Improving Outcomes for People with Diabetes in Primary Care National Conference - Wednesday, 8 February 2017 Programme

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2 Improving Outcomes for People with Diabetes in Primary Care National Conference - Wednesday, 8 February 2017 Programme

3 Improving Outcomes for People with Diabetes in Primary Care National Conference - Wednesday, 8 February 2017 Programme

4 Improving quality: what works? Martin Marshall Professor of Healthcare Improvement, UCL Improving quality for people with diabetes in primary care 8 th February 2017

5 Challenges 1. What we do to improve patient care and health services is insufficiently influenced by science 2. Science is insufficiently focused on the needs of those undertaking improvement activities

6 Translation gap T1 T2 Basic Sciences Clinical Sciences implementation benefit for patients The sciences that influence patient care 6

7 Translation gaps T1 T2 T3 Basic Sciences Clinical Sciences Improvement Sciences benefit for patients The sciences that influence patient care 7

8 Some things that we know from theory and empirical evidence about how to organise and deliver high quality health care (but often fail to put into practice)

9 1. We know what good looks like High Performing Healthcare Systems: Delivering Quality by Design Baker GR, MacIntosh-Murray A, Porcellato C, Dionne L, Stellmacovich K, Born K, Develop quality as a core strategy 2. Ensure organisational skills to support improvement 3. Use information as a platform for change 4. Focus on learning 5. Develop leadership

10 2. We know that there are many ways of improving quality Clinical teams Education and training Clinical audit Peer review/ collaboration Guidelines Health system Performance management Regulation Incentives/sanctions Competition Commissioning Organisations Org. development TQM/CQI, BPR, PDSA, Lean, 6 sigma

11 but most of them are usually only moderately effective

12 4. We know that improvement efforts are most effective when we use multiple interventions which combine technical and social elements

13 5. We know that improvement requires more than an effective intervention Intervention Implementation Context

14 6. We know culture is important and changing it is difficult Journal of Health Services Research and Policy, 2007

15 7. We know that we need to adopt a whole systems approach to change

16 Sources of behaviour Intervention functions Policy categories Behaviours Psychological Physical

17 8. We know that all efforts to improve have unintended consequences

18 9. We know that restructuring health services can be a distraction

19 10. We know something about different approaches to achieving change Stacey, 2012

20 So how can we put these lessons more effectively into practice?

21 Mobilising knowledge Problem Nature of evidence Nature of decision process Solution Knowledge transfer A product One-off event Improved dissemination of evidence to users ( Push ) or demand for evidence from users ( Pull ) Knowledge production A process Iterative social process Work together to define, refine, generate and implement evidence ( Co-creation ) Adapted from Canadian Health Services Research Foundation, 2003

22 Researcher-in-Residence Model Barnsley FC Poet-in-residence All England Tennis Club Artist-in-residence British Library Innovator-in-residence

23 Researcher-in-Residence Model 1. The researcher is a core member of an operational team 2. They are explicit about their expert contribution to the team: the evidence base theories of change evaluation, both formal and informal use of data 3. Their focus is on negotiation and compromise of their expertise rather than imposition a meeting of experts

24 Examples Operational Researcher-in-Residence at Great Ormond Street Hospital for Children Health Service Researcher-in- Residence in Whittington Health Integrated Care Organisation Anthropologist-in-Residence at UCLH

25 Examples Critical Discourse Social Scientist-in- Residence in an East London integrated care programme Political Scientist-in-Residence in Newham general practice Health Service Researcher-in-Residence in Essex care homes

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27 National Diabetes Audit, Some findings and their implications Bob Young NDA Specialist Clinical Lead

28 National Diabetes Audit National Diabetes Footcare Audit Transition Audit National Pregnancy in Diabetes Audit nda_trans National Diabetes Audit Insulin Pump Audit National Diabetes Inpatient Audit

29 National Diabetes Audit National Diabetes Footcare Audit Transition Audit National Pregnancy in Diabetes Audit nda_trans National Diabetes Audit Insulin Pump Audit National Diabetes Inpatient Audit

30 NDA Continuous Linked Data GP and Specialist Electronic Records (Routine Records) Core NDA (2004): NHS number, Diabetes Type, Year, Sex, Post Code (IMD), YoB BMI, Smoking, BP, HbA1c, TC, egfr, UACR Education, Pump Data, Foot (& Eye) checks NPiD: Antenatal NDFA: Foot Ulcers Transition: NPDA (2015) Hospital Episode Statistics/PEDW NHS number Admission for DKA, Amputation, Dialysis/Transplant, Angina, MI, HF, Stroke Deliveries; NNC Foot Disease Admission Specialist Care OPD ONS (MRIS) NHS number Date of death Cause of Death Unlinked (snapshot): NaDIA: Inpatients (2011)

31 Participation Participation 82.4 per cent (2,721,292 PWD). Participation <50% in only 16 CCGs. For more information on the level of participation in by CCG and LHB please see the participation report. A dashboard showing participation over the last 3 years for CCGs and LHBs can be found here. 31

32 What this talk will cover NDA Type 2 Diabetes core reports Variation and possible explanations for Variation in: Blood Glucose Treatment Target achievement rates (HbA1c<58mmol/l (7.5%) Blood Pressure Treatment Target achievement rates (BP<140/80) 32

33 Characteristics of People with Type 2 Diabetes Age and gender of patients with Type 2 and other diabetes England and Wales, Male > Female Older > Younger 36% < 65yr 33

34 Care Processes Time Series Percentage of people with diabetes receiving NICE recommended care processes by care process, diabetes type and audit year England and Wales Type 2 and other HbA1c Blood pressure Cholesterol Serum creatinine Urine albumin * Foot surveillance BMI Smoking Eight care processes * There is a health warning regarding the screening test for early kidney disease (Urine Albumin Creatinine Ratio, UACR) prior to ; please see the NDA Data Quality statement 3,4. Please see full list of footnotes in the definitions and footnote section. 34

35 Treatment Target Time Series Percentage of people with diabetes achieving their treatment targets by diabetes type and audit year England and Wales Type 2 and other HbA1 c < 58 mmol/mol Blood pressure < 140/80* Cholesterol < 5mmol/L Meeting all three treatment targets

36 Treatment targets - Locality Variation, Type 2 The range of CCG/LHB treatment target achievements for people with Type 2 and other diabetes, England and Wales HbA1c <48mmol/mol (6.5%) HBA1C <=58mmol/mol (7.5%) HbA1c <=86mmol/mol (10.0%) Treatment target BP <=140/80 Cholesterol <4mmol/L Cholesterol <5mmol/L Meet all three treatment targets 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of patients 36

37 T2 Treatment Targets Salford Practices % HbA1c<58mmol/mol (7.5%) 65.2% % BP<140/ % 37

38 T2 Treatment Targets Newham Practices % HbA1c<58mmol/mol (7.5%) 63.4% % BP<140/ % 38

39 T2 Treatment Targets Barnet Practices % HbA1c<58mmol/mol (7.5%) 67.4% % BP<140/ % 39

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41 So What Could Explain the Variation? Just Normal Cause Variation? Multivariate Statistical models show that Care Process Completion Rates ARE appreciably explained by age, sex, duration and type of diabetes, ethnicity, social deprivation (C statistic 0.8+) Multivariate Statistical models show that Treatment Target Achievement rates ARE NOT appreciably explained by age, sex, duration and type of diabetes, ethnicity, social deprivation (C statistic 0.6) Can that Really be True? 41

42 Closing off the escape routes I see that our diabetes treatment targets are lower than others, but we have an older population Our practice is sitting in a deprived area which is very different to practices generally. There s variation here because we re not the same as our local CCGs we re different with high numbers of ethnic minorities.

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44 CCG average T2 DM Treatment Target Rates % lowest two IMD Quintiles %HbA1c <58mmol/mol %BP<140/80 England Salford Newham Barnet

45 T2 Treatment Targets Salford Practices % HbA1c<58mmol/mol (7.5%) % BP<140/80 45

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47 Treatment Target By Age Percentage of people with Type 2 diabetes achieving all three treatment targets by age England and Wales Percentage 100% 90% 80% 70% 60% 50% 40% Type 2 and Other 30% 20% 10% 0% Age of person with diabetes 47

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55 Conclusions Treatment target variation does not seem to be explained by differences in patient characteristics Some of the treatment target variation is not normal cause People of Working Age and younger should be a priority for improving Treatment Target Achievement Rates Change is possible Practices can use NDA results with confidence: to benchmark themselves and select priorities for improvement to measure the effectiveness of their improvement projects 55

56 RCGP QUALITY IMPROVEMENT TOOLKIT FOR DIABETES CARE Roger Gadsby, NDA GP Lead

57 WHY NOW? Increasing demand Increasing complexity Increasing elderly Pressure on resources Improvements need to be effective, efficient and sustainable

58 THE QUALITY IMPROVEMENT WHEEL

59 PILOT PROJECT Betsi Cadwaladr 8 practices Wiltshire 5 practices Southport 9 practices Walsall 7 practices

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61 USEFUL QI TOOLS Context Process Mapping Fishbone diagrams Model for improvement/pdsa

62 CONTEXT CHECKLIST Culture Leadership Team working Technological Capacity Social/demographics Capability Opportunity Motivation

63 CONTEXT CHECKLIST Immediate solutions leader technology Solutions difficult involve patients time motivate others in QI

64 PROCESS MAPPING Used in process of review appointments Used in first abnormal HbA1C to first appointment with diabetes nurse Areas to improve invitations dealing with results

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66 FISHBONE DIAGRAM PATIENT Poor concordance with therapy Side effects Maximum Tolerated therapy Abnormal BPs not being followed up BP not being taken No search for abnormal BP No prompt on computer screen DOCTOR Not aware of target Does not believe target appropriate TARGET BP NOT MET Abnormal BPs not being followed up PROCESS

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68 COMMON AREAS FOR IMPROVEMENT Process ACRs Numbers attending for review Responding to HbA1C results Targets cholesterol HbA1C Blood pressure

69 SATISFACTION WITH TOOLS

70 DATA National Diabetes Audit QOF PRIMIS Eclipse Audit plus Practice soft ware systems

71 DISPLAYING DATA Line graph - % HbA1C < 58 cumulative ACRs Run chart foot checks cholesterol < 5 Visual display

72 QI GUIDES - GENERIC Quality improvement for General Practice Mini guides run charts process mapping MFI/PDSA

73 TRAINING MATERIALS Presentation Group work context process mapping MFI/PDSA run charts

74 PROJECT MANAGEMENT TOOLS Follow up check list Multi practice plan

75 REPORT AND EVALUATION As submitted to HSCIC (Now NHS Digital)

76 EVALUATION TOOLS Reflection template Interview template Baseline questionnaire Follow up questionnaire

77 EVALUATION

78 CONTEXTUAL FACTORS

79 PROCESS FACTORS

80 IMPACT

81 Percentage of cholesterol results 5mmol/l

82 PATIENT INVOLVEMENT Little evidence in project Webinar

83 ENGAGEMENT CCG or LHB involvement can have a significant impact on success.

84 FURTHER INFORMATION RCGP QI toolkit for diabetes care

85 Diabetes care in general practice: a person with diabetes experience Marianne Littleford Patient representative, NDA Partnership Board

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87 GET IN TOUCH: