Creating Person-Centred Co-ordinated Care. the role of BIG DATA

Transcription

1 Creating Person-Centred Co-ordinated Care the role of BIG DATA Kevin Hudson Business Solutions & Innovation SaWCS LPF Bid Director

2 CONTEXT: Learning from Integrated Care Information in Somerset If commissioning a new model of integrated care, it is fundamental to clearly understand the affected patient population, their encounters with the overall Health and Social Care system, their clinical conditions, and their cost An evidence base for the design of integrated care, the budget, any accompanying contracts and subsequent evaluation of change A holistic databases that fully integrates health and social care with full Episode Treatment Group condition classification for all patients For Symphony, data changed the way in which clinicians thought about commissioning integrated care from a frail-elderly approach to a clearer focus on treating the effects of multi-morbidity Partnerships have been crucial: All local providers, commissioners and commissioning support plus Centre for Health Economics (University of York), LH Alliances, Optum Health, Ordnance Survey then also Monitor, South West Academic Health Science Network, Experian 2

3 Holistic Data Model Most data analysis (and commissioning) is episodic Episodes of care, amalgamated over time and categorised by provider Holistic data model where the patient is the base unit, not the episode In Somerset the RISC system already collates all secondary and primary care data and reports by patient Full holistic model including health and social care Purpose of the model is to: Help set the scope of Outcomes Based Commissioning Help set the focus within any agreed scope Help develop an evaluation methodology to measure change

4 The Data-Set Dataset for the whole of Somerset but with focus on South Somerset GP Federation (109,000 patients): Fully pseudonymised; age, gender, ward of residence (LSOA) What s in (for 2013/14) o o o o Primary Care: Number and cost of GP contacts; Prescribing activity and costs; Clinical conditions and diagnoses Sourced through RISC system Community hospitals: Minor injuries; Outpatients; Interface clinics (Partnership only); Inpatient admissions; Length of stay; Tariff costs for minor injuries and outpatients; Bed-day cost for community hospitals Sourced through SUS / cost per bed day from Partnership Mental health cluster costs: Community and inpatient activity Sourced from Partnership Acute Care activity and costs: A&E; outpatients; elective inpatients; daycases; non-elective admissions); Clinical coding; Length of stay; Tariff costs Sourced through SUS o Social Care activity and costs: residential care (net costs); home care; day care; direct payments; professional support; equipment; on costs ; - Sourced from Somerset CC o (For 2013/14) Community Services: ALL SERVICES INCLUDING - District nursing and health visiting; Ambulance service; Podiatry; Dietetics; Community diabetes service; Rehab; Community therapies; Tissue viability; Speech and language therapies; Continence; End of life; Voluntary sector

5 Episode Treatment Groups A comprehensive condition classification tool that organises all relevant medical services across all sites of care (GP Practice, Inpatient, Outpatient, A&E) for over 400 specific medical conditions. Using routinely collected encounter data as input, the ETG software captures the relevant services (including prescriptions) provided during the course of a patient s treatment, and organizes the encounter data into meaningful episodes of care. For Symphony, 49 subsets of ETGs were identified and included in the analysis.

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7 Patients (%) Morbidity (number of ETGs) by age band 100% 90% 80% 70% Number of conditions 60% 50% 40% 30% % 10% 0% Age band (Years)

8 What drives cost age or conditions? Regression variables Age Number of conditions Age, Number of conditions Variation explained 3.36% 18.76% 19.30%

9 Starting point: Different conditions / characteristics

10 Starting point: Multi-morbidity and Cost

11 But not all follow this pattern

12 For multi-morbidity, number type

13 For multi-morbidity, number type

14 For multi-morbidity, number type

15 For multi-morbidity, number type

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18 And for the LIG s (Local Implementation Groups) we map where the patient s are Number of Patients

19 their cost Cost of Patients

20 and the setting of care ( at home or in a home ) Cost of Patients

21 Key Customer Groups: Type M56 Older people living on social housing estates with limited budgets Key Features: State pensioners Low use of credit Enjoy reading Small housing Basic education Shop locally Traditional Lifelong council tenants Face to face contact Communication Preferences: Access Information Local Papers and Face to Face Not Internet, Telephone, Magazines, SMS Text Service Channels Face to Face Not Internet, Mobile Phone or Telephone 2% Population, 8% of full cohort, 12% of high cost patients

22 Complex Maths is needed to model the Future Analytical model We apply multivariate regression models to analyse each person s total costs and costs incurred in each of the eight settings. As is typical in modelling health costs, we use a logarithmic transformation to reduce skewness and make the distribution more symmetric and closer to normality. A high proportion of people in inpatient, outpatient, A&E, mental health, community care, social care, and continuing care incur no costs. To analyse costs in such settings, we employ two-part models (Charlton et al., 2013, Brilleman et al., 2012, Duan et al., 1983) which allow us to account for the large number of zeros found in the data. The two parts are assumed to be independent and can be estimated separately. The first part, estimated by a logistic regression, models the probability of incurring any expenditure and the second part models the amount of expenditure only for those with positive costs. The conditional mean independence assumption is then given by: E(ln y y 0, X ) X i i where y i are costs for individual i, X i is a vector of explanatory variables and indicates the parameter estimates. Primary care costs and total costs exhibit a very small proportion of zeros and are estimated by a simple log-linear regression. i i The science bit. An econometric approach to modelling health data based both on the non-normal distribution of the cost of patient care (logarithmic modelling) and the high prevalence of zeros when looking across setting of care for individual patients (probability modelling).

23 National Coverage Symphony data analysis was covered on the front page of HSJ April 2014 Professor Andrew Street also recorded a video: watch?v=cr7aevrgbqm Published in International Journal of Integrated Care January 2015 (after academic peer review)