Supporting Better Care Fund resubmissions Risk Stratification and - PowerPoint PPT Presentation

Supporting Better Care Fund resubmissions Risk Stratification and information governance Webinar 28 August 2014 CONFIDENTIAL AND PROPRIETARY

Overview of webinars Several webinars will be held across 3 topics over the next 3 weeks; Today’s webinar will focus on Risk stratification and IG related to it Topic Dates Facilitator 1 Section 75 28, 29 Aug 12.00-13:30 3, 5 Sep � David Owens � Olwen Dutton 2 Risk stratification and 28 Aug, 9.00-10:30 � Oleg Bestsennyy information governance Additional dates TBC � Debbie Terry 3 Financial analysis TBC � Oleg Bestsennyy 1

Today’s content 1 Risk stratification 40 mins + 10 mins Q&A 2 Information governance 30 mins + 10 mins Q&A 2

Risk stratification contents 1 A How risk stratification helps? B How do you do it to a gold standard? C What can be achieved in 2 weeks? 3

How risk stratification helps? A McKinsey research shows that there are 3 building blocks to a successful integrated care system Success in coordinated care 1 2 Understand Needs Organise Delivery Individual Multi-disciplinary care plans teams Care Self- Coordi- empowerment nation and education Support with Enablers 3 Payment Governance Information Leadership Support SOURCE: Carter, Chalouhi, Richardson – What it takes to make integrated care work (McKinsey Health 4 International, 2011); Amended and updated in 2014

How risk stratification helps? A A robust segmentation/stratification is the foundation for ensuring patient-centred planning In depth understanding of 2 Create evidence population needs with 1 2 based plans by segmentation/ understanding the stratification Evidence- right evidence- based Use best available 1 backed planning data to understand interventions for population needs segments of the quantitatively as well populations with Financial as qualitatively, making analysis expected impact, use of risk stratification 3 timing and cost and segmentation Outcomes and impact Financial analysis should 4 modeling set out the overall impact of initiatives (in terms of activity, 3 Outcomes should be selected to commissioner spend and crystalise the goals the HWBB sets for investment) by segment and the population; they should be stretching the costing and assumptions but achievable based on impact modeling of specific initiatives over the informed by the evidence based and next year, but should link to understanding of the population needs the five year plan 5

How risk stratification helps? A Two approaches to understanding patient needs: risk stratification and patient segmentation 1 2 Risk stratification: Grouping population Patient segmentation: Grouping population based on how likely people are to use based on common characteristics (e.g., age, services condition, demographics) Patient segmentation: Distribution of population of a certain CCG into 18 various segments Severe Mostly More than Learning Age One LTC SEMI Dementia Cancer Physical healthy one LTC disability Disability Data unavailable 18.4k 1.6k 0.1k 0.1k 0k <16 49.2k 17.1k 7.0k 0.9k 0.1k 2.7k 16-69 3.7k 4.5k 5.3k 0.1k 0.8k 3.2k 70+ Total ~ 115,000 71,252 23,213 12,382 1,198 897 5,932 SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13 15 � Better clinical decision-making: � Better clinical decision-making: innovative prioritisation of efforts and focus care delivery models � Identification of intensity of care support � Realignment of resources with patient required needs � Prioritisation of resources � Payment innovation for various segments based on need They are not mutually exclusive! Best in- class examples do them both in concert 6

B How do you do it to a gold standard? RISK STRATIFICATION Risk stratification: 20% of population with the highest risk of an acute admission in one locality drive 70%+ of health and social care expenditure… ����������������� ������ ������������ ���������� ��������� ��������� ��������� £26,587 4,450 £118.3m 11% ����� ����� ���� £8,007 ��� 40,044 £320.6m 29% ��������� £2,600 ��������� 133,473 £347.0m 31% ���� £714 �������� 266,950 £190.6m 17% ��������� ���� £303 88x 444,916 £134.6m 12% 889,883 £1,249 £1,111.2m Total SOURCE: McKinsey team analysis, HES 2011/12, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs 7

B How do you do it to a gold standard? … But only 36% of primary care RISK STRATIFICATION ������������� !���������������������� Rest of the ����������� Top 3 strata population �������� 80% Population 20% ��������������� ��������������� Emergency hospital 97% 3% ���������� spend ����� 14% Total hospital spend 86% ����������� ������������� Social care spend 87% 13% ������ ��������� ���������������� Community care 27% 73% ��������������� spend ����������� Primary care spend 36% 64% ���������������� ������������ Total spend 71% 29% SOURCE: McKinsey team analysis, HES 2011/12, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs 8

B How do you do it to a gold standard? SEGMENTATION, AGE AND CONDITION Patient segmentation: Independent variables included in regression analysis Diagnosis Other Not included Comment Reason for exclusion Number of Epilepsy Total spend � � Socially excluded groups, Not available in data LTCs Depriva- like the homeless may tion and have distinct care needs Heart failure and be a significant driver Asthma Age social and LVD of demand exclusion Hyperten- � � There is evidence to Not available in data Cancer Death and suggest end of life care sion end of life is a significant driver of care spend care CHD Stroke � � Main determinant of care No indicator that is Unpredict- demand among those who independent of spend Severe and do not have chronic outputs (so inclusion CKD able conditions would be circular) enduring � episodic No forward predictive mental power – an episode require- illness COPD of care in one year is ments not a good predictor (SEMI) 1 for the next � � Dementia Whether an individual had Not available in data Learning a learning disability was found to be significant in disability other sites Depression � � Whether an individual Not available in data severe physical disability Physical for which they received disability Diabetes social care was found to be significant in other sites 1 Psychosis, schizophrenia and bipolar disorders SOURCE: Nuffield trust research, clinical input 9

B How do you do it to a gold standard? SEGMENTATION, AGE AND CONDITION Example: Average patient spend (£k) varies dramatically between various segments in one UK locality Severe Mostly More than Learning Age One LTC SEMI Dementia Cancer Physical healthy one LTC disability Disability Data £0.5k £1.0k £3.3k £3.8k £18.2k unavailable <16 £0.7k £1.4k £2.9k £9.7k £23.2k £3.6k 16-69 £2.5k £2.7k £15.3k £19.4k £6.1k 70+ £5.5k £781 £1,612 £4,050 £9,542 £19,681 £5,000 Avg. Total £ 1,758 SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13 10

C What can be achieved in 2 weeks? What can you do in the next 2 weeks? 1 “Quick and dirty” segmentation 2 Monitor’s “Ready Reckoner” Using your most recent HES data, JSNA or QOF registry data: � Identify proportion of the population that is � Monitor will be releasing a tool, elderly (75+) OR has a long-term condition the “Ready Reckoner”, that can – Use QOF or JSNA to assess the be used to facilitate a basic prevalence of major long-term conditions segmentation analysis – Alternatively, look for specific diagnoses � It can help your locality codes associated with major long term estimate the average per-capita conditions in your HES data spend for various segments � Working with your CSU or your analytics based on the type of locality, team, analyse HES data to assess how total population size and total many non-elective (NEL) admissions, Firms-reported budgeted outpatient appointments and A&E visits � Watch out for link to this tool on were associated with the elderly or people the BCF website with major LTCs and what proportion of the total number of NEL/OP/A&E activity this represents 11