How Real Data Will Transform Predictive Models

Individualized, Automated Care Management

Charles DeShazer, MDVP, Quality, Medical Informatics & Transformation

Dean Health System

Madison, WI

Key Industry Assumptions

Premise: Advanced Predictive Modeling leveraging EHRs will be essential to evolving care management to the next level

Drivers of transformation

Current cost inflation curve is unsustainable

Payers (esp CMS) are moving towards paying for value rather than volume

EHR will become a standard tool

Quality will become not only the “ticket to play” but also a key basis of competition (value = quality/cost)

Primary care will be the engine for quality

Challenges

% ofPopulation

1%

15%

70%

14%

25%

15%

10%

50%

Population vs. Costs vs. Interventions

1000 Lives 14,000

Lives

15,000 Lives

70,000 Lives

Complex Case Management

Disease/Demand Management

Health

Mgmt

% of

Cost

Example of 100,000 People in a Population

24 hours in the life of a PCP

“The Impending Collapse of Primary Care Medicine and Its Implications for the State of the Nation’s Health Care,” a report from the American College of Physicians, 2006

Yarnall KS, et al. Primary care: is there enough time for prevention? Am J Public Health 2003; 93:635

Ostbye T, et al. Is there time for management of patients with chronic diseases in primary care? Ann Fam Med 2005; 3:209

Preventive care (7.4 hrs)

Chronic care (10.6 hrs)

Leftover (6 hrs)

Facts per Decision

Next generation care management will require managing costs (risk) and improving quality by managing chronic and complex conditions (and the data about those conditions)

• Predictive modeling is about identifying these patients early through data analysis and proactively providing resources and guidance to reduce/eliminate risk and manage conditions

• PM + EHR data will enable complex, proactive clinical decision making otherwise not feasible

• You will have to fix the PCP’s day in the process

Ingredients for Transformation

If we assume the hard work will be done…

Cultural change

Shift from paying for volume to paying for value

Physician compensation

Physician leadership

Patient engagement and activation

Effective accountability and governance structure

PCMH + ACO

Then the stage is set for transformation…

Meaningful Use

Creating infrastructure for transformation of healthcare over next 3-5 years

Stage 1: Data capture in structured format

Stage 2: Advanced clinical processing

Stage 3: Improved outcomes

Important aspects for Predictive Modeling

More electronic clinical data, more structured data

Problem List Management

Clinical Decision Support

Patient Health Record & sharing data with patients/family

ICD-10

One of the most comprehensive regulatory changes in the history of healthcare in the US

Unlike MU, it is an unfunded regulatory event

Replaces 30 year old ICD-9-CM, which is outdated and lacks clinical granularity

Important aspects for Predictive Modeling

Provides granularity to diagnostic information that should greatly enhance predictive models

Improved ability to specify and measure healthcare services

Enable better integration of predictive modeling and clinical decision support

Richer data structures for research

ICD-10 Asthma Codes

More granular clinical information will enhance predictive models as well as enable real-time program referrals especially when followed serially and combined with other data.

EHR Data for Predictive Modeling

Predictive models will be enhanced in several ways by leveraging comprehensive, real-time data for better models and then tightly coupling and embedding this knowledge in care processes

Eliminate claims lag

Real-time identification of changes in status

Service utilization enhancement of models

More accurate and comprehensive coding (ICD-10) due to clinical use of data (MU) will enhance models

Feedback of care process (validation of models)

HealthCare Partners Medical Group use of PM for high need patients

Created comprehensive care center and homecare team informed by PM for highest need patients

This occurred after optimizing transitions of care and use of hospitalists

Reduced hospital use by 20% and saved the system $2 million per year for every 1,000 patients

Source: Health Affairs 30, No.3 (2011): 416-418

Next Generation: Individualized Guidelines Individualized guidelines outperform general guidelines

Patients with complex clinical profiles may not fit easily into well-defined guideline categories

Individualized guidelines take into account all of an individual’s information (e.g., lab values, biomarkers, demographics, history, medications, etc.) as well as the continuous nature of risk factors and expected benefit from treatments.

David M. Eddy, MD, PhD, from Archimedes, San Francisco, California, conducted a person-specific, longitudinal analysis of participants in the Atherosclerosis Risk in Communities study, which included 15,792 patients between the ages of 45 and 64 years.

Compared with patients simulated to receive random care, the researchers found that individual guidelines based on the CV Guidelines Calculator could reduce MIs and strokes at the same rate as JNC 7 guidelines, but at a 67% cost savings, or for the same medical costs, individualized guidelines could prevent 43% more MIs and strokes than JNC 7 guidelines.

Source: Ann Intern Med May 3, 2011 154:627-634

Model is being tested at Kaiser Permanente in conjunction with their EHR & panel management system.

Model is only feasible with a full EHR. This would enable automated, proactive and near real-time individualized care management.

Addition of genomic data will enhance models tremendously.