DOI: 10.4018/IJBDAH.2019010101

International Journal of Big Data and Analytics in HealthcareVolume 4 • Issue 1 • January-June 2019

Copyright©2019,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.

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The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in HealthcareChaojie Wang, The MITRE Corporation, McLean, USA

https://orcid.org/0000-0001-8521-9420

ABSTRACT

ImprovingtheperformanceandreducingthecostofhealthcarehavebeenagreatconcernandahugechallengeforhealthcareorganizationsandgovernmentsateverylevelintheUS.Measurestakenhaveincludedlaws,regulations,policies,andinitiativesthataimtoimprovequalityofcare,reducecostsofcare,andincreaseaccesstocare.CentraltothesemeasuresisthemeaningfulandeffectiveuseofBigDataanalytics.Toreap thebenefitsofbigdataanalyticsandalignexpectationswithresults,researchers,practitioners,andpolicymakersmusthaveaclearunderstandingoftheuniquecircumstancesofhealthcareincludingthestrengths,weaknesses,opportunities,andthreats(SWOT)associatedwiththeuseofthisemergingtechnology.ThroughdescriptiveSWOTanalysis,thisarticlehelpshealthcarestakeholdersgainawarenessofbothsuccessfactorsandissues,pitfalls,andbarriersintheadoptionofbigdataanalyticsinhealthcare.

KeyWORDSBig Data, Data Analytics, Data Breaches, Data Ethics, Electronic Health Records, Health Information Exchange, Health IT, Healthcare, Machine Learning, SWOT Analysis

1. INTRODUCTION

TheUShealthcaresystemhasbothstrengthsandweaknesses.Itenjoysalarge-scale,well-trained,andhigh-qualityworkforceofclinicians,nurses,andspecialists,robustmedicalresearchprograms,andtheworld’sbestclinicaloutcomesinselectmedicalservices.Yet,itsuffersfromhighexpenditure,lowperformance,anddisparityinhealthstatus,accesstocare,andoutcomesofcare(Barnes,Unruh,Rosenau,&Rice,2018).

1.1. High Cost of the US Healthcare SystemAccording to a recent report published by The Organization for Economic Co-operation andDevelopment(2018),in2017theUSspendingonhealthcarewasthelargest,measuredbyboththespendingpercapitaandthepercentageofthegrossdomesticproduct(GDP)amongits37membernations.Figure1showsthattheUSspentover$10,000percapitaonhealthcarethatyear,orabout17%ofGDP.

EvenmorealarmingistherapidgrowthinUShealthcarespending.AccordingtotheCentersforMedicareandMedicaidServices(CMS),healthcarespendingisprojectedtogrowatanaveragerateof5.8percentfrom2012-2022,1.0percentagepointfasterthantheexpectedaverageannualgrowthintheGDP.By2022,UShealthcarespendingisprojectedtobenearly20%ofGDP(CentersforMedicareandMedicaidServices,2012).

Thisarticle,originallypublishedunderIGIGlobal’scopyrightonMay24,2019willproceedwithpublicationasanOpenAccessarticlestartingonJanuary20,2021inthegoldOpenAccessjournal,InternationalJournalofBigDataandAnalyticsinHealthcare(convertedtogoldOpenAccessJanuary1,2021),andwillbedistributedunderthetermsoftheCreativeCommonsAttributionLicense(http://cre-

ativecommons.org/licenses/by/4.0/)whichpermitsunrestricteduse,distribution,andproductioninanymedium,providedtheauthoroftheoriginalworkandoriginalpublicationsourceareproperlycredited.

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1.2. Low Performance of the US Healthcare SystemThisextremelyhighspendingissharplycontrastedwiththelowperformanceintheUShealthcaresystem.In2000,theWorldHealthOrganization(WHO)publishedareportthatmeasuredandrankedthehealthsystemperformanceof191countries.Accordingtothisreport,theUShealthcaresystemwasunimpressivelyrankedat37,belowmost industrializedcountries includingFrance, theUK,andCanada,andevenbelowsomelessdevelopedcountriessuchasColombiaandChile(Tandon,Murray,Lauer,&Evans,2000).Almosttwodecadeslater,therehasnotbeenmuchimprovementintheperformanceoftheUShealthcaresystem.Accordingtoa2017reportfromtheCommonwealthFund,theUSisrankedlastoutof11high-incomeindustrializedcountriesbasedonmeasuresincludingcareprocess,accesstocare,administrativeefficiency,equity,andhealthoutcomes(Schneider,Sarnak,Squires,Shah,&Doty,2017).Figure2showsthattheUShealthcaresystemperformsatthebottomonfourofthefivemeasures.

1.3. efforts to Improve the US Healthcare SystemIn2007,theInstituteforHealthcareImprovement(IHI)launchedtheTripleAiminitiativetoimprovethepatientexperienceofcare(includingqualityandsatisfaction),improvethehealthofpopulations,andreducethepercapitacostofhealthcare.TheTripleAiminitiativedirectlytargetsthecriticalmeasuresofhealthcareperformanceincludingbothqualityofcareandefficiencyofcare(InstituteforHealthcareImprovement,2007).

In2008,CongressenactedtheMedicareImprovementsforPatientsandProvidersAct(MIPPA).AspartoftheimplementationofMIPPA,CMSintroducedthevalue-basedpurchasing(VBP)planlinkingpaymentsdirectlytothequalityoutcomesofthecareprovided.Thispay-for-qualityorpay-for-performanceplanaimstomoveawayfromthetraditionalfee-for-serviceplanwhichprovidesnoincentivesfortheproviderstoimprovecarequalityandcontributestothehighcostofhealthcare(Terhaar,2018).

In2010,toaddressthedisparityandinequityinhealthcareandtoincreaseaccesstocarefortensofmillionsofuninsuredandunderinsuredAmericans,CongressenactedthePatientProtectionandAffordableCareActof2010,alsoknownas“Obamacare”(“PatientProtectionandAffordableCareActof2010,”2010).

Whilehealthcareinitiatives,regulations,andpoliciesmayhelpdrivethequalityandperformanceimprovementatthemacrolevel,effectiveimplementationsrequireconcertedeffortsatthemicrolevelbyallstakeholdersincludingpolicymakers,providers,payer,patients,andthepublic.Inaddition,thesediversestakeholdersmustbeempoweredandenabledbyinnovativesolutionsandtechnologies

Figure 1. 2017 health spending per capita as share of GDP (Organization for Economic Co-operation and Development, 2018, p. 2)

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todealwiththecomplexproblemsinhealthcare.Bigdataanalyticsasanemergingtechnologyisoneofmanyindispensabletoolsinthetoolboxandcanplayanimportantroleinimprovinghealthcare.However,aswithanynewtechnology,benefitsoftencomewithlimitations,opportunitieswithrisks,andhopeswithhypes.Itisimportanttounderstandthefullspectrumofthisnewtechnologysothatitcanbeefficientlyandeffectivelyadoptedandapplied.

MotivatedbythetremendouschallengesfacingtheUShealthcaresystemandthegreatpotentialbigdataanalyticshasinimprovingitsperformance,thispaperpresentsahigh-levelanalysisofthestrengths,weaknesses,opportunities,andthreats(SWOT)associatedwiththeuseofbigdataanalyticsinhealthcare.Thegoalofthispaperistohelppolicymakers,careproviders,researchers,andthepublicgainbroaderanddeeperunderstandingofthemanydimensionsoftheuseofthisemergingtechnologyinhealthcaresothattheycanmakeinformedandsensibledecisionsintheireffortstoimprovehealthcarequalityandperformance.

2. BIG DATA ANALyTICS AS eNABLeR TO IMPROVe HeALTHCARe

2.1. Big Data AnalyticsDuringthepastdecade,bigdataemergedasanewtechnologytrendthankstotherapidinnovationandadvancementininformationandcommunicationtechnology(ICT).BigdatawasinitiallydefinedwiththreeessentialcharacteristicsknownastheThreeV’s–Volume,Velocity,andVariety.Sincethen,moreV’shavebeenadded.ASixV’smodelwidelyusedinhealthcareaddsthreeadditionalV’s–Veracity,Variability, and Value (Senthilkumar, Rai, Meshram, Gunasekaran, & Chandrakumarmangalam,2018).Dataanalyticsisanumbrellatermcommonlyusedtoreferencebusinessintelligence,businessanalytics,datamining,knowledgediscoveryindatabases,anddatascience.Aslargevolumesofdatainwidevarietiesarecollectedandmadeavailable,thedemandincreasesforextractingvaluesfrombigdatatoimprovebusinessperformanceanddriveorganizationalchanges.Dataanalyticsanswersthechallengebyintegratingcomputerscience,informationtechnology,statistics,domainknowledge,andhumancollaborationinastreamlinedprocessofknowledgediscovery,creation,andapplication(Wang,2018).

2.2. Applications of Big Data Analytics in HealthcareHealthcaredata analytics is the applicationofbigdata anddata analytics tohealthcare.Similartermswithminordifferencesalsoappearbothinacademiaandindustry.Amongthemarehealthcareanalytics, health analytics, healthcarebigdata analytics, bigdata analytics in health, andhealth

Figure 2. 2017 healthcare systems performance ranking (Schneider et al., 2017)

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informatics.Knowledgecanbediscovered,andinsightscanbegainedfrombigdataanalyticstoimprovehealthcarequalityandperformance.Basedonthecontentanalysisof26casestudies,Wang,Kung,andByrd(2016)identifiedfivepotentialbenefitsofhealthcaredataanalytics:ITinfrastructure,operational, organizational, managerial, and strategic benefits. Lebied (2018) provided detailedaccountsoftwelveapplicationsofbigdataanalyticsinhealthcaresuchaspreventionofopioidabuse,telemedicine,preventionofERvisits,andfrauddetection.Theseserveasexemplarsofthemanyapplicationscurrentlyrecognizedandmanymoreinnovativeapplicationscontinuingtoemerge.

TheUniversityofPennsylvaniaHealthSystem(UPHS)’suseofmachinelearningandElectronicHealthRecords(EHR)datatopredicttheonsetofseveresepsisisanexampleofusingbigdataanalyticsinhealthcare.Sepsisisalife-threateningillnessduetobloodinfection.AccordingtotheCenterforDiseaseControl&Prevention(CDC),eachyear,1.7millionAmericanadultsdevelopsepsis,270,000Americansdieofsepsis,andoneinthreepatientswhodieinhospitalshassepsis(Dantes&Epstein,2018).SepsiscostsAmericanhospitals$27billionannually,makingitthenumberonecostdriverforhospitals(Reinhart,2018).UPHSdevelopedapredictivemodelbytrainingamachinelearningalgorithmusinghistoricpatientdatastoredin itsEHRincludinglabs,clinical,anddemographicdata.Thetrainedmachinelearningmodelwasthenusedtopredictseveresepsis12hourspriortotheclinicalonsetbycontinuouslypullingreal-timepatientdatafromtheEHR.Alertsweresenttocliniciansandnursestoenableadditionalmonitoringandearlyintervention(Gianninietal.,2017).

2.3. The Adoption Model for Analytics Maturity (AMAM)Whenitcomestotheadoptionandutilizationofbigdataanalytics,eachorganizationisconstrainedbyitsuniqueorganizationalcharacteristicsincludingsize,financialresources,technicalcapability,andleadershipandenvironmentalcharacteristicssuchasgeographicallocation,community,andpatientpopulations.Tohelpassessthematurityofhealthcareorganizationsinadoptingbigdataanalytics,HIMSSAnalytics,awhollyownedsubsidiaryoftheHealthInformationandManagementSystemSociety(HIMSS),developedtheAdoptionModelforAnalyticsMaturity(AMAM)(HIMSSAnalytics,n.d.). AMAM describes eight stages representing increasing levels of maturity. An organizationtypicallystartsoutatstagezeroinwhichitonlyusesanalyticsinsparseandsiloedpointsolutionsandgraduallyclimbsuptostageseven,whereanalyticsisusedforprescriptiveandpersonalizedcareforindividualpatients.

3. THe SWOT FRAMeWORK AND SUMMARy OF ANALySIS

3.1. The SWOT FrameworkStrengths-Weaknesses-Opportunities-Threats(SWOT)isamanagementscienceframeworkoriginallyusedforcorporatestrategicplanningdatingbacktothe1960s.Itisananalysistooltoassessabusiness’sfitnessasmeasuredbyhowwellitsinternalqualities(thestrengthsandweaknesses)matchupwithexternalfactors(theopportunitiesandthreats)(Hill&Westbrook,1997).

Strengthsandopportunitiesareconsideredpositive,favorable,andsynonymoustosuccessfactorswhile weaknesses and threats are considered negative, unfavorable, and synonymous to pitfalls,challengesorbarriers.Theobjectiveofstrategicplanningistomitigateorminimizetheunfavorablefactorsandutilizeormaximizethefavorablefactors.TheSWOTframeworkhasbeenappliedtotheuseofbigdataanalyticsingeneral(Wang,Wang,&Alexander,2015)andinaspecificindustryordomain(Collins,2016).ThispaperappliesSWOTanalysistotheuseofbigdataanalyticsinhealthcare.Figure3showstheframeworkinfourquadrants,knownasaSWOTmatrix.

3.2. Strengths and Weaknesses of the SWOT FrameworkSWOTisasimpleyetpowerfulmodelforanalyzingacomplexsituation.Itreflectsthecomplexityoftherealityandpresentsadialecticallybalancedperspectiveforanalyzinganddealingwithcomplex

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problems.However,SWOTanalysisisnotconsideredarigorousempiricalresearchmethod.Itisdifferentfromthequalitativemethodaimingatthegenerationofnewtheoriesorthequantitativemethodaimingattheconfirmationorfalsificationofexistingorproposedtheories.SWOTprovidesawayof thinkingakin tosystems thinkingorcritical thinking.Thedefinitionandclassificationofwhatconstitutesstrengths,weaknesses,opportunities,andthreatsaresubjectiveandreflecttheresearchersandpractitioners’personalexperienceandunderstandingofthecomplexsocioeconomic,cultural,andmanagerialproblems.SWOT’spowerliesinitssimplicityindealingwithcomplexity.Itprovidesasimpleframeofreferenceforanalyzinganddealingwithcomplexsituationssothatpotential opportunities can be explored, potential risks can be assessed, and potential outcomes(intendedorunintended,desirableorundesirable)canbeilluminatedbeforeinterventionscanbedevelopedandactionscanbetaken.

3.3. SWOT Analysis of Big Data Analytics in HealthcareThis SWOT analysis was performed based on review of literature, industry reports, and expertopinions.Theauthoralsodrawsuponhisprofessionalexperiencesinsystemsengineering,healthIT,dataanalytics,andhealthcarequalitymanagement.AligningwiththefourquadrantsoftheSWOTmatrix,thispapersetsouttoanswerthefollowingfourquestions:

1. Whatarethestrengthsofhealthcarethatmakeitfavorablefortheadoptionofbigdataanalytics?

Figure 3. The SWOT matrix

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2. Whataretheweaknessesofhealthcarethatmayhindertheadoptionofbigdataanalytics?3. Whataretheopportunitiesthatfavortheadoptionofbigdataanalyticsinhealthcare?4. Whatarethethreatsassociatedwiththeadoptionofbigdataanalyticsinhealthcare?

Theextantliteratureonsimilarsubjectstendstobeatalowerlevelorwithtechnicalimplementationdetails.Collins(2016)performedaSWOTanalysisofbigdataandhealtheconomicsintheUKwithafocusoncostsandincludedcoverageofdrugs,biomonitoring,anddatarepositories.Yangetal.(2016)performedaSWOTanalysisofwearabledevicesinhealthcare.WhiletheflexibilityoftheSWOTframeworkallowsfordifferentlevelsofanalysis,thispaperfocusesonthebigpicture(theforest)insteadoflow-leveldetails(thetrees).TheresultsoftheSWOTanalysisaresummarizedinFigure4.Detaileddescriptionsareprovidedinthefollowingfoursections.

4. STReNGTHS

4.1. Tradition of evidence-Based MedicineWesternmedicinehasthetraditionofapplyingscienceandtechnologyintheprevention,control,diagnosisandtreatmentofdiseases.FromthedevelopmentofmedicaldevicessuchasMagneticResonanceImaging(MRI)andtheAutomatedExternalDefibrillator(AED)totheuseofexperimentaldesignandstatisticalinferenceinclinicaltrials,scienceandtechnologyremainattheheartofmodernmedicine.

A spirited movement called Evidence-Based Medicine (EBM) began within the healthcarecommunityintheearly1990s.EBMisdefinedas“theconscientious,explicit,andjudicioususeofcurrentbestevidenceinmakingdecisionsaboutthecareofindividualpatients.Thepracticeofevidence-basedmedicinemeansintegratingindividualclinicalexpertisewiththebestavailableexternalclinicalevidencefromsystematicresearch”(Sackett,Rosenberg,Gray,Haynes,&Richardson,1996).EBMhasbecomeanessentialcodeintheDNAofhealthcareeversince.Itsprinciples,processes,andpracticesarewellalignedwiththoseofbigdataandanalytics.Theincreasingvolume,variety,andavailabilityofdataalongwiththeadvancementofdatastorage,computingpower,andanalyticstoolsandtechniqueswillmakeEBMmorepracticalandpowerful.

Figure 4. Summary of the SWOT analysis on big data analytics in healthcare

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4.2. Prevalence of electronic Health RecordsElectronic Health Records (EHR) evolved from traditional Electronic Medical Records (EMR).WhileEMRwaslimitedtomedicalrecords,EHRexpandsthescopetoallrecordsgermanetohealthincludingmedical, administrative, claim, and socioeconomic records. In2009, theUSCongressenactedtheHealthInformationTechnologyforEconomicandClinicalHealth(HITECH)ActaspartoftheAmericanRecoveryandReinvestmentAct(ARRA)afterthe2008globalfinancialcrisis.HITECHwascreatedtopromotetheadoptionofhealthITandimplementationofEHRtomodernizetechnologyinfrastructureandcapabilitiesofhealthcare.FederalinvestmentwasprovidedtohealthcareproviderstoincentivizehealthITadoptionandafederalcertificationprocesswasputinplacetoensureITsystemsaredevelopedaccordingtotechnological,functional,andsecurityrequirements.

SincetheenactmentoftheHITECHAct,morehealthcareorganizationshaveadoptedhealthITandimplementedEHRs.AccordingtotheOfficeoftheNationalCoordinatorforHealthInformationTechnology(2018),“In2017,96percentofallnon-federalacutecarehospitalspossessedcertifiedhealthIT.”ThepenetrationofhealthITandEHRsprovidesthetechnicalfoundationanddatasourcesfortheadoptionofbigdataanalyticstoimprovehealthcare.Atthesametime,bigdataanalyticsaimstodelivertherightamountofinformationtocareprovidersattherighttimeintherightplaceandhelprealizetheintendedbenefitsofEHRsandalleviatetheunintendedburdenassociatedwithEHRssuchasadministrativeoverheadandinformationoverload.

4.3. Widespread Use of Mobile TechnologyTheubiquitousInternetcoupledwithGlobalPositioningSystems(GPS),mobiletelecommunicationnetworks,andcloudcomputingleadtotheproliferationofmobiledevices(includingwearablefitnesstrackers)andmobileappsthatarebecominganintegralpartofhealthcareandourdailylife.AccordingtoStatista(2018),asofJanuary2018therewere3.7billionmobileusersworldwide.Whiletheglobalmobilebroadbandsubscriptionpenetrationrateisaround50%,theAmericasandEuropehavethehighestrates,around78.2percentand76.6percent,respectively.

Amongmillionsofmobileapps,healthandwellnessappsarebecomingincreasinglypopular.Therearecloseto48,000mobilehealthandwellnessappsavailablefromtheAppleAppStorealone(Statista,2018).Mobilehealthandwellnessappsnotonlychangethewayhealthcareisdeliveredandhowhealthismonitoredandmanaged,butalsoprovideadditionalbiometricsandlifestyledatatotheclinicians,researchers,andpolicymakersintheireffortstoimprovecareforindividualpatientsandpopulationsatlarge.

5. WeAKNeSSeS

5.1. Complex System of HealthcareTheUShealthcaredeliveryandpaymentsystemiscomplexwithmanyinterdependent,interactingstakeholders.Asacomplexadaptivesystem, itexhibitsnon-linear,dynamic,and indeterministicbehaviorsthatareunpredictableanddifficulttomanageandcontrol(Rouse,2008).Figure5showsthemanystakeholdersandhoweachof themplaysadifferent role inacomplex relationship todeliverhealthcare.

Improvinghealthcarerequiresthecollaborationandconcertedeffortsofallstakeholdersthroughconsensus building. Government policymaking should be conducted by taking inputs from allstakeholdersandanypotentialunintendedadverseconsequencesshouldbeevaluatedandremedied.Forexample,CMS’sbundledpaymentinitiativeintendedtoreducecostofcaremayleadtocareproviders’discriminationinpatientselectioncommonlyknownas“lemondropping”and“cherrypicking”,wherehighriskpatientsareturnedawayinfavoroflowriskpatients.Bigdataanalyticscanprovideevidenceandinsightstohelppolicymakersassesstheintendedbenefitsandunintendedharmofhealthpolicies.

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5.2. Complex Determinants of HealthWhilemodernmedicinehasgreatsuccessattreatingsymptomsandmanagingbothacuteandchronicconditions,itisnotassuccessfulinpreventingandcuringdiseases.Thisisduetothecomplexnatureofdiseasesandthemultiplecontributingfactorsincludingclinical,behavioral,andsocioeconomicfactors.Thereisagrowingbodyofresearchlinkingsocioeconomicfactors,suchasanindividual’slifeconditionandsocial standing, tohisorherhealth statusunder thegeneral schemeof socialdeterminantsofhealth(Barr,2014;Marmot,2005;McGovern,Miller,&Hughes-Cromwick,2014).Thereisamyriadoffactorsthataffecthealthandinfluencehealthcare.Somearenaturalwhileothersarecultural;somearemeasurablewhileothersarenot.Machinelearningandartificialintelligencemaybeeffectiveinanalyzingnaturalormeasurablefactorsbutmustrelyonhumanintelligenceandhumanjudgementtodealwiththeculturalorunmeasurablefactors.

5.3. Complex Process of CareHealthcareisateamsportwheremultipleprofessionalsmustworktogethertodeliverqualitycaretopatients(Nancarrowetal.,2013).Forexample,thedialysiscareforpatientssufferingfromEnd-StageRenalDisease(ESRD)requirescoordinatedeffortsbyadministrative,medical,andsocialprofessionalsincluding facility administrators, medical directors, nephrologists, nurses, dialysis technicians,dietitians,andsocialworkers(MarylandDepartmentofHealth,n.d.).Inaddition,ESRDpatientstendtohavecomorbiditiessuchasdiabetesandhypertensionandarevulnerabletohospitalizationsandERvisits.Theirqualityoflifedependsonthecoordinatedcarebydialysisfacilities,hospitals,communitiesandtheirfamilies.Thisprocessofcareiscomplexandmakesthequalityofcaremuchhardertoquantitativelymeasureandanalyze.

Inaddition,patientengagementplaysacriticalroleinachievingoptimalhealthoutcomesinthepatient-centeredcareprocess.Patientsmustbeinformedofhowtheirtreatmentisbeinginfluencedbydata,evidence,andanalyticsandbeengagedinthehealthcaredecisionmakingprocess.Thisrequiresongoingpatienteducationtoimprovebothhealthliteracyanddataliteracy.

Thecomplexityarticulatedintheabovethreeinterrelatedareasposesgreatchallengestotheeffectivenessofbigdataanalytics.Asmuchaswewouldliketousetheinsightsgainedfrombigdataanalyticstoimprovetheperformanceofhealthcare,actionscannotbetakensolelybasedontheoutputsofnondeterministiccomputeralgorithms.Forexample,IBMWatsongeneratedconsiderablebuzzforitspurportedutilityinhelpingdoctorsmorerapidlyandaccuratelydiagnoseillness,butarecentarticlerevealedthatWatsonfailedtoliveuptothoseexpectations(Hernandez&Greenwald,2018).

Figure 5. Stakeholders and interests in health care (Rouse, 2008, p. 19)

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6. OPPORTUNITIeS

6.1. Promise of Health Information exchangesWhileEHRsenablehealthcareorganizationstocentralizethemanagementofpatienthealthdata,theHealthInformationExchanges(HIEs)goonestepfurtherbyprovidingthetechnicalinfrastructuretoconnectthesedisparateanddiverseEHRssothatpatienthealthdatacanbeexchanged,aggregated,andsharedacrossthewholehealthcaredeliverysystem.“Electronicexchangeofclinicalinformationallowsdoctors,nurses,pharmacists,otherhealthcareproviders,andpatientstoaccessandsecurelyshare a patient’s vital medical information electronically - improving the speed, quality, safety,coordination,andcostofpatientcare”(TheOfficeoftheNationalCoordinatorforHealthIT,n.d.).HIEsmakeitpossibletoestablishcomprehensive,inclusive,andlongitudinalpatientandpopulationhealthdatafortheeffectiveapplicationofbigdataanalytics.

Since2009,theSocialSecurityAdministration(SSA)hasbeenusingHIEstoautomaticallyandtimelyrequestandobtainmedicalevidencerecords(MERs)fromhealthcareprovidersnationwidetosupportdisabilityclaimsadjudication(Feldman&Horan,2011).AsofDecember2018,over18,000healthcareproviders representedby150healthcareorganizationsparticipated in theexchangeofMERswithSSA.Comparedtoapaperprocess,theuseofHIEsgreatlyspeedsupmedicalevidenceacquisition to support disability determination. Faster availability of benefits resulting from theexpediteddeterminationhelpsdisability claimants pay formuchneededhealthcare services andsupportshealthcareprovidersinthedeliveryofhealthcare(SocialSecurityAdministration,n.d.a).Inaddition,SSAusesthevastamountofelectronichealthdataalongwithmachinelearningtoprovidedata-drivendecisionsupporttoitsdisabilityadjudicators.Thisuseofbigdataanalyticsfurtherspeedsupthedisabilityadjudicationprocessandhelpsincreasetheaccuracyandconsistencyofdisabilitydeterminationdecisions.SSAestablishedinteroperabilityguidelinesandacertificationprocesstoensureparticipatingorganizationscomplywithindustryinteroperabilitystandards(SocialSecurityAdministration,n.d.b).

6.2. Abundance of Data from All SourcesOneofthemanyV’sofbigdataisvariety.Forbigdataanalyticstobeeffective,manykindsofdatathatarerelatedandrelevanttohealthandhealthcareshouldbeincluded,especiallythesocioeconomicdatathatmeasuretheimportantsocialdeterminantsofhealth.EHRsmaycontainindividualpatient’sdemographicdatabutlacksthemacrosocioeconomicdatathatcanbeobtainedfromgovernmentcensusandsurveydata.

Forexample,theAmericanCommunitySurveydatafromtheUSCensusBureaucontainrichsetsofsocioeconomicdataaboutcommunitiesintheUSandcouldbeusedinconjunctionwithclinical,administrative,andclaimsdatatogainabetterunderstandingofthestateofhealthandhealthcare.Inaddition,datafromlawenforcement,non-governmentalorganizations,andsocialmedianetworkscanalsobeleveragedforbigdataanalyticsinhealthcare.

In January 14, 2019, President Trump signed into law the Foundations for Evidence-BasedPolicymaking(FEBP)Act.AspartoftheFEBPAct,theOpen,Public,ElectronicandNecessary(OPEN)GovernmentDataActrequiresallnon-sensitivegovernmentdatatobemadeavailableinopenandmachine-readableformats.Thesuccessful implementationof this federal lawwillhelppropeltheadoptionofbigdataanalytics.

6.3. Availability of Technology InnovationsThepastdecadehasseena rapidadvancement incomputerscienceand information technology.Theconfluenceofcloudcomputing,mobilecomputing,machine learning,artificial intelligence,andInternetofThingshasgivenrisetoaplethoraofnascenttools,techniques,andplatformsforperformingproductiveandeffectivebigdataanalytics.Therearemanyreadilyavailablechoicesof

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bothcommercial-off-the-shelf(COTS)productsandfreeopensourcesoftware(FOSS)products.Therearemanyvirtualcommunities,blogs,forums,andtutorialsavailableontheweb.Datasciencehasemergedasaburgeoningvibrantprofession,andhundredsofdatascienceprogramshavesproutedupincollegesaroundtheworld.

7. THReATS

7.1. Cyberattacks and Data BreachesWhilebigdataanalyticshasthepotentialtohelpimprovethequalityandperformanceofhealthcare,therisksofcyberattacksanddatabreachesarehighandshouldnottobeoverlooked.Accordingtoarecentstudyoverafive-yearperiodfrom2012to2017,therewere1,512reporteddatabreachesofprotectedhealthinformationaffectingatotalofmorethan154millionpatientrecords(Ronquillo,ErikWinterholler,Cwikla,Szymanski,&Levy,2018).Thesestolendatacanbeusedfor“identitytheft,criminalimpersonation,taxfraud,healthinsurancescams,andahostofothercriminaloffenses”(Goodman,2016,p.109).

ThecyberattacksonhealthcareITsystemsnotonlyputpatients’privacyandsecurityatrisk,theyalsoposeeconomicharmstohealthcareproviders,insurers,andtaxpayers.A2016studybythePonemonInsfitute(2016)estimatedthatabout90%ofthehealthcareorganizationsrepresentedinthestudysuffereddatabreachesinthepasttwoyearsandtheaveragecostofdatabreachesforcoveredentitiessurveyedwasmorethan$2.2million,whiletheaveragecosttobusinessassociatesinthestudywasmorethan$1million.

7.2. Unethical Use of Health DataExternalmaliciouscyberattacksarecertainlygraveconcerns,howeverinsiderthreatsandunethicaluseofhealthdatatodiscriminatehealthinsurancecoverageandtoboostcorporaterevenuesandprofitsshouldnotbeoverlooked.Bigdataanalyticsisadouble-edgedsword.Whenappliedproperlyandethically,ithasthepotentialtodogood;otherwise,itmaycauseharmtopatients,providers,andtaxpayers.Oneemergingapplicationistheuseofpredictiveanalyticstodrawinsightsforprofitswithoutregardtoprivacylawsandprotectionofpatients’personalandhealthinformation.

A2017reportbyTheCenturyFoundation(Tanner,2017)paintedagloomypictureofbigdatainhealthcare.Thereexistsamulti-billion-dollarindustrythatcollects,mines,buys,andsellsanonymizedpatienthealthdata.Thepatienthealthdataaretradedroutinelyforprofit.Whilethesharinganduseofde-identifiedpatienthealthdataforsecondaryuseinmedicalandpolicyresearchareallowedbytheHealthInsurancePortabilityandAccountabilityAct(HIPAA)(Cohen&Mello,2018),thereisarealdangerforthepatienthealthdatatobere-identifiedthroughtheprocessofdatalinkingwithadditionaldatasourcesfromsocialmedianetworksandmobilehealthandwellnessappsandtheuseofmachinelearningalgorithms.Thisunethicalfor-profitdataminingalongwiththeupsurgeinmalicioushackinganddatabreachescanresultindevastatingimpacts.

7.3. Biases in Data and AlgorithmsRealityiscomplexandunknowable.AsstatedbyLaoziinthe2500-yearoldTaoisttextTao Te Ching,“ThetaothatcanbetoldisnottheeternalTao.ThenamethatcanbenamedisnottheeternalName.Theunnamableistheeternallyreal”(Laozi,Mitchell,Roig,&Little,1989).EchoingLaozi,statisticianGeorgeBox(1976)wasfamouslyquotedforthemaximthat“allmodelsarewrong”becausetheyareonlyapproximationsoftruereality.Bigdataanalyticsisinherentlybiasedsinceitreliesondataasinput,andalgorithmsastheenablers.Limitationsandbiasesexistinbothdataandalgorithmswhichcanbetracedbacktothecognitivelimitationsandbiasesinhumanminds(Gianfrancesco,Tamang,Yazdany,&Schmajuk,2018).

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Whethercollectedthroughhumanobservationsorsensorydevices,dataareapproximatemeasuresoftheactualpropertiesoftheobservedentities.Evencorrectlymeasuredandcurateddatacannotescapefromthebiasesofthosewhodefinethemeasurementandthosewhodesignthedatacollectioninstruments. Inaddition, incomplete, inaccurate,andmissingdataare typicalandcandistort theoutcomeoftheanalytics(affectionatelyknownas“garbagein,garbageout”).

8. CONCLUSION

Thispaperpresentedahigh-levelanalysisofthestrengths,weaknesses,opportunities,andthreats(SWOT)intheapplicationofbigdataanalysisinhealthcare.Whilethestrengthsandopportunitiesarepositive,desirable,andeasiertocomprehend,theweaknessesandthreatsarenegative,undesirable,anddemanddiligenceandvigilance.Althoughthispaperprovidesevencoverageofallfourfactors,itshouldbestressedthatmoreattentionmustbepaidtotheweaknessesandthreats.Thisisespeciallytrueintheeraofrapidinnovationsininformationtechnologywhichoftengiverisetoanunrealisticexpectationofapanaceawithquick,autonomous,andmagicalcuresforacomplexsocialproblem.

Technologysuchasmachinelearningandartificialintelligencehavebeeneffectiveindealingwith“hard”problemssuchaswinning“Go”games,recognizinghumanvoicesandfaces,andeventranslatinglanguages.Theseproblemshavewell-definedboundaries,well-understoodrulesofgameorcausalmechanisms,andcanbesimulatedorprogrammedusingsoftwarewithhighdegreesofaccuracyandcertainty.However,socialproblemsaremuchmorecomplexwithill-definedboundariesandpoorly-understoodcausalmechanisms(Kirk,1995).Healthcareisaperfectexampleofacomplexsystemwithnon-linear,dynamic,andindeterministicbehaviorsinvolvingmultiplestakeholderswithconflictsofinterestsandundertheinfluenceofmultitudesofintertwinednaturalandsocialforces.TheseweaknessesareinherentinhealthcareandshouldbekeptinmindwhenapplyingtechnologiessuchashealthITandbigdataanalytics.Thereisnosimpleandstraight-forwardsolutiontothehighcostandlow-qualitychallengesfacingtheUShealthcaresystem.Bigdataanalyticsispromisingandcanhelpuncoverpartialandlimitedknowledgefrombigdatatoinformclinicalandpolicydecisionmaking,butitdoesnotprovidethewholetruthabouthealthcareandisnotasilverbullet;overcomingthe weaknesses requires the conscious and judicial use of political skills, business acumen, andcollaborativespiritinadditiontotechnicalandanalyticalcompetency.

Whilethispaperprovidesonlycursoryexposuretothesubjectofbigdataanalyticsinhealthcare,oneofitsobjectivesistodrawattentiontosomeofthemanyinterrelatedfactorsthatmustbeconsideredwhenseekingtoleveragebigdataanalyticstoimprovehealthcarequalityandperformance.Amoredetailedanddeeperanalysisonanyofthefactorsmentionedintheabovehigh-levelanalysiswouldrepresentaworthwhileresearchsubject.Forexample,healthcareisahighlypersonalizedprocessinvolvingthecoordinationofmultipleproviderssuchasacutecarehospitals,primarycarephysicians,andnursepractitioners.Carecoordinationisaknownfactorinfluencingsuchhealthcareoutcomesas unplanned hospital readmission (Fluitman et al., 2016). Quantitatively measuring health carecoordinationisachallengingtask.Comparedtoreadily-quantifiableclinicalmeasuressuchasbloodpressureorbodymass index, individualhumanbehaviorsaremuchharder toquantify,andcarecoordinationinvolvesmultipleparties.Moreover,fairandjustattributionofqualitymeasurescorestomultipleprovidersisalsoadifficulttask.Thisisfurthercomplicatedbypatientbehaviorssincepatientsarealsopartofthecoordinationprocess.

DISCLAIMeR

Theauthor’saffiliationwithTheMITRECorporationisprovidedforidentificationpurposesonlyandisnotintendedtoconveyorimplyMITRE’sconcurrencewith,orsupportfor,thepositions,opinionsorviewpointsexpressedbytheauthor.ApprovedforPublicRelease;DistributionUnlimited.CaseNumber19-0249.

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Chaojie Wang is a seasoned software engineer, systems architect, data scientist, researcher, and project manager with over thirty-year experience in industry, government, and academia. Dr. Wang currently works for The MITRE Corporation, an international thinktank and operator of several Federally Funded Research and Development Centers (FFRDC). In his capacity as a principal systems engineer, Dr. Wang advises the federal government on IT Acquisition & Modernization, Data Analytics & Knowledge Management, and Emerging Technology Evaluation & Adoption. Previously, Dr. Wang worked for Lockheed Martin Corporation and participated in multiple large-scale IT projects for the federal government. Dr. Wang is a certified Project Management Professional (PMP), a certified SAFe Agilist (SA), and currently serves on the Editorial Review Board for the International Journal of Patient-Centered Healthcare (IJPCH) by IGI Global and the Issues in Information System (IIS) by the International Association for Computer Information Systems (IACIS). Dr. Wang holds a Bachelor of Engineering in MIS from Tsinghua University, a Master of Art in Economics and a Master of Science in Statistics both from the University of Toledo, an MBA in Finance from Loyola University Maryland, and a Doctor of Science in Information Systems and Communications from Robert Morris University.

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