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

1

Framework for a Healthcare Services

TECHNOLOGYEHR, HIE, Privacy, Security, Clinical Decision Support

REGULATIONHITECH Act, HIPAA,

NIST Technical Standards

PATIENT CENTERED CARE

Care Coordination, Quality, Safety and

Cost

SERVICE INNOVATION

Accountable Care Organizations

FINANCIAL Incentives

and Penalties

2

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

3

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.

4

Problem 2: Clinical Decision Support for Kidney

Allocation in the context of Constrained Supply

5

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)

Predicting the outcome for a

hypothetical patient

SKLT Scenario (kaltyn=0)

Predicting the outcome for a

hypothetical patient

KALT Scenario (kaltyn=1)

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

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

Natural Language Processing applied

to COPD

Document

Preprocessor

Lexical

Analyzer

Assertion

COPD Term

Spotter

Smoking

Status

Recognition

COPD Drug

Name

Recognition

Readmission

Predictive

Model

NLP Extraction Results (N=1695 over 18 months)

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

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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