advanced laboratory analytics — a disruptive solution for health systems
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Advanced Laboratory Analytics —A Disruptive Solution for Health Systems
Eleanor Herriman, MD, MBAChief Medical Informatics Officer
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
Agenda
The Age of Advanced Analytics
Lab Analytics Rule
The Lab Disruptive Solution
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
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 NashFounding Dean, Jefferson School of Population Health
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
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
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.
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
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
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
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
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
Lab Analytics Rule
Lab Data Rule in Advanced AnalyticsLab data Radiology
dataMedication data
Physician exam data
Claims data
Timely
Structured
Ubiquitous (settings, providers)
Predictive potency
Personalized med apps
Population care apps
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
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
• 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
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
The Lab Disruptive Solution
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
Health System Advanced Analytics – Disruptive Opportunities
Large Health SystemsEnterprise Data Warehouse +
Advanced Analytics
Mid-Size Health Systems Lab-Driven, Advanced Analytics
Programs
Small Systems / HospitalsLab-Based Point Solutions
“Disruptive” – simpler solution that fits user’s needs at lower cost point
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 MedicinePredictive Analytics
Architecture for Lab-Based Advanced Analytics System
LIS Path Micro, MDx, … EMR Billing
Population Health
Chronic disease mgmt
