This document discusses predictive analytics in population health management. It begins by stating that predictive analytics can reduce expenditures and enhance patient quality of life. It then outlines the key components of predictive analytics for PHM including patient data integration, data cleansing, building predictive models using artificial intelligence, and creating dashboards. Examples of applying predictive analytics include predicting mortality for heart patients, influenza outbreaks, and reducing hospital readmissions. Challenges to implementing predictive analytics in healthcare include lack of budget, incomplete data, and lack of skilled employees. The document concludes that predictive analytics has potential to revolutionize healthcare by predicting future health issues.

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Effective PHM Means Being Able to
Predict the Future
20th March, 2018 | Author: Sonal Govil, Healthcare Business Analyst, CitiusTech
CitiusTech Thought
Leadership

2. 2
Objective
 Population Health Management (PHM) market is expected to grow tremendously at a
Compound annual growth rate (CAGR) of 25.2% between 2016 to 2021
 It is expected to reach USD 42.54 billion by 2021
 Following trends shall have a key role in the growth of Population Health Management (PHM):
 This document discusses the concept of Predictive Analytics in Population Health Management
(PHM), describes its various key components, application in PHM model, use cases, challenges
and conclusion
 This document is intended to emphasize the effectiveness of Predictive Analytics on population
health management programs
Top trends – PHM 2017 & beyond
Real-Time Analytics with Big Data
The Internet of Things (IoT)
Artificial Intelligence (AI)
Lean Operations
1
2
3
4
5
6 Tightened Coordination
Source: marketsandmarkets

3. 3
Agenda
 Predictive Analytics in PHM
 Key Components
 Application of Predictive Analytics in PHM
 Use Cases
 Top 5 challenges
 Summary

4. 4
Predictive Analytics in PHM
 Isn't healthcare a predictive science since ages?
A patient with some symptoms visits provider. The provider assess the patient’s
healthcare data, prescribes treatment and delivers a prognosis. Thus, provider predicts
and cures/avoids a developing disease
 With the CMS MIPS-MACRA regulations for mandatory adoption of 2014/2015 certified
EHRs, Predictive Analytics in population healthcare is the next big obvious trend
 Predictive Analytics in PHM can reduce expenditures while enhancing the quality of life of
patients as masses
 As per one of the case studies of HIMSS 2016, predictive risk analytics was able to reduce
the hospital admission rates by 40% in the high risk patients
 According to a study published in the Journal of the American Medical Informatics
Association (JAMIA), EHR Predictive Analytics flag 32% fall in nursing home patients

5. 5
Key Components of Predictive Analytics for PHM
Predictive
Analytics
Patient
data
integration
Data
cleansing
Big data
repository
Artificial
Intelligence
modelling
Real time
analytics
Dashboard
/ Index for
action
Patient data integration:
 Collect patient population data
from various fronts i.e., labs,
claims, demographic, clinical,
social, economical, and
geographical such that it is
complete and exhaustive
 It can be from disparate
sources in different forms like
HL7, X12, XML etc.
Data cleansing:
 Perform cleansing of patient
data using techniques like
reconciliation so as to avoid
duplicates and have good
quality of data for each patient
Big data repository:
 Store historical and current
heath information of all
patients in data warehouse so
as to have ample and
complete data for analysis
Artificial Intelligence (AI) modelling:
Construct a prediction algorithm using AI
from past individual records e.g., To
determine:
 Risk stratification of patients
 Patients who are likely to visit ED in
next 6 months
 Patients who are likely to be readmitted
in next 30 days
 Patients who are likely to develop type-
2 diabetes in 1 year etc.
Real time analytics:
Perform run-time prediction on a
continuous stream of patient data so as to
enable decision making in real-time
Dashboard/Index for action:
Create visualization of the processed
meaningful patient information in an easy
to read form so as to aid the clinicians to
drive inference as to what is likely to
happen to patient in the future

6. 6
Application of Predictive Analytics in PHM
Using Predictive Analytics to
Manage Care
Define
Population
Population
Health
Management
Measure
and Report
Population
Analytics
Manage
Care
Engage
Patients
Stratify
Risks
Leveraging Predictive Analytics
can help to close some of the gaps
of Care Management in PHM like:-
 Consolidate patient data
from multiple
sources/formats in a single
platform
 Formulate tailored care plans
for patients based upon their
risk factor, non-adherence to
medication/tests, sedentary
lifestyle, genetics etc.
 Close monitoring of results
being achieved by various
care management teams and
updating care plan as needed
 Foresee and prevent
infectious diseases outbreaks

7. 7
Use Cases of Predictive Analytics in PHM (1/3)
Objective:
Predict mortality of patients with heart disorder
PHM trends seen:
Artificial Intelligence + Big Data
Proposal:
 As per a report published in a radiology journal, researchers have developed an AI software. This
software analyse blood samples and scans of beating heart so as to investigate signs of failing
heart. This software could predict for about five years into the future.
 The input to this AI software was heart MRI scans & blood test results of 256 patients. During
each heartbeat, it measured movement of 30,000 different points in the heart and combined this
data with that particular patient’s eight years of health records. The software analysed blood
tests and scans of beating hearts to spot signs when the organ was about to fail.
 So far the researchers have tested this software on patients with pulmonary hypertension and
would also like to use the technology in other forms of heart failure, such as cardiomyopathy. The
technology could save lives by identifying patients that might need a pacemaker or more
aggressive treatment in near future.

8. 8
Use Cases of Predictive Analytics in PHM (2/3)
Objective:
Predict Influenza outbreak timeframe among patients
PHM trends seen:
Real-Time Analytics + Big Data + Predictive Analytics
Proposal:
 As per a study published in Scientific reports, researchers have developed the Auto Regressive
Electronic Health Record Support vector machine (ARES), which uses EHR and Predictive
Analytics to identify peak weeks of Influenza
 ARES used information extracted from cloud-based electronic health record databases, machine
learning techniques and historical epidemiological information to provide near real-time regional
estimates of flu outbreaks in the United States
 ARES accurately predicted flu peak weeks with 0.148% error at national level while with 0.445%
error at regional level
 Thus, predictive real-time flu surveillance system could provide care to patients in advance and
improve population health management programs

9. 9
Use Cases of Predictive Analytics in PHM (3/3)
Objective:
Reduce hospital readmission rate
PHM trends seen:
Lean Operations + Tightened Coordination + Predictive Analytics
Proposal:
 As per a report published in Joint Commission Journal on Quality and Patient Safety, clinicians at
Brigham and Women's Hospital (BWH) were able to reduce the hospital readmission rate by 9%
over three years
 The hospital performed screening tests for its entire patient population to identify patients with
delirium, alcohol withdrawal, and suicide harm (DASH) symptoms. The patients identified as high
risk for DASH were treated with collaborative approach such as standardized assessment tools
and clinical guidelines by nurses, physicians and hospital leadership to effectively improve
outcomes
 Thus, by correctly predicting and treating patients in advance with potential DASH conditions, the
hospital readmission rate was decreased while improving the quality of life of patients

10. 10
Top 5 Challenges of Predictive Analytics in PHM
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Lack of
budget
Regulatory
issues (e.g.
HIPAA)
Incomplete
data
Lack of
skilled
employees
Lack of
sufficient
technology
TotalRespondents
Source: soa.org
 As per a survey conducted by ‘’The
Society of Actuaries (SOA)” in 2017,
57% healthcare executives expect to
save 15% or more while 26% forecast
to save 25% or more of their total
budget over the next five years by
using Predictive Analytics in
healthcare
 Due to issues like disparate data
sources, bulk of complex
clinical/claims data, new
technologies, few professionals with
new skillset, regulatory issues and
budget constraints; healthcare
organizations find it tough to
implement Predictive Analytics in
healthcare

11. 11
Summary
 The future of Predictive Analytics in Population Health Management is still a steep curve
considering the complexity and the diversity of healthcare data
 But with our boundless zeal to enhance population health and with the advent of technology,
we are not far from availing benefits of predictive analytics in population health management
 It has the potential to revolutionize our lives by predicting our future health or acute illnesses
just as another predictive operational solution called ‘Google Maps’ which has already
revolutionized our travel in terms of finding best route, predicting accurate travel time, and
saving us from being lost in this big world!!!

12. 12
References
 https://www.beckershospitalreview.com/population-health/trends-that-will-impact-
how-the-healthcare-industry-approaches-population-health-management.html
 https://www.marketsandmarkets.com/Market-Reports/population-health-
management-market-263411936.html
 https://healthitanalytics.com/news/93-of-payers-providers-say-predictive-analytics-is-
the-future
 http://www.bbc.com/news/health-38635871
 https://healthitanalytics.com/news/ehr-analytics-advance-population-health-
management-of-the-flu
 https://healthitanalytics.com/news/care-coordination-plan-cuts-dash-hospital-
readmissions-by-9
 https://www.beckershospitalreview.com/data-analytics-precision-medicine/4-common-
roadblocks-to-adopting-predictive-analytics-in-healthcare-organizations.html
 https://healthcare-analytics.healthcaretechoutlook.com/vendors/most-promising-
healthcare-analytics-solution-providers-2016.html

13. 13
References and Keywords
Relevant CitiusTech references:
 https://www.beckershospitalreview.com/h
ealthcare-information-
technology/succeeding-with-predictive-
analytics-in-healthcare-10-steps-to-get-
started.html
 https://us.hitleaders.news/predictive-
analytics-roadmap-to-realize-value-from-
healthcare-big-data/
 AI in healthcare
 Predictive Analytics
 Machine Learning
Keywords

14. 14
Thank You
Author:
Sonal Govil
Healthcare Business Analyst
thoughtleaders@citiustech.com
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