diabetes screening– CHD risk

PRIMIS



National Diabetes Audit

• Queries to assess activity for Diabetes NSF– web interface to transmit to NCASP database– merging primary and secondary care data– analysis available online (PIANO)– started April 2004– report on first year due out June 2005– this year’s queries being prepared

PRIMIS



Flu campaign

• Health Protection Agency– recommended use of PRIMIS Flu query sets

• at-risk cohort• vaccine uptake in at-risk groups

– done in report-style, analyse-style and CHART formats

– September 2004 – January 2005– now discussing this year’s campaign

PRIMIS



Smoking and obesity

• Health Improvement Directorate, Department of Health– Phase 1: query sets to support preparation of Local

Delivery Plans• report-style with LDP analysis tool and email feedback

proforma• analyse-style with feedback proforma• January – March 2005

– Phase 2: query sets to support monitoring of LDPs• queries and tools as above• new CHART version with possibility of web transmission later• starts soon, awaiting new guidance

PRIMIS



Pneumococcal vaccine

• Vaccine Tracking Unit, Department of Health– Phase 1: query sets to support reporting of

achievement to 31/3/05• subset-style for transcription of data into VTU website• CHART with summary sheet for transcription of data into VTU

website• starts April 2005

– Phase 2: development plan for next year• queries and tools as above• discussing new CHART version to try out web transmission

PRIMIS



Future plans

• Continue work on all the above• Diabetes screening

– research collaboration– screening uptake rates for at-risk group

• obese patients aged over 40

• CHD primary prevention– Health Improvement Directorate, Department of Health

• in discussion about criteria

• Need for co-ordination• Future through SUS

PRIMIS



Dynamic Data in Action: better patient care, improved public health

Sheila Teasdale

Julie Richardson

Dr Michael Soljak

PRIMIS



Using Dynamic Data:Case Study

Julie RichardsonGreenwich Primary Care Trust

PRIMIS



Immunising against influenza: why bother?

•Deaths in vulnerable people

•Increased morbidity with other conditions

•Increased vulnerability to external injuries

•Source of transmission to others

•PCT/GP targets!

PRIMIS



Flu vaccination scheme - background

• Local reporting for incentive scheme • Focus on over 65s only

– (65% - 50p/patient. 70% - £1/patient)

• National reporting to feed the machine

PRIMIS



How I got involved

• Complaints / requests for help from practices around reporting

• All filling in forms• All trying to find their own

solutions

PRIMIS



SELHPU

• Flu co-ordinators’ meeting

• Explain how PRIMIS / MIQUEST can help

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Benefits of MIQUEST

• Identify all at-risk patients

• Raise profile of under 65s at risk

• Proactive not reactive

• Simpler for practices

• Uniform reporting

• Robust data for planning

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SELHPU

• Agreed good idea but: -– 3 PCTs wanted to do their own thing– 2 PCTs had no PRIMIS facilitator– Greenwich went on alone

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Action

• Flu seminar - explain process

• September - identify at-risk patients

• October - uptake query set

• November - uptake query set

• December - uptake query set

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Process• Download query set from PRIMIS website• Use MIQUEST Query Manager to customise

query set for individual practices• Email queries to practices with instructions

on how to run through MIQUEST• Practice runs queries & views results in

Excel• Practice either fills in form or anonymises

data & returns to PCT

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"The problem is not that there are problems. The problem is expecting otherwise and thinking that having problems is a problem." -Theodore Rubin

PRIMIS



All practices need an e-mail account

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… and they need to know how to use it

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Need to be able to send query files…

Dr X flu.zip Dr X flu.julie

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…and flu co-ordinators need to learn how to read

Instructions

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…and practices need the software to open them

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… and to use them

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Emphasise deadlines

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Maximising the impact of immunisation

we want to identify those practices for the greatest impact

•Practice A: 414 immunised out of 444

•Practice B: 44 immunised out of 144

•Practice C: 144 immunised out of 444

PRIMIS




% Not Immunised

6.76

69.4467.57

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Practice A Practice B Practice C

practice

% o

f at

ris

k p

atie

nts

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Real People Not Immunised

30

100

300

0

50

100

150

200

250

300

350

Practice A Practice B Practice C

practice

nu

mb

er o

f at

ris

k p

atie

nts

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What else can we learn?

Integrity of practice-based registers

Relationship of immunisation to consultation rates• Influenza • Other morbidity

Indication of practice capacity

Alternative strategies to reduce transmission

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Outcome

• For PCT– Uniform, comparable data from all practices– Baseline for future years– Targeted planning

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Outcome

• For Practice– Identify and vaccinate at-risk patients– Vaccine ordering– Simpler reporting

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Outcome

• For Patients– Better health?

PRIMIS



The human element

• November ’03 – January ’04 St Thomas’ Hospital had 27 confirmed cases of flu - all under 65 (3 adults, 24 children)

• 12 of the 27 were in ‘at-risk’ groups• 3 had hospital acquired infection, 24 were

community acquired• Of those that were community acquired, 1

attended a special needs playgroup where none of the children were vaccinated. He died.

Dr Michael Soljak

Health Improvement Directorate

Why “Choosing

Health” Needs You

What On Earth Is A PSA Target?

• Every two years (2004) HM Treasury conducts a Spending Review

• As part of the review, Public Service Agreement targets are agreed with Government Departments

• PSA targets were converted into the 2005-8 Priorities & Planning Framework

• The PPF is converted into PCT Local Delivery Plan (LDP) “lines” like the smoking and obesity queries

The Wanless Report: Securing Good Health For The Whole

Population3.135 “…practice based patient registers could be developed to record

information on disease, medication and risk factors. Such knowledge could be used not only to improve chronic disease management, but to guide local activity aimed at health improvement and the primary prevention of disease”.

Recommendation 9.14 “An experiment should be established across primary care to assess the benefits of monitoring risk… It would also produce evidence about the effectiveness of information to assist personalised risk management and disease prevalence in local populations. The experiment should be directed towards areas of inequality”. PRIMIS IT Facilitator

The National Priority Areas

Local Delivery Plan Lines

Smoking status among people aged 15 to 75 years, as recorded in GP records

• Line 1: Number of people aged 15 to 75 years on a GP register, recorded as being a smoker in the last 15 months

• Line 2: Number of people aged 15 to 75 years on a GP register, with a smoking status recorded in the last 15 months

• Line 3: Total number of people aged 15 to 75 years on a GP register

Obesity among people aged 15 to 75 years, as recorded in GP records

Line 1: Total number of people aged 15 to 75 years on GP register, recorded as having a BMI of 30 or greater in the last 15 months

Line 2: Total number of people aged 15 to 75 years on GP register, with a BMI recorded in the last 15 months.

Line 3: Total Number of people aged 15 to 75 years on GP register.

Synthetic Smoking Prevalence

PCTSmoking

PrevalenceChiltern and South Bucks 19.9%Wokingham 20.2%East Elmbridge and Mid Surrey 20.9%Harrow 21.1%Islington 38.1%Eastern Hull 39.6%North Manchester 39.8%North Liverpool 40.0%

Source: Health Development Agency

Baselines & Trajectories: Smoking

Percent On Register With Smoking Status Recorded In Last 15 Months

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mar-05 Mar-06 Mar-07 Mar-08

Baselines & Trajectories: Smoking

Percentage on a GP register recorded as smokers

0%

5%

10%

15%

20%

25%

30%

Mar-05 Mar-06 Mar-07 Mar-08

Health Survey = 26% smokers

Baselines & Trajectories: Obesity

Percentage on GP register, with a BMI recorded in the last 15 months

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2004/05 2005/06 2006/07 2007/08

Baselines & Trajectories: Obesity

Percentage on register recorded as having a BMI of 30 or greater in the last 15 months

0%

2%

4%

6%

8%

10%

12%

14%

16%

2004/05 2005/06 2006/07 2007/08

Health Survey = 21.4% obese

The Future• There is a big task ahead, firstly in loading queries/CHART and obtaining

baseline data from all practices• Secondly in beginning to improve data quality and timeliness• A submission has been made to the QOF review to incentivise recording

and intervention for smoking and obesity from April 2006• In the meantime, PCTs will need to support practices to improve data

quality through the “traditional” routes- overtime, loan of data entry staff etc

• A Primary Prevention Query Library will be developed to enable interested PCTs to obtain further data e.g. age-sex breakdowns, other health risk factor prevalences etc

Dynamic Data in Action: better patient care, improved public health

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