hxr 2016: data insights: mining, modeling, and visualizations- niraj katwala

© 2016 Talix. Confidential and Proprietary.

Data Insight: Mining, Modeling and VisualizationsNiraj Katwala, EVP & CTO, Talix

April 5, 2016

About Talix

– PEOPLE Sixty passionate Medical Professionals, Informaticists, Software Engineers, and Product Leaders

– PRODUCTS Coding InSight Improves risk management,

leading to better patient outcomes and optimized reimbursement for healthcare providers and payers

HealthSearch Powers search and discovery for healthcare professionals and patients

– ACCOMPLISHMENTS Serving major Healthcare brands through products powered by our HealthData Engine

2

HealthData Engine: Unlocking the Value of Unstructured Data

3

Inpu

ts

Unstructured Patient Data(e.g., Treatment

Authorization Request)

Clinical Rules

NLP

HealthTaxonomy

Structured Patient Data

(e.g., EMR Data, Formulary Lists of Drugs)

OUTPUTS WHAT IT ENABLES

INPUTS

Pharmaceuticals:Pharma insights on clinical trial,

prescription and other data

Publishers:Content search across large bodies

of content

Providers:Group patients into cohorts based

on key attributes / factors

Health Plans: Match treatment authorization

requests to clinical policy bulletins

Patient data and other clinical data

extracted, normalized,

categorized, and enriched

4

22 2324

25

Clinical Inference Engine using Clinical Rules on Patient Data (22 - 25)

Patient Data Inference Engine Master

Patient Data Inference Visualizer (Outcomes with

Confidence Score)

Configuration Database forSelecting Encoded Rules

By Use Case (Coding, UM, F/W/A, Re-Admission)

Loop over IndividualPatient Data Records

HealthData Engine: Leveraging Taxonomy, NLP, Clinical Rules

Structured ClientPatient Data CaptureMethods/Adaptors

UnstructuredClient Patient

Data in Text Format

StructuredClient Patient Data

Client Clinical Data Systems(EMR, Claims, Rx Systems, Laboratory, Radiology, Social Networks)

Unstructured ClientPatient Data CaptureMethods/Adaptors,

Including OCR/Speech Recognition

1

Talix HealthTaxonomy

6

5

2 3

4

Patient Data Acquisition Module (1 – 6)


Automated Machine Learning from Published Literature,

Claims, EMR Data

Clinical GuidelinesCapture through

Clinical Guidelines Editor

Manual Entryby Smart Tags Editor

Clinical Rules (Diagnostic and Treatment Rules) Capture Mechanisms

Raw Rules Database

Rules Enrichment Process

Enriched Rules Database

Rules Codifier using HL-DSL

Clinical Rules Assembly Module (12 – 21)

13 14 15

1816

20

17

19

21Encoded Rules in HL-DSLwith Confidence Scores

Final StructuredClient Patient Data

Records Enriched and Normalized (LPR)

Talix NLP Engine Master


Non-Healthcareand Use Case-

SpecificKnowledge bases

Healthcare andGeneral Annotators

Use Case and DocumentSpecific Parsers

8

9

10

11 12

NLP Engine for Patient Data Enrichment and Normalization (6 – 12)

7

HealthData Engine: Leveraging Taxonomy, NLP, Clinical Rules

5

Structured and Enriched Content and Data

10

Talix NLP Engine (Process Steps in next diagram)

8

Talix Health Taxonomy

6

Clinical Rules

Database

13

Clinical Guidelines Editor (Machine Readable Form)

12

Terminology Editor

5

Back-End Terminology Integration Engine

CPT-4 LOINCOMIM NDC ICD-10

NCI-T HCPCS Gene Ont.ICD-9 SNOMED1

Medical Informatics Team (Human)1. Semantic Relationship Build-out2. Consumer-Friendly Names3. Clinical Quality Control4. Acronym and Abbreviations5. Stemming Correction Lists6. Homonyms and Negation7. Term and Query Specific Rules

2

Data Mining from Published Literature (Suggestions Only)

3

Organization SpecificTerminologies

4

Clinical Rules

Database

7

Organization-Specific Clinical Guidelines

11

Unstructured Content and Data

9

Health Taxonomy: A Robust Knowledge Base

6

INDUSTRY STANDARDS CALIBRATIONMost precise and comprehensive healthcare taxonomy for all healthcare segments mapping multiple industry standard terminologies including

• ICD-9, ICD-10• MESH, NCI THESAURUS, GENE ONTOLOGY, OMIM• SNOMED, LOINC, HCPCS, DRG• RXNORM, NDC

1+ MILLION CONCEPTS BASED ARCHITECTURE Concepts with many attributes including

• Synonyms, Abbreviations, Acronyms• Misspellings• Homonym Identification• Stemming Correction Lists

2+ MILLION SEMANTIC RELATIONSHIPS WITH RANKINGS Unique in the industry and include

• Disease to Drugs • Disease to Symptoms • Disease to Treatments • Disease to Diagnostic Procedures • And many others with Ranking strength

HISTORY OF THE TAXONOMYHighly-iterative effort over 15 years in development by a dedicated team of Medical Professionals and Data Scientists and tens of millions of R&D dollars in multiple implementations & domains. 3rd party verifications

Clinical Rules

7

TalixHealthData Engine (HDE)

Machine Readable GuidelinePDF Clinical Guideline

NODE 143ER Positive: ICD 9: V86.0, ICD 10: Z17.0, CPT4: 3315F, Synonyms: Estrogen Receptor Positive, ER+, ER +ve

PR Positive: CPT4: 3315F, Synonyms: Progesterone Receptor Positive, PR+, PR +ve

NODE 134

Tubular Mucinous: ICD 9: 189, ICD 10: C64

Enriched Clinical Guideline Node Data

3

2

1

8

Beyond NLP: Natural Language Understanding (NLU)

Documents annotations written out into Cassandra and Solr, from which search and analytics toolkits can consume this data.

Lucene Search Index

Cassandra Data store

Summary Generators

Automatically generates document summaries using concept aggregation.

9

In-Document Abbreviation Recognizer

Maps abbreviations to concepts per document.

8 Coordinate Expansion

Expands “Diabetes Type I and II” into “Diabetes Type I” and “Diabetes Type II.”

7 Word Sense Disambiguator

Uses taxonomy to disambiguate ambiguous terms, e.g., disambiguate between Cold Temperature and Common “Cold.”

6 Rule Based Annotators (next slide)

Drug Dosage Module, Laboratory Test with Values, Family History, Negation, Demographics, Past Medical History etc.

5

Clinical Rules Database

Various Named Entity Extractors

Dictionary based named entity extraction: names, geographical entitles, molecules, etc.

4

3rd Party Dictionaries

Finds concepts in document by looking up graph view of taxonomy, sets base and relationship scores, normalizes and adjusts scores.

3 Concept Mapper

Produces various views of the document. i.e.: Paragraph, Sentence, Word, etc.

Tokenizer2Document Preprocessor (CIP Conversion)

1

Various Types of Documents – Clinical Policy Bulletins, Clinical Guidelines, Input Patient History with Treatment Recommendations

Produces various views of any document. i.e.: Section, Paragraph, Sentence, other defined fields

Talix Health Taxonomy

9

NLP Stack: Annotators and Knowledge Repositories

Chief Complaint Annotator

Laboratory Test and Results

Annotator

Drug and Dosage

Annotator

Past Medical History

Annotator

Social History Annotator

Family History Annotator

Pre and Post Surgery

Observations

Conditions Annotator

Treatment Procedures Annotator

Negation Annotator

Age Group Annotator

Gender Annotator

Geographic Annotator

Temporal Value

Annotator

Code Translations(ICD9, CPT4,

RxNORM, etc.)

Semantic Type Concepts (e.g. Diseases, Labs,

Drugs)

Regular Expression

Patterns(e.g. Drug

Dosage Patterns)

Temporal Values, Age

Values, Georgraphic Entities, etc.

Document Types and Sub-Headings

Stemming Corrections, Homonyms

Condition Specific Rules and Patterns

Use Case Specific Data,

Rules and Patterns

Document Section Specific Annotators

Level 1

Semantic TypeAnnotators

Level 2

Base Term TypeAnnotators

Level 3

Knowledge Bases Level 4

Access 1

Access 2

Access 3

Vital Signs and

Observations Annotator

Use Case: Leveraging Data Analytics for Risk Adjustment

10

a

EHR

/ Cl

inic

al S

yste

ms

Unstructured Patient Data

Semi-Structured Patient Data

Structured Patient Data

Clinical Rules

INPUTS

INPUTS

NLP

HealthTaxonomy

Risk Adjustment Model

RISK COMPUTATION

CODING OPTMIZATION

ANALYTICS & REPORTING

The Challenges of Risk Adjustment

11

Time-consuming, inefficient and

error-prone

Retrospectiverather than prospective

Significant impact on reimbursement and

patient care delivery

Overlooked clinical factors in unstructured

narratives and patient histories

Inferior analytics technology, leading to a significant number of

missed or inaccurate codes

Not integrated at the point

of care

Coding InSight Application: Addressing Risk Adjustment

• Automate coding gaps detection for more accurate coding and risk scoring

• Conduct prospective and retrospective coding optimization

• Analyze projected coding patterns and provider documentation gaps

• Integrate into the physician workflow at the point of care

• Improve care planning and patient outcomes

12

Analyzing Unstructured Patient Data

13

Peripheral Neuropathy

• Novolog Mix 70-30• Flexpen

Insulin Injection

HbA1c 7.3

• Metformin 1,000 mg tablet• Actos 30 mg tablet

Endocrinologist

Onglyza

BMI 38.86

Hemoglobin A1c

ComplicationPeripheral Neuropathy

Medication• Novolog Mix 70-30• Flexpen

Treatment ProcedureInsulin Injection

Lab ResultHbA1c 7.3

Medications• Metformin 1,000 mg tablet• Actos 30 mg tablet

SpecialistEndocrinologist

MedicationOnglyza

Risk FactorBMI 38.86

Diagnostic Procedure Hemoglobin A1c

Optimizing CMS Payments

14

Scenario 1: What Was Coded Scenario 2: What Should Have Been Coded

Condition ICD-10 Code

HCC Risk Score

Diabetes Mellitus with diabetic nephropathy E11.21 0.368

Peripheral Vascular Disease, unspecified I73.9 0.299

Chronic Obstructive Pulmonary Disease, unspecified

J44.9 0.346

Condition ICD-10 Code

HCC Risk Score

Diabetes Mellitus with diabetic nephropathy E11.21 0.368

Peripheral Vascular Disease, unspecified I73.9 0.299

Chronic Obstructive Pulmonary Disease, unspecified

J44.9 0.346

Sick Sinus Syndrome I49.5 0.295

Chronic Viral Hepatitis C B18.2 0.251

BMI 40.0-44.9, adult Z68.41 0.365

RAF Score: 1.013Total Payment: $10,130

RAF Score: 1.924Total Payment: $19,240Source: Data based on a Talix customer