BigData:Implica.onsforNursingInforma.cs

BonnieL.Westra,PhD,,RN,FAAN,FACMIAssociateProfessorandDirector,CenterforNursingInforma?cs

April29,2016

Objec?ves

• Definebigdataasitrelatestonursing• Iden?fychallengesinuseofnursingdataforbigdatascience

• Exploreexamplesofbigdatanursingresearch

• Iden?fystrategiesfornurseinforma?cianstoshareknowledgeacrossseQngstocreatenursingbigdata

BigDataSources-Nursing• Volume• Velocity• Variety• Veracity• Value

https://infocus.emc.com/william_schmarzo/thoughts-on-the-strata-rx-healthcare-conference/

Geocodes

DataSources• CTSA–hXps://ctsacentral.org/

• NCATS-hXps://ncats.nih.gov/• PCORnet-hXp://www.pcornet.org/

•  13clinicaldataresearchnetworks(CDRNs)•  22pa?entpoweredresearchnetworks(PPRNs)

• OptumLabs–140millionlivesfromclaimsdata+40millionfromEHRs(delaney@umn.edu)

• hXp://www.data.gov/-Searchover192,872datasets

BigData&BigDataScience

• Applica?onofmathtolargedatasetstoinferprobabili?esforassocia?ons/predic?on

• Purposeistoacceleratediscovery,improvecri?caldecision-makingprocesses,enableadata-driveneconomy1

•  Three-leggedstool•  Data•  Technology•  Algorithms

http://www.sciencemag.org/site/special/data/ScienceData-hi.pdf

•  inareasofeSciencesuchas•  [datacapture],•  Databases,•  Workflowmanagement,•  Visualiza?on•  Compu?ngtechnologies.

Harnessing the EHR for Research

Nursing Research Journal!

© Westra, 2014 Nursing Informatics & Translational Science

BigDataAnaly?csforNursing

RequirementsforUsefulData

• Commondatamodels• Standardizedcodingofdata• Standardizequeries

http://www.pcornet.org/resource-center/pcornet-common-data-model/

DataStandardiza?on

• Demographics–OMB• Medica?ons-RxNorm• Laboratorydata-LOINC• Procedures–CPT,HCPCS,ICD,SNOMEDCT

• Diagnoses-ICD-9/10-CM,SNOMEDCT• Vitalstatus–CDC• Vitalsigns-LOINC

11

Vision–InclusionofNursingData

Clinical Data NMDS

Management Data

NMMDS

Other Data Sets

Con?nuumofCare

Challenges

Challenges-Standards

ANA Position Statement – Inclusion of Recognized Terminologies Supporting Nursing Practice within Electronic Health Records and Other Health Information Technology Solutions

hXp://z.umn.edu/bigdata

Challenges-Architecture

Challenges-Reinven?ng

UMNClinicalDataRepositoryCohortdiscovery/recruitmentObserva?onalstudiesPredic?veAnaly?cs

Data available to UMN researchers via the Academic Health Center Information Exchange (AHC-IE) 2+ million patients

MHealth/FairviewHealthServices

ExampleFlowsheet

DataSourceClinicalDataModels-Flowsheets

T562

Groups2,696

FlowsheetMeasures14,550

DataPoints153,049,704

•  10/20/2010 - 12/27/2013 •  66,660 patients •  199,665 encounters

UMNCTSI-ExtendCDMTeam:Nursing(DNP/PhD),ComputerScience,HealthInforma?cs

Iden?fyClinicalDataModelTopic

Iden?fyConcepts

MapFlowsheets

toConcepts

Present Validate

MakingDataUseful200KPa?entEncounterPainInforma.onModel309observa?ons80Uniqueconcepts–assessments,goals,interven?ons(notincludingvaluesets–choicelists)

CardiovascularSystem Pain

Falls/Safety PeripheralNeurovascular(VTE)

Gastrointes?nalSystem

PressureUlcers

GenitourinarySystem/CAUTI Respiratorysystem

NeuromusculoskeletalSystem VitalSigns,Height&Weight

FlowsheetInforma?onModels

Informa.onModelName NumberFlowsheetIDs

MappedtoObservables

NumberInforma.onModelClasses/

Observables

Classes Concepts

CardiovascularSystem 241 8 84

Falls 59* 4 57

Gastrointes.nalSystem 60 3 28

GI/CAUTI 79 3 38

MusculoskeletalSystem 276 9 72

Pain 309 12 80

PressureUlcers 104 6 56

RespiratorySystem 272 12 61

VTE 67 8 16

VitalSigns/Anthopometrics 85 10 48

NursingBigDataResearch

NursingResearch•  SevereSepsisComplianceguidelinesandimpactonpa?entcomplica?onsandmortality

•  Unan?cipatedICUadmissionsforelec?vesurgerypa?ents

•  Pa?entandnursestaffingfactorsassociatewithCAUTI•  FactorsassociatedwithurinaryandbowelIncon?nenceimprovement

•  Predic?nghospitaliza?onforfrailelders•  DemonstratevalueofWound,Ostomy,Con?nenceNursingforimprovingwoundsandincon?nence

•  Improvementinmanagingoralmedica?ons

HomeCareEHRDe-Iden?fiedData8,9

ReasonforRemovingRecords n

Incompleteepisoderecords 464,485

Assessmentoutsidestudydates 125,886

Incorrecttypeofassessment 51,779

Maskedormissingdata 16,302

Duplicaterecords 2,748

Age<18orprimarydxrelatedtopregnancy/complica?ons

822

Ini.alDataSet808agencies,1,560,508OASISrecords,888,243pa?entsListofpa?entswithandwithoutWOCNurse

Final Data Set 785 agencies, 447,309 patients,

449,243 episodes of care, 0.6% re-admissions

Cer?fiedWOCNursesInfluenceonIncon?nence&Wounds

OutcomeVariables Descrip.on

PressureUlcers Totalnumberofpressureulcers(M0450a-e)

StasisUlcers Totalnumberstasisulcers(M0470/M0474)

SurgicalWounds Totalnumberofsurgicalwound(M0484/M0486)

UrinaryIncon?nence Presence/managementofurinaryincon?nenceorneedforacatheter(M0520)

UrinaryTractInfec?on TreatedforUTIinpast14days(M0510)

BowelIncon?nence Frequencyofbowelincon?nence(M0540)

Improved/NotWorse(Stabilize)Outcomes

Score BowelIncon.nenceFrequency ImprovedNotWorse(Stabilize)

0Veryrarely/neverhasBIorhasostomyforbowelelimina?on

1 Lessthanonceweekly

2 Onetothree?mesweekly

3 Fourtosix?mesweekly

4 Onadailybasis

5 Moreopenthanoncedaily

Aim1:Prevalence

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

PU SU SW UI BI UTI

Prevalence of Condition by Agency

No WOC (n = 13,261) WOC (n = 281,552)

PressureUlcer(PU),StasisUlcer(SU),SurgicalWound(SW),UrinaryIncon?nence(UI),BowelIncon?nence(BI),UrinaryTractInfec?on(UTI)

Aim2:Incidence

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

PU SU SW UI BI UTI

Incidence of Conditions by Agency

No WOC (n = 13,261) WOC (n = 281,552)

EffectofWOCNursesonAgencyOutcomes

IndividualPa?entOutcomes

IndividualPa?entOutcomes

LessonsLearned

• Obtainingdata•  TrackingWOCnursepa?entvisits• Dataquality

• Matchingpa?entsstartanddischarge• Duplicatepa?entrecords•  Encrypteddata• Missingdata

•  Selec?ngvariables-theoryanddomainexper?se•  Typeofanalysis-Researchques?on,structureofthedata

MobilityOutcomes

• Discoverpa?entsandsupportsystemcharacteris?csassociatedwiththemobilityoutcomes

•  Findnewfactorsassociatedwithmobilitybesidescurrentambula?onstatusduringadmission(OR=5.96)

•  Ineachsubgroupofpa?entsdefinedbycurrentambula?onstatusduringadmission(1-5)

•  Tocomparethepredictorsacrosseachpa?entsubgrouptofindtheconsistentbiomarkersinallsubgroupsandspecificfactorsineachsubgroup

MobilityOutcome

ComparisonofOutcomesbyGroup

OverallSteps

39 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, pp. 37 – 54. http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf. P. 41

OASISdataextractedfromEHRsfrom270,634pa?entsservedby581Medicare-cer?fiedhomehealthcare

agencies

Standardizingdata,de-iden?fyingpa?ent,impu?ngmissingvalue,binarizingdatainto98variables

DataMiningTechniques

•  Identify risk variables significantly associated with mobility outcomes - varied among the groups

•  Group the single predictors based on whether they cover same or different patient group –  Clustering

•  Based on similarity of patients •  Not discriminative •  High frequency variables got merged

–  Pattern mining based approach •  Discriminative •  Coherence (similarity of patients)

SubgroupVariability

ClusteringGroupsVa

riables

Variables

ImprovementGroup2

Olderadultswithnoproblemsindaily

ac?vi?es

Healthierphysiologicalandpsychosocialelderly

HouseholdManagement

NoImprovementGroup2

44

Incapabletotoiletandtransfer

HelpwithfinancialandlegalmaXers

Cogni?vedeficitsandbehavioralproblems

PaidHelp

Frailpa?entswithfunc?onaldeficiency

•  Transform data into binary variables •  Selection of variables – remove if

•  Too little variation or high inter-correlations of predictors

•  Medical diagnoses used to describe patients, not predict •  Analysis by subgroup •  Interpretation of results is critical – requires domain

experts •  Different clusters point to the need to tailor interventions

for subgroups •  Lack of standardized interventions precluded

understand how care provided effects outcomes

LessonsLearned

SharingExperiences

47

z.umn.edu/bigdata

Vision•  BeXerhealthoutcomesfromthestandardiza?onand

integra?onoftheinforma?onnursesgatherinelectronichealthrecords•  EHRincreasinglythesourceofinsightsandevidence•  Usedtoprevent,diagnose,treatandevaluatehealthcondi?ons.

•  OtherIS-Richcontextualdataaboutpa?ents(includingenvironmental,geographical,behavioral,imagingdata,andmore)

•  Leadtobreakthroughsforthehealthofindividuals,families,communi?esandpopula?ons.

CreateaNa?onalAc?onPlan•  Implementnursinginforma?oninEHRsandotherinforma?onsystemsusingstandardizedlanguage•  Streamlined,essen?al,evidence-based,ac?onable,anddemonstratesvalueofnursing’scontribu?ontohealth

•  Standardizenursinginforma?cseduca?ontobuildanunderstandingandcompetences

•  Influencepolicyandstandardsfordocumen?ngandcodingnursinginforma?oninhealthcareknowledgesystems

•  Usestandardizednursingdatawithotherdatasourcesforbusinessanaly?csandresearch

Conferences*•  Workingconferenceswithvirtualworkgroupstakingac?onbetweenannualmee?ngs

•  Allfocusedonthesamevision•  Strategicinclusionofstakeholders

•  Prac?ce-leaders•  Industry,par?cularlysopwarevendors•  Professionalorganiza?ons

•  Na?onal–nursing,interprofessional,informa?cs

•  Academia

*Proceedings:hXp://z.umn.edu/nbd2k

2014–2015Accomplishments•  Educa.on

•  Surveyedaccredita?on,cer?fica?onandcreden?alingprogramsinfluencinginforma?cs

•  Facultyresourcesforteachinginforma?csavailable:hXp://www.nursing.umn.edu/con?nuing-professional-development/nnideepdive/

•  ScienceofNBD2K•  CompletedNMMDSupdates/LOINCcodingforpublicdistribu?on

•  hXp://z.umn.edu/nmmds

•  StartedNINRNursingInforma?csSIG•  Created“BigDataChecklistforChiefNurseExecu?ves”

•  QualityMeasures•  ForwardedeMeasureforPressureUlcers

•  HealthITPolicy•  GuidingPrinciplesforBigDatainNursing:UsingBigDatatoImprovetheQualityofCareandOutcomes.hXp://www.himss.org/big10

•  ANAPosi?onStatement:InclusionofRecognizedTerminologiesSuppor?ngNursingPrac?cewithinElectronicHealthRecordsandOtherHealthInforma?onTechnologySolu?ons.

•  ANA/ANI/AANInforma?csexpertpanelcollaboratedonappointmentsandcommentsonpolicies

•  Harmoniza.on/Standardiza.onofNursingData/Models•  Focusoncarecoordina?on&standardiza?on•  IntegratePNDSintodataandmodelstandards

2014–2015Accomplishments

2014–2015Accomplishments•  ValueofNursing

•  Createddatamodeltodemonstratevalueofnursingatindividualnurselevel

•  LOINC/SNOMEDCTMinimumAssessment•  DevelopedminimumphysiologicalassessmentencodedwithLOINC/SNOMEDCT

•  WorkforceData•  Developdissemina?onplanforImplementa?onGuideforNMMDS

•  TransformNursingDocumenta.on•  Developasetofrecommenda?onsforleveragingEHRsandclinicalintelligencetoolstopromoteevidencebased,personalizedcareacrossthecon?nuum

NewWorkgroups•  Social/BehavioralDeterminantsofHealth

•  Developatoolkittosupportinclusionofthisdataintoelectronichealthrecords,includingexpectedCMSMeaningfulUseprogramrequirements

•  Nursing&CareCoordina?on•  Iden?fynursingimplica?onsrelatedto“bigdata”associatedwith“carecoordina?on.”

•  ConnectNursingInforma?csLeaders•  Provideaplavormforemergingandexpertinforma?csnursestodiscussopportuni?estoenhancenursingknowledge

•  mHealthData•  Exploretheuseofmobilehealthdatabynurses,includingnursing-andpa?ent-generateddata,andincorpora?ngmHealthdatauseintoworkflows

YouAreInvitedtoGetInvolved• Workinggroups–

• ContactLisianePruinellipruin001@umn.edu• NursingKnowledge:2016BigDataScienceConference•  June1-3,2016 • Minneapolis,Minnesota• Registra?onopen!EarlybirddiscountthroughApril1,2016

• hXp://z.umn.edu/bigdata

Summary

• Bigdataisincreasing•  Exis?ngandnewermethodsfordataanalysis• Bigdatascienceusefultoaddressprac?ceques?ons

•  Lessonslearned• Dataquality–originatesinprac?ce•  Standardizeddataandcommondata/informa?onmodelsneededforusabledata

•  “Takesavillage”–combinedexper?seimportant

Acknowledgments

• Mul?plefundingforstudies:•  GrantNumber1UL1RR033183fromtheNa?onalCenterforResearchResources(NCRR)oftheNa?onalIns?tutesofHealth(NIH)totheUniversityofMinnesotaClinicalandTransla?onalScienceIns?tute(CTSI).

•  TwoNSFgrants:NSFIIS-1344135andNSFIIS-0916439aswellasaUniversityofMinnesotaDoctoralDisserta?onFellowship.

• Wound,Ostomy,Con?nenceNursingAssocia?on

Ques.ons?BonnieL.Westra,PhD,RN,FAAN,FACMI

westr006@umn.edu

References•  WestraBL,Chris?eB,JohnsonSG,etal.ExpandinginterprofessionalEHRdatainI2B2.AMIAJointSummitsonTransla2onalScienceproceedingsAMIASummitonTransla2onalScience.2016.

•  DeyS,CoonerJ,DelaneyCW,FakhouryJ,KumarV,SimonG,SteinbachM,WeedJ,WestraBL.MiningPaXernsAssociatedWithMobilityOutcomesinHomeHealthcare.NursRes.2015Jul-Aug;64(4):235-45.PubMedPMID:26126059.

•  WestraBL,La?merGE,MatneySA,ParkJI,SensmeierJ,SimpsonRL,SwansonMJ,WarrenJJ,DelaneyCW.Ana?onalac?onplanforsharableandcomparablenursingdatatosupportprac?ceandtransla?onalresearchfortransforminghealthcare.JAmMedInformAssoc.2015May;22(3):600-7.PubMedPMID:25670754.

•  WestraBL,DeyS,FangG,SteinbachM,KumarV,SavikK,OanceaC,DierichM.InterpretablePredic?veModelsforKnowledgeDiscoveryfromHomecareElectronicHealthRecords.JournalofHealthcareEngineering.2011;2(1):55-74.