TransformingHealthcare

Administration:IndustryLanguageModelfor

MedicalCodeExtraction

RachitGupta,GenerativeAIIndustrySolutionsLead,CognizantSentheeshLingam,ChiefArchitect-GenerativeAI,Cognizant

1?2025-2027Cognizant|Private

ScalingEnterpriseAIwithCognizant&NVIDIA

Innovationmeets

manufacturingoperations

RevolutionizingServicesatScale

AIAgents

Agentfactory

DigitalTwin

NVIDIAOmniverse

IntegratingFoundationalelements,platformsandsolutions

Democratizationthroughplatformonplatform

Tailormademodels,deepdomainrelevance

IndustryLLMs

HealthcareLLM

·Al·

Cognizant?NeuroAI

NVIDIANIMTMl

AIFoundation

Data

RewireforAI-NVIDIARAPIDSm

Infrastructure

AIfactory-ServiceasaSoftware

2?2025-2027Cognizant|Private

TheeraofindustryspecificLLMs

IndustriesneedAIthatspeakstheirlanguage–Onesizedoesn’tfitall!

RELEVANT&EFFICIENTRESPONSES

ContextualAwarenessTaskOptimization

CUSTOMIZATION&ADAPTABILITY

FinetunedforspecifictasksEasierIntegration

EFFICIENCYGAINS

ReducedInferenceCostEfficientprocessing

HIGHERACCURACY&DEEPDOMAINEXPERTISE

UnderstandsComplexJargonsReducesHallucinations

IMPROVEDCOMPLIANCE&SECURITY

RegulatoryAlignment(LikeHIPAA,GDPR,SOX)BetterDataSecurity

Asvendorsbeginto

adaptcoreGenAItechnologiestoindustries

andbusinessdomains,expectthemarkettobe

complementedbyarichsetofsolutions

specializingbyrole,businessunitand

industrythroughH12025.

By2025,two-thirdsof

businesseswillleverageacombinationof

GenAIandretrieval-augmentedgenerationto

powerdomain-specificself-service

knowledgediscovery,improvingdecision

efficacyby50%.

By2027,over50%ofthegenerativeAI

modelsusedbyenterpriseswillbedomain-

specific(industryorbusinessfunction),

upfrom1%today.

3?2025-2027Cognizant|Private

Transforminghealthcaremanagementthroughoutthepayervaluechain

GenAIsolutionsacrossthehealthcarevaluechainreadytobepoweredbyhealthcarelanguagemodel

SalesandmarketingoperationsEstimated25-30%productivityincrease

ContractManagementandAdministration

Estimated40-50%decreaseincycletimes

AppealsandGrievance

Estimated30-40%productivityimprovement

MemberPlanShoppingAssistant

Estimated50-60%improvementinplanselection

AutomatedStarProfiling

Intuitiveinsights,benchmark

carequalityandcontextualrecommendations

MedicalCodeExtraction

20-60%effortsavingscomparedtomanualcodeextraction

FWAAssistant

Estimated40-50%effort

reductioninFWAcaseidentifyingandresearch

PriorAuthorizationAssistantEstimated20-30%productivity

improvement

Personalizedcareplangeneration

Improvedcaremanagerefficiencies(>20%)andhealthoutcomes

SupportedbyDeepDomainExpertiseandStrongEngineeringCapabilities

RichSMEBase

DataScienceCoE

NVIDIAAcceleratedComputing

NVIDIANeMoTM

NVIDIANIMTM

TriZetto

4?2025–2027Cognizant|Private

Medicalcodeextractioncontinuestobeamajorchallengeinhealthcare

…aproblemthatwearecansolvethroughindustrylanguagemodel

OPERATIONALCHALLENGES

Voiceof

MedicalCoders

“Thecodingsystemsaresocomplexanddynamiccodingstandardsmakesusmorepronetomistakes”

“Thepressuretomeetproductivitystandardssometimescompromisesouraccuracy”

“There'sashortageofcertified

coders,andtheworkloadcanbeoverwhelming”

Codingmistakescontributeto63%

ofallbillingerrors

Thereisanestimated30%

shortageofprofessionalmedicalcodersacrosstheUnitedStates

TECHNICALCHALLENGES

Modelspronetoerrorsduetolackofdomain-specificknowledge,resultingin

incorrectorirrelevantcodeassignments

Highhallucinationratesingeneralpurposemodels

Longertimeforretrievaland

processingrelevantinformation,

whichcanslowdownthecoding

process

Theannualmaintenance&

retrainingtokeepmodeluptodate

ishighforGeneral-purposemodels

orRAGmodels

HEALTHCAREORGANIZATIONAL

CHALLENGES

~$60+Billion

perAnnumLossforHospitalsduetocodingerrors

Constantupdatestocodingstandardsandnavigating

complexregulations

42%ofdenialsarecausedbycoding

issues,leadingtosignificantrevenue

losses

Around11%ofallclaimsaredenied,

withsomeprovidersexperiencing

denialratesashighas30%

5?2025–2027Cognizant|Private

Certifiedmedicalcodersdiscussobstaclesinextractingmedicalcodes

“Difficulttoextractspecific

“Combinationcodesare

codesduetolackofcontext

stemmingfromincomplete

medicalrecords”

complexinnatureandeven

tougherwhentryingtofindactive

conditions”

“MissingCodesdue

tomanualreadingof

largeparagraphs”

“Codershavetoparsethroughmultiple

documentssuchastreatmentplan

option,medicationrecordstoprovide

proofofconditionbeingactiveduring

theparticularencounter.

“MisinterpretingICD-10-CM

descriptions,leadingtoerrorsin

theguidelines,may

documentation.”

“Missingadd-oncodes,whichaffects

reimbursementanddataaccuracy”

“Notcapturingmorespecificdiagnoses,

impactcodingquality,leadingto

increasedauditreviews,

potentialcompliancerisks,anddelaysindatasubmission”

mappingcodestoclinical

unintentionally

overlookkeydetails”

leadingto

underreportingofpatient

conditions”

“Evenexperienced

coders,despiteknowing

“Thesechallengescansignificantly

6?2025–2027Cognizant|Private

Revolutionizingmedicalcodingwithprecisionandefficiency

Improvedoperationalefficiencyandconsistencythroughautomationandfinetuning

30%-40%

Improvedmedical

codingaccuracyandconsistencyas

comparedtogeneralpurposeLLMs

30%-75%

40%-45%

FastertimetomarketbyutilizingNVIDIA

NeMoTM

Reductionineffortforaccuratemedicalcodeextraction

*Dependingoncomplexity

Benefits

?ReducedLatency

?Significantcostandeffortsreduction

?EfficientRevenueCycleManagement

?AccurateRiskAdjustmentDetermination

?Improvementinquality-of-CareReportingCompliance

?Decreasedexposureofproprietaryhealthdata

7?2025-2027Cognizant|Private

3

Strategicfine-tuningapproachenhancedbyCognizant'sindustryknowledge

1DataPreparation

?Collection&InjectionofMedicaldomaintaxonomy,guidelinesandICDcodedesc.

?DatacurationusingNemoCurator

?Preparedredacted2k+clinical

notes&generated10k+syntheticdatausingNemoCurator

ModelFine-Tuning

?Medicaldomainadaptationusingmedicalterminologies,guidelinesandICD-10coding

?Fine-tuningmodelwithmedicalcodeextractiontasksutilizingsynthetic

data

?LeveragedParameter-EfficientFine-Tuning(PEFT);Low-Rank

Adaptation(LoRA)techniques

Strongtechnicalexpertiseinfine-tuningandcustomizinglanguagemodels

acrossdomains

1000+resourcestrainedandcertifiedacrossNVIDIA,GCP,GenerativeAI.

4ModelInference

?Modeldeployment,provisioning&configurationusingNIMcontainer

?Medicalcodeextractionbusinessappbuildanddeployment

?ModelMonitoring,performance&scalability

Agenticmodulararchitectureforeasyscalability

SeamlessintegrationwithenterpriseappsandTriZettoproductecosystem

2Benchmarking

?Selectionofmedicaldomainandopen-sourcemodels

?Baseliningusingdifferent

approachessuchasRAG,CoT,fewshot

?Defineandevaluatemodels

ExperiencedSMEbase-300+AAPCcertifiedcoderswitha

proventrackrecordonsensitivesubmissions(e.g.,toCMS)

Accesstorichand

diverse2000+datasetsandontologies

Cognizant’sValue

TriZettoproductandhealthcaredomainexpertiseinmodelvalidation

LeverageCognizant’sbenchmarkingframeworkonNVIDIAevaluator

NVIDIA

NeMoCurator

NeMoCustomizer,Evaluator

NeMoEvaluator

NeMoRetriever,Guardrails,NIM(LoRA)

TechStack

8?2025–2027Cognizant|Private

Blueprintforsuccessfulfine-tuning:solutionarchitecture

NVIDIAAccelerated

Computing&NVIDIANIMTM

?ParallelProcessingPower-Training

epochsupto28timesfasterthan8-coreCPU

?EnergyEfficient-NVIDIANIMTMwith

TensorRT-LLMoptimizesLLMsinferencereducingenergyconsumptionbyupto3x

?Highmemorybandwidth-Fastertransferbetweencoreandmemory,MinimizeAI

modeltrainingandinference

2BENCHMARKINGSME

1DATAPREPARATION

NVIDIAAccelerated

Computing

NVIDIAAcceleratedComputing

GradioUI

UploadMedical

Notes

CloudRun

CloudStorageData

Model

Evaluator

SyntheticDataGenerator

Indexer

VectorSearch

VertexAI

NeMo

Curator

NeMo

Customizer

NeMo

Evaluator

NeMo

Retriever

NeMo

Guardrails

BigQuery

NVIDIA

NIM

NeMo

Customizer

NeMo

Evaluator

NeMo

Retriever

NeMo

Guardrails

NVIDIANIM

NeMo

Curator

NVIDIANeMoTM

NVIDIANeMoTM

NVIDIAAIEnterpriseTM

NVIDIAAIEnterpriseTM

4MODELINFERENCE

NVIDIANGCRegistry

FINE-TUNING

3

NVIDIA

AcceleratedComputing

NVIDIAAcceleratedComputing

CloudOps

Operations

Security

IAM

NVIDIANeMoTM

?LowCode,FasterBuild

?Time&EffortEfficiency-maximizesthroughputandminimizestrainingtimewithmulti-node,multi-GPUtrainingandinference

FineTunedModel

Fine-TuningJobs

OpenSourceModel

UI

MedicalCoder

NeMoCurator

NeMo

Customizer

NeMo

Evaluator

NVIDIA

NIM(LORA)

NeMo

Retriever

NeMo

Guardrails

Rules

Firewall

NeMo

Evaluator

NeMo

Retriever

NeMo

Guardrails

NVIDIANIM

NeMo

Curator

NeMo

Customizer

NVIDIANeMoTM

LocalStorage

NVIDIAAIEnterpriseTM

NVIDIANeMoTM

NVIDIAAIEnterpriseTM

NVIDIANGCREGISTRY

?ComprehensiveAIresources

?AcceleratedWorkflows

?EaseofIntegration

?RichDocumentationandSupport

9?2025-2027Cognizant|Private

Datasetpreparationformedicalcodeextraction&evaluation

SyntheticDataGeneration

MedicalDocumentsTypes

SamplingGenerationMethodology

?Samplesgeneratedfroma

subsetofICD-10codeschosenfromasetof~80kcodes

?ICDcodesweregrouped

togetherbasedonpotential

medicalconditionsthatcanco-exists.

?Samplenoteswereeach

groupingdescribingoneormorepossiblemedicalcondition

?SyntheticnotesweregeneratedforallICDcodegroupings.

?SyntheticcodeswerevalidatedbySME

?WellnessForms

?PhysicianConsultProgressNote

?DischargeSummary

?PatientHistory

?ExaminationFindings

ComplexityEvaluation

?Identifyingfactorscontributingtothecomplexity

?Grouping&scoringtheindividualfactorcontributiontowards

complexity

?Identifyingthepresenceof

contributingfactorsinmedicaldocuments

?ClassifyingandScoringdocuments

MedicalCodingHierarchy

10?2025-2027Cognizant|Private

EvaluatingLLMsbasedondocumentintricacyandpromptingmethod

Accuracybycomplexity

35

39%

40%

14%

36

7%

%

%

13%

22%

%

5%

15

8%

22%

2

ParentLevelAccuracybyComplexity

MedLM

Llama-2

Llama

Gemini

DeepSeek-R1

0%10%20%30%40%50%

MediumLow

High

1

3%

1

ChildLevelAccuracybyComplexity

MedLM

Llama-2

Llama

Gemini

DeepSeek-R1

15%

19%

1%

1%

5%

9%

13%

8%

2%

10%

29%

MediumLow

High

0%5%10%15%20%25%30%

AccuracybyApproach

MedLMLlama-2Llama

Gemini

DeepSeek-R1

ParentLevelAccuracybyApproach

5%

12%

13%

2

29%

6%

30%

52%

0%10%20%30%40%50%60%

RAG

Direct

MedLMLlama-2Llama

Gemini

DeepSeek-R1

ChildLevelAccuracybyApproach

RAG

Direct

11%

37%

5%

6%

12%

15%

2%

0%10%20%30%40%

KeyConsiderations:

?Benchmarkingacrossbothopensourceandcommercialmodels

?Medicaldocumentscategorizedbycomplexitylevelsforevaluation

?MultipleapproachesconsideredforevaluatingmodelperformanceincludingRAGapproach

?Extractedmedicalcodes(ICD-10)werematchedbothatparent&childlevels

KeyObservations

?Opensourcemodelstrainedinmedicaldomainperformbetterthangenericmodels

?RAGapproachimprovestheperformanceofallmodels

?Datacomplexityhashugeimpactonmodel

performanceparticularlywhenmultiple

scenariosarepresentinthemedicaldocuments

?Extractingaccuratechildcodesischallengingformostmodels

11?2025-2027Cognizant|Private

Challengesimpactingtheaccuracyofmedicalcodesextraction

HIGHCOMPLEXITY

MissedDiagnosesinDischargeNotes

Missedcodingspecificity

InaccurateICDCodePrediction

Missed'InitialEncounter'Coding

Missed'Z'CodesforHealthStatus

UninterpretedAbbreviations

MEDIUMCOMPLEXITY

Invalidcodeprediction

Hallucination

Entity-CodeMismatch

Missed'Acute'vs.'Chronic

LOWCOMPLEXITY

Missed's/p'(StatusPost)

Condition

Missedcommonterms

12?2025-2027Cognizant|Private

Ourapproachaimstoreplicatethereasoningprocessofamedicalcoder

MedicalCoder

SearchwithintheAlphabetic

IndextofindtheICD-10codeforspecificmedicalentitiesor

conditions.

IdentifyMedicalEntities

fromclinicalnotesfor

diagnosis,symptoms,

support,historical

statements,referrals,plans,andhealthstatuschanges.

Chapter-wiseGuideline

ChecktovalidatetheICD-10CMcodeagainstchapter-

specificguidelinesandgeneralcodingconventions.

ReviewICD-10CodefromtheICD-10-CMTabularListofDiseasesandInjuriestoensureitmatchesthe

patient’sdiagnosis.Identifyanynotesimpactingthecodeselectionalong

withidentificationofadditional

characters/codestobestsuitthediagnosis.

Finalization

?FamiliarizewithmedicaltaxonomytomapclinicaltermstoICD-10codes.

?Identifycommonmedicalabbreviationstoaccuratelyinterpretclinical

conditions.

?StudyconventionsandgeneralcodingguidelinesfromtheICD-10-CMOfficialGuidelines.

ExtractionLookup

Validation

Foundation

ICD-10CodesGeneration

?LeveragedtheenhancedfinetunedLLMsknowledgebasetoidentifytheICD-10codesfromtheextractedmedicalentities.

ICD-10CodesFinalization

?CatalogthefinallistofICD-10whicharerelevanttothecontextofthedocumentandadheringtotheadditional

requirementstobestsuitthecontent.

ICD-10GuidelineValidations

?ValidatedtheidentifiedICD-10codesagainstchapter-

specificguidelinesand

generalcodingconventionsusingPromptEngineeringtoensureadherencetocodingprotocols.

MedicalEntityExtraction

?SuccessfullyimplementedaLoRAbasedfinetuning

approachtofurthertraintheLLMsharnessingSyntheticMedicalDocuments.

?Leveragingtheaugmented

knowledgebase,specific

MedicalentitiesareextractedviaQ&Aprompting.

DomainSpecificFinetuning

?AugmentedtheexistingknowledgebaseoftheLLMswithICD-10-CM

OfficialGuidelines,AMAGlossaryofMedicalterms,ICD-10-CMIndex

documents

?SupervisedFinetuningapproachisimplementedtoexpandtheLLM’sexistingknowledgebank.

HealthcareLanguagemodel

13?2025–2027Cognizant|Private

ICD10CodeExtraction

Pre-trainedmodel

ExtractedICD10Codes:

['T446X5S','T444X5S']

ExpectationMatch:0%

Fine-tunedmodel

ExtractedICD10Codes:

['I497','K850','M6282','I2690','E119','N185','I420','H4410','I230','N400']

ExpectationMatch:40%

Analysisoffine-tunedmodeloutputforhighlycomplexclinicalnotes

Expectedentitiesinclinicalnote

BeforeFinetuning

AfterFinetuning

Acuterenalfailurerequiringhaemodialysis

√

√

Amiodaronetoxicityoflungandliver

√

√

Anaemiaduetobloodloss

?

√

Atrialfibrillation

?

√

Benignprostatichypertrophy

?

√

Cardiomyopathy

?

√

Chronicobstructivepulmonarydisease

?

√

Chronicrenalfailure

?

√

Diabetestype2withretinopathy

?

√

Hypercholesterolemia

?

√

Hypertension

?

√

NPHinsulin5mgq.a.m.,3mgq.hs.

?

?

Peripheralvasculardisease

?

√

Pneumonia

?

√

Systoliccongestiveheartfailure

?

√

Tachy-bradysyndromestatuspostDDDpacemakerplacement

?

√

14?2025-2027Cognizant|Private

Overviewoftheresultsfromthefine-tunedmodel

Accuracy&CoverageComparison:Llama(8b)Model

38%22%

10%

LowComplexity

100%

80%

60%

40%

20%

0%

78%64%

43%

30%

18%

%Parent

Coverage

%Child

Coverage

ParentAccuracy

Child

Accuracy

BaselineModelFinetunedModel

18%

70%

60%

50%

40%

30%

20%

10%

0%

MediumComplexity

63%

52%

32%33%27%27%

13%

%Parent

Coverage

%Child

Coverage

ParentAccuracy

Child

Accuracy

BaselineModelFinetunedModel

59%

Increaseinoverallaccuracy

78%

IncreaseinOverallAccuracy&

Coverage(weightedaccuracy)

HighComplexity

100%

80%

60%

40%

20%

0%

83%

59%

42%

28%

23%

19%

13%

11%

%Parent

Coverage

%Child

Coverage

Child

Accuracy

ParentAccuracy

BaselineModelFinetunedModel

KeyConsiderations:

?Themodelwasrefinedprogressivelyusingvarioussyntheticdatasetsthatvaryincomplexityandbatchsizes.

?TechniquessuchasLoRaandSFTwereappliedformodelfine-tuning.

?Themodelunderwentinstructionfine-tuningforICD10codeextraction.

?Chainofthoughtstrategieswereemployedtoenhancethe

model'sperformanceincodeextraction,ensuringreasoningandjustificationduringinference.

KeyObservations

?TheRAG-basedmethodappliedtothefine-tunedmodel

enhancedtheoverallperformanceacrossallmodels.

?

Fine-tunedmodelsdemonstratedsuperiorcapabilitiesinhandlingcomplexstructureddocumentsthatnecessitatelinking

informationfoundindifferentsections.

?Thesemodelsachievedimprovedoutcomeswithoutrelyingon

extensivepromptingtechniquesorintricateRAGmethods.

15?2025-2027Cognizant|Private

Applicationofafine-tunedmodel-extractingmedicalcodes

?AutomatedICDCoding:Thesystem

automatesICDcodeextractionfrom

medicaldocuments,reducingmanualeffortandpotentialerrors.

?Two-PassApproachforAccuracy:Atwo-passapproachleveragesGenAItoinitiallyidentifypossibleICDcodesandthenrefinetheselectionwithfulldocumentcontext.

?DiagnosisTermRecognition&Mapping:UtilizesGenAItoidentifydiagnosisterms

withinmedicaldocumentsandmapsthemtocorrespondingICDcodesviaanExceldatabase.

?ContextualFilteringforPrecision:

SecondpassincorporatesthefullmedicaldocumentascontexttofilterandrefinetheinitialsetofICDcodes.

?User-FriendlyInterfacewithExclusionFunctionality:

TheUIoffersafeaturetoexclude

specificdiagnosisterms,providingusercontroloverthecodeextractionprocess.Model

16?2025-2027Cognizant|Private

CognizantTriZetto?agenticAIsystemleadingthetransformationofcore

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