Advanced laboratory analytics can provide a disruptive solution for health systems facing challenges under value-based care models. Laboratory data is well-suited for advanced analytics due to its timeliness, structured format, ubiquity across settings and providers, and predictive potential. Laboratory-based predictive algorithms and clinical decision support tools can help optimize outcomes like readmissions, adverse events, costs, and disease management. By leveraging laboratory data and analytics, health systems can better manage patient populations, make personalized medical decisions, and support value-based care goals of improving quality while reducing costs.

1. Advanced Laboratory Analytics —
A Disruptive Solution for Health Systems
Eleanor Herriman, MD, MBA
Chief Medical Informatics Officer

2. L. Eleanor J. Herriman, M.D., M.B.A.
• Physician executive with 20 years of varied healthcare experience
• Former faculty member at Harvard Business School’s Institute for
Strategy and Competitiveness
• Market research and strategy services to the pathology and
laboratory industries at G2 Intelligence
• Healthcare strategy consulting at Bain & Company
Education
• Doctor of Medicine degree from Baylor College of Medicine
• Presidents Scholarship with honors in Neurology, Psychiatry and
Neuropathology
• Masters in Business Administration from Harvard University
Graduate School of Business Administration as a Baker Scholar
Chief Medical Informatics Officer

3. Agenda
The Age of Advanced Analytics
Lab Analytics Rule
The Lab Disruptive Solution

4. Medical Payment and Delivery is Undergoing a Massive Upheaval
Traditional Model: Fee For Service New Model: Value-­Based Care
Diabetic Patient
Set Fee Split Between Providers:
Bottom Line and Outcomes are Drivers
$ One Year Diabetic Care $
Fee For Service:
Paid by Volume Regardless of Quality
$$
$
$
$
$$$
$
$
$
Diabetic Patient

5. Population Health is the “New Mandate”
“There is nothing more important [in healthcare] than the
transition from traditional medicine to population health and
the implications that will have. No outcome, no income.”
Dr. David Nash
Founding Dean, Jefferson School of Population Health

6. Providers and Services Now Driven by Bottom Line and
Outcomes
Laboratory
• Operational efficiencies
• Testing utilization management
• Demonstrate value of testing to
payers, health organizations
Healthcare Providers
• Decrease avoidable clinical
costs
• Improve outcomes
• Project and manage population
costs
Requires New, More Advanced Analytical Tools

7. Health System “C-­Suite” – Key Issues
Substantially Reduce Costs
• Targeting 15% OpEx cuts
• Move to less expensive
settings (inpatient to out,
nursing home to post-­
acute, home care)
• Restructure care delivery
and work “top of license”
Integrate Care Delivery
• Across settings – hospitals
and physician groups
merging to care for
populations
• Across service lines –
coordinated delivery for
bundled care
• Across license tiers –
coordinated care teams
with RNs, mid-­levels, etc.
Maximize Quality
• Ensure achievement of
quality, reimbursement-­
linked targets
• Minimize occurrence of
poor quality / unpaid
events
• Consumer satisfaction and
transparency

8. Overburdened Clinicians are Struggling with Decision Making
Clinicians
struggling
to make
optimal
decisions
Patient
information
overload
Complexity of
molecular
testing,
genomics
New models
require
forecasting
costs and risks
“The pace at which
new knowledge is
produced outstrips
the ability of any
individual clinician
to…manage
information that
could inform
clinical practice.”
IOM, 2012
IOM (Institute of Medicine). 2012. Best care at lower cost: The path to
continuously learning health care in America.

9. The Age of Advanced Analytics
•Integrate predictive,
population and/ or
personalized tools to
guide provider
decisions
•Molecular / genetic
testing to optimize
therapeutic decisions
•Machine learning
applications that predict
readmissions, adverse
events, mortality, ER
visits
•Predict costs for cohort,
episode, …
•Patient risk triage tools
•Chronic care – provider
gap management tools
•Care coordination tools
– tracking across settings,
providers
Population
Management
Analytics
Predictive
Analytics
Clinical
Decision
Support
Personalized
Medicine
Analytics

10. Rapid Adoption Driven by Value Based Care (VBC)
Health system analytics The missing key to
unlock value-­based care
Findings from the Deloitte Center for Health Solutions 2015
US Hospital and Health System Analytics Survey

11. Advanced Analytics Showing Results and Increasing
Investment
A March 2015 survey on analytics in
healthcare:
The top analytical priority for providers in
clinical analytics and data capture. Risk
management, quality improvement, and
business process innovation are key
areas for analytics in payer
organizations.
The report highlighted that by using
analytics, 82% of the respondents saw
improved patient care, with 63% seeing
reduced readmission rates
Market research and surveys
further indicate that:
65% of healthcare providers and
60% of healthcare payers plan to
increase analytics spend in 2015

12. Predictive Analytics Adoption Taking Off
“Virtually every major healthcare delivery system in the
country is either considering, or in the early stages of
implementing predictive-­analytics programs.”
Melanie Evans, “Data collection could stump next phase of predictive analytics.”
Modern Healthcare, July 12, 2014

13. Challenges in Advanced Analytics Adoption
• Technology interoperability / data integration
expensive, lengthy and difficult due to variation
in terminology, data structures, etc.
• IT resources overwhelmed
• Lack of analytics experts -­ What to do with “big
data” after creating datalake / EDW?
• Need for analytics NOW – urgency of move to
value-­based reimbursement / population health
Multiple studies have highlighted
this to be the #1 challenge in the
adoption of analytics
McKinsey report -­ 2018 U.S.
shortage of 190,000 skilled data
scientists and 1.5 M advanced big
data analysts
Interoperability between
technologies is one of the major
factors impacting the adoption of
analytics

14. Lab Analytics Rule

15. Lab Data Rule in Advanced Analytics
Lab data Radiology
data
Medication
data
Physician
exam data
Claims data
Timely
Structured
Ubiquitous
(settings,
providers)
Predictive
potency
Personalized
med apps
Population care
apps

16. Lab-­based Advanced Analytics
•“Smart” test panels by disease
indication
• Interpretive, integrative lab
reports
•Molecular / genetic testing to
optimize therapeutic decisions
•Test-­driven therapy selection
•Lab-­based predictive
algorithms for readmissions,
adverse events, mortality, ER
visits
• Diabetes care management
lab tools – testing pathways,
missing Dx, registries
•Real-­time antibiograms
•Blood product personalized
utilization
Population
Management
Analytics
Predictive
Analytics
Clinical
Decision
Support
Personalized
Medicine
Analytics

17. Lab Test Results for
Mr. Jones
App pulls data from lab
LIS, Path etc. systems
Probability that Mr.
Jones will experience
event X (readmission,
death, adverse event,
disease progression)
Care protocol
specific to
event
activated –
outcome
optimized
Trained
computer
prediction
engine
App delivers probability
score to clinicians via
EHR, mobile device, etc.
Lab-­based Predictive Analytics

18. • 11%-­14% of U.S. adults have chronic
kidney disease (CKD) and are at higher
risk for cardio events and renal failure
• Proven therapies to improve outcomes
in CKD patients exist, but they have
clinical risks and add costs
• CKD clinical decision is
challenging due to the
heterogeneity of kidney diseases,
variability in rates of progression,
and the competing risk of cardio
mortality
CKD Patient’s Labs
• Est GFR, albuminuria, serum calcium
• Serum phosphate, serum bicarbonate,
and serum albumin
Risk prediction
• Single score
• Individualized
• Risk of developing renal failure
• ROC = 91%
Clinical intervention
• Lower risk patients followed by PCPs
• Higher risk patients treated by
nephrologists and closely monitored
Renal Failure Prediction Application

19. Lab Advanced Analytics Diabetes Program
Basic
Management
Ensure abnormal tests
not “missed”
Test value protocols for
referral to
endocrinologist
PGx test to increase
patient statin adherence
(KIF6 from Medco,
Celera)
Avoid
Admissions,
ERs
Biomarker prediction
panels for cardio, renal,
coagulation, etc.
Aggressive, precise
treatment for all other
disorders – e.g. use
PGx and MDx in
GERD, COPD, etc.
Better
Management of
Infections
Rapid, targeted therapy
guided by point of care
MDx for inpatient
infections, including
decub ulcers, UTIs
Pathogen surveillance
program with frequent
antibiograms -­
community PCPs
Avoid Adverse
Events
Consider preemptive
pharmacogenetic
testing of diabetics for
key genes
Quality program for
bedside / critical care
glucometers for
hospital glycemic
control

20. The Lab Disruptive Solution

21. Health System Advanced Analytics Needs and Capabilities
Large Health Systems
• Needs
• ACO-­level analytics
• Enterprise-­wide coordination
• Distribute knowledge IP
• Capabilities
• EMR integration
• Analytics group
• Substantial IT budget -­ $Bs
Mid-­Size Health Systems
• Needs
•Program-­based analytics
•Condition-­centered coordination
•Insource expertise
•Capabilities
•Some integration
•Limited internal analytics
•IT analytics budget < $500M
Small Systems / Hospitals
• Needs
•Application targeted analytics
•Professional services
• Capabilities
•Little IT integration
•Small analytics cap budget – need SAAS

22. Health System Advanced Analytics – Disruptive Opportunities
Large Health Systems
Enterprise Data Warehouse +
Advanced Analytics
Mid-­Size Health Systems
Lab-­Driven, Advanced Analytics
Programs
Small Systems / Hospitals
Lab-­Based Point Solutions
“Disruptive” – simpler solution that fits user’s
needs at lower cost point

23. Lab Integration Platform
Genomic
Variant +
Lab Data
Infectious
Disease
Hospital
Re-­
admission
Test
Algorithm
Rx
Support
Renal
Failure
Prediction
Mortality
Prediction
Blood
Product
Analytics
Personalized
Medicine
Predictive Analytics
Architecture for Lab-­Based Advanced Analytics System
LIS Path Micro, MDx, … EMR Billing
Population Health
Chronic
disease
mgmt

24. Thank you
Click to watch on-­demand webinar
eleanor.herriman@viewics.com