Stephen Sutch: Risk stratification and model development: Potential of new data and predictive modelling

Risk Stratification and Model Development: Potential of “new” data and Predictive Modelling

Stephen Sutch, MAppSc, BSc. Doctoral Student

Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland 21205 USA

[email protected]

Presented at Nuffield Trust 13 June 2012

mailto:[email protected]

Copyright 2008 Johns Hopkins University 2

Themes

• Risk stratification of whole population • Improving the use of clinical data in predictive

modelling – Use of other data, Rx, Labs, frailty ….

• Build models for specific purposes/outcomes • Classification and Predictive Modelling, contextual

information


Working Definitions

• Case mix / risk adjustment (RA) - taking health status / risk into consideration for health care finance, payment, provider performance assessment and patient outcome monitoring.

• Predictive modeling (PM) - prospective (or concurrent) application of risk measures and statistical technique to identify “high risk” individuals who would likely benefit from care management interventions.

3


Needs Assessment

Quality

Improvement

Payment/ Finance

The risk measurement pyramid

Case- Management

Disease Management Practice

Resource Management

High Disease Burden

Single High Impact Disease

Users

Users & Non-Users

Management Applications

Population Segment


Using Predictive Modeling to Assign Persons Within the Care Management Pyramid

5%

Level 3 High risk

with multiple chronic illness

15% Level 2

Moderate risk patients with single chronic

illness or risk factors

80% Level 1 Low risk

Intensive Case and Disease Management

Health Coaching and Lifestyle Management

Health Education and Promotion


Purposes of Predictive modeling

• Clinical prediction - Individual patient, to improve clinical decision-making

• Population predictive models - Groups of patients, to forecast healthcare trends and identify candidates for healthcare interventions (e.g. DM programs)


Key non statistical considerations for model selection if it is to be used administratively

• Transparency – How easily can the model be understood and

explained?

• Clinical Texture – Does the system make sense to clinicians?

• Flexibility – Does the system support a range of applications?

• Customisable – Adjusts to local data, new models easy to derive and

validate?


Prior High Cost Year-1 (Prior Use)

Predicted High Risk

Year-2 (Using Year-1

Data)

Actual High Cost

Year-2 Not High Risk

High Risk, Current Costs Low, Future Costs High

Value of Predictive Modeling Population of Persons Across Two Year Period


Data

• Secondary Care – Acute Hospitals, Inpatient, Outpatient,

– Mental Health, Rehabilitation, Community care

– Diagnoses, Procedures

• Primary Care – Attendances, Diagnoses, Prescribing

– Labs, Examinations, Findings, Dispensing

• Patient Data – Risk factors, lifestyle factors, Health Status, Rx

Possession, Self Care


Distribution of READ Codes: Illustration Drugs 39%

Findings 23%

Procedures 17%

Administration 11%

Clinical findings 8%

Other 2%


GP diagnosis Coding and Drug prescribing

Diagnosis coding & drug prescribing by GP

PCT data US data

Prevalence Diags/Drugs Prevalence Diags/Drugs

Asthma 8.69% 3.60% 0.71% 4.38%

Dx + Rx Dx Only Rx Only

9.77% 2.67% 1.48% 5.63%


Congestive Heart Failure 2.52% 0.18% 0.05% 2.29%


1.85% 0.30% 0.85% 0.70%


Depression 6.23% 1.36% 0.25% 4.62%


10.38% 1.28% 0.66% 8.43%


Diabetes 3.91% 0.60% 3.25% 0.06%


5.45% 2.77% 2.23% 0.44%


Hyperlipidemia 5.32% 1.28% 0.22% 3.82%


14.87% 5.23% 6.85% 2.78%


Hypertension 13.09% 4.53% 0.45% 8.11%


18.95% 8.78% 6.05% 4.12%



Stratifying Whole Populations

• Multimorbidity – Understanding and measuring

• Classification of health need – Stratification of disease popultions

• Multiple purposes • Validation on whole populations

– Generalisable?


Co-Morbidity is key – Multiple morbidities encountered in UK GP practices

Average consultation in elderly involves someone with 1.9 QOF diseases and 6.7 chronic diseases using ACG/EDC chronic disease designations

Source: Salisbury et al. From GPRD data, 488 practices 2005-2008


17%

12%

11%

9%

24%

22%

21%

22%

23%

23%

25%

21%

20%

22%

24%

21%

16%

21%

19%

27%

0% 20% 40% 60% 80% 100%

Hypertension

Arthritis

Heart Disease

Diabetes

Single Condition Condition + 1 Condition + 2 Condition + 3 Condition + 4+

Source: From US Medicare (65+) data . Partnership for Solutions, Johns Hopkins University

Co-morbidities are the norm for those with common “index” chronic conditions (US 65+)


Risk Stratification – Endocrine Disorders

Source: Ashton Leigh Wigan PCT, Pilot Project


Case Management and Disease Management: Identification of individuals at risk

• Disease Management, Wellness Program Identification – E.g. Diabetes, Hypertension Pharmacy Gaps, Poorly

Controlled Asthma, Untreated Schizophrenia

• Case Management Program Identification – E.g High Medical Needs, Emerging Risk, High Risk for Poor

Coordination, Potential Home Health Needs

• Pharmacy Management Program Identification – E.g. Poly-pharmacy and Medication Gaps / No Ambulatory

Care, High Rx Users

• Utilization Management Program Identification – E.g. High Risk for Hospitalization, Emergency Room for

Primary Care, Risk for High Utilization


Identify high risk members of population based on multi-morbidity oriented “Relative Risk Score”

• Risk predicted to increase • Total costs predicted to increase • 7 chronic conditions • 13 doctors


Patient risk information in support of GPs, Community Matrons

• Numerous co-morbidities • At risk for future hospitalization • ER Visit with no admission • Poly-pharmacy use • Tobacco Use


Patient View: Comprehensive Patient Clinical Profile

Context for Forming Care Management Strategies.


Current Challenges

• Recognizing Multimorbidity – Recording of diagnoses, patterns

• Cost data • Pharmacy data

– Prescribed v Dispensed (possession?)

• Integrated records – GP, OP, A&E, IP, MH, Social Care

• Other data – Functional status, Health Risk factors, Health

Status, Individual Data


The Future

• Ensuring Risk Stratification is fit for purpose • Complimenting case management • A means to an end, not an end in itself, supporting

effective care management and equity • Integrated care, integrated data and information

support • Understanding individuals’ morbidity burden

Health & Medicine

Stephen Sutch: Risk stratification and model development: Potential of new data and predictive modelling