The document discusses how healthcare organizations are increasingly relying on data analytics and data scientists. It notes that while analytics can help improve patient care and reduce costs, the healthcare industry lags behind other sectors in adopting new technologies and analyzing data due to privacy concerns and differences in prioritizing risks. The document outlines some current uses of analytics including clinical decision support, fraud detection, and personalized treatment plans. It also explores challenges to wider adoption such as establishing standards and gaining access to data.

1. Health Analytics
February 2019
Analytics in Action
http://DSign4.education

2. • Why do healthcare establishments need to develop
in-house data science expertise, or become
increasingly reliant on external consultants and
software editors.
• How do the authors explain the lack of specialists
in this field?
• How do they support their contention that the
industry's senior leadership would rather rely on
their own instincts rather the data?
• What is the nature of the build vs buy dilema?
Using It or Losing It?
Introduction
Martin Heusch and Timothy Mauser, «Using It or
Losing It? The Case for Data Scientists Inside
Health Care"

3. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
Definitions
Infrastructure
Use Scenarios
Limits

4. • The use of data, analysis, and predictive
modeling to improve teaching and
learning
• Analytics models aggregate data in new
ways
• Help students and institutions
understand past, present and future
academic performance
• Impact on personalized learning,
pedagogical practices, curriculum
development, institutional planning, and
research
Health Analytics
Technology

5. Inputs
Prediction
Evaluation
Actions
Outcomes

6. • Health care analytics arims to improve
clinical care while limiting excessive
spending
• Healthcare Activities that can be
undertaken as a result of data collected
from four areas within healthcare;
 Claims and cost data,
 Research and development (R&D)
data,
 Clinical data (collected from electronic
medical records (EHRs)),
 Patient behavior and sentiment data
Context
Technology

7. • The journey from fee-for-service to value-
based contracts
• Understanding the wants and needs of health
care consumers
• Defining the role of patient experience in
marketing
• Embracing transparency
• Creating patient loyalty in health care
organizations
Market Challenges
Technology
NRC Health

8. • The Assistance Publique-Hôpitaux de Paris has
been using data to predict daily and hourly patient
admissions
• Propeller Health, which has started to use inhalers
with GPS-enabled trackers in order to identify
asthma trends help clients identify novel trade
patterns
• Flatiron Health has developed a service called the
OncologyCloud, based on the idea that 96% of
potentially available data on patients with cancer is
not yet analyzed
• Optum Labs has collected EHRs of over 30 million
patients to create a database for predictive analytics
to improve healthcare delivery
Whose doing it?
Technology

9. • Using collaborative analytics to personalize
treatment plans
• Analytics can help derive meaningful insights
to attract customers, as well as manage costs
and risk of health plans
• Detect fraud based on analysis of anomalies in
patient records
• Open up new diagnostic landscapes for the
automated interpretation of x-rays, CAT scans,
and MRIs
Use Scenarios
Technology

10. • 3865 Healthcare Data Scientist jobs
• The core data science skillset of machine
learning, data visualization, and statistics
• Healthcare refers to dozen sub-industries such
as hospitals, health plans, pharmaceuticals,
medical devices,
• The most important information is hidden in
unstructured data
• The need to work closely with their end users
• It is critical to really understand what
constitutes success
Working in Healthcare
Technology
D’Avolio, L. (2017) What Data Scientists Need to Learn

11.  Cutting down administrative costs
 Clinical decision support
 Reducing fraud and abuse
 Better care coordination
 Improving patient wellbeing
Value Levers
Technology
HealthFore Technologies

12. • The industry is far behind other sectors in
terms of adopting the latest technology and
analytics
• For privacy reasons, it can often be difficult to
obtain access to data
• There are very different costs to false-
negatives and false-positives
• Establishing common technical standards
• Increasing confidence in safety and safe use of
health IT
• Developing an international communications
structure
• Stakeholder collaboration
What are the risks?
Technology

13. • A boon in wearable devices that track activity
and biometric data
• The evolution of electronic medical record
systems
• The creation an interoperability roadmap
• Increasing privacy and security
Future trends
Technology

14. • Bianchard, T., (2017), Data Science and Predictive
Analytics in Healthcare
• Huffington Post, How Big Data Could Transform The
Health Care Industry, (video)
• Heusch, M., (2017), Using It or Losing It? The Case
for Data Scientists Inside Health Care
• Lebied, M., (2017), 9 Examples of Big Data Analytics
in Healthcare That Can Save People
• Montgomery, M., (2016), The Future Of Health Care Is
In Data Analytics
• Rao, V., (2015), Healthcare Data Analytics
• Raghupathi, W. (2014), An Overview of Health
Analytics
Bibliography
Next Steps

15. • What is the organization’s business
model?
• Why does the organization focus on
data?
• How is the Data Science team
organized?
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How does the organization use Data
Science to propel growth
Case Study Questions
Technology