Ravi S. Behara

1. DATA-DRIVEN INNOVATIONS
IN HEALTHCARE SERVICES
Dr. Ravi S. Behara, Ph.D.
Department of IT & Operations Management, College of Business
Florida Atlantic University, Boca Raton, FL, USA
rbehara@fau.edu
ISSIP Service Innovation Weekly Speaker Series
26th August 2015
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2. Framework for a Healthcare Services
TECHNOLOGY
EHR, HIE, Privacy,
Security, Clinical
Decision Support
REGULATION
HITECH Act, HIPAA,
NIST Technical
Standards
PATIENT CENTERED
CARE
Care Coordination,
Quality, Safety and
Cost
SERVICE
INNOVATION
Accountable Care
Organizations
FINANCIAL
Incentives
and Penalties
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3. Problem 1: Medication related Care Coordination
• Care delivery process & patient sentiment
• Using unstructured data (text) analysis to uncover patient
sentiment as it related to pain medication management
• Text analysis reveals underlying care process
• Care delivery process innovation of developing a
“discharge triage” to facilitate care coordination between
hospital and home
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4. Patient Pain-Medication Management
Patient
Comment
Categories
Patient Comments
Communication
(Physician, Nurse,
Administration/St
aff)
 I had poor pain control immediately post-op, despite the fact
that I repeatedly told the nurses that I had my pain meds
(prescribed by my doctor of 8 1/2 yrs.) with me. I was in
severe pain for a day and a 1/2.
Treatment
Protocol
 …..Two different nurses tried to give me a pain med I was
allergic too.
Personnel (Nurse)  The only bad experience I had was when trying to get my pain
medication administered.
Care Process
(transition)
 I felt that I was sent home w/improper pain management
medications.
 Was sent home w/weak pain meds (4 day supply). I was still in
bad pain 8 days later.
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5. Problem 2: Clinical Decision Support for Kidney
Allocation in the context of Constrained Supply
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6. Predicting the outcome for a
hypothetical patient
• The patient is 62 years old with a BMI of 20 (lower due to
illness)
• The recipient is diabetic and currently treatment for
hypertension. The recipient is also hepatitis-C positive
with a creatinine of 4, serum albumin of 1.8, and MELF of
40 reflecting liver disease
• The donor is a 35 year old diseased individual who had a
BMI of 40 (assuming a diseased-donor kidney
transplantation)

7. Predicting the outcome for a
hypothetical patient
SKLT Scenario (kaltyn=0)

8. Predicting the outcome for a
hypothetical patient
KALT Scenario (kaltyn=1)

9. Results
• This study developed a NN model to predict the outcomes
in dual organ (Kidney-Liver) transplant patients
• The model predicted outcome for a hypothetical patient
found that the recipient would have a lower likelihood of
death with KALT while having a higher likelihood of being
alive with a successful transplantation with SKLT
• Appropriate predictive models like those developed in this
study may be used to create individual/clinical decision
support solutions, to help physicians addressing the organ
allocation problem in an environment of significantly
constrained supply

10. Problem 3: Improving COPD Readmission
Prediction
• High levels of avoidable hospital readmissions of patients
with Chronic Obstructive Pulmonary Disease (COPD) is a
significant challenge
• Patient data include significant amount of unstructured
data, such as physician’s notes and patient discharge
summaries
• Accuracy of models to predict the risk of readmission can
be improved using such data by developing a natural
language processing based approach to extract such
information from patient records
• But this is not a simple task due to the ambiguity and
variety of language used in the description and evaluation
of any specific patient condition

11. Natural Language Processing applied
to COPD
Document
Preprocessor
Lexical
Analyzer
Assertion
COPD Term
Spotter
Smoking
Status
Recognition
COPD Drug
Name
Recognition
Readmission
Predictive
Model

12. NLP Extraction Results
(N=1695 over 18 months)

13. Results
• This study developed a component-based domain-
independent text analysis system for processing of the
natural language known as Domain-independent Natural
Language Processing System (DINLP)
• It was effectively applied to Chronic Obstructive
Pulmonary Disease (COPD) patient data in the context of
hospital readmission
• Continuing research in this domain extends to integrating
DINLP with other machine learning modeling approaches

14. Conclusion
• Advanced data analytics provides opportunities for
innovations in patient care
• These methods can be applied to other complex services
• I would like to thank my co-authors in the various studies
• Dr. Ankur Agarwal, Ph.D.
• Dr. Fabio Potenti, M.D.
• Dr. Vinaya Rao, M.D.
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