1. Self Management, Multimorbidity, Shared Decision Making and Care Planning with People who have Long Term Conditions Nigel Mathers Professor of Primary Medical Care, University of Sheffield Vice Chair, Royal College of General Practitioners South Yorkshire GPSTP June 2013 Academic Unit of Primary Medical Care

2. 2 Long Term Conditions and Personalisation of Care Background ”the ageing population and the increased prevalence of chronic diseases require a strong reorientation away from the current emphasis on acute and episodic care towards prevention, self care, and care that is well-coordinated and integrated.” The King’s Fund, 2011

3. 3 Shared Decision Making, Care Planning and the use of Patient Decision Aids 1. Long Term Conditions: 15.4m people in England have one or more long term conditions (LTCs) Utilisation of health services is high amongst the LTC group – they account for 30% of the population, but 70% of NHS spending (c. £70bn) The number of people with multiple conditions is projected to increase and this will put pressure on NHS budgets LTCs are strongly linked to health and economic inequalities While the majority of people with LTCs are elderly by no means all

4. The person who lives with an LTC: Day to day management is self management

5. Our grossly underutilized workforce: [ people who live with LTCs]

6. 2. Self management ; many tasks, many challenges

7. The domains of self management: My condition (Biological) What I do (Social / Behavioural) The way I feel (Psychological)

8. 3. Patient Activation = knowledge, skills and confidence to manage one’s own health and healthcare Knowledge (Biological) Skills (Social / Behavioural) Confidence (Psychological)

9. Strategies to support people on their ‘journey of activation’

10. Public services face unprecedented challenges The commonest long term condition is: Multiple long term conditions

11. 11 Shared Decision Making, Care Planning and the use of Patient Decision Aids 4. Multimorbidity and Long Term Conditions: The Picture in Scotland Clinical data from 310 Scottish general practices for 1,754,133 registered patients was provided by the Primary Care Clinical Informatics Unit (“PCCIU data”) Clinical data from 40 Scottish general practices linked to hospital admissions data (“ISD and PCCIU data”) Stewart Mercer, Professor of Primary Care Research, University of Glasgow: SSPC National Lead for Multimorbidity Research stewart.mercer@glasgow.ac.ukstewart.mercer@glasgow.ac.uk Bruce Guthrie, Professor of Primary Care Medicine, University of Dundee: Living Well with Multimorbidity Epidemiology work-stream lead b.guthrie@dundee.ac.ukb.guthrie@dundee.ac.uk Sally Wyke, Professor of Interdisciplinary Research, University of Glasgow: sally.wyke@glasgow.ac.uksally.wyke@glasgow.ac.uk

12. 12 Shared Decision Making, Care Planning and the use of Patient Decision Aids Multimorbidity and Long Term Conditions

13. Shared Decision Making, Care Planning and the use of Patient Decision Aids How they relate

14. 14 Shared Decision Making, Care Planning and the use of Patient Decision Aids Multimorbidity and Hospital Admissions

15. 15 Shared Decision Making, Care Planning and the use of Patient Decision Aids 5. Shared Decision Making Shared decision Making is ‘a process in which clinicians and patients work together to select tests, treatments, management or support packages, based on clinical evidence and the patient’s informed preferences. It involves the provision of evidence-based information about options, outcomes and uncertainties, together with decision support counselling and a system for recording and implementing patients’ informed preferences.’ Coulter A and Collins A. 2011. Making shared decision-making a reality: no decision about me, without me [pdf] London. The Kings Fund. Available at http://www.kingsfund.org.uk/publications/nhs_decisionmaking.html [Accessed 25 April 2012]

17. 17 Shared Decision Making, Care Planning and the use of Patient Decision Aids Shared Decision Making NHS patient Surveys (2002-9) 46-49% patients want more involvement in treatment decisions 2010 1 in 3 patients in Primary Care 1 in 2 patients in Hospital

18. 18 Shared Decision Making, Care Planning and the use of Patient Decision Aids Benefits of Shared Decision Making Better Consultations Clearer Risk Communication Improved Health Literacy More Appropriate Decisions Fewer Unwanted Treatments Healthier Lifestyles Improved Confidence and Self-efficacy Safer Care Reduced Costs Better Health Outcomes

19. 19 Shared Decision Making, Care Planning and the use of Patient Decision Aids

20. 6. What are Patient Decision Aids (PDAs)? Evidence base for treatment options Clarification of people’s values Systematic guidance to inform decisions Shared Decision Making, Care Planning and the use of Patient Decision Aids 20

21. 21 Shared Decision Making, Care Planning and the use of Patient Decision Aids

22. Shared Decision Making, Care Planning and the use of Patient Decision Aids The PANDAs decision aid: For doctors and nurses in General Practice For people with Type 2 diabetes (T2DM) who are making treatment choices Purpose of the study: To determine the clinical effectiveness of the PANDAs decision aid. Primary Research Question: “Does the use of the PANDAs decision aid improve decision quality in patients with T2DM who are making decisions whether or not to start insulin in General Practice?” 22

23. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [1] Design: A cluster randomised controlled trial Intervention: Brief training of clinicians Pre-consultation familiarisation with the PDA Use of PDA by patients and clinicians in the consultation Control: Usual care (no PDA) Participants: 175 people with T2DM from 49 General Practices randomised into intervention (n=25) and control (n=24) groups. 23

24. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [2] Inclusion criteria: Practices: >4 partners List size >7,000 T2DM > 1% of Practice population Patients: People with T2DM (age >21) taking at least 2 oral glucose- lowering drugs at maximum tolerated dose Most recent HbA1c >7.4% (>57 mmols/mol) or Advised in preceding 6 months to add or consider changing to insulin 24

25. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [3] Outcome measures and follow-up: Primary outcome measure: Decisional conflict based on the Decisional Conflict Scale score (indicator of decision quality) Secondary outcome measures Knowledge: which treatment option most effective in reducing blood glucose and diabetic complications? Realistic expectations: self-report of chances of experiencing hypoglycaemia, gaining weight and developing complications Preference option: preferred treatment of initiating insulin, adhering more to diabetes advice, or making no change Participation in decision making (Control Preference Scale) Regret: for decision made (Regret Scale) 25

26. 26 Shared Decision Making, Care Planning and the use of Patient Decision Aids

27. InterventionControl Number of Practices2524 List Size7,510 (3,129-20,900)7,325 (1,974-13,500) People with diabetes350(96-912)356 (143-634) No of partners5 (1-13)5 (2-10) No of practice nurses3 (1-6)3 (1-5) IMD* score30.35 (range 8.9 - 59.5)30.20 (range 6.5 - 55) Study practice profile (mean and range) * Index of Multiple Deprivation 27 Shared Decision Making, Care Planning and the use of Patient Decision Aids

28. Make No Change Follow the diabetes advice more regularly Start insulin I am not sure Total Control 33 (42.3%) 29 (37.1%) 9 (11.5%) 7 (9%) 78 Intervention 32 (34.7%) 38 (41.3 %) 17 (18.4%) 5 (5.4%) 92 Total 65 67 26 12 170 (X 2 3 =2.88, p =0.410 ) Preferred choices of patients in intervention and control groups post-consultation 28 Shared Decision Making, Care Planning and the use of Patient Decision Aids

29. InterventionControl Mean difference in HbA1c unadjusted Mean difference in HbA1c adjusted* 95% CI 8.64 (SD 1.37)8.40 (SD 1.31)0.2440.351-0.088 to 0.789 * adjusted for age, education, gender, baseline HbA1c, insulin status and clustering. P=0.117 The effect of the PANDAs decision aid on HbA1c at 6 months 29 Shared Decision Making, Care Planning and the use of Patient Decision Aids

30. How did you make your decision about your diabetes treatment? (n = 169) PassiveCollaborativeAutonomousTotal Control16 (21%)28 (36%)33 (43%)77 (100%) Intervention8 (9%)25 (27%)59 (64%)92 (100%) (X 2 =8.9, df=2, p=0.012) Decision making roles of patients in the intervention and control groups, post consultation with their doctor/nurse 30 Shared Decision Making, Care Planning and the use of Patient Decision Aids

31. CONCLUSIONS In people with diabetes who are making treatment choices in General Practice, use of the PANDAs decision aid: Reduces decisional conflict Improves knowledge Promotes realistic expectations Promotes autonomy without prolonging consultation time 31

32. 32 Shared Decision Making, Care Planning and the use of Patient Decision Aids

33. 33 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 1. Co-ordinated care Desired outcomes: people feel that the care they receive is seamless because it is organised around them and their needs.

34. 34 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 2. Patients engaged in decisions about their care Desired outcomes: all patients and carers can take an active role in decisions about their care and treatment because they are given the right opportunities, information and support.

35. 35 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 3. Supported self-management Desired outcomes: people with long term conditions can manage their condition appropriately because they have the right opportunities, resources and support.

36. 36 Shared Decision Making, Care Planning and the use of Patient Decision Aids 7. What is Care Planning? 1.Prepared pro-active Practice team 2.Informed engagement by people in their own care 3.Partnership working between Doctors/Nurses [HCPs] and people with Long Term Conditions [LTCs]

37. 37 Shared Decision Making, Care Planning and the use of Patient Decision Aids ‘The House’ IT: Clinical record of care planning & able to feed data into commissioning Consultation skills/attitude Integrated, multi- disciplinary team & expertise Senior buy-in & local champions to support & role model Emotional & psychological support Information/ structured education ‘Prepared’ for consultation Identify and fulfill needs Procured time for consultations, training and IT Quality assure and measure

38. 38 Shared Decision Making, Care Planning and the use of Patient Decision Aids Care Planning: the Sheffield experience (Stephenson, 2013)

40. Care fragmentation

41. R3 Shared Decision Making, Care Planning and the use of Patient Decision Aids RCGP Care Planning Programme: The Vision: A joint strategic approach to health improvement based on the concerted implementation of care planning in general practice, within the context of multimorbidity, and in partnership with a range of disease specific organisations; covering, for example, cardiovascular conditions, respiratory and musculo-skeletal conditions and cancer.

42. 42 Long Term Conditions and Personalisation of Care The RCGP Care Planning Programme Aims: To embed care planning into the ‘core business’ of General Practice To incorporate the development of care planning skills into the GP training curriculum and facilitate other educational initiatives for established GPs.

43. 43 Long Term Conditions and Personalisation of Care The RCGP Care Planning Programme: Objectives: Communities of Practice ‘Natural Laboratories’ Leadership facilitation Active Championing (“diffusion of innovation”) Primary Healthcare Team involvement Service redesign/delivery models Learning and training resources (GP curriculum) Improvement research (evaluation) Development of IT/Metrics Communication strategy

44. 44 Shared Decision Making, Care Planning and the use of Patient Decision Aids 8. Practice Variation

45. Understanding variation: the bad and the good. Mulley, 2011 Bad Variation (care not evidence-based) Poor research  professional uncertainty Poor knowledge  professional ignorance JAMA, 1988 Good Variation (care is patient-centered) Clinical differences among patients Personal differences among patients If all variation were bad, it would be easy to stop it. What is difficult is reducing the bad variation while keeping the good. Shared Decision Making, Care Planning and the use of Patient Decision Aids 10

46. Practice variation: when there is little or no evidence When to order a diagnostic test…? How often to see a patient with chronic disease…? When to admit a patient to a hospital…? When to admit a patient to intensive care…? How long a patient should stay in the hospital…? Shared Decision Making, Care Planning and the use of Patient Decision Aids 11

47. Variation: decreasing the bad and increasing the good Mulley, 2011 Decreasing bad variation (making care evidence- based) Improve knowledge management Improve communication No avoidable ignorance Increasing good variation (making care patient- centered) Recognize clinical differences among patients Honor personal differences among patients The only efficient way to reduce overuse, underuse, and misuse of care 12 Shared Decision Making, Care Planning and the use of Patient Decision Aids

48. 48 Shared Decision Making, Care Planning and the use of Patient Decision Aids Patient ‘Empowerment’ [Personalisation of Care] Long Term Conditions and Multimorbidity Shared Decision Making (Patient Activation) Use of Patient Decision Aids Care Planning Practice Variation

49. 49 Long Term Conditions and Personalisation of Care It’s time for change! Thank You

50. 50 Shared Decision Making, Care Planning and the use of Patient Decision Aids Questions?

51. 51 Shared Decision Making, Care Planning and the use of Patient Decision Aids

52. 52 Shared Decision Making, Care Planning and the use of Patient Decision Aids

53. Clinical Practice variation J Allison Glover, 1874-1963 1938: 10-fold variation in tonsillectomy 8-fold risk of death with surgical treatment The response: “…these strange bare facts of incidence…” “… tendency for the operation to be performed for no particular reason and no particular result.” “…sad to reflect that many of the anesthetic deaths… were due to unnecessary operations.” Shared Decision Making, Care Planning and the use of Patient Decision Aids 7

54. John E. Wennberg, 1973 Shared Decision Making, Care Planning and the use of Patient Decision Aids 17-fold variation in tonsillectomy 6-fold variation in hysterectomy 4-fold variation in prostatectomy “The need for assessing outcome of common medical practices” “Professional uncertainty and the problem of supplier-induced demand” Clinical practice variation: it’s rediscovery by Wennberg 8

55. 55 Shared Decision Making, Care Planning and the use of Patient Decision Aids

56. The PANDAs Decision Aid contains the following information in line with the International Patient Decision Aid Standards criteria: 1. Information about insulin and other treatment options Reasons for starting insulin The procedure for insulin injection Common concerns about insulin Treatment options: Make no change; lifestyle modification; insulin therapy 2. Presents probabilities of outcomes The advantages and disadvantages of each option are described in words, numbers and pictures (‘smiley faces’) 3. Patient value clarifications A list of patients’ values about the advantages and disadvantages of insulin therapy 4. Structured guidance 56 Shared Decision Making, Care Planning and the use of Patient Decision Aids Content of the PANDAs decision aid

57. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [7] Instruments Decisional Conflict Scale (DCS) 16 item scale with 5 subscales: uncertainty, informed, values clarity, support, effective decision Control Preference Scale (CPS) 5 item scale: 2 items active role, 1 item shared role, 2 Items passive role Regret Scale 5 item scale: measures distress or remorse after a healthcare decision 57

58. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [8] Statistical Analysis Using total DCS score as primary outcome: total number of participants 86 and total cluster size 17 Outcome variables treated as continuous Multiple regressions with generalised estimating equations (GEE) and exchangeable correlation to allow for clustering Multiple logistic regression with GEE was used for binary outcomes in the secondary analysis Analysis according to intention to treat principle 58

59. SubscoreInterventionControl Mean difference unadjusted Mean difference adjusted* 95% CI p value Uncertainty20.1 (16.6) 29.4 (20.8) -9.29-8.72 -14.9 to -2.53 p=0.006 Informed18.1 (13.3) 26.0 (16.6) -7.65-8.69 -13.3 to -4.10 p<0.001 Values Clarity16.7 (13.9) 26.7 (18.2) -9.74-9.84 -14.8 to -4.84 p<0.001 Support17.4 (13.1) 20.8 (15.3) -3.41-3.66 -8.58 to 1.25 p=0.144 Effective Decision 16.1 (14.4) 23.3 (15.2) -9.70-9.80 -16.8 to 2.75 p=0.006 Total Score17.4 (12.6)25.2 (14.9) -7.67-7.72-12.5 to –2.97 p<0.001 * adjusted for age, education and gender Comparison of decisional conflict scores between the intervention and control groups (0=no decisional conflict, 100=maximum decisional conflict). 59 Shared Decision Making, Care Planning and the use of Patient Decision Aids

60. Intervention Decision Aid Control Usual Care Unadjusted Odds Ratio Adjusted + Odds Ratio (95% CI) ICCp value Knowledge Number9580 Which choice has the greatest chance of lowering your blood sugar? 49 (51.6%) 23 (28.8%) 2.63 1.31 (1.14 to 1.50) 0.071<0.001 Which choice has the greatest chance of lowering your complications? 29 (30.5%) 23 (28.8%) 1.091.20 (0.07 to 19.05)0.2020.90 Realistic expectations If you take insulin, about how many times might you experience ‘hypos’ in a year? 77/95 (81.0%) 4/75 (5.2%) 77 ^-<0.001 * If you take insulin, about how much more weight might you gain in a year? 67/95 (70.5%)4/75 (5.3%) 42.5-<0.001 * Out of 100 people like you who take insulin, how many may get complications in five years? 25/95 (26.3%)4/80 (5%)^-<0.001 * + adjusted for clustering, insulin initiation, age, gender and education level ^ Numbers answering correctly in the control group were too few to control for clustering. * Chi-squared p value Secondary outcomes: Knowledge and realistic expectations (Questions answered correctly) 60 Shared Decision Making, Care Planning and the use of Patient Decision Aids

61. InterventionControl Mean difference unadjusted Mean difference adjusted* p value Regret Score44.6344.570.06 0.22 (-2.48 to 2.93) 0.872 Persistence with chosen option 68.1%56.3%1.65 † 1.17 ^ (1.00 to 1.36) 0.041 * adjusted for age, education, gender, baseline HbA1c, insulin status and clustering † Crude odds ratio ^ Adjusted odds ratio Comparison of the decision Regret Score and persistence with chosen option between the intervention and usual care groups after six months 61 Shared Decision Making, Care Planning and the use of Patient Decision Aids

62. Acknowledgements: Funding body: National Institute for Health Research (NIHR), Research for Patient Benefit Programme UK [PB-PG-0906-11248] NIHR National Trials Register: 14842077 Sheffield Health and Social Care NHS Foundation Trust Ethics permission: North Sheffield Research Ethics Committee (07/Q2308/53) Expert specialist advice: Professor Simon Heller Members of the PANDAs Advisory Group Members of the Sheffield Diabetes UK Group ALL DOCTORS, NURSES AND PEOPLE WITH DIABETES WHO PARTICIPATED IN THE PANDAs TRIAL R1 Shared Decision Making, Care Planning and the use of Patient Decision Aids

64. R2 Shared Decision Making, Care Planning and the use of Patient Decision Aids SINGLE DISEASE SPECIFIC SOLUTIONS WILL NOT WORK

65. R5 Shared Decision Making, Care Planning and the use of Patient Decision Aids Decision plane showing the distribution of simple consent, informed consent, and shared decision making within 4 types of medical decisions Quadrant A: high risk, high certainty Consent type: Informed Shared decision making: absent Interaction: intermediate, enough for an adequately informed decision Example: laparotomy for gunshot wound of abdomen Quadrant B: high risk, low certainty Consent type: Informed Shared decision making: present Interaction: extensive, including discussion of patient values, preferences, hopes and fears Example: mastectomy or lumpectomy plus radiation for early breast cancer Quadrant C: low risk, high certainty Consent type: simple Shared decision making: absent Interaction: minimal or none Example: lower diruetic dose for patient with low serum potassium level Quadrant D: low risk, low certainty Consent type: simple Shared decision making: present Interaction: intermediate Example: lifestyle changes vs. medication for lyperlipidemia Zone of informed consent Zone of shared decision making Combined zone Certain (1 clear best choice) Certainty Uncertain >2 alternatives Risk HIgh Low

66. R6 RCGP Care Planning Programme Communities of Practice – Tasks Redesign the condition-specific pathway Contribute to evaluation Collect feedback and use agreed metrics Develop local systems of project management Medical ‘musts’ in multimorbidity Determine resource use within/between Practices Use agreed IT Participate in learning sets Develop and share local commissioning mechanisms Shared decision making, care planning and the use of patient decision aids

67. 67 Long Term Conditions and Personalisation of Care Context 15.4m people in England have one or more long term conditions (LTCs) Utilisation of health services is high amongst the LTC group – they account for 30% of the population, but 70% of NHS spending (c. £70bn) The number of people with multiple conditions is projected to increase and this will put pressure on NHS budgets LTCs are strongly linked to health and economic inequalities While the majority are elderly by no means all

69. 69 Long Term Conditions and Personalisation of Care Multimorbidity and Long Term Conditions The Picture in Scotland Clinical data from 310 Scottish general practices for 1,754,133 registered patients was provided by the Primary Care Clinical Informatics Unit (“PCCIU data”) Or clinical data from 40 Scottish general practices linked to hospital admissions data (“ISD and PCCIU data”) Stewart Mercer, Professor of Primary Care Research, University of Glasgow: SSPC National Lead for Multimorbidity Research stewart.mercer@glasgow.ac.ukstewart.mercer@glasgow.ac.uk Bruce Guthrie, Professor of Primary Care Medicine, University of Dundee: Living Well with Multimorbidity Epidemiology work-stream lead b.guthrie@dundee.ac.ukb.guthrie@dundee.ac.uk Sally Wyke, Professor of Interdisciplinary Research, University of Glasgow: sally.wyke@glasgow.ac.uk sally.wyke@glasgow.ac.uk

70. 70 Long Term Conditions and Personalisation of Care Multimorbidity and Long Term Conditions

71. 71 Long Term Conditions and Personalisation of Care Multimorbidity and Hospital Admissions

72. 72 Shared Decision Making, Care Planning and the use of Patient Decision Aids Wagner, 2004

73. 73 Long Term Conditions and Personalisation of Care ‘The House’ IT: Clinical record of care planning & able to feed data into commissioning Consultation skills/attitude Integrated, multi- disciplinary team & expertise Senior buy-in & local champions to support & role model Emotional & psychological support Information/ structured education ‘Prepared’ for consultation Identify and fulfill needs Procured time for consultations, training and IT Quality assure and measure

74. 74 Long Term Conditions and Personalisation of Care Care Planning: the Sheffield experience (Stephenson, 2013)

75. 75 Long Term Conditions and Personalisation of Care NHS Funding  arctic’ scenario: real funding cuts (-2 per cent for first three years, -1 per cent for second three years)  ‘cold’ scenario: 0 per cent real growth in six years  ‘tepid’ scenario: real increase (+2 per cent for first 3 years, then +3 per cent for the next three years). Appleby J, Crawford R, Emmerson C. (2009) How cold will it be? http://www.kingsfund.org.uk/research/publications/ how_cold_will_it_be_html (Last accessed on 11 October 2009).http://www.kingsfund.org.uk/research/publications/ how_cold_will_it_be_html

76. 76 Long Term Conditions and Personalisation of Care National QIPP Programme for LTC Personalisation Risk profiling and stratification of risk Integrated community teams with single lead professional contact for Care Planning Transferring knowledge and control back to the patient Enabled by Change in tariff moving to “A Year of Care” Supported by Futures Forum report on Integration

77. 77 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 1. Co-ordinated care Desired outcomes: people feel that the care they receive is seamless because it is organised around them and their needs.

78. 78 Long Term Conditions and Personalisation of Care RCGP Care Planning Programme: The Vision: A joint strategic approach to health improvement based on the concerted implementation of care planning in general practice, within the context of multimorbidity, and in partnership with a range of disease specific organisations; covering, for example, cardiovascular conditions, respiratory and musculo-skeletal conditions and cancer.

79. 79 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 2. Patients engaged in decisions about their care Desired outcomes: all patients and carers can take an active role in decisions about their care and treatment because they are given the right opportunities, information and support.

80. 80 Long Term Conditions and Personalisation of Care The Richmond Group of Charities Principles: 3. Supported self-management Desired outcomes: people with long term conditions can manage their condition appropriately because they have the right opportunities, resources and support.

81. 81 Long Term Conditions and Personalisation of Care RCGP Care Planning Programme Communities of Practice – Tasks Redesign the condition-specific pathway Contribute to evaluation Collect feedback and use agreed metrics Develop local systems of project management Medical ‘musts’ in multimorbidity Determine resource use within/between Practices Use agreed IT Participate in learning sets Develop and share local commissioning mechanisms

82. 82 Long Term Conditions and Personalisation of Care RCGP Care Planning Consortium: British Heart Foundation British Lung Foundation Macmillan Cancer Support Arthritis Research UK King’s Fund Health Foundation Primary Care Rheumatology Society Diabetes UK RCGP

83. 83 Long Term Conditions and Personalisation of Care SINGLE DISEASE SPECIFIC SOLUTIONS WILL NOT WORK

84. 84 Long Term Conditions and Personalisation of Care How they relate

85. 85 Long Term Conditions and Personalisation of Care Principles: 1.Co-ordinated Care 2.Patients engaged in decisions about their care 3. Supported self-management

86. 86 Shared decision making, care planning and the use of patient decision aids The RCGP Care Planning Programme Aims: To embed care planning into the ‘core business’ of General Practice To incorporate the development of care planning skills into the GP training curriculum and facilitate other educational initiatives for established GPs.

87. 87 Shared decision making, care planning and the use of patient decision aids The RCGP Care Planning Programme Objectives: 1. Build communities of Practice (‘Natural Laboratories’) Leadership facilitation Active Championing (“diffusion of innovation”) Primary Healthcare Team involvement Service redesign/delivery models 2. Develop a central reference (evaluation) group Learning and training resources (GP curriculum) Improvement research (evaluation) Development of IT/Metrics Communication strategy

88. 88 Shared decision making, care planning and the use of patient decision aids RCGP Care Planning Consortium: British Heart Foundation British Lung Foundation Macmillan Cancer Support Arthritis Research UK King’s Fund Health Foundation Primary Care Rheumatology Society Diabetes UK RCGP

89. Public services face unprecedented challenges The commonest long term condition is: Multiple long term conditions

90. Care fragmentation is the norm and waste is endemic

92. The person who lives with LTCs is the ultimate delivery mechanism: Day to day management is self management

93. The development of the Patient Activation Measure Other constructs Locus of control Self efficacy Readiness to change Tend to be used as predictors of individual behaviours and do not capture the broad range of knowledge, skills, beliefs and behaviours needed to manage LTCs

94. Patient Activation Measure: 22 items Development of the PAM. Hibbard J et al. Health Services Research 2004; 39(4): 1009-1032

95. Impact of shared decision making: some examples Surgery for benign prostatic hyperplasia in the United States and United Kingdom Hysterectomy for benign uterine conditions in the United Kingdom Surgery and percutaneous intervention for coronary disease in Canada Surgery for back pain in the United States Surgery for hip and knee pain in Canada Shared Decision Making, Care Planning and the use of Patient Decision Aids 15

96. 7% of population 14% of population

97. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [3] Intervention Training of doctors and nurses (1-2 hours): Principles of shared decision making Importance and clinical effectiveness of decision aids Evidence for treatment options in poorly controlled T2DM Essential skills in risk communication 97

98. Shared Decision Making, Care Planning and the use of Patient Decision Aids METHODS [6] Baseline data: Practice and clinician profile Patient sociodemography Diabetes profile (duration, complications, prescription, glycaemic control) Co-morbidities (hypertension, coronary artery disease, dyslipidaemia, chronic kidney disease) Previous T2DM education 6 month follow-up data: HbA1c Regret score Persistence with decision 98

99. Shared Decision Making, Care Planning and the use of Patient Decision Aids Shared Decision Making and Care Planning Patient decision aids promote: Realistic expectations Value decision concordance Patient involvement in decision making They also improve knowledge 99