Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.

1. Unleashing Human Potential:
The Philosophy, Psychology, and Technology
of Data in Healthcare
Northwestern Masters in Health Informatics Guest Lecture
Feb 25, 2020
Dale Sanders
Chief Technology Officer, Health Catalyst

2. -Dale's Career path
-Overview of Health Catalyst and their
Entrepreneurial Journey
-Emerging trends in Health Analytics and
Informatics
-Use of HL7 FHIR standard in the industry
-Real-world case studies that helped improve
health and business outcomes through Analytics
-Healthcare policy for IT and Analytics
-Career advice for students
Potential Topics

3. 1. My Career, Career Advice, and
the Health Catalyst Journey
2. The Psychology and Philosophy
of Data
– Becoming “Data-Driven” in Healthcare
3. The Technology of Data
– The State of Data in US Healthcare
4. Health Catalyst from the
Trenches
Today’s Chapters

4. © 2020
Health
Catalyst
• 15 years in space, defense, and national intelligence
• 23 years in the healthcare
My Background: Data Fusion and Decision Support
1983
B.S. Chemistry,
biology minor
US Air Force Command,
Control, Communications,
& Intelligence (C3l) Officer
TRW/National Security Agency
• Nuclear weapons policy & decision
making
• START Treaty
• Nuclear non-proliferation
• US nuclear command & control
system threat protection
Director of Medical Informatics,
Intermountain Healthcare
“Health Catalyst v1”
CIO, Cayman Islands
National Health System
CTO, Health
Catalyst
Reagan/Gorbachev
Summits
• Space Operations
• Nuclear Warfare
Planning and Execution;
NEACP & Looking Glass,
”Doomsday Planes”
CIO, Northwestern
University Medicine
“Health Catalyst v2”
2020
4

5. © 2020
Health
Catalyst
Underground
Command Center
for US Nuclear Forces
5

6. © 2020
Health
Catalyst
US National
Emergency
Airborne Command
Post System
“Doomsday Planes”
6

7. © 2020
Health
Catalyst
Different “Command Centers”, Same Concepts
7
Intermountain’s LDS Hospital
Northwestern Medicine Campus
George Town Hospital Cayman Islands
Health City Cayman Islands

8. © 2020
Health
Catalyst
Military + Healthcare
• Subjective information from
human sources
• Objective data from sensors
• False positives
• False negatives
• Time critical
• Life critical
Conceptual Decision-Making Similarities
8

9. The Psychology and
Philosophy of Data:
The Humanistic ”Why” and
“How” of a Data Strategy

10. © 2020
Health
Catalyst
What often we humans define as “the truth” in life is
just irrefutable mistakes and errors
We stumble upon the truth by making a big mistake
10

11. My personal life motto applies to healthcare data, too…
• Find The Truth
• Tell The Truth
• Face The Truth
Humans & Their Biology: Elusive Truth
• The truth in healthcare data is rarely “The Truth,”
but we talk to physicians as if it were
• When you communicate the truth, realize that it’s
only an approximation, and be sensitive to the
human who’s receiving the message
• Help people face the truth without feeling
threatened and over-measured
11

12. © 2020
Health
Catalyst
The Strength of Digital Social Connections
• During her pregnancies, my wife spent 95%
of her “pregnancy data and technology time”
interacting with “mothers like her”
• At most, she spent 5% of her technology
time interacting with her traditional
healthcare delivery ecosystem
• She is still connected to those mothers, and
almost completely disconnected from her
healthcare system
• We need to bring these two worlds together,
technically and socially
12

13. © 2020
Health
Catalyst
• Netherlands study
• Rate of patient requests for a
specific therapeutic or
diagnostic intervention, 1985-
2014
• Significant increase in
requests by patients
• Significant increase in
compliance by GPs
Owning Their Care: Patients Are Becoming More Data-Driven
13
Requests for blood tests: 2x increase
Requests for urine tests: 26x increase
Requests for radiology/imaging: 2.4x increase
Requests for medication prescription: 1.2x
increase

14. © 2020
Health
Catalyst
We’re losing our physicians…
they are losing their freedom and autonomy
• 271 measures in QPP, 86 related
to General Internal Medicine
• 65% are either invalid or of
questionable clinical validity
• Highest suicide rate of any
profession
• American Psychiatric Association (APA) 2018. Abstract 1-227,
presented May 5, 2018
• >50% burnout in all specialties
• Carefully manage the tension
between variation reduction and
physician autonomy
• Minimize your physician quality
measures
14 June 2018

15. © 2020
Health
Catalyst
Goals of the Report: This report outlines three primary goals
for reducing health care provider burden:
1) Reduce the effort and time required to record information in EHRs
for health care providers during care delivery.
2) Reduce the effort and time required to meet regulatory reporting
requirements for clinicians, hospitals, and health care organizations.
3) Improve the functionality and intuitiveness (ease of use) of EHRs."
Workgroups: This report was informed by, and will be followed by,
several initiatives at HHS/ONC to implement the recommendations,
coming from four workgroups:
1) Clinical Documentation
2) Health IT Usability and the User Experience
3) EHR Quality Reporting
4) Public Health Reporting
15

16. © 2020
Health
Catalyst
Attributes of a Change Negotiator (and Consultant)
Believability: Is this person honest, sincere, trustworthy, and
transparent? If I don’t know them well enough, is there evidence
of that in their background?
Relatability: Is this person similar to me? Can this person
empathize directly with my situation and/or role? Can I relate to
this person along other dimensions of empathy, e.g., upbringing,
religion, age, gender, race, ethnicity, experiences, hobbies,
education, etc.?
Credibility: Even though we’re similar, does this person bring a
diverse view, expertise or knowledge that I don’t have; and that I
value, respect, and need?
Inspired by a RAND Corp study, ~1985…?
16

17. © 2020
Health
Catalyst
A Few Sanders-isms
• “When times are tight, goodwill takes flight.”
• Whatever data strategy you undertake, it must be aligned
with sustainable economics– and it must account for
behavioral economics-- tangible and social currency
• “People will rarely change if you keep paying
them to stay the same.”
• ”Predictions of risk without interventions are a
liability to the decision maker, not an asset.”
• My cultural motto: “Chronic, constructive,
dissatisfaction”
Which oddly apply to data strategies
17

18. © 2020
Health
Catalyst
• Every company is “innovating”, but… 90% of innovation program fail*
• Instead of swinging for the homerun fence of innovation, look around at the
everyday violations of common sense in healthcare, that we hypnotically
walk past everyday, and fix those
Become Common Sense Contrarians
18
• Appoint a Chief of Common Sense Officer?
• The accumulated relief from ambient violations of
common sense will be profound for patients and
staff
*

19. The Technology of Data
Data Assets and How to Leverage Them

20. © 2020
Health
Catalyst
20
AI Boils Down to Pattern Recognition
Detecting a pattern then reacting to it, exactly like human cognition

21. © 2020
Health
Catalyst
21

22. © 2020
Health
Catalyst
Data Volume is Key to AI
“The Unreasonable Effectiveness
of Data”, March 2009, IEEE
Computer Society; Alon Halevy,
Peter Norvig, and Fernando
Pereira, of Google
“Invariably, simple models and a
lot of data trump more elaborate
models based on less data.”
22

23. © 2020
Health
Catalyst
• Every 10 hours, Tesla collects
1 million miles of driving data
• 30Tbytes per car per 8 hours
• “We can fix problems in your
car and make it safer, long
before you know you need it.”
Vehicle Health Monitoring – Human
Health Monitoring
23

24. © 2020
Health
Catalyst
Properties of Satellite (and Human) Telemetry Data
• High Dimensionality: Hundreds to thousands of data variables
• Multimodality: Day and night modes; pediatric & adult
• Heterogeneity: Continuous, real values; discreet, categorical values
• Temporal Dependence: At what time you collect the data matters;
the temporal dimension between heterogeneous data also matters
• Missing Data: Is the missing data expected to be missing, or not?
Spacecraft Health Monitoring – Human
Health Monitoring
24
TRW/Northrup Grumman
DSP Satellite

25. © 2020
Health
Catalyst
25
“…newest generation aircraft…
five-to-eight terabytes per flight”
“Airplanes like the 787 and A350 collect 10,000
times more data than 1990s or early 2000s-era
aircraft. That is because more parameters are
being measured at higher frequencies, using
broader transmission pipelines.”
– Joel Reuter, Vice President of Public Affairs, Rolls-Royce North America

26. © 2020
Health
Catalyst
Creating the Patient’s Digital Twin
Developing three fundamental AI pattern recognitions in healthcare
26
Patients like
this
[pattern]
Who were
treated like
this
[pattern]
Had these
outcomes
and costs
[pattern]
Less about predictions, more about patterns
hpcwire.com

27. © 2020
Health
Catalyst
Turn this into your strategic
data acquisition roadmap
• In the US, our digital view of
the patient is stuck in the lower
left quadrant
• On average, we collect data on
patients about 3x per year in
the US, during visits to the
clinic or hospital
• We collect almost no data on
healthy patients, who rarely
visit the healthcare system
The Human Health Data Ecosystem:
Creating Our Digital Twin
27

28. © 2020
Health
Catalyst
Our Digital Understanding of Patients is Poor
This is my life.
This is healthcare’s
digital view of my life.
28

29. © 2020
Health
Catalyst
We Are Not “Big Data” in Healthcare, Yet
29
Citation: Dale Sanders, CIO, Northwestern
Medicine. Calculating annual storage
requirements for the Northwestern electronic
health record, 2011
5-8TB per 4 hrs.
30TB in 8 hrs.
29
100 MB per year

30. © 2020
Health
Catalyst
• July 2019
• U of Toronto, Microsoft,
Johns Hopkins, Harvard,
MIT, New York University
30
“…diseases in EHRs are poorly labeled,
conditions can encompass multiple
underlying endotypes, and healthy
individuals are underrepresented. This
article serves as a primer to illuminate these
challenges and highlights opportunities for
members of the machine learning community
to contribute to healthcare.”

31. © 2020
Health
Catalyst
Clinical Text Data: Questionable Quality
31
In a typical note, 18% of the text
was manually entered; 46% copied;
and 36%, imported

32. © 2020
Health
Catalyst
EHR Documentation = Observed
Physician Behavior
32
• 38.5% of review of systems (ROS)
were confirmed (61.5% of the time, the
EHR data did not reflect reality)
• 53% of physical exams (PE) were
confirmed (47% of the time, the EHR
data did not reflect reality)
• Sept 2019
• UCLA, Stanford, UC Santa Cruz
Perception
Reality

33. © 2020
Health
Catalyst
49% of randomized clinical trails were
deemed high risk for wrong conclusions
because of missing or poor measurement
of outcomes data
The Importance of Outcomes Data
33
18 Sep 2019

34. Microns-thin, one-inch skin-pliable sensors
with integrated Bluetooth antenna, CPU,
physiologic monitors, and wireless power
34

35. © 2020
Health
Catalyst
Microns-thin, one-inch skin-
pliable sensors with integrated
Bluetooth antenna, CPU,
physiologic monitors, and
wireless power
35

36. © 2020
Health
Catalyst
36
Feb 2019

37. © 2020
Health
Catalyst
Rise of The Digitician and Patient Data Profiles
37
• Different patient types have
different data profiles required for
the active management of their
outcomes and health
• I’m not talking about quality
measures
• I’m talking about telemetry,
diagnostics and functional status
about the state of the patient, not
the state of healthcare processes
• It’s the Digitician’s job to
prescribe the right sensors and
proactively collect this data for
patients in their panel, and feed
the analytics of that to the care
team and patient

38. Health Catalyst
From the Front Lines

39. © 2020
Health
Catalyst
Generally speaking,
computer science hasn’t
addressed the last and
critically important layer in
the technology stack,
especially for the
incredibly complex world
of healthcare and life
sciences data…
User Interface
Application Software
Operating System
Infrastructure
39
Where Does Health Catalyst Fit in the Tech Stack?

40. © 2020
Health
Catalyst
InteroperabilityData Analytics
& AI
Data-First
Application Dev
The Health Catalyst Data Operating System™ (DOS) is a single, cloud-
based, API-based architecture with a common, curated, consistent layer of
data content, to support the Three Missions of Data…
Three Missions of Data—One Vendor, One Platform
40
Workflow transactions and
ambient analytics in the same
software user experience
Interoperability, portability,
and source-to-subscriber,
transaction-level
exchange of data

41. © 2020
Health
Catalyst
Healthcare decision
making should occur in
three closed-loops…
1. Populations
2. Protocols
3. Patients
4141

42. © 2020
Health
Catalyst
The Healthcare Analytics Adoption Model
This is a proven recipe for success… follow it, deliberately
Level 9 Direct-to-Patient Analytics & Artificial Intelligence
Level 8 Personalized Medicine & Prescriptive Analytics
Level 7 Clinical Risk Intervention & Predictive Analytics
Level 6 Population Health Management & Suggestive Analytics
Level 5 Waste & Care Variability Reduction
Level 4 Automated External Reporting
Level 3 Automated Internal Reporting
Level 2 Standardized Vocabulary & Patient Registries
Level 1 Enterprise Data Operating System
Level 0 Fragmented Point Solutions
42
• Flash Data Engine: Source Mart Designer
• Flash Data Engine: Subject Area Mart Designer
• IDEA
• Atlas Data Governance
• DOS Operations Console
• Population Builder
• DOS Marts Level 1
• Standardized Terminology (support all products)
• DOS Marts Level 2
• Measures Manager
• Leading Wisely
• Touchstone Suite
• Touchstone
• CORUS Suite
• Population Health Foundations
• Community Care
• 45+ Analytic Accelerators
• EHR Closed Loop and AI/Data Science (support
all products)
• Patient Safety Monitor Suite
• Care Management Suite
• Touchstone

43. © 2020
Health
Catalyst
3-5 Year Customer Strategy
ExpansionVolume Labor
Supply
Chain
OtherPayment
Population
Health
Revenue Cost Quality
Generate Actionable Analytics Insights2
Integrate All of Your Data1
Measure, Monetize, & Market Value4
Apply Expertise to Drive Durable Improvements3
⇩ Pharmacy
Supply Costs
⇩ Surgical
Supply Costs
⇩ General
Supply Costs
⇩ Blood
Utilization
⇧ Capacity
⇧ Access
⇩ Referral
Leakage
⇧ Care
Expansion
⇧ Collection
Rate
⇧ Cash
Acceleration
⇧ Payer
Contracts
⇧ Service
Lines
⇧ M&A
⇧ Trials
Revenue
⇧ Digital
Retail
⇩ Vendor Costs
⇩ Clinical Support
Services Costs
⇧ Analytics
Efficiency
⇩ Building &
Equipment Costs
⇩ Low Value Care
⇧ Care
Management
⇧ Quality
Measures
Performance
⇧ Financial &
Operations
–––– ⇧ Cost Accuracy and Transparency ––––
Patient
Safety
Clinical
Operations
⇩ Readmissions
⇧ Outcomes
Excellence
⇧ Research &
Operations
⇩ Safety
Events &
Infections
⇧ Voluntary
Reporting
⇩ Liability
⇧ Safety
Excellence
⇩ Labor Costs
⇩ Staffing
Contracts
⇧ Provider
Contracts
⇧ Outsourcing

44. © 2020
Health
Catalyst
ExpansionVolume
Patient
Safety
Clinical
Operations
Labor
Supply
Chain
OtherPayment
Population
Health
Revenue Cost Quality
⇩ Labor Cost
• CORUS: Labor Mgmt
• Department Labor
Costs
• Surgical Services
• Emergency
• Provider Productivity
⇩ Staffing Contracts
• Labor Contract
Models
⇧ Provider Contracts
• Provider Contract
Consulting
⇧ Outsourcing
• SmartSourcing
• Nurse Abstraction
• Analytics
• Clinical Expert
Reviewers
⇩ Pharmacy Supply
Costs
• Medication Therapy
Mgmt (MTM)
⇩ Surgical Supply
Costs
• Supply Chain Mgmt
Expert Services
• Supply Chain Mgmt
Explorer
⇩ General Supply
Costs
• Supply Chain
Explorer
• Purchasing Optimizer
⇩ Blood Utilization
⇧ Capacity
• Capacity Mgmt
• Patient Flow
• Surgery Ops
• Pop Builder –
Stratification
⇧ Access
• Outpatient Services
• Practice Mgmt
• Access
• Ops
• Telehealth
⇩ Referral Leakage
• Practice Mgmt
Suite
• PMPM Analyzer
• Competitive
Analysis Service
⇧ Care Expansion
• Community Care
• Practice Mgmt
Suite
• Care Mgmt Suite
⇧ Collection Rate
• Revenue Cycle Mgmt
• Bad debt reduction
• Denial reduction
• Clinical
documentation
improvement
• Unbilled
receivables
• Late/missing
charges
• Clinical chart
abstraction service
⇧ Cash Acceleration
• Revenue Cycle Mgmt
• A/R Mgmt
• Patient collections
• DNFB
⇧ Payer Contracts
• Quality Measures
• OPPE Services
• Financial Mgt
Explorer
• Revenue Cycle Mgmt
• Payer Contract
Consulting
• Market
Assessment
⇧ Service Lines
• Service Line
Strategic
Consulting
• Financial Mgmt
Explorer
⇧ M&A
• M&A Strategic
Consulting
• EMR Integration
Alternatives
• Ambulatory
Acquisition
⇧ Trials Revenue
• Enable Pharma /
Device Trials
⇧ Digital Retail
• Digital Retail
Expert Services
⇩ Readmissions
• Readmissions
Explorer
⇧ Outcomes
Excellence
• Cardiovascular
• Surgery
• Chronic Disease
• Musculoskeletal
• Neuroscience
• Oncology
• Acute Care
• Women’s & Newborn
• Gastrointestinal
• Pediatrics
• Respiratory
• Outcomes
Improvement Training
⇧ Research &
Operations
• Pop Builder
• Pre IRB
• IRB
• Grant production
• Life Sciences
• Precision Medicine
• Digital Medicine
• Trials Recruitment
• Disease
Collaboratives
• Evidence Based
Outcomes Center
⇧ Care Management
• Care Mgmt Suite
• Pop Builder -
Stratification
• Care Mgmt Workflow
• Care Mgmt Analytics
⇧ Quality Measures
Performance
• Quality Measures
• Readmissions
• Community Care
⇧ Financial & Operations
• Claims Measures
• Population Builder
• PMPM Analyzer
• HCC Insights
• Population Health
Opportunity Analysis
• Solution Optimization
Services
• Bundled Payments
⇩ Safety Events &
Infections
• Patient Safety Monitor
• Medication safety
• Pressure ulcers
• Falls
• Ambulatory Safety
• Sepsis
• CLABSI/CAUTI
• C-Diff
• SSI
• HAP/VAP
⇧ Voluntary Reporting
• Patient Events
• Workforce Events
• Visitor Events
⇩ Liability
• Culture of Safety
• Patient Safety
Organization
⇧ Safety Excellence
• Rating Improvement
Expert Services
• STAR
• Leapfrog
• High Reliability
Organization
Data, AI &
Analytics
Platform
Data Operating System (DOS™) Data Services
• Data Acquisition & Integration
• EHR & 300+ sources
• Data Operations
• Master Data Mgt & Terminology
Analytics Services
• Analytics Accelerators
• Analytics Optimization
• Custom Analytics
• Analyst Training
• Analyst SmartSourcing
Analytics Tools
(Rapid Response Analytics)
• Population Builder
• Leading Wisely
• DOS Marts
• EHR Closed Loop
• Data Warehouse
• AI/Machine Learning
• App Dev Platform
• Interoperability
• Flash Engine
• Source Mart Designer
• SAM Designer
• Atlas (Data Governance)
Multi-Client DOS™ Platform
• Touchstone (Benchmarking)
• Life Science
• AI / Machine Learning
• Data Quality
Data Science Services
• Data Science Consulting
• Data Science Optimization
• AI Tool Kit
• AI Leadership Decision Support
• AI for Health Equity
⇩ Vendor Costs
• Eliminate Vendors
• Eliminate
Purchased
Services
⇩ Clinical Support
Services Costs
• Patient Flow
• Lab & Imaging
• Therapeutic
• Invasive
• Acute Medical
• Ambulatory
⇧Analytics
Efficiency
⇩ Buildings &
Equipment Costs
• Geo-Spatial Facility
Optimization
• Equipment
Optimization
⇩ Low Value Care
• Diagnostic
• Therapeutic
• Procedural
--------------– ⇧ Cost Accuracy and Transparency –-------------
• Activity Based Costing: CORUS

45. © 2020
Health
Catalyst
AI Algorithms are Commodities, Data Platforms are Not
“…it is dangerous to think of these
quick wins as coming for free. Using
the software engineering framework
of technical debt, we find it is
common to incur massive
ongoing maintenance costs in
real-world ML systems.”
Neural Information Processing Systems (NIPS)
Advances in Neural Information Processing Systems 28 (NIPS 2015)
45

46. © 2020
Health
Catalyst
The machine learning code, in the black box, is a small fraction of
the machine learning investment and ecosystem

47. AI/Data
Science
Commoditize
access to AI
models
IDEA
Add missing data
DOS
Operations
Console
Schedule, define,
monitor, and
troubleshoot ETL
processes
Atlas
Data
Governance
Govern data via
this metadata index
and browser
Metadata- & Task-
Management Tools
Health Catalyst DOS Ecosystem for Providers
Touchstone
Suite
Leading
Wisely
Data to the Edges:
Rapid Response Analytics
EHR
Closed Loop
Data
published
back to DOS
Population
Builder
Client-
developed
analytics &
apps
3rd-party apps
DOS Developer
Program 1.0
(Q1 2019)
Data
published
back to DOS
Patient Safety
Monitor Suite
Care Management
Suite
Population Health
Foundations
CORUS Suite
(Activity-based Costing)
Community Care
(Compulsory Measures)
45+ Analytic
Accelerators
Data published
back to DOS
Data to the Domains
Subsets of data for
specific analytic use
cases & standardized
terminology
Level 1: DOS Marts
Clinical, cost,
claims, etc.
Level 2:
Population SAMs
Sepsis, diabetes, CHF,
COPD, etc.
Customized SAMs
Measures
Manager
View and manage
all measures
in one place
>2,000
potential
compulsory
& internal
measures
Requirements drive
data content needs
DOS Tools
>300 data
sources Text
Processing
Integrate text &
discrete data
DOS Data
Lake
Flash Data Engine
Subject Area
Mart Designer
Aggregate &
manage data
Flash Data Engine
Source Mart
Designer
Enable real-time
data ingestion
Raw
text
Data
of all
types
HC
Interoperability
Community-based
data integration
Ambulatory
data

48. © 2020
Health
Catalyst
Where Are Your Circles of Influence?
• If you hope to be successful
in healthcare, your strategy
must address and be
realistic about the entire
ecosystem
• The road to failure is paved
with the bricks of naivete
• Lack of a strategy in the
outer two circles means
you’ve surrendered to their
influence
As a Board, these are the three levers you can pull

49. © 2020
Health
Catalyst
Lessons Learned: Health Catalyst
• Our Intermountain skills play
well in some cultures, but not
in others
• Most clients are not in an IDN
economic model
• Many cultures are not as
”beehive” as Intermountain–
collaborative and communal
49

50. © 2020
Health
Catalyst
• We dismissed the importance of compulsory quality measures as they
relate to revenue
• We didn’t focus enough on the reality need of generating revenue
• We were too top-down… we overlooked the value that comes from
unleashing data to the edges of the organization; and grass roots,
culturally ambient improvements
• We under-sold the value of the DOS ecosystem to translational research
• We focused too much on in-patient clinical variation reduction
• Complex cultural and process change, protracted ROI
• Not enough focus on ambulatory care and population health
The Health Catalyst Journey:
A Few Lessons Learned

51. © 2020
Health
Catalyst
Build a Tactical and Strategic Battle Plan
Short, Medium, Long-Term Data-Driven Strategy
51
51

52. • April 2018 Health
Affairs
• Vermont
Accountable Health
Community
• Balanced portfolio
of interventions by
determinant of
health and time
frame
52

53. © 2020
Health
Catalyst
“Despite interest in addressing social
determinants of health to improve patient
outcomes, little progress has been made in
integrating social services with medical
care.”
“…the ACOs were frequently “flying blind,”
lacking data on both their patients’ social
needs and the capabilities of potential
community partners.”
Population Health Challenges
53

54. Raising Emphasis on Low Value Care
Evidence Based Medicine: We’re asking docs, “Do more of this”
Low Value Care: We’re asking docs, “Do less of this”
The constantly shifting definition of evidence-based medicine makes it
VERY difficult to comprehensively and persistently implement as a core
strategy
I believe the reduction of Low Value Care offers significant, easier
progress
And by implication, patient safety

55. • Oct 2017 Health Affairs
• RAND, U of Michigan
• 44 low value health
services were studied
• $586M in unnecessary
direct costs
• Virginia APCD
• 2014 claims
• 5.5M beneficiaries

56. • May 2018
• $210B in direct
unnecessary costs per year
in the US from Low Value
Care

57. Medicare Definitions of LVC
~31 measures, 6 categories
1. Cancer Screening
2. Diagnostic, Preventive
Testing
3. Preoperative Testing
4. Imaging
5. Cardiovascular Testing
6. Other Surgical Procedures
Guided by the US Preventive Services Task Force

58. • University of Washington
• April 2019 JAMIA
• Low Value/High Cost
Medication Prescribing
• Best Practices Alerts
• Cost of care @ point of care
• 32% reduction in low
value/high cost medication
prescriptions

59. © 2020
Health
Catalyst
In Closing…
59
1. Career paths: Random, planned
opportunity
2. 60% psychology, 40% technology
3. Technology is easier but the data
and challenges are bigger
4. Lessons from the trenches of
Health Catalyst