revenue streams new laboratory methods to generate using ... · and this presentation includes...

Using “Big Data” Methods to Generate New Laboratory Revenue Streams

April 29, 2014Destry Sulkes, MD, MBA

http://www.executivewarcollege.com/sessions/using-sophisticated-reporting-tools-and-big-data-feeds-of-lab-test-results-to-generate-new-revenue-streams-for-your-clinical-lab/





Confidential. Trade Secret. All Rights Reserved. Medivo, Inc.

Disclosures

Destry Sulkes, MD, MBA

● 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


The Emergence of Big Data

Big Data, for better or worse: 90% of world's data

generated over last two yearsDate: 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

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


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

http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf


Examples of Big Data to Establish Scale

“Petabyte”...or PB1 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 formathttp://en.wikipedia.org/wiki/Petabyte

http://en.wikipedia.org/wiki/DNA

http://en.wikipedia.org/wiki/MP3

http://en.wikipedia.org/wiki/Walmart

http://en.wikipedia.org/wiki/Human_brain

http://en.wikipedia.org/wiki/Google

http://en.wikipedia.org/wiki/AT%26T_Inc.

http://en.wikipedia.org/wiki/Microsoft



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.

http://www.youtube.com/watch?v=Gz83UimvY-Y


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.


Pharma key performance indicators

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

Evolution of Big Data in Healthcare

50’s-70’s



80’s-90’s:


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


Pharma key performance indicatorsEvolution of Big Data in Healthcare

Anonymous Patient-level (APLD) and Diagnosis (Dx) data from longitudinal and claims offered. 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 and partners becomes available. Roll-up of large lab networks provides scale.


10’s =>


Big Data Becomes Actionable Data

IBM’s Watson using a global compendium of healthcare research to guide decision-

making for individual patients

United Healthcare and Cigna have both conducted analyses of their

call center data to identify common issues that can be

proactively addressed to decrease ER visits and readmissions

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 by improving the pre-approval of healthcare insurance coverage.

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

● Patients get more appropriate care, higher quality metrics.

Watson & WellPoint: Improving pre-approval processes



Watson & NY Genome Ctr: Better informed drug choices



● 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.

Walgreens / Inovalon: Patient Assessment Tools



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.

Express Scripts: Predictive Modeling


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 well-established (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 payors→ NEW INTEREST IN RESULTS, NOT

JUST ACTIVITIES


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


http://www.cms.gov/clia



How to Unlock the Value of Lab Data Assets


Conditions where biomarkers are utilized in diagnosis and/or monitoringCondition 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 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 lab data? PATIENTS

● Track their progress towards goals.

● Better understand their condition(s).

Will we see an uptick in patients requesting results once CMS 2319-F takes effect??

Patients can leverage lab data to:


Who values lab data? LABS

● Gain insights on Lab market dynamics.

● Increase reimbursed test volumes & revenues.

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

You can leverage the data you collect to:


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: Increase Test Volumes

● Physicians who receive lab data that easily identifies patients in need of tests are more likely to order tests.

● Patients who are engaged in their health are more likely to seek medical care, resulting in more tests ordered.

Making the data you collect more actionable can increase testing volumes



Compared to control groups, clinicians who received organized, color coded 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)

Physicians are more likely to order tests when actionable lab data is available

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


Medivo HCPs 50% more likely to identify & follow-up with all patients who have not responded to office outreach about abnormal labs and/or symptoms.

(2012 Survey results, p=0.02)


Physicians are more likely to follow-up when actionable lab data is available


Labs: New Revenue Channels

● Assess patient outcomes.

● Measure physician quality.

● Manage lab utilization and costs.

PAYORS - NEW STAKEHOLDER to VALUE your data


Labs: New Revenue Channels

● Recognize practice level targeting opportunities.● Increase sales force effectiveness.● Gain competitive advantage.

PHARMA - NEW STAKEHOLDER to VALUE your data

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 - Congolink , 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 InitiativeA New Phase

JVHL designed a program to use lab data for diabetic patients to create tools to monitor:1. Disease management2. Guideline compliance



Diabetes Practice GuidelinesAdopted 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)™● 55M+ 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 monitoringCondition 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


Q&A

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