using “big data” to generate new opportunities for your lab · and this presentation includes...

Using “Big Data” to Generate New Opportunities for Your Lab April 29, 2014 Jason Bhan, MD

Disclosures

Jason Bhan, MD

●  I am a co-founder of Medivo, a laboratory analytics company, and this presentation includes information about Medivo and its services.

●  I am a Medivo employee and a Medivo private equity holder.

Gary S. Assarian, MD

●  I am a co-founder of The Joint Venture Hospital Laboratories (JVHL), a network comprised of more than 130 hospital-affiliated laboratories.

●  I have nothing to disclose.

Learning Objectives

1.  Increase knowledge of how big data is used in the healthcare industry broadly and in laboratory analytics specifically.

2.  Demonstrate how laboratories can obtain useful insights on marketplace dynamics and in turn share in revenues derived from their data assets.

3.  Guide laboratory professionals to successful methods to unlock the value in their information systems, based on a case study.

Emergence of Big Data in the Healthcare Marketplace

What is Big Data?

“High volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”

Coined in 2001, revised in 2012 - Doug Laney, Gartner, Inc. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

The Emergence of Big Data

Big Data, for better or worse: 90% of world's data generated over last two years Date: May 22, 2013

Source: SINTEF http://www.sintef.no/home/Press-Room/Research-News/Big-Data--for-better-or-worse/

Each and every one of us is constantly producing and releasing data about

ourselves. We do this either by moving around passively – our behaviour being

registered by cameras or card usage – or by logging onto our PCs and surfing the

net. The volumes of data make up what has been designated 'Big Data' – where

data about individuals, groups and periods of time are combined.

- Petter Bae Brandtzæg of SINTEF ICT

Examples of Big Data to Establish Scale

“Petabyte”...or PB 1 PB:

to store the DNA of the entire US population - twice

of MP3-encoded songs would play 2,000 years

was required to create the 3D CGI in the movie Avatar

2.5 PB:

to store the entire Library of Congress - 167 times

is the size of Walmart’s database of >1M transactions/hr

is also the human brain's ability to store memories

24 PB is how much data Google processes per day

30 PB is how much data AT&T transfers per day

150 PB to move 14yrs of Hotmail accounts to Outlook format http://en.wikipedia.org/wiki/Petabyte

Big Data in Healthcare

Daniel Garrett, US healthcare I.T. practice leader for PwC, discusses the possibilities and challenges of "big data" for the health industries, 4/30/13.

Examples of Big Data in Healthcare

- >5M licensed healthcare professionals seeing patients1

- >256.2M covered lives in the U.S.2

- >4B prescriptions written per year3

- >7B lab tests per year, with >100 fields of data each4

- >12.9B healthcare billing/payment transactions per year5

Sources: (1) AMA, ANA, AANP, AAPA, APhA, (2) United States Census Bureau, (913/11). Income, Poverty and Health Insurance Coverage in the United States: 2010. (3) IMS Health, National Prescription Audit, Dec 2012, (4) CMS CLIA OSCAR Database (5) The U.S. Healthcare Efficiency Index© (USHEI), estimated volumes per year.

IBM’s Watson using a global compendium of healthcare

research to guide decision-making for individual patients

Express Scripts’ 1.5B annual prescriptions provide insights into patients most likely to fail to refill & the best intervention methods that

facilitate adherence

Implemented solution at 400 retail clinics that would provide their NPs/PAs with advanced analytics at the point of care in real time, in order to achieve goals in improving quality outcomes and risk score accuracy

●  Reducing waste in the healthcare system

●  Challenging due to the volume of data analyzed in making these decisions.

●  Patients get more appropriate care, higher quality metrics.

Improving pre-approval processes: Watson & WellPoint

Better informed drug choices: Watson & NY Genome Ctr

●  Implemented at 400 Walgreens Clinics.

●  Analysis of 8.3B+ medical events.

●  A patient assessment tool with individualized predictive analytics at the point of care.

●  Goal is to improve quality outcomes and risk score accuracy.

Patient assessment tools: Walgreens / Inovalon

Designed to identify and intervene with patients likely to be non-compliant ●  Looks at 400 variables,

including Rx history and socioeconomic factors.

●  Predictions correct about 90% of the time.

Predictive modeling: Express Scripts

Healthcare Big Data Pioneers: Pharma

●  2012 U.S. pharma sales = ~$350B1

●  2012 U.S. pharma marketing spend = ~$27B2

●  2012 U.S. pharma R&D spend = ~$48B3

●  Constituents are: ○  Providers -- HCPs, Nurses/PAs ~800K prescribers ○  Payers/Managed Care Organizations

■  Health Insurers, IDNs, PBMs ○  Specialty Pharmacies ○  Patients ○  GPOs, Distributors

Sources: (1) IMS (2) Cegedim Strategic Data (3) Pharmaceutical Research and Manufacturers of America 2013 Profile

U.S. Pharmaceutical Industry: Quick Facts

Evolution of Big Data in Pharma Pharma key performance indicators

Drug Distribution Data from wholesalers. Pharmacy account-level volume information collected manually (think McKesson, Cardinal)

50’s-70’s

80’s-90’s Physician-level Rx Data from pharmacies. Advent of electronic data capture and scale of large pharmacy networks (think IMS, Source (now SHS))

Anonymous Patient-level (APLD) and Diagnosis (Dx) data from claims. Emergence of big data from payor networks (think Emdeon, Verispan)

90’s-00’s

Physician-level and Anonymous Patient-level Lab data (Lx) from labs. Roll-up of large lab networks provides scale.

10’s =>

Why Lab Data Analytics?

Clinical pathways improve

View physician-level and regional analytics

Connect with new customers

Low cost service drives system-wide benefits

Empowers consumers

Increase reimbursed testing

$70Bn in Lab revenues in 2012

Why Now?

- Electronic data standards (HL-7, LOINC) → LAB DATA IS STANDARDIZED

Why Now?

- EMR datasets lack consistent access to structured lab data → STRUCTURED DATA IS REQUIRED FOR SCALABLE ANALYTICS

Why Now?

- Increased quality focus from pharma and payors → NEW INTEREST IN RESULTS

Why Now?

- Growth in personalized medicine, no more blockbuster drugs → PHARMA INTEREST IN SPECIFIC PATIENT SEGMENTS

Why Now? 7 PB of US Lab data is available for analysis right now

With >7B tests/yr, and 100+ fields of data per test http://en.wikipedia.org/wiki/Petabyte and www.cms.gov/clia

How to Unlock the Value of Lab Data Assets

Conditions where biomarkers are utilized in diagnosis and/or monitoring Condition Analytes Monitored Example Patient Profiles

Diabetes HbA1c New dx, poor control, test gap Hep C HCV Ab, HCV VL, HCV Gen Tx resistant, new dx, warehoused HIV HIV WB, HIV VL, HIV Gen, CD4 New dx, V breakthrough, test gap,

requires prophylaxis Dyslipidemia LDL, Trig, non-HDL New dx, poor control, test gap CML BCR-ABL New dx, poor tx response, test gap

Anticoagulation INR New dx, poor control, bleed risk, test gap

Rheumatoid Arthritis

ESR, CRP, RF, anti-CCP New dx, flare, poor tx response

Mixed HemOnc BRAF, KRAS, EGFR, JAK2 New dx, test gap

Mixed Endo GH, Cortisol, TSH New dx, poor tx response, test gap

Other Conditions: Hep B, Low T, Gout, SLE, CKD, IBD, CAD, CPP, CRC, Flu, Mult Myeloma, HAE, Prostate Ca

Who values analysed lab data? PROVIDERS

●  Develop personalized treatment plans.

●  Understand how patients respond to treatment.

●  Identify patients who are non-compliant.

●  Demonstrate improving outcomes.

Healthcare Providers can leverage lab data to:

Who values analysed lab data? PATIENTS

●  Track their progress towards goals.

●  Better understand their condition(s).

●  Empowerment = better outcomes?

Patients can leverage lab data to:

Who values analysed lab data? PHARMA

●  Inform and direct timely conversations between providers and Life Science reps.

●  Optimize call plans and marketing campaigns.

●  Demonstrate impact of pharma driven clinical interventions.

Pharma can leverage lab data to:

BUT...Pharma requires SCALABLE DATA

●  Drug Distribution Data - coverage is ~80% of all U.S. sales

●  HCP-level Drug Data collected from pharmacies - coverage is ~75% of all U.S. Rx

●  Anon. Pt-level Diagnosis Data (APLD) from claims - coverage is ~50% of all U.S. claims

●  HCP- & Anon. Pt-level Lab Data from labs - Lab physician/patient-level results & ICD-9 from national lab networks, coverage for any one contributor is poor

Lab Market is very fragmented. Opportunity to aggregate.

Who values analysed lab data? PHARMA

Who values analysed lab data? LABS

●  Create new revenue channels by licensing de-identified data.

●  Gain insights on Lab market dynamics.

●  Maximize appropriate testing.

The more scalable your data, the more value you can derive.

Labs can leverage their own data to:

Labs: New Revenue Channels

●  leverage your existing data assets

●  non-competitive with your core business

●  data feeds from your existing LIS

Pharma values target analytes with the most complete geographical coverage and highest volumes.

New Stakeholders VALUE scalable lab data

Labs: Gain Insights

●  Testing trends on the national, regional and local levels.

●  New opportunities to gain market share.

●  Benchmarks within defined geographical areas as well as lab specific time period comparisons.

Analyzing your lab’s data can provide insights into:

Labs: Maximize Appropriate Testing

Compared to control groups, clinicians who received lab data analysis reports ordered significantly more tests per year1:

●  11% more LDL tests (p<0.02) ●  21% more triglyceride tests (p<0.02) ●  22% more testosterone tests (p = 0.03) ●  38% more BCR-ABL tests for patients with CML (p = 0.02) ●  55% more serum uric acid tests for patients with gout

(p<0.001)

More patients tested when actionable lab data is available

Source: (1) Analysis of the Medivo Lab Value Exchange, 2013

Case Study Results from the JVHL-Medivo Collaboration

Dr. Gary Assarian, JVHL

●  Co-founder, Medical Director and Director of Informatics. ●  Consultant to BCBS, Trinity Health systems and Healthcare

consulting groups - Cognolink , Gerson Lehrman Group, Inc. ●  Member of NCQA - Expert Lab Panel. ●  Serve on the Joint Uniform Medical Policy committee for BCBS/

BCN for lab related issues.

Who is JVHL?

The Joint Venture Hospital Laboratories (JVHL) network is comprised of more than 130 hospital-affiliated laboratories committed to providing managed care plan members and participating physicians with the highest quality, convenient and efficient laboratory services.

JVHL / Medivo Collaboration

Individually, Medivo and JVHL have conducted analyses to show value to their respective clients.

JVHL → focus on payor reporting & quality improvement

Medivo → focus on increasing reimbursed lab test volumes & lab market insights

Together, Medivo and JVHL are partnering to aggregate nationwide datasets to better inform payors, healthcare providers & partner labs

JVHL - Disease Management Initiative A New Phase

JVHL designed a program to use lab data for diabetic patients to create tools to monitor:

1.  Disease management 2.  Guideline compliance

Diabetes Practice Guidelines Adopted from ADA – Clinical Practice Recommendation 2009

Standards of Care require that a number of parameters be evaluated on a continuous basis annually and that certain thresholds and criteria are achieved to reduce disease morbidity.

These guidelines serve as a basis of this grading and monitoring tool developed by JVHL.

JVHL study design:

●  13 payor groups were reviewed for ~75,000 diabetic patients’ A1c testing rates per ADA Guidelines, across ~9,000 MDs.

●  Avg. non compliance rate across payors: 67%. ●  Range of non compliance: 54 – 74 %.

JVHL study findings:

●  Only ~1/3 of the diabetic patients being followed up properly according to ADA guidelines.

●  There are significant differences in compliance when patient groups are compared by plan, and in follow up and monitoring compliance between physicians.

Compliance Score Average by Numbers of Physicians Who Ordered Studies According to ADA Guidance Standards for DM Patients

Count of Physicians by Average Compliance Score

JVHL study conclusions:

●  Using patient and claims data will result in targeted improvement of monitoring compliance and offers an objective means to compare physicians and physician groups.

●  Compliance information could be offered to clients to help in efforts to monitor patients.

●  Benefits of having this information available to the providers and payors is limitless and an important link between a client and the lab.

Medivo Studies Support JVHL Observations

Mining the Lab Value Exchange (LVX)™ ● 27M+ De-identified patient records

Follow up & monitoring compliance varies among physicians

Study #1: Abstract #1130 at 2013 AACE Annual Meeting

●  Data source: Medivo Lab Value Exchange

●  Timeframe: Jan 1 - Dec 31, 2012

●  Physicians: 2,562 total (180 endocrinologists and 2,382 PCPs)

●  Lab Tests: 171,697 HbA1c tests for 39,556 Patients with Diabetes

●  Analysis: ANOVA was performed to compare HbA1c order rates between the two physician groups

Endocrinologists vs PCPs: HbA1C Testing Rates Significantly Different

●  Over the 1 year study period, as compared to PCPs, endocrinologists ordered more HbA1c tests/patient with diabetes (3.24 vs. 2.81, p<0.0001)

●  Endocrinologists order significantly more tests than PCPs for patients both <65 years (p<0.001) and ≥65 years (p=0.03)

Study #2: BCR-ABL Testing Frequency Lower than NCCN Recommendations in Lab Network Review of CML Patients ●  Data source: Medivo Lab Value Exchange ●  Timeframe: 3/10 - 2/13 ●  Patients: 8,414 CML patients were categorized by the number

of annual BCR-ABL tests per year. Findings: ●  50% of patients were tested 1X/year or less. This is between

one half and one quarter of the recommended frequencies in NCCN guidelines, depending on the patients’ CCyR status.

●  Testing frequency varied significantly across regions.

Follow up & monitoring compliance varies among physicians regionally

Conditions where biomarkers are utilized in diagnosis and/or monitoring Condition Analytes Monitored Example Patient Profiles

Diabetes HbA1c New dx, poor control, test gap Hep C HCV Ab, HCV VL, HCV Gen Tx resistant, new dx, warehoused HIV HIV WB, HIV VL, HIV Gen, CD4 New dx, V breakthrough, test gap,

requires prophylaxis Dyslipidemia LDL, Trig, non-HDL New dx, poor control, test gap CML BCR-ABL New dx, poor tx response, test gap

Anticoagulation INR New dx, poor control, bleed risk, test gap

Rheumatoid Arthritis

ESR, CRP, RF, anti-CCP New dx, flare, poor tx response

Mixed HemOnc BRAF, KRAS, EGFR, JAK2 New dx, test gap

Mixed Endo GH, Cortisol, TSH New dx, poor tx response, test gap

Other Conditions: Hep B, Low T, Gout, SLE, CKD, IBD, CAD, CPP, CRC, Flu, Mult Myeloma, HAE, Prostate Ca

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