Population Health Analytics and Usage of the ACG System in the UK Stockholm, 13 th June, 2017 Alan Thompson, Director of User Support

Transcription

1 Population Health Analytics and Usage of the ACG System in the UK Stockholm, 13 th June, 2017 Alan Thompson, Director of User Support

2 Content Summary of main uses of the ACG System in the UK Population Health Profiling Case Finding Questions & Answers

3 ACG System Use in the UK ACG System used by 40 Clinical Commissioning Groups (CCGs) 1200 GP Practices/clinics Population of approx. 11 million Evidence base for specific applications is increasing Use to support population profiling is the greatest area of growth

4 Challenges & Opportunities = New Organisational Models Need to profile the population and to undertake case finding

5 Challenges & Opportunities Informationssäkerhet = New Organisational Models Need to profile the population and to undertake case finding

6 Use of the ACG System in the NHS NHS users have identified four main roles for the use of the ACG System: 1. To support population profiling 2. To support case finding activities 3. To support resource management and our understanding of relative performance 4. Potentially to support a more equitable formula for resource allocation

7 The Objective Profile the population, identify homogeneous groups/segments Commission additional or new services as appropriate. Decommission services if necessary Define programmes of care and agree admission and discharge criteria Identify gaps in provision and/or over provision Populate programmes with right patients (and discharge those who do t fit riteria)

8 POPULATION PROFILING

9 Phenomena we are Seeing in Populations 1. The need for healthcare varies a small percentage of people consume a large amount of resource 2. Multimorbidity is the norm it is more common for people to have multiple chronic conditions that to have just one 3. Not all patients with a particular disease are the same multimorbidity affects cost and resource use 4. Co-morbidity impacts resource use exponentially 5. Multimorbidity is not distributed evenly across a population and case-mix varies quite significantly between GP practices 6. Multimorbidity more than age is a key driver of cost, activity and future risk and multi-morbidity occurs across the whole adult age range 7. Top of pyramid is not homogeneous there s ot as u h o erlap et ee different risk groups as you may think

10 The Need for Health Care Varies % of Population Percentage of Healthcare Spend US UK Top 1% Top 5% Top 10% Top 25% Top 50% A small percentage of people consume a large amount of resource Source of data: US figures - UK figures -

11 Multimorbidity is the Norm 100% Common Chronic Conditions & Co-Morbidity Count 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Diabetes COPD Chronic Renal Failure Ischaemic Heart Condition Rhuematoid Arthritis Single Condition Condition +1 Condition +2 Condition +3 Condition +4 Condition +5 Condition +6 Condition +7

12 Multimorbidity is the Norm 100% 90% 80% 70% 60% 50% Common Chronic Conditions & Co-Morbidity Count Only 21% of people with diabetes have no other chronic condition Only 12% of people with COPD have no other chronic condition 40% 30% 20% 10% 0% Diabetes COPD Chronic Renal Failure Ischaemic Heart Condition Rhuematoid Arthritis Single Condition Condition +1 Condition +2 Condition +3 Condition +4 Condition +5 Condition +6 Condition +7

13 Multimorbidity is the Norm 100% 90% 80% 70% 60% Common Chronic Conditions & Co-Morbidity Count Over 50% of people with COPD have at least 3 other chronic conditions 50% 40% 30% 20% 10% 0% Nearly 80% of people with diabetes have at least one other chronic condition they have to manage Diabetes COPD Chronic Renal Failure Ischaemic Heart Condition Rhuematoid Arthritis Single Condition Condition +1 Condition +2 Condition +3 Condition +4 Condition +5 Condition +6 Condition +7

14 Not All Patients With a Particular Disease Are The Same No of Patients % of Patients Average of Inpatient Emergency Activity Average of Inpatient Elective Activity Average of OP First Attendance Activity Average of GP Visit Count Average of Total Cost Average of Pharmacy Cost Average of Distinct Drug Count All Patients with Diabetes % Diabetes Only % Diabetes + 1 other CC % Diabetes + 2 other CC % Diabetes + 3 other CC % Diabetes + 4 other CC % Diabetes + 5 other CC 924 8% Diabetes + 6 other CC 618 5% Diabetes + 7 other CC 410 3% Diabetes + 8 or more other CC 794 6%

15 Not All Patients With a Particular Disease Are The Same No of Patients % of Patients Average of Inpatient Emergency Activity Average of Inpatient Elective Activity Average of OP First Attendance Activity Average of GP Visit Count Average of Total Cost Average of Pharmacy Cost Average of Distinct Drug Count All Patients with Diabetes % Diabetes Only % Diabetes + 1 other CC % Diabetes + 2 other CC % Diabetes + 3 other CC % Diabetes + 4 other CC % Diabetes + 5 other CC 924 8% Diabetes + 6 other CC 618 5% Diabetes + 7 other CC 410 3% Diabetes + 8 or more other CC 794 6%

16 Morbidity is Not Distributed Evenly 100% Resource Utilisation Band Distribution Across GP Practices 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% CCG Mean Healthy & Non Users Low Moderate High Very High

17 Morbidity is Not Distributed Evenly 100% Resource Utilisation Band Distribution Across GP Practices 90% 80% 70% 60% 50% 40% 30% 20% Morbidity and multimorbidity are not distributed evenly across a population Casemix varies between GP practices/clinics Clinics on the right have relatively sicker patients 10% 0% CCG Mean Healthy & Non Users Low Moderate High Very High

18 Average Cost Per Person in Previous Year ( ) Multimorbidity Drives Cost Average Cost by Segment Multimorbidity more than age drives cost Multimorbidity 8000 does not just occur in the elderly Number denotes number of chronic conditions: 0 = 0 1 = 1 2 = = 4-6 7= 7+ Letter denotes age band: A = 0-19 B = C = D = E = A 0B 0C 0D 0E 1A 1B 1C 1D 1E 2A 2B 2C 2D 2E 4B 4C 4D 4E 7B 7C 7D 7E Segment

19 High Risk Groups are Not Homogeneous Three cohorts of patients: - Those most at risk of an emergency admission in coming year (n=2,730) Risk of Emergency Admission Those at risk of highest costs (n=2,730) 210 1,154 - Those who are flagged by efi as being moderately or severely frail (n=1,991) Total number of unique individuals = 4,539 Frail 154 Risk of High Costs 725 Match the Search Criteria to the Cohort of Interest

20 Population Profiling & Segmentation Children with Chronic Diseases Multimorbidity = Uncoordinated Care Emerging Risk End of Life Care Frailty New Organisational Models Need to profile the population AND further segment Level 1

21 How This Intelligence is Used Commissioners/payers are: Matching services to need Reallocating resources Buying new types of services Locating services where there is greatest need Undertaking casemix adjusted performance analysis Addressing issues of equity Doctors and other clinicians are: Taking more notice of multimorbidity Being more sophisticated in their case finding activities Real opportunity to use this intelligence to support emerging accountable care systems

22 CASE FINDING

23 Example 1 Unplanned Hospital Admissions National policy related to unplanned hospital admissions NHS worked with JHU to develop new predictive model to predict risk of unplanned admission Now used to create lists of top 2% most at risk for review Patients reviewed at multi-disciplinary meeting Care plan developed to provide right health or social care input to prevent admission to hospital

24 Example 2 Care Coordination Identify people in 2-5% risk band for care coordination Criteria for enrolment include: All adults 6 or more chronic conditions In the high or very high predictive risk bands Not part of the unplanned admission DES programme Enrolled in care programme and reviewed by Clinical Care-Coordinator Medication review Education of patient and carers Improve communication and coordination amongst those providing care Initial feedback from patients and doctors is good Indication is a reduction in cost and improvements in quality

25 Example 3 Disease Orientated Searches The ACG System groups all diagnoses into one of about 270 Expanded Diagnostic Clusters (EDCs) Searches for patients with any of these EDCs can be carried out easily But, people with a particular disease can also be stratified by their overall level of disease Over 50% of people with diabetes are in the healthy and low risk groups Information used to identify and target the smaller percentage of diabetic patients who are high & very high RUBs

26 Example 4 Complex Case Management Scheme in Berkshire Established a new primary care based service to support multi-morbid patients Analysis undertaken to identify a cohort of patients where the potential impact of GP intervention was high and the number was manageable Search criteria used were based on patients with certain co-morbidities: Each patient will have a series of primary care consultations focussing on: Clinical review based on holistic needs rather than management of individual diseases Medication review Improving coordination of care Education of patient & family

27 Example 5 Polypharmacy & High Cost Pharmacy People are looking at: People with polypharmacy Those with highest costs in past year People predicted to be in the top 5% of patients with highest drug costs in coming year Review by primary care based clinical pharmacist Liaison between GP and pharmacist Large, recurring, financial savings Improved patient safety

28 Summary Profiling the population identifies segments with similar care needs and cohorts of potential interest Population Profiling Case Finding Clinicians identify opportunities for different/more effective ways of providing care which informs population level analytics to identify how many patients there are and where they are Two Sides of the Same Coin

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30 Questions? Follow Johns Hopkins Solutions on: HopkinsACG.org JohnsHopkinsSolutions.com