predicting hospital readmission using cascading

Predicting Hospital Readmission using Cascading

by H. Michael Covert and Victoria Loewengart

September 3, 2015

Proprietary and Confidential

Agenda• Analytics Inside• Predictive Analytics Use Case: Hospital

Readmissions• Technical Solution Overview • Why Cascading and Alternatives Explored• Implementation Considerations• Best Practices for Operational Readiness

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Analytics Inside• A Westerville Ohio base company founded in late 2011.• Self-‐funded, profitable, and growing. • The outgrowth business intelligence and advanced analytics

consulting (2007), big data consulting (2010) , and academic research.

• Our mission is to:– Be data scientists and to develop and deliver innovative advanced technologies to

vertical industries.• Health Care• Cyber-‐security• Intelligence and Law Enforcement

– Engage and focus on endeavors that deliver social value.– To make some money and have some fun.– To partner with organizations that have delivery muscle, vertical industry

expertise, and share common values.

• We provide products, services, and training.


Analytics Inside• We are Concurrent business partners

– We deliver Concurrent Cascading training services– We also have an advanced Cascading course– We have now 18 Big Data Technology modules that can be assembled into

customized training curriculums


Phase Course Title Description

Planning Hadoop PlanningSteps involved in identifying need, planning for introduction of Hadoop into your environment, systems integration and architecture.

Introduction to Linux A comprehensive review of the Linux Operating System. Introduction to Java A comprehensive review of the Java technology stack.

Introduction to Hadoop An detailed overview of the Hadoop technology stack.Hadoop Software Development MapReduce programming, NoSQL (HBase) programming, Sqoop

Hadoop Quality Assurance Overview of standard QA processes and how they are different from standard QA.Introduction to YARN Development of Hadoop version 2 YARN development.Introduction to Spark Development of Spark systemsIntroduction to Storm Development of Storm systemsIntroduction to Kafka Development of Kafka message queueing systems

Natural Langauge Processing Overview of computational linguisticsIntroduction to Graph Theory Overview of using Hadoop and Spark to do advanced graph theoryAdvanced Text Analytics Text analytics using big data frameworks.

Real time Big DataOverview of next generation big data soultions that utilize real-‐time (non-‐batch) technology.

Cascading Developer training for the Concurrent Cascading product set.Advanced Cascading Developer training for advanced usages of the Cascading frameworkAdvanced Analytics Using Mahout to do predictive analytics.

Hadoop Systems Administration Installing, configuring, and maintaining a Hadoop clusterAdministration

Comprehension

Development

Analytics Inside Solutions• Our solution family, collectively known as RelMiner™,

consists of a set of software components that are designed to be integrated into domain specific products

• Specialization in:– Big data solutions

• Hadoop, YARN, Tez, and Spark• NoSQL Databases• Streaming – Kafka, Storm, and Spark Streaming

–Machine Learning• Predictive and Prescriptive Analytics

–Natural language processing (Computational Linguistics, Text Analytics, and Text Mining)–Graph theory and graph databases

• Cascading is core to all of our development–We started with 1.3 and have now seamlessly migrated to 3.0 using Tez


Predictive Analytics Use CaseHospital Readmissions

• A hospital patient readmission is a costly event that health care providers are attempting to reduce. – A readmission is defined as ANY reentry to a hospital 30 days or

less from a prior discharge. • The US Affordable Care Act mandates lower readmission

– If not achieved, providers face fines or reduced government reimbursement.

– Specifically, the US Medicare and Medicaid will either not pay or will reduce the payment made to hospitals for expenses incurred when readmissions occur.

– By the end of 2014, over 2600 hospitals incurred in excess of $24B of losses from a Medicare and Medicaid expenses, now expected to rise to $50B by the end of this year.

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• Predictive analytics is now being used to categorize and prioritize those patients with the highest likelihood of readmission– Impact is both clinical (better outcome for the patient) AND financial

• And better financially performing hospitals generally have better outcomes!

• Many such predictive systems exist. One is the LACE score.– Invented by the Ottawa Hospital Research Institute, Institute for Clinical

Evaluation Sciences, University of Toronto, University of Ottawa and University of Calgary

– It is a calculation that predicts the probability of readmission or death following a hospital discharge based on:• Length of Stay (days in hospital)• Accuity (acuteness or severity of condition)• Comorbidity (all of a patient’s diagnosed conditions)• Emergency Visits


Predictive Analytics Use CaseHospital Readmissions

Why This is a Big Data Problem• Typical patient can have several gigabytes of data

– Much information is hidden in unstructured chart data and clinical notes

– 68,000 diagnosis codes and a very large number of modifiers, and new diagnosis codes are constructed to contain a lot of information • Site – where on the body the diagnosis applies• Combination codes – encode two or more related conditions

– 87,000 procedure codes, again now highly encoded with information

• Machine learning uses wide and variable vector lengths• Multiple models are desirable

– Possibly trained by segmentation (Neonatal, geriatric, etc.)• Data variation is quite large – HCPCS, ICD-‐10, CPT, and more

– And researchers want to add more data!


Hospital Readmissions


LACE Subassembly

LACE Score =Length of Stay Score + Acuity Score + Emergency Visits Score + Comorbidity Score

Length of Stay in DaysAcuity (serious condition)

# Visits to Emergency Room over some period of time

Comorbidity Score = Charlson Comorbidity Index = Age Score + ∑ Diagnosis ScoresPatient Demographic Records

Patient Admission Records

Patient Diagnosis Records

MedPredictAdvanced Analytics for Health Care

Fundamentals

Classification and prediction of care outcome. It provides dashboard level reporting, early identification of potential undesirable outcomes, and actionable suggestions for remediation. It combines a variety of available data sources including:

• Patient biometric data and historic data• Value Based Purchasing scores• ICD-‐9 and ICD-‐10 diagnosis and procedures • DRG and MDC classifications• NCQA metric categories and HEDIS standard data• Pharmaceutical usage• Patient and facility demographic data• Patient psychographic data from chart data• Re-‐admissions and mortality data• Inpatient and emergency department discharge data• Patient satisfaction survey data• Meaningful Use Summary of Care records• Incorporates LACE score through a sophisticated LACE calculation

engine

MedPredict™


Advanced.Analytical.Intelligence.

MedPredict™


MedPredict™

LACE EnginePatient RecordsName/IDDOB/Age

Gender, Race, Ethnicity…


Admittance and Discharge DateAdmission Source and Type

Admission TypeDischarge Disposition

HospitalExpenseInsurer…


Date of DiagnosisLocation of Diagnosis

Diagnosis CodePhysician NotesDRG and MDCCC/MCC…

Age Tier Scores

ER Buckets

Diagnosis Scores

Patient Summary Record

Diagnosis Vector

LACE Metrics

Patient Data

ETL

ETL

ETL

PredictionEngine

Length of Stay

Expected Expense

ExpectedOutcome

Readmission Probabilities

Look back time

Look back time


MedPredict™

Patient RecordsName/ID

DOB/Age, Gender, Race, Ethnicity


Admittance and Discharge DateAdmission Source and Type

Admission TypeDischarge Disposition

HospitalExpenseInsurer


Date of DiagnosisLocation of Diagnosis

Diagnosis CodePhysician NotesDRG and MDC

CC/MCC

Transform and Score

Transform and Score

Transform and Score

Age Tier Score

Look back timeER Buckets

Diagnosis Score

Patient Prediction Record

Diagnosis Vector

LACE Metrics

Patient Data

Procedure Vector

LACE

ETL

ETL

ETL

PredictionEngine

Length of Stay

Expected Expense

ExpectedOutcome

Readmission Probabilities

Look back time

Look back time

Look back time

Patient Procedure Records

Date, Procedure Codes, Pharmaceuticals, Patient Chart Data

Transform and Score

Procedure Score

Appropriateness of Care Index

MedPredict™

Profitability

MedExtractAdvanced Analytics for Health Care

Fundamentals

Clinical information extraction from unstructured text.The Problem -‐ Even with the advent of data management technology, most of the patient information is recorded as unstructured text. That includes health care plans, prescriptions, doctor’s observations, and patients’ communications with their health care providers. A wealth of information is buried within these documents, yet it is difficult to find because, unlike a database, it cannot be queried with a uniform method.

The Solution -‐ MedExtract utilizes advanced Text Analytics techniques and technologies for effective information extraction from unstructured text . From the free form text it can extract:

• Diseases, diagnoses, and procedures• Names, addresses, phone numbers, locations• Drugs, dosages, and usage• Patient observations of sentiment (depression, anger, etc.)

Advanced.Analytical.Intelligence.

MedExtract™


Technology Underpinnings


Technical Solution Overview• MedPredict™ contains several parts:

– ETL• Ingestion and cleansing using subassemblies to trap and record errors• Creation of the patient diagnosis record from patient diagnosis history• Creation of the patient admittance record from hospital discharge records• Creation of full patient scoring record• Text extraction from unstructured sources

– LACE calculation• Multiple subassemblies

– Predictive modeling• Training predictive models• Model testing• Model deployment

– Nightly run• Compute patient metrics and insert new record• Sorting and prioritization -‐> Reporting and alerting• Trend analysis

– Analysis and Refinement• Adding new data• Adding new calculations• Adjusting parameters• Retraining…


ETL Training

ModelModelModel

Scoring

Reporting

Alerting

Analysis

Discharge Chart Diagnosis Procedure Patient History

Kafkapub/sub

Technology Underpinnings


Cascading

MedPredictand

MedExtract

Workflow control

TechnologyMigration

Driven

MonitoringPerformance and Tuning

MedMiner infrastructure

Mahout

OpenNLP

Why Cascading?• We had already been using Cascading!

– We started here in 2011, so we had four years under our belt– Reliability and stability was an issue. Cascading is mature, and

unlike other systems, we understand how it works.• We literally “wrote the book” on Cascading J

– Cascading has been cost-‐effective and has preserved a large initial investment for us.• Core product set was written in version 1.3 in 2011.• We moved to version 2 in 2013.• Since June 2015, we are now running under version 3 using Tez

– Cascading Test Driven Design principles have made development easier.

– Cascading subassemblies and cascades have provided us with tremendous code reuse.


Why Cascading?

• We have one (portable) language and framework to learn and maintain.

• We use Cascading dynamic control.– We heavily instrument our flows, and these metrics control the

number of iterations that we use– We tried to do this in Pig and MapReduce, but found it to be very

difficult to impossible

• RelPredict was written in Mahout. Cascading wraps this functionality and gives us “hyper-‐parallelism”

• RelExtract uses a heavily augmented OpenNLP and Cascading also wraps this functionality in a customizable “pipeline” much like UIMAfit.


Why Cascading?• Modules in MedPredict seemed naturally to fit into the Cascading model – Several discrete steps must be formed and fed forward– Pipe metaphor is ideal manner of expression– Hashjoins give huge performance benefits during calculation phase

– Checkpointing is used extensively due to expected high error rate in data

– Cascading Local mode allows smaller hospitals to use the system without requiring a full Hadoop stack


Implementation Considerations• Occasionally, we find disk spills to impact performance due to two large

multi-‐dataset joins that the LACE calculation performs. • We make extensive usage of Buffer operations, so we need to monitor

these phases closely due to memory and compute requirements. • Usually at some predefined trigger point, we have to run a very large

predictive retraining job. It is very resource intensive. Data lineage is a big issue since MedPredict's data originates from many sources.

• ETL is complex and is customized relative to the data that the app is provided. – It must be transformed into a common format before MedPredict can consume it. – Errors in incoming data streams can be quite baffling at times.

• MedExtract and Natural Language Processing is very resource intensive. – Named Entity extraction uses both machine learning and dictionaries– We produce indices of searchable terms (usually consumed by Solr/Lucene).– We produce records that augment the other ETL streams.

You have to manage and monitor all these things.20September 3, 2015 Proprietary and Confidential

MedMiner Deep Learning


InpatientAssociations

AndRelationships

Health Care Team Model

Chart Model

Pharmacy Model

VBP Model

Diagnosis, Treatment, and

Outcome

Comparative Performance

Financial Impact

Status and Discharge Disposition

Financial Cost

Emergency Room

VBP Metrics

Hospitals and

Facilities

DataMgmt

RelMiner Router

Fees and Services

Pharmacy

Diagnostic and

Procedural

Outcome Outcome

Length of Stay

Readmittance

NeonatalJuvenile

AdultGeriatric

Learning Feedback Loop

Chart and Notes

NLP

NLP

Best Practices for Operational Readiness

• We monitor end-‐to-‐end performance and use Driven to tune some of the larger flows.

• Because we use flow skipping extensively, and we monitor when steps have not been skipped (indicating that a data refresh has occurred).

• Errors in incoming data streams can be quite baffling at times. We use Traps and Checkpoints to help here extensively.


Driven, from Concurrent, is used to track the performance of MedPredict™ to solve these operational problems

We are now testing our system using Cascading version 3 and Tez.

Best Practices for Operational Readiness

• Visualize your pipelines to make sure your applications are executing as expected in dev, test and prod environments.

• If you are in a regulated industry, like Healthcare, track data lineage. You will have to report on it for internal and external audits


Driven, from Concurrent, is used to track the performance of MedPredict™ to solve these operational problems

Technical Overview -‐ Driven• MedExtract and Natural Language Processing is very resource

intensive. – Named Entity extraction uses both machine learning and dictionaries– We produce indices of searchable terms (usually consumed by

Solr/Lucene).– We produce records that augment the other ETL streams. Driven is

used here for perf/tune and to monitor the overall NLP pipes.

• We are now testing a Tez port of our system. Driven plays a roll here as well.


Questions and Answers

September 3, 2015 25Proprietary and Confidential

[email protected]@AnalyticsInside.us

http://www.AnalyticsInside.us