1.
A Joke…

2.
Big Data Healthcare
What is it?
What are the opportunities?
What are the Challenges?

3.
Big Data It is everywhere!
Not only Health
care

4.
Let’s
Exercise

5.
Let’s
Exercise

6.
Several Use Cases

7.
Creating risk scores based on lab testing,
biometric data, claims data, patient-generated
health data, and the social determinants of health
can give healthcare providers insight into which
individuals might benefit from enhanced services
or wellness activities.
AVOIDING 30-DAY HOSPITAL READMISSIONS
predictive analytics can warn providers when a
patient’s risk factors indicate a high likelihood for
readmission within the 30-day window.
GETTING AHEAD OF PATIENT DETERIORATION
Machine learning strategies are particularly well suited to
predicting clinical events in the hospital, such as
the development of an acute kidney injury (AKI) or sepsis.
PREVENTING SUICIDE AND PATIENT SELF-HARM
Early identification of individuals likely to cause harm to
themselves can ensure that these patients receive the
mental healthcare they need to avoid serious events,
including suicide.
DEVELOPING PRECISION MEDICINE AND NEW THERAPIES
AS PRECISION MEDICINE AND GENOMICS GAIN STEAM,
PROVIDERS AND RESEARCHERS ARE TURNING TO
ANALYTICS TO SUPPLEMENT TRADITIONAL CLINICAL
TRIALS AND DRUG DISCOVERY TECHNIQUES.

8.
 Personalized medicines
 Cost reduction
 Care in Real Time
 Increase transparency
 Improve patient outcomes
 Research and development

9.
Is there a Relation between High Blood
Pressure to Coronary Heart Disease
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
50,000 people
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
5000 people
Usual SBP (mmHg)
120 140 160 180
1
2
4
8
16
32
64
128
256
Age at risk:
80-89
70-79
60-69
50-59
40-49
500,000 people

10.
The Risk of
Discrimination

11.
The Risk of Bias

12.
The Risk of Privacy
(linkage)

13.
Conclusion

14.
Ten simple rules for responsible
big data research
Acknowledge that data are people and can do harm
Recognize that privacy is more than a binary value
Guard against the reidentification of your data
Practice ethical data sharing
Consider the strengths and limitations of your data;
big does not automatically mean better
Debate the tough, ethical choices
Develop a code of conduct for your organization,
research community, or industry
Design your data and systems for auditability
Engage with the broader consequences of data and
analysis practices
Know when to break these rules