This document discusses the use of data science in healthcare. It provides background on the speaker, Gaurav Garg, and his experience in healthcare consulting, product development, and data warehousing projects. It then addresses why healthcare data is messy, with many different systems from various vendors. It provides examples of how data warehousing can be used for clinical and syndrome surveillance, optimizing resource utilization, improving physician and patient satisfaction. Finally, it outlines a data pipeline and discusses interface engines, predictive analytics use cases, and poses several high value questions that data science could help address across ambulatory care, high acuity care, digital pathology, radiology and revenue cycle.

1. DATA SCIENCE
IN HEALTHCARE
Seattle Code Camp 2016
1

2. ABOUT THE SPEAKER
2
Gaurav Garg (GG) is a business/technology leader playing the role of trusted
advisor and product development manager.
• Leads a Healthcare consulting practice for a national IT consulting firm
(www.calance.com).
• Participated in product strategy team - identified High Value Questions resulting in
150+ new products and features in $16B GE Healthcare product portfolio.
• Played the role of IT Director at UCLA Health System, acting as an interface
between the Clinical Directors and the IT Department.
• Led the delivery of 8 products and 70+ Enterprise Data Warehouse projects in the
Healthcare industry.
• Presented and won grants from the NIH under SBIR funding to build a product in
partnership with UCLA Health System.
• As a thought leader, GG publishes white papers, appears in speaking
engagements and guides healthcare startups at Cambia Grove.
Gaurav Garg (GG)
https://www.linkedin.com/in/gauravkinatus @gaurav_kinatus

3. PAST PROJECTS
3
USC
UNIVERSITY
HOSPITAL

4. HEALTHCARE BASICS Technologies & Flow of
Information
4

5. WHY IS HEALTHCARE DATA MESSY?
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Integrated
Clinical
Information
System
Financial
System
Lab
Anatomic
Pathology
(6 vendors)
EMPI
(5 vendors)
Dictation
(4 vendors)
Interface
Engines
(7 vendors)
Ambulatory
Specialty
(4 vendors)
Hospice
(5 vendors)
Advanced
Visualization
(5 vendors)
Budgeting
(3 vendors)
Labor and
Delivery
(7 vendors)
Time and
Attendance
(3 vendors)
Ambulatory
EMR
(Small
practice)
(18 vendors)
Anesthesia
(1 vendor)
PACS
(31 vendors)
Patient
Monitoring
System
(1 vendor)
Laboratory
Blood Bank
(9 vendors)
CT Ultrasound
Other
medical
Devices
Ventilators
Fetal
monitors
Core
Measures
Rapid
Response
PHR
integration
Ambulatory
Inpatient
Throughput
Tracking
Infection
Tracking
Quality
Scorecard
Care
Quality
Analysis
Charge &
Payment
Analysis
IHI
Triggers

6. BI & DATA WAREHOUSE EXAMPLES
6
Clinical and
Syndrome
Surveillance
MEWS; HAC Risk Scoring (DVT,
DU, CLBSI, CA-UTI, CTI, PU,
Wound); Sepsis; school
absenteeism; bio-surveillance
1 Patient 1
Chart
Optimizing
Resource
Utilization
Imaging Tracking; Room
Utilization; Capital
Investment Optimization;
Payer Compliance; Leakage;
LOS; Re-Admissions Manager
(RAM)
Improving
Physician
Satisfaction
Protocol
Compliance
Tracking
Pharmacy Protocol, Core
Measures (AMI, HF, PN, SCIP,
MU), Nurse Admission
Tracking
Improving
Patient
Satisfaction
Data explosion from EMR adaption has created lots
of data. Calance has implemented over data
warehouse projects in over 70 hospitals and HIEs.
• EMPI Integration – we have experience integrating with
popular EMPI solutions and troubleshooting performance
issues.
• Enterprise Interface Engine – implementation, upgrade,
migrate, configure interfaces. Caradigm Intelligence
Platform (aka Amalga), Orion Rhapsody, eGate,
Intersystem Ensemble, BizTalk, SSIS.
• Data Warehouse/Data Lake – we have implemented
and managed some of the largest data
warehouse/data lake infrastructure in the healthcare
industry (largest @ 3 petabyte data).
• Scorecard/Reports – data visualization for scorecards,
dashboards and reports on SharePoint, Roambi,
tableau.
• Predictive Analytics – build risk stratification and other
predictive analytics algorithms using hadoop ecosystem
and RStudio.

7. 7
DATA PIPELINE
EMPI
Staging Enterprise
Service Bus
Data
Warehouse
BI Analytics,
Dashboards,
Visualization
& Report
Coordination
Browser
Mobile Device
Redshift,
NoSQL,
SQL Server
Authorization and DevOpsData Governance
Products and Logos acknowledged to respective owners. Logos used to for illustration purpose.

8. 8
ESB VS. INTERFACE ENGINE
Courtesy of

9. 9
ESB VS. INTERFACE ENGINE
Courtesy of

10. 10
FINANCIAL FLOW
https://www.youtube.com/watch?v=RJKIjxl1KeE
Who is paying for your healthcare?
Watch the Video

11. 11
CASE STUDIES Retrospective & Predictive

12. 12
PREDICTIVE ANALYTICS EXAMPLE –AUTOBED
https://www.youtube.com/watch?v=eI1l_s4zo_s
Predictive Analytics Example –AutoBed
Watch the Video

13. 13
CHANGING BUSINESS MODEL

14. Know Your Patients Clinical ProtocolsBusiness Intelligence & Predictive Analytics
Cohort Identification Standardized Care PathPatient Demographics
Lifestyle
Medical History
Risk Stratification
Adverse Event Prediction
Family History
Proactive Appointments
Treatment Effectiveness
Patient Generated Data
Variance Management
Financial Review
Patterns
Treatment Adjustment
Actionable Intelligence
POPULATION HEALTH EXPLAINED

15. PROTOCOL COMPLIANCE
Clinical desktop integration using “Knowledge
Hub”
Protocol Definition and Matching
Clinician view, compliance officer view,
reporting moduleLanguage & Terminologies
Data Aggregation
TouchPoint360
Data existing in hospital systems
Calance Protocol Compliance Framework

19. IDEAS High Value Questions
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20. 20
AMBULATORY
• Is my practice setup to be successful as a Pay-
for-Performance contract or should I stay as
pay per service?
• Should I go an form an Accountable Care
Organization (ACO) with 75 other physicians
or join an Integrated Delivery Network (IDN)?
• What are my compliance requirements with
different contracts?
• How do I rapidly understand the business
implications of (program de jour) for my
specific practice so I can rapidly make
informed decisions to participate (or not)?

21. 21
HIGH ACUITY CARE
• What is the probability of patient developing
an infection?
• Do I have everything for tomorrow?
• How are we doing on infection management
(SEPSIS)? Is there anything we can do to use
the devices to proactive alerts?
• What is the correlation between cost index
and length of stay?

22. 22
DIGITAL PATHOLOGY
• Find similar cases
• What other information do you need for
diagnosis? Additional images, molecular
image, additional test results, additional
symptoms
• How to close the loop between Radiologist
and Pathologist to reduce discordance?

23. 23
RADIOLOGY
• Automatically detect abnormalities in the
image
• Which Radiologist results in good/bad
follow-up actions?
• What are my compliance requirements with
different contracts?
• Is this the right procedure, modality and
protocol?

24. 24
REVENUE CYCLE
• Recommend an immediate step to avoid
hospitalization. e.g. cab for picking up Rx
• Who is the best provider to treat this patient?
• What is the best site for this patient?
• Coding accuracy dashboard