REDUCING READMISSIONS WITH BI AND ANALYTICS - Webinar

23 March 2018 Copyright © 2016 AAJ Technologies All rights reserved.Reducing Readmissions with BI and Analytics
www.aajtech.com
Copyright © 2016 AAJ Technologies All rights reserved.
Webinar
Reducing Readmissions
with BI and Analytics

www.aajtech.com
Hospital Readmissions
Michele Russell, CEO, Russell Consulting Group

Hospital Readmissions Overview
Under Hospital Readmissions Reduction Program (HRRP), CMS withholds up to 3 percent of
regular reimbursements for hospitals if they have a higher-than-expected number of
readmissions within 30 days of discharge for seven conditions:
● Chronic lung disease
● Coronary artery bypass graft surgery
● Heart attacks
● Heart failure
● Acute myocardial infarction
● Hip and knee replacements
● Pneumonia
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Hospital Readmissions Overview
● About 80 percent of the 3,241 hospitals evaluated by the Centers for Medicare and
Medicaid Services (CMS) this year will face penalties
● Medicare under the Hospital Readmissions Reduction Program (HRRP) will reduce
reimbursement for 2,573 hospitals for fiscal year (FY) 2018
● An analysis of the data also showed CMS under HRRP will withhold $564 million in
payments over the next year
Source: Kaiser Health News
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HIPAA Privacy & Security / Risk Mitigation
In addition to creating a culture that focuses on the security and privacy of Protected
Health Information (PHI), our technology plays a significant role in preventing data
breaches.
● Tracking and audit trails
● Physical security of the data
● Limited user access to data
● Role-based security
● Protection of sensitive subsets of PHI
● Ongoing control of user access regardless of the hosting environment
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Where does / will your data live?
The three major types of cloud storage used in enterprise deployments are:
● Public
● Private
● Hybrid Cloud
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A Care Transition System (CTS) to Reduce
Hospital Readmissions
Ed Kirchmier, VP Global Delivery, AAJ Technologies

Care Transition Problem
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Pre-
Discharge
Discharge
EMR/ADT
(Hospital)
Order Management
(Home/Community
Providers)
Rx Dispensing
(Pharmacy)
Practice Mgmt Sys
(PCP/Specialists)
Hospital
Visit
SNF
Mental
Health
Home
ALF
Insufficient Education
and
Lack of Coordination
Leads to Readmission
Patient
Discharge Instructions
● PCP/Specialist Follow-up
● Rx Scripts
● Nutrition Guidelines
● Wound Care
● PT Orders

Care Transition Platform
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Care Coordination & Coaching
Pre-Discharge Discharge
Post-
Discharge
Follow-Up
Medication
Management
Nutrition
Management
PCP/Specialist
Follow-up
Red Flags / Signs
& Symptoms
Home & Community
Services
Personal
Health Record
EMR/ADT
(Hospital)
• Case
• Visits/Assessment
• Care Plan
• Appointments
• Orders
• Workflows
• Reminders
• Notifications
• Alerts
Order Management
(Home/Community
Providers)
Rx Dispensing
(Pharmacy)
Practice Mgmt Sys
(PCP/Specialists)
Goal: Reduce Re-admissions
HOSPITAL VISIT HOME/FACILITY VISIT VISIT(S)/CALLS

www.aajtech.com
Dashboards to Forecast Healthcare
Outcomes
Kevin Oppenheimer, Principal/Owner KGO Consulting Group

Dashboard 30 Day CMS Readmissions Report
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www.aajtech.com
Data Analytics to Predict When a Patient Will
Readmit
Andrew Satz, Co-Founder, Data Scientist and Futurist, Metrix Labs

Causation versus Correlation
A patient is hospitalized for pneumonia with a history of chronic COPD. The patient
is rehospitalized within thirty days.
● Only 7.4% of patients readmitted had pneumonia. 92.6% of those readmissions
were caused by comorbidities rather than the pneumonia
● While a prior pneumonia case is highly correlated to readmission, it’s not
necessarily the cause
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Data Dimensionality
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Sex Weight Ethnic City BP Smoker COPD Pneumonia Heart Disease GI
F 142 H M 130/80 Y 2 4 3
M 178 A F 170/90 N 5 1
F 203 C M 130/90 N 1 3 2
M 187 A P 170/90 Y 3 2
F 162 A P 170/90 Y 3 4 1
F 120 H M 80/50 N 4 2
M 263 A F 80/50 Y 2 5 2 4
M 207 C P 130/80 N 5 4 3

Data Dimensionality
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Reducing Data Dimensionality:
Feature Importance
If we want to identify these additional factors for readmission, we need to
evaluate ‘feature importance’. This lets us find causal and correlated features
associated with the outcome. Then we can build an algorithm off of those
features, which can be used to classify patients and predict readmission
As a result, we can identify those patients most likely to be readmitted and why.
This would enable caregivers to deliver focused interventions to vulnerable
patients
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Using A.I. to Avoid Readmissions
The effort:
A health system aggregated and integrated their clinical, financial, administrative,
patient experience, and other relevant data
Important features were algorithmically selected based upon their statistical
importance. Features included: Medicare severity diagnosis related group (MS-DRG),
use of tobacco, residential zip code, financial risk, healthcare utilization, age, marital
status, admission source, medication, provider, and more
More than two dozen machine learning models were built to predict readmission
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Using A.I. to Avoid Readmissions
The Results:
The team’s hypothesis was that patients on a larger number of medications would
be at the greatest risk for readmission
But the data revealed that patients with no medications were at the greatest risk
The data showed similar patterns regarding age. While the team assumed older
patients are at the highest risk, the data showed that its younger patients were at
greatest risk
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Questions & Answers
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www.aajtech.com Additional Information:
Jim McKeen
Director of Healthcare Solutions
jim.mckeen@aajtech.com
954.448.4974
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REDUCING READMISSIONS WITH BI AND ANALYTICS - Webinar

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REDUCING READMISSIONS WITH BI AND ANALYTICS - Webinar