Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda

(1)

ComputersinIndustry122(2020)103290

ContentslistsavailableatScienceDirect

Computers in Industry

j o ur na l h o me pa g e :w w w . e l s e v i e r . c o m / l o c a t e / c o m p i n d

Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda

Anushree Tandon

^a

, Amandeep Dhir

^b,c,e,∗

, A.K.M. Najmul Islam

^d,f

, Matti Mäntymäki

^a

aTurkuSchoolofEconomics,UniversityofTurku,Finland

bSchoolofBusinessandManagement,LUTUniversity,Lappeenranta,Finland

cOptentiaResearchFocusArea,North-WestUniversity,Vanderbijlpark,SouthAfrica

dDepartmentofFutureTechnologies,UniversityofTurku,Finland

eNorwegianSchoolofHotelManagement,UniversityofStavanger,Stavanger,Norway

fLUTSchoolofEngineeringScience,LUTUniversity,Lappeenranta,Finland

a r t i c l e i n f o

Articlehistory:

Received4January2020

Receivedinrevisedform22May2020 Accepted13July2020

Availableonline27July2020 Keywords:

Blockchain Healthcare

Systematicliteraturereview Medicaldata

a b s t r a c t

Thisstudypresentsasystematicliteraturereview(SLR)ofresearchonblockchainapplicationsinthe healthcaredomain.Thereviewincorporated42articlespresentingstate-of-the-artknowledgeoncurrent implicationsandgapspertainingtotheuseofblockchaintechnologyforimprovinghealthcareprocesses.

TheSLRfindingsindicatethatblockchainisbeingusedtodevelopnovelandadvancedinterventionsto improvetheprevalentstandardsofhandling,sharing,andprocessingofmedicaldataandpersonalhealth records.Theapplicationofblockchaintechnologyisundergoingaconceptualevolutioninthehealthcare industrywhereithasaddedsignificantvaluethroughimprovedefficiency,accesscontrol,technological advancement,privacyprotection,andsecurityofdatamanagementprocesses.Thefindingsalsosug- gestthattheextantlimitationsprimarilypertaintomodelperformance,aswellastheconstraintsand costsassociatedwithimplementation.Anintegratedframeworkispresentedtoaddresspotentialareas whereinfutureresearcherscancontributesignificantvalue,includingaddressingconcernsregarding regulatorycompliance,systemarchitecture,anddataprotection.Finally,theSLRsuggeststhatfuture researchcanfacilitatethewidespreaddeploymentofblockchainapplicationstoaddresscriticalissues relatedtomedicaldiagnostics,legalcompliance,avoidingfraud,andimprovingpatientcareincasesof remotemonitoringoremergencies.

1. Introduction

Blockchainwasoriginallyintroducedasamechanismtopower Bitcoin(Nakamoto,2008),buthasnowevolvedtothepointofbeing referredtoasafoundationaltechnologyformultipledecentralized applications(Iansiti&Lakhani,2017).Blockchainisbeingtouted as a useful technology for managing sensitive data, especially withinthesectorsofhealthcare,medicalresearchandinsurance (Meinert etal., 2019).Healthcare maybeunderstood asa sys- temthatincludesthreeprimaryconstituents:(a)coreprovidersof medicalcareservices,suchasphysicians,nurses,hospitaladmin- istrations,andtechnicians,(b)criticalservicesthatareassociated

∗Correspondingauthorat:SchoolofBusinessandManagement,LUTUniversity, Lappeenranta,Finland.

E-mailaddresses:anushree.tandon@utu.fi(A.Tandon),amandeep.dhir@lut.fi (A.Dhir),najmul.islam@utu.fi(A.K.M.N.Islam),matti.mantymaki@utu.fi (M.Mäntymäki).

withmedicalservices,suchasmedicalresearchandhealthinsur- ance(Campbelletal.,2000),and(c)beneficiariesofmedicaland health-orientedservices,i.e.patients,orthepublic.Inthepresent study,weconsiderthehealthcaresystemtobeinclusiveofcontact- basedandtechnology-basedremotemonitoringservicesextended byconstituentserviceprovidersinanefforttopromote,maintainor restorethehealthofbeneficiaries(Liaoetal.,2012;Devadassetal., 2017).Inthefieldofhealthcare,privacyandsecuritybreachesare purportedlyincreasingeveryyear,withover300breachesreported in2017and37millionmedicalrecordsaffectedbetween2010and 2017(Talesh,2017; McCoy&Perlis,2018).Theincreasingdigi- tizationofhealthcarehasfurtherledtotheacknowledgmentof concernsrelatedtosecurestorage,ownership,sharingofpatients’

personalhealth records,andalliedmedicaldata(Meinertetal., 2019).Blockchain hasbeensuggestedasa waytosolvecritical challengesfacedbyhealthcare,suchassecuredsharingofmed- icalrecordsandcompliancewithdataprivacylaws(Rupasinghe etal.,2019).

https://doi.org/10.1016/j.compind.2020.103290

(2)

Yet,priorresearchhasmadelimited attemptstoholistically encapsulateextantknowledge byutilizing systematic literature reviews(SLRs)(e.g.Angraaletal.,2017;Hölbletal.,2018;Agbo etal.,2019;O’Donoghueetal.,2019;Jaoude&Saade,2019).For instance,Hölbletal.(2018)employedbibliometrictechniquesto presentanoverviewofblockchainelementsandresearchtrends pertainingtotheapplicationofblockchaininhealthcare.Angraal, Krumholz,andSchulz(2017)detailedthevariousplatformsthat havebeendevelopedtodeployblockchaininhealthcare.Agboetal.

(2019)discusseddifferentinstancesoftheadoptionofblockchain technologyinhealthcare,thechallengesfaced,andpossiblesolu- tions.O’Donoghueet al.(2019)discussedspeciﬁctradeoffs and designchoicesexecutedbyresearchersinvariousscenarioswhere blockchain technology was applied. Jaoude and Saade (2019) curatedstudiespertainingtoblockchainapplicationsacrossmul- tipleindustriesandbroadlydiscussedthedifferentusagecontexts forthistechnology.Recently,Hasselgrenetal.(2020)analyzed39 studiestopresent summarystatistics onpopularplatformsand targetedareaswhereinblockchainhasbeenappliedtoimprove healthcare.

WhiletheseSLRshavecontributedtotheextantbodyofknowl- edge,theirfocushasprimarilybeenonsynthesizingordelineating trends(e.g.Hasselgrenetal.,2020)andareasofblockchainapplica- tion(seeRisius&Spohrer,2017;Hölbletal.2018;Agboetal.2019;

Jaoude&Saade,2019).However,duetotheextentanddiversity ofpriorresearchonblockchain,researcherswouldbeneﬁtfrom afocuseddiscussionontheramiﬁcationsofitsadoption(Risius&

Spohrer,2017),aswellasspecificchallengesandareasforimprove- ment foradvancing thefield (Agboet al., 2019).Review-based studiescanassistinmeetingtheseneedsbyassimilatingexisting knowledgeandexplicatingfocalareasthatneedsignificantschol- arlyattention(Agboetal.,2019;Ozdagogluetal.,2020).

We address this need by conducting an SLR on the use of blockchaininhealthcare(Kitchenhametal.,2009).SLRscanpro- videavaluablesummarizationofcurrentknowledgeinafieldof research(Aznoli&Navimipour,2017)andallowfortheidentifi- cationofexistingknowledgegapsand,consequently,avenuesfor futureresearch(Gopalakrishnan&Ganeshkumar,2013).Thisstudy contributestothecurrentliteratureonblockchaininhealthcare byaddingtopriorSLRsintwoways.First,itprovidesathemati- callyorganized,state-of-the-artclassificationofpriorstudieswith respecttotheirapplicationareas,limitations,andrecommenda- tions. Second, based on the findings of the SLR, we propose a synthesizingframework todetail potential themesthat require scholarlyattentiontoadvancethecurrentbodyofknowledge.This contributionismadebyaddressingfourresearchquestions:RQ1.

Whatisthestate-of-the-artresearchproﬁleforblockchainappli- cationsinthehealthcaredomain?RQ2.Whataretheprimaryareas ofhealthcarewhereinblockchainhasbeenapplied?RQ3.Whatare theemergentlimitationsandchallengesthattheliteratureposits forthisresearcharea?RQ4.Whatarethefutureavenuesinhealth- carethatmightbeneﬁtfromtheapplicationofblockchain?

Theremainderofthepaperisstructuredasfollows.Section2 providesanoverviewofblockchaintechnology. Section3expli- catesthemethodologyadoptedforthecurrentSLR.Thefindings arepresentedintheSection4,followedbyadiscussionofthese findingsinSection5.Section6presentsadetaileddiscussionof theimplicationsoftheinsightsderivedfromthefindingsofthis study,limitationsandfuturescopeofresearch.Thelastsectionis dedicatedtodiscussingconcludingremarks.

2. Blockchaintechnology

Blockchainisadistributedpublicledgerdatabasethatismain- tainedbyanetwork ofveriﬁedparticipantsornodes(Jaoude&

Saade,2019)andstoresimmutableblocksofdatathatcanbeshared securelywithoutthird-partyintervention(Hölbletal.,2018).Data arepreservedandrecordedwithcryptographicsignaturesanduse ofconsensusalgorithms thatare enactedaskey enablersofits application(Mendlingetal.,2018).Thisabilityfordatapreserva- tionisasigniﬁcantreasonthathasdriventheuseofblockchainin healthcare(Kuoetal.,2019a),whereinasigniﬁcantamountofdata issubjecttoextensiveexchangeanddistribution(Meinertetal., 2019).

Theevolutionofblockchaintechnologyanditsapplicationin diversecontextshasoccurredinvariousphases.Thefirstphaseof blockchainevolutionwasrelatedtocryptocurrencyandthesecond pertainedtotheapplicationofsmartcontractsinareassuchasreal estateandfinance(Swan,2015;Agboetal.,2019).Thethirdgen- erationofevolutionwasfocusedontheapplicationsofblockchain innonfinancialdomains suchasgovernment,healthcare(Swan, 2015;Miau&Yang,2018),andculture(Efanov&Roschin,2018).

Additionally,drivenbyinnovativetechnologicalfeaturessuchas dataimmutability(Yli-Huumoetal.,2016),blockchainisnowcon- sideredtobeinitsfourthstageofevolutionwiththeincorporation ofartiﬁcialintelligence(AI)(Angelis&daSilva,2019).Blockchain’s asserteddiversityinitsscopeofapplicationsmaybeattributed toitspotentialforcreatingdecentralized(Silvaetal.,2019)and trustlesstransactionenvironments(Zhangetal.,2018).

Thehealthcare industryis a prime candidate for blockchain technology(Kuoetal.,2017;Allaetal.,2018;Ciosetal.,2019);

asblockchainhasthepotentialtoaddresscriticalconcerns,such asautomated claimvalidation (Angraalet al.,2017)and public healthmanagement (Mettler,2016).Thistechnologymayallow patientstoowndataandchoosewithwhomitisshared(Dimitrov, 2019),therebyaddressingextantconcernsaboutdataownership and sharing(Zhang etal. 2018; Jiet al.,2018).Concurrently,it enablesdatarecordstobeuniﬁed,updated,securelyexchanged, andaccessedinatimelybyappropriateauthoritieswiththeuse ofconsensusprotocols(Allaetal.,2018).Thisisamajoradvan- tageaffordedbytheapplicationofblockchaintechnologywithin thehealthcarespacebecausecurrentpracticesrequiredatatobe storedwiththirdparties(Hölbletal.,2018).Finally,blockchaincan potentiallybringtransparencytodatamanagementprocesses(Ito etal.,2018)whilealsoreducingthechancesofdatamishandling ormisusebecauseofpossiblehumanerror(Allaetal.,2018).

Despitethepositiveconnotationsofblockchain’seffectonsoci- etalandbusinesstransformation,thereseemstobeadebateon itsprevalentadvantages and derived benefitsin comparisonto previouslyestablishedexpectations.Arecentreportsuggeststhat althoughorganizationswillundertakesignificantinvestmentsin implementingblockchain-basedtechnologiesinthefuture, they willlikely adopt a cautiously pragmaticapproach becauseof a prevalentbeliefthatthebenefitsmaybeover-hyped.Itmaybesaid thatthistechnologyisyettomeetitstoutedexpectations(Iansiti&

Lakhani,2017),afactthatmaybeattributedtocertainchallenges tothewidespreadimplementationofthistechnology,especiallyin termsofregulatorybarriers(Pawczuketal.,2019).Anotherimpor- tantchallengeinpromulgatingthedeploymentofblockchainisthe unfamiliarityofthepublicandindividualusers,suchaspatients ordoctors,withthewaythistechnologyworks,itstechnicalfea- tures(Allaetal.,2018)oritsbeneﬁtsfordatamanagement.Iansiti andLakhani(2017)suggestthatduetosocial,organizational,and implementationbarriers,suchassecurityorgovernance,signiﬁ- canttimemayberequiredforblockchaintogeneratetheexpected levelsofbusinesstransformation.Thismaybeadditionallycom- poundedbyageneraluncertaintyaboutblockchain’susagewith respecttolegalcomplianceand governmentregulations(Swan, 2015;Allaetal.,2018).Currentresearchisfocusedonaidingthe operationalevolutionofblockchainandacceleratingitsprevalence byaddressingthesechallengesandbarriers.

(3)

A.Tandon,A.Dhir,A.K.M.N.Islametal./ComputersinIndustry122(2020)103290 3

Fig.1. Theprotocolforasystematicliteraturereview.

3. Methodology

SLRsofferreaderscomprehensiveknowledgeoftheliteraturein afieldthroughaholisticandorganizedprécisthatadherestostan- dardprotocols(Afrooz&Navimipour,2017;Aznoli&Navimipour, 2017;Ahmadetal.,2018;Mehta&Pandit,2018).SLRsalsoassistin explicatingexistingknowledgegapsandconsequentidentification ofavenuesforfutureresearch(Gopalakrishnan&Ganeshkumar, 2013).ThecurrentstudyadaptedprotocolssetforthbyBehera,Bala andDhir(2019),whichsynthesizedcomprehensivearticleassess- mentcriteria frompreviously publishedSLRs. OurSLR protocol consistedofthreemainphases,namelyplanning,execution,and reportingassimilatedinformation(seeFig.1).ToaddressRQ1,the presentSLRaddressedtheobtaineddescriptivestatisticsrelated tothefollowing:(a)numberof articlespublishedperyear; (b) average citations per year received by a reviewed article; and (c)scholarlycontributionsinthefieldregardingpublishers,jour- nals,andcountries.ToaddressRQ2,RQ3,andRQ4,fourmodular querieswerecreated,assuggestedbypriorliterature(seeAfrooz

&Navimipour,2017):(a)identifyingpreviouslyinvestigatedstudy contextsandprimaryconstructs;(b)identifyingcurrentintellec- tualcapitalbysummarizingﬁndingsandlimitations;(c)extracting focalimplicationsfrompracticalandtheoreticalperspectives;and (d)identifyingemergentresearchgapsandpotentialavenuesfor futureresearch.Thesequeriesallowedforafocusedsynthesisand analysisofselectedstudiesandderivepertinentinsightstoanswer theRQs.

Four databases—PsycINFO, PubMed, Scopus, and Web of Sciences—wereidentified bypriorstudiesaspopularsourcesof informationforarticlesrelatedtohealthinformatics(Zhangetal., 2017;Beheraetal.,2019).Articleselectionwasbasedonspecific inclusionandexclusioncriteria,asrecommendedbypriorresearch (Zhangetal.,2017).ThesewereadaptedfromBeheraetal.(2019) (seeFig.1)whereintheadaptationsweredevelopedandagreed uponbytheauthors(seenoteinTable2Threekeywordcombina- tionswerefoundtobeappropriateforadatabasesearchperformed inJuly2019—“Healthmanagement”,“blockchaininhealthcare”, and“medicalmanagement”(seeTable1).Thesekeywordswere drawnfromareviewofpriorstudies(i.e.SLRs)inthisfieldthatused

similarkeywords,i.e.blockchain,healthcare(orhealth*),andmed- ical(ormedic*)(seeHölbletal.,2018;Allaetal.,2018;Agboetal., 2019;Hasselgrenetal.,2020).Subsequently,appropriatearticles werescreenedaccordingtospeciﬁcselectioncriteriafordetermin- ingquality,relevance,androbustness(Webster&Watson,2002).

Thesummaryprotocolandthereviewandselectionprocessforall threephasesareillustratedinFig.1.

Thequalityofarticlesselectedfortheﬁnalsamplewasassessed toensurethattheoutcomesofthecurrentSLRpresentedtranspar- entandunbiasedresults(Beheraetal.,2019;Mehta&Pandit,2018).

Twoauthorscompletedthisassessmentandresolveddifferences inindividualevaluationthroughdiscussiontoreachconsensuson theﬁnalinclusionorexclusionofastudy.Incaseswherethetwo authorscouldnotachieveconsensus,athirdauthorwasinvolved inreviewanddiscussion.TheFleiss’Kappavalueforinter-coder agreementwas0.87,whichindicatesastrongagreementbetween thetwo coders(Landis&Koch,1977).Qualityscoreswerecal- culatedfor allarticlesperthecriteria presentedin Table2.Six articleswereremovedatthisstagefornotmeetingthepredeter- minedthresholdvalueof4.5(50%ofmaximumscore,seedetails inTable3),and41studiesremainedafteraqualityassessment.

Backwardandforwardcitationchainingwasconductedtoaddress feedbackloops.Thisresultedintheidentiﬁcationofthreearticles, ofwhichtwowereexcludedfornotmeetingthequalityevaluation criteria.Theﬁnalsamplecomprised42articles.

3.1. Researchproﬁle

Reviewed articleswere profiled tounderstand thestatus of researchonblockchainapplicationsinhealthcare.Thisreviewsug- geststhatblockchainhasseenarecentintegrationinthehealthcare domainbecausetheearliestarticleincludedinthesamplewaspub- lishedin2016.However,asharpincreaseinthenumberofyearly publications(seeFig.2)andaveragecitationsfortheselectedstud- ies(seeFig.3)suggeststhattheacademicfocusonthisfieldhas intensifiedoverthelastfewyears.CristianoAndrédaCosta(Brazil) andAlexRoehrs(Brazil)emergedasthetopauthorsinthefieldwith twopublicationseach(seeTable3).Thissuggeststhatthisareaof

(4)

Table1

Databasesearchsummary.

Database Keywords Totalhitsappeared Abstractsread* Fulltextdownloaded^a

PsycINFO

“Blockchain”,“Healthcare” 39 39 31

“Blockchain”,“Healthmanagement” 30 30 22

“Blockchain”,“medicalmanagement” 37 37 16

PubMed

Scopus

WebofScience

“Blockchain”,“healthmanagement” 55 55 12

Note:Resultsincludearticlesfrommultipledisciplinessuchasmedicine,genomics,informationscience,bankingetc.Multiplesourcesanddocumenttypeswerereﬂected inthesearchresultsincludingjournals,trademagazines,booksetc.

Resultsofthesearchweresortedfor“relevance”beforereviewingabstracts.

aIndividualstudiesmaybeduplicatedinmultiplecellsofthiscolumn.

Table2

QualityEvaluation(QE)criteria.

QE# Criterion

QE1 Explicitdiscussionofdataanalysis:“quantitative(+2)”,

“qualitative(+1.5)”or“noevidence(+0)”.

QE2 Discussionofadvantagesandchallengesofthetopicof interest:“yes(+2)”,“partially(+1.5)”and“no(+0)”.

QE3 Arethediscussedoutcomesalignedwithandvalidwith respecttotheutilizedmethodologyandtopicofinterest:

“yes(+2)”,“partially(+1.5)”and“no(+0)”

Note:partialjustiﬁcationpertainstoalimitedorunavailable explanationforanemployedtechniqueormethodology QE4 Peer-recognitionofthearticleandsourcereliability:

(+2)sumofcitationsandHIndexis>100

(+1.5)sumofcitationsandHIndexis>=50and<=99 (+1.0)sumofcitationsandHIndexis>=1and<=49 (+0)sumofcitationsandHIndexis0

QE5 Comparabilityoftheutilizedmethod(s)withmethods popularlyusedinpriorstudies:

“yes(+1)”,and“no(+0)”

[Note:basedonBehera,BalaandDhir(2019withthefollowingadaptations:

QE1:Quantitativedataanalysiswasconsideredequivalenttotheavailabilityof adetailedalgorithm (protocols,architecture, codingscript),ANDresultsfrom experimentalorsimulation-basedperformanceevaluation;Qualitativeanalysiswas consideredequivalenttoevidenceof(detailedalgorithm)OR(limiteddetailson algorithmwithlimitedresultsofperformanceevaluation).

QE2:Discussionofadvantageshasbeenadaptedtocurrentcontextas:yes(detailed discussiononapplicabilityofresultstohealthcarecontext),partially(limiteddis- cussiononapplicabilityofresultstohealthcarecontext),no(nodiscussionon applicabilityofresultstohealthcarecontext).

QE5:Intermsofcomparabilityofutilizedmethods,thescoringwasadaptedto thecontextofmethodsusedbyotherstudiesfocusedonapplyingblockchainin healthcare.].

researchiswitnessingconceptualandtheoreticaldevelopmentas awaytoidentifypossibleavenuesforfurthercontribution.

Theﬁrstauthorsofthereviewedarticleswerefoundtobeafﬁl- iatedwithinstituteslocatedacross17 countries.Fivecountries, namelyChina(n=12),UnitedStates(n=6),SouthKorea(n=4), India(n=3),andBrazil(n=3),cumulativelyrepresented65%of thesample(seeFig.4).Theinclusionofbothdevelopedanddevel- opingeconomies inthesamplesuggestsaglobalrecognitionof blockchain’spotentialapplicationwithinhealthcare.Theselected studieswerepublishedinmultiplejournals(seeFig.5).However, theleadingsourceswereJournalofMedicalSystems(n=10),IEEE Access(n=6),andAppliedSciences(n=3),withSpringer(n=12), Elsevier(n=8),andIEEE(n=7)emergingastheleadingpublishers (seeFig.6).

Furthermore,analysisofauthor-indicatedconstructsthrough wordcloudsshowedthattheprimaryfocusofresearchpertained to“patients,”“data,”“hospital,”“provider,”and“devices,”which

Fig.2. Yearlydistributionofpublications.

Note:Publicationcountfor2019isinclusiveofstudiespublishedandavailable onlineuntilJuly2019.

Fig.3.Averagecitationsperyear. Note:DetaileddescriptioninTable3.

aregraphicallypresentedinFig.7(a).Similarly,author-indexed keywordsofferedbypriorstudiesrevealedthat“medical,”“health,”

“data,”and“sharing”werethemostfrequentlyusedkeywords(see Fig.7b).

(5)

A.Tandon,A.Dhir,A.K.M.N.Islametal./ComputersinIndustry122(2020)1032905 Table3

Qualityassessmentresults.

Authors QualityEvaluation(QE) Citationsperyear Averagecitations

peryear

TotalCitations H-index QE1 QE2 QE3 QE4 QE5 QSScore 2016 2017 2018 2019

Badr,Gomaa&Abd-Elrahman(2018) 3 51 2 1.5 1.5 1.5 1 7.5 0 0 1 2 1.5

Brogan,Baskaran&Ramachandran(2018) 12 31 2 2 2 1 1 8 0 0 4 8 6

Casado-Vara&Corchado(2019) 1 8 0 1.5 2 1 0 4.5 0 0 0 1 1

Chattuetal.(2019) 0 0 0 1.5 2 0 0 3.5

Dagheretal.(2018) 39 36 0 2 2 1.5 1 6.5 0 0 16 23 19.5

Dhagarraetal.(2019) 0 28 0 2 2 1 0 5 0 0 0 0 0

Dimitrov(2019) 1 21 0 1.5 1.5 1 0 4

Dwivedietal.(2019) 10 84 0 2 2 1.5 1 6.5 0 0 0 10 10

Fanetal.(2018) 2 50 2 1.5 2 1.5 1 8 0 0 0 2 2

Firdausetal.(2018) 11 45 0 2 2 1.5 1 6.5 0 0 20 19 19.5

Griggsetal.(2018) 32 45 0 2 1.5 1.5 1 6 0 0 11 21 16

Guoetal.(2018) 46 57 2 1.5 1.5 2 0 7 0 0 22 24 23

Hangetal.(2019) 0 0 1.5 0 1.5 0 1 4

Husseinetal.(2018) 37 19 2 2 2 1.5 1 8.5 0 0 23 14 18.5

HylaandPeja´s(2019) 0 18 2 1.5 2 1 1 7.5 0 0 0 0 0

Islametal.(2019) 0 68 2 1.5 2 1.5 1 8 0 0 0 0 0

Jamiletal.(2019) 0 0 1.5 0 1.5 0 0 3

Jietal.(2018) 8 45 2 2 2 1.5 1 8.5 0 0 2 6 4

Kauretal.(2018) 15 45 0 1.5 1.5 1.5 0 4.5 0 0 2 13 7.5

Kuo,Gabriel&Ohno-Machado(2019) 2 62 2 1.5 2 1.5 1 8 0 0 0 2 2

S.HLee&Yang(2018) 3 42 2 2 2 1 2 9 0 0 2 1 1.5

S.J.Lee,Cho,Ikeno&Lee(2018) 1 29 2 1.5 1.5 1 1 7 0 0 0 1 0.5

H.H.Lietal.(2018) 18 45 2 1.5 1.5 1.5 1 7.5 0 0 4 14 9

X.Lietal.(2019). 4 57 2 2 1.5 1.5 1 8 0 0 0 4 4

Mamoshinaetal.(2018) 45 86 2 2 2 2 1 9 0 2 17 25 22

Nagasubramanianetal.(2018) 0 54 2 2 1.5 1.5 1 8 0 0 0 0 0

Nguyenetal.(2019) 0 57 2 2 2 1.5 1 8.5 0 0 0 0 0

Nohetal.(2017) 1 27 2 2 2 1 1 8 0 0 1 0 0.33

AlOmaretal.(2019) 4 68 2 2 2 1.5 1 8.5 0 0 0 4 4

Patel(2019) 25 21 0 1.5 1.5 1 0 4

Pourvahab&Ekbatanifard(2019)* 0 89 1.5 0 0 1.5 0 3

Quainietal.(2018) 0 0 1.5 1.5 2 0 1 6 0 0 0 0 0

Rahmadika&Rhee(2019) 0 22 2 2 2 1 1 8 0 0 0 0 0

Roehrsetal.(2017) 50 50 2 2 2 1.5 1 8.5 0 1 29 20 16.67

Shen,Guo&Yang(2019) 2 29 2 2 2 1 1 8 0 0 0 2 2

Silvaetal.(2019) 0 22 1.5 1.5 1.5 1 1 6.5 0 0 0 0 0

Siyaletal.(2019)* 2 0 0 1.5 1.5 1 0 4

Tian,He&Ding(2019) 0 45 2 1.5 1.5 1 1 7 0 0 0 0 0

Uddinetal.(2018) 10 57 2 2 2 1.5 1 8.5 0 0 2 8 5

H.Wang&Song(2018) 15 45 2 1.5 1.5 1.5 1 7.5 0 0 1 14 7.5

S.Wangetal.(2018) 9 0 0 1.5 1.5 1 0 4

Wong,Bhattacharya&Butte(2019) 3 240 1.5 1.5 1.5 2 1 7.5 0 0 0 3 3

Xiaetal.(2017) 121 57 2 1.5 1.5 2 1 8 0 3 60 27 30

Yangetal.(2019) 1 29 2 2 1 1 1 7 0 0 0 1 1

Yueetal.(2016) 246 45 0 1.5 1.5 2 0 5 4 36 115 91 61.5

A.Zhang&Lin(2018) 21 45 2 1.5 2 1.5 1 8 0 0 5 16 10.5

P.Zhang,etal.(2018) 41 31 1.5 2 2 1.5 1 8 0 0 10 31 20.5

Zhang,Xue&Huang(2016)* 77 57 2 1.5 2 2 1 8.5 0 11 31 35 19.25

Zhengetal.(2019) 0 82 0 2 1.5 1.5 0 5 0 0 0 0 0

Zhou,Wang&Sun(2018) 14 45 2 1.5 2 1.5 1 8 0 0 4 10 7

Totalcitations 4 53 383 451

Avg.citationcount 0.09 1.23 8.91 10.49

Note:Articlesmarkedby*wereconsideredthroughbackwardcitationchainingsearchresult.Norelevantarticlewasfoundthroughforwardcitationsearch.

Articlesmarkedbyabrokenunderlinefailedtomeetqualitycriteriaandwereexcludedfromfurtheranalysis.

(6)

Fig.4.Publicationsbycountry.

Note:Countryreflectsthelocationoftheinstitutetowhichthefirstauthorisaffili- ated.

Fig.6. Articlesperpublisher.

4. Findings

ThecurrentSLRutilizedameta-ethnography-basedapproach (Noblit & Hare, 1988) to review and synthesize insights from thepool of 42 studies that qualiﬁed for inclusion. The process resultedinthedevelopmentofresearchthemesandidentiﬁcation of research gaps and limitations. Further, extantrecommenda- tionsfromreviewedstudiesweresynthesized,andinconjunction withtheexplicatedgapsinpriorresearch,wereusedtodevelop aresearchframeworktoadvancescholarlyworkinthisdomain.

Theseﬁndingsarediscussedatlengthinthefollowingsections, andabriefoverviewispresentedinTable4and5.

Fig.5.Numberofarticlesperpublication.

(7)

A.Tandon,A.Dhir,A.K.M.N.Islametal./ComputersinIndustry122(2020)103290 7

Fig.7. (a)Keyframeworkconstructs(b)Authorprovidedkeywords.

Table4

Summarizingpastresearch:Themes,limitations&futureresearchrecommendations.

Thematicclassiﬁcationofscopeofstudy Limitations Futureresearchrecommendations

Conceptualevolution

• Conceptdevelopment

• Beneﬁtbasedapplication

• Promotingdecentralization

Performance

• Platform

• Algorithmfeatures

• Nodemanagement

Technicaladvancements

• Frameworkoptimization

• Replication&extension

• Nuancedtechnologydevelopment TechnologyAdvancement

• Developingintelligenthealthcareecosystems

• Technicalimprovementstoarchitecture

• Buildingpredictivecapabilities

Assumptions

• Frameworkdevelopment

Enhanceddata&privacyprotection Improvingmedicaldiagnostics Efﬁciencyenhancement

• Process

• System

Constraints

• Costs

• Data&Analysis

• Platformelements

• Societal DataManagement

• Dataprivacy

• Dataprotection

• Datahandling

Ethics&security

(8)

Table5

Suggestedfutureresearchagenda.

ResearchGaps

(identiﬁedbasedontheSLR)

Directionsforfutureresearch

Managingmedicaldataobtained frommultipleIoT-basedsources, e.g.sensors

Holisticpurviewforadoption

Investigatinguser-centricand societalbarriersforadoption

Blockchainarchitecture optimization Strategicperspectiveofblockchain

adoptionbyanorganization

Datamanagement&legal compliance

Integrationwithothertechnologies Methodologicaladvancementsfor

obtainingreal-timedataonactual performanceandusage

Nuancedapplication Contributiontovaluechain&

supplychain

4.1. Researchthemes

Contentanalysis(Krippendorff,2018)wasemployedtoanalyze thereviewedstudiesand delineate fourexplicitthematicareas ofresearchthatrepresentfocalissuesaddressedbythereviewed studies(Table4).Thesethematicareasareconceptualevolution, technologyadvancement,efficiencyenhancement,anddataman- agement.Theresultsofthereviewindicatethatcontinualscholarly effortshave been directed at refiningconceptual and technical knowledgetoenhancetheefficiencyofhealthcare,theassociated processes,anddatamanagementprotocolsthroughtheapplication ofblockchain.

4.1.1. Conceptualevolution

Theresultsofthereviewindicatethatresearchinthedomain ofblockchaininhealthcarehasbeenlargelydirectedatpromoting thedevelopmentofconceptsthatassistscholarsinderivingmulti- domain(Kauretal.,2018)andfeasibleapplicationsforblockchain inhealthcare.Thefeasibilityofapplications(Quainietal.,2018) hasbeendevelopedandtestedthroughresearcheffortsthatcan beclassiﬁedintothreesub-themes:conceptdevelopment,beneﬁt- basedapplications,andbuildingpredictivecapabilities.

4.1.1.1. Conceptdevelopment. Theresultsofthereviewshowthat signiﬁcantattentionhasbeenpaidtodevelopingnewproofsand algorithms,e.g.proofofprimitivenessofdata(H.Lietal.,2018;Z.

Lietal.,2018),proofoffamiliarity(Yangetal.,2019),andsimpli- fiedworkloadforproofofwork(Lee&Yang,2018).Studieshave alsofocusedonrefiningframeworksthatenableblockchainexe- cution throughinclusion, aswellas testing ofnovel constructs andelementsinsystemarchitectures.Examplesincludepreviously utilizedattribute-basedcryptosystems(Wang&Song,2018),the Stackelberggameapproach(Lietal.,2019),siblingintractablefunc- tions(Tianetal.,2019),and homomorphiccomputations(Zhou etal.,2018)formoreefficientframeworks.Forinstance,Xiaetal.

(2017)proposedanewblockchain-baseddatascheme(BBDS)for securityandprivacypreservationfordatatransactions.Zhang,Xue andHuang(2016)invokedtheideaofthehumanbodyasatrans- missionmediumintheirattempttodevelopanovelprotocolfor apervasivesocialnetwork(PSN)basedblockchainnetwork.Islam etal.(2019)utilizedfogcomputingtoconstructacomputation- allyefficientandaccuratemodelforhumanactivityrecognitionfor promotingremotee-healthmonitoring.Fanetal.(2018),inturn, focusedonincorporatingmultipletimesourcesintheirframework toavoidasinglepointoffailure.Inconclusion,studiesclassified underthisthemehavefocusedonexplicatingwaystomaximizethe efficiencyof previously developed blockchain-based algorithms andframeworks.

4.1.1.2. Benefit-based applications. Extant studies have incorpo- rated blockchain in healthcare to derive specific benefits by

identifyingandtestingnewavenuesofapplicationforblockchain technology. This includes studies focusing on enhancing technical benefitsderived fromblockchain application,e.g. through advancedimageprocessing(Lee&Yang,2018),efficientactivity recognition(Islametal.,2019),andsynchronizationofInternet-of- Things(IoT)devices(Fanetal.,2018).Additionally,moststudies classifiedunderthisthemehavefocusedonblockchainapplication for developing healthcare-specific benefits, such as collabora- tive medical decision-making (Yang et al., 2019). For example, blockchainadoptionhasbeenpositedtohavepositiveconnota- tionsinclinicaltrialmanagement(Wongetal.,2019),DNAdata transmission (S.J. Lee et al., 2018), remote patient monitoring (Griggsetal.,2018;Dwivedietal.,2019)aswellasdrugdiscovery, biomarkerdevelopment,andpreventivehealthcare(Mamoshina etal.,2018).

4.1.1.3. Promoting decentralization. Extant research has also focusedonpromulgatingthemajorbeneﬁtsofblockchainwithin healthcareecosystemstopromote fairnessand effectivedecen- tralization(Nohetal.,2017;Zhangetal.,2018;Zhengetal.,2019).

ForinstanceLietal.(2019)developed aframeworktopromote revenue maximization and decentralized fair trading, whereas Dagheretal.(2018)discussthenecessityoftrade-offsformining incentives.Scholarshavealsodiscussedthepotentialofblockchain for promoting transparency in data transactions (Hyla & Peja´s, 2019),e.g.byincorporatingfairclientroles(Kuoetal.,2019a,b).

Thus,itmaybesaidthatpriorstudiesontheevolvingadoption of blockchain in healthcare have focused on disseminatingthe conceptsofdecentralizationanditsassociatedbeneﬁts.

4.1.2. Technologicaladvancements

Existingresearchhasmadesignificantinroadsinadvancingand refiningblockchain technologyin terms of developing targeted applicationsinthefieldofhealthcare. Basedonourreview,we proposethatpriorstudiesclassifiedunderthisthemeareoriented towardsthreekeytopicalissues:

4.1.2.1. Developingintelligenthealthcareecosystems.Somescholars havefocusedontheincorporationofblockchainplatformgateways intohealthcareecosystems(Badretal.,2018).Suchincorporations mayenablethecreationofintelligenthealthcaresystems(Yueetal., 2016).Forinstance,Casado-VaraandCorchado(2019)suggestthat blockchainadoptioncanassistinthecreationofanoptimizede- healthecosystem. Priorstudieshavealsoproposedframeworks todevelop blockchain based e-health(Hyla &Peja´s, 2019)and tele-medicalinformationsystems(Jietal.,2018)thatmayallow healthcareserviceproviderstoincreasethereachofprovisionof servicesinfuture.

4.1.2.2. Technicalimprovementstoblockchainarchitecture. Major- ity of research in this domain has focused on enhancing the performance of developed systems and architectures through technicalimprovements,suchasunknownrootexploitationdetec- tion(Firdauset al.,2018), useofsmallerdatablocksizes(Yang etal.,2019),and improvementoftransactionpropagationdelay (Rahmadika&Rhee,2019).Someattentionhasalsobeendirected towardsaddressingissuesthathavebeenpreviouslyidentifiedas probableproblemsintheeffectivedeploymentofblockchainarchi- tectures. Such issues addressed bystudies classifiedunder this themeincludestorageloads(Zhangetal.,2016),memoryandCPU requirements(Zhouetal.,2018),temperature(Zhangetal.,2016), andreliablenodeidentification(Uddinetal.,2018).Insomecases, theeffectivenessofproposedsolutionstotheseissueshasalsobeen demonstratedthroughanalysesdirectedatnetworkandalgorithm comparison(Kuoetal.,2019a,b;Yangetal.,2019).However,we positthatthisthememayseefurtheradvancesinthefutureand

(9)

A.Tandon,A.Dhir,A.K.M.N.Islametal./ComputersinIndustry122(2020)103290 9 callforaconcurrentneedtofocusoncomparativeanalysesthat

canascertainoptimaleffectivenetworksandalgorithms.

4.1.2.3. Building predictivecapabilities. Asblockchain technology movesintoitsfourthphaseofevolutionwiththeincreasingincor- porationofAI(Angelis&daSilva,2019),asimilartrendcanbeseen foritsuseinhealthcare.Recentstudieshavebegun toincorpo- rateanalogousandperipheraltechnologiesintoblockchain-based systemarchitecturesthatincludeIoT(Dwivedietal.,2019),sensors(Casado-Vara&Corchado,2019),wirelessbodyareanetworks (Griggsetal.,2018),(Mamoshinaetal.,2018),bigdata(Dhagarra etal.,2019),edgecomputing(Zhengetal.,2019),andcloudtech- nology(Kauretal.,2018).

Theuseofsuchtechnologiesisassistingresearchersinbuild- ingblockchain-basedframeworkswiththepredictivecapabilities toimprovemedicalinformatics(Leeetal.,2018)aswellasdiag- nostics(Lee et al.,2018; Zhang &Lin, 2018).Such frameworks havebeenpreviouslyinvestigatedforspeciﬁcutilitarianfunctions thatarehealthcare-oriented,suchasprescriptionfraudavoidance (Casado-Vara&Corchado,2019),veriﬁabledatageneration(Zhou etal.,2018),andautomaticclaimsettlement(Wang&Song,2018).

Additionally,studieshavealsofocusedonadvancingblockchain technologytoassisthealthcareserviceprovidersinotherfunctions, suchaspopulationleveldatacollection(Broganetal.,2018)and useridentitydeﬁnition(Zhangetal.,2018).

4.1.3. Efﬁciencyenhancement

Multiplestudiesintheextantliteraturehavefocusedonunder- standinghow blockchain adoption can effectually enhance the efﬁciencyofhealthcareprocesses.Thisreviewindicatesthatthe focusofscholarshasbeendirectedtowardstwo aspectsofefﬁ- ciencyimprovement:processesandsystems.

4.1.3.1. Process. Prior research has paid signiﬁcant attentionto enhancingtheeffectivenessoftechnicalaspectsoftheprocesses requiredforexecutingablockchain-basedhealthcaresystem.For example,studieshavefocusedonexplicatingsolutionsforreducing computationalloads(Zhangetal.,2016),communicationoverload (Uddinetal.,2018),convergencetimeandoverheads(Fanetal., 2018),andreducedenergyconsumption(Uddinetal.,2018).

Additionally,priorstudieshavefocusedonimprovingprocesses fortimelyupdates(Wang&Song,2018)andreportingforadverse events(Wongetal.,2019).Somestudieshavefocusedonimproving thecomputationalefﬁciency(Islametal.,2019)ofprocesses,and accuratetestingofproposedarchitectures(Lee&Yang,2018)to ensurethattheproposedblockchainarchitecturesprovidemore reliable processing than traditional architectures (Uddin et al., 2018).

Furthermore,studieshave madeinroadsintoaddressingthe purported challenges associated with managing time, managing data, and associated costs by proposing improvements in blockchainframeworks.Forinstance,somereviewedstudieshave developedframeworkstolowerthecostofexecutionaftertheini- tialsetup(Yangetal.,2019),lowerstoragecost(Nagasubramanian etal.,2018)andfacilitatethepreservationandstorageofﬁlesof unlimitedsize(H.Lietal.,2018;Z.Lietal.,2018).Thesedeveloped frameworkshavepurportedtoprovidesigniﬁcantimprovements inruntime(Zhangetal.,2016),deliverytime(Roehrsetal.,2017), andresponsetime(Nagasubramanianetal.,2018).

4.1.3.2. System. Ourreviewoftheexistingliteraturesuggeststhat multiplemeasureshavebeenadoptedfortheholisticimprovement oftheblockchain-basedhealthcaresystem.Forinstance,studies havefocusedonimprovingsysteminteroperability(Dhagarraetal., 2019;Silvaetal.,2019),andmanaginginter-institutionalaccess

privileges(Quainietal.,2018;Lietal.,2019)aswellasdatacontrol (Broganetal.,2018;Mamoshinaetal.,2018;Zhangetal.,2018).

Scholarlyattentionhasalsoaddressedenhancingsystemscala- bility(Xiaetal.,2017)aswellasperformance(Nagasubramanian etal.,2018;Leeetal.,2018).Researchershavefocusedondevelop- ingintegratedservice-orientedarchitectures(Hyla&Peja´s,2019) andimprovingthegeneralizabilityaswellasﬂexibilityofexecuted blockchainsystems(Kuoetal.,2019a,b;Silvaetal.,2019).

4.1.4. Datamanagement

Based onourreview, weposit that managementof medical recordsanddatahasreceivedthemostsubstantialamountofschol- arlyattentioninthisﬁeld.Priorstudieshavepromulgatedtheuse of blockchainasa proﬁcientmethodofmanagingmedicaldata (Quainietal.,2018;Husseinetal.,2018;Silvaetal.,2019;Shen etal.,2019;Tianetal.,2019)andelectronicpersonalhealthinfor- mationorrecords(PHRs)(Roehrsetal.,2017;Dagheretal.,2018;

Husseinetal.,2018;Guoetal.,2018).Further,blockchainmayassist increatingaviableinformationecosystemformanagingsuchPHRs (Mamoshinaetal.,2018)byincorporatingheterogeneousformsof data(Yueetal.,2016;Shenetal.,2019;Silvaetal.,2019),including medicalbigdata(Kauretal.,2018).BasedontheSLR,wedelineate threefocalaspectsofextantresearchinthistheme:

4.1.4.1. Dataprivacy. Preservingdataprivacybyensuringautho- rizedaccesstomedicalrecordshasbeenasigniﬁcantfocalarea ofpriorliteratureonthedatamanagementaspectsofblockchain technologyinhealthcare.Thereviewsuggeststhatmanagement ofaccesscontrol(Zhengetal.,2019)hasespeciallyseenexten- siveinvestigation(Badretal.,2018;Broganetal.,2018).Thisissue isespeciallycriticalinhealthcareduetotheneedtopreservethe privacyofsensitivemedicaldatathroughgreateraccountability, immutability,andaccesscontrol(Husseinetal.,2018).Inresponse tothiscriticalneed,priorstudieshavedevelopedblockchain-based frameworkstoensuretheprovisionofefﬁcient(Roehrsetal.,2017), user-centric (Noh et al., 2017), and secure/encrypted access to patientPHRsandothermedicaldata(e.g.Guoetal.,2018;Badr etal.,2018;Dwivedietal.,2019)

4.1.4.2. Data protection. The prevention of unauthorized access andpreservationofdatasecuritytoensuredataprotectionhasbeen anotherkeyissueaddressedinthestudiesonthedatamanage- mentaspectsofblockchaininhealthcare.Themajorityofreviewed studieshavefocusedtheirattentiononpreventingunauthorized access(Nguyenetal.,2019)andprotectingagainsteavesdropping (Uddinetal.,2018).Multiplemeans,suchasefﬁcientauthenti- cation (Nagasubramanianetal., 2018), biometric authentication (Dhagarraetal.,2019),userveriﬁcation(Husseinetal.,2018),and theuseofdualsignatures(Lietal.,2019)havebeensuggestedto achievethisobjective.However,relativelylessattentionhasbeen paidtothepreventionofexternalattacks,suchasattacksonsensor data(Broganetal.,2018),escrow,andcollusionattacks(Guoetal., 2018).

4.1.4.3. Datahandling. Priorresearchhas,tosomedegree,attended totheneedtoensurelegally,aswellasethicallycompliantprocess- ing,sharing,andhandlingofhealthcaredata.Ourreviewsuggests thatfewstudieshaveacknowledgedtheneedforregulatorycom- pliance (Yue et al., 2016) or even the standards and goals for compliancerequirements(Zhang&Lin,2018;AlOmaretal.,2019;

Lietal.,2019).

However,signiﬁcant attentionhasbeenpaid totheneed to maintain data integrity (Hyla &Peja´s, 2019; Tian et al., 2019;

Shenetal.,2019).Forinstance,priorstudieshaveaddressedissues pertaining to authentic data mobilization(Brogan etal., 2018), avoidanceofdataleakage(Zhangetal.,2016;Zhouetal.,2018)

(10)

ordouble spending on storage(Rahmadika &Rhee, 2019), and perpetualdatapreservation(H.Lietal.,2018).Withtheincreas- inginter-institutionaladoptionofblockchain,scholarshavealso focusedontheissueofstorageandpreservationofsensitivedata (AlOmaretal.,2019)fromdifferentsourcessuchasmedicaldevices (Firdausetal.,2018),andmedicalinsurance(Zhouetal.,2018).A fewstudieshavealsodirectedattentiontothefacilitationofcross- institutionaldatasharing(AlOmaretal.,2019),andimprovements inefﬁciencyaswellasﬂexibilityofdatasharing(Shenetal.,2019).

Furthermore,priorstudieshavealsoattendedtotheneedfor improvements in data processing (e.g. Zhou et al., 2018). The reviewedstudieshavesuggestedsomemeasuresofinducingthese improvements,forinstance throughtheeffective integrationof heterogeneousdatafrommultiplesources(Quainietal.,2018),and theintegrationofsmartcontracts(Dagheretal.,2018).

Thesethemessuggestthatpriorresearchinthisfieldpresents anemphasizedfocus on(a)improvement oftechnical features, (b) management of medical data, and (c) identification of dis- tinctcapacitieswithinthefieldofhealthcare,whereinblockchain cangeneratesignificantcontributions.Basedontheseemergent themes,wepositthatresearchinthisdomainiscurrentlyinatrans- formativestate,withcontemporaneousaspectsofhealthcarebeing continuallyidentifiedaspotentialbeneficiariesofblockchain’suse throughtechnologicaladvancements.

4.2. Limitationsofthecurrentliterature

The limitations acknowledged by prior research indicate technically-orientedchallenges(seeTable4).Weattributethisto thefactthat thereviewedstudieshavemainlyconcentratedon developingnovelalgorithms,frameworks,andproofsofconcept fordeployingblockchaininhealthcare.Basedonthereview,we categorizetheextantlimitationsintofourcategories:performance, assumptions,constraints,andethicsandsecurity.

4.2.1. Performance

Studieshaveimpliedthatcertainaspectsofarchitecturalframe- worksdevelopedforadoptionofblockchaininhealthcarecanaffect theperformanceefﬁciencyoftheproposedframework (Jietal., 2018;Mamoshinaetal.,2018;Zhang&Lin,2018).Forinstance,high compressionratiosmayaffecttheinherentstabilityofaframework, andsubsequently,itsperformance(Leeetal.,2018).Thedesignof theframework andauthorizationsfordatamovementmayalso becontingentonmanualapprovalsbyusers,whichmayimpact theefﬁciencyoftheframework’sperformance(Shenetal.,2019).

Mamoshina etal. (2018) alsoacknowledgethat theirproposed frameworkdirectedatdetectinganomaliesmayunderperformin certaincaseswheredatasetsarenotlabelled.Thescalabilityand performanceefﬁciencyof aframework mayalsobeaffectedby issuessuchasrequirementsforcontinualupgradesbytheutilized system(Kauretal.,2018),thecomputationalloadofsensordata (Zhangetal.,2016),keywordsetsize(A.Zhang&Lin,2018),the amountofdiskspace,andthenetworkset-uprequiredbythetype ofblockchain,e.g.Ethereum,usedinthemodel(Quainietal.,2018).

Similarly,Dagheretal.(2018)suggeststhatincorporatingcertain features,suchastheuseofglobalsmartcontracts,indeveloped frameworksmayset-offhigherperformance-relatedcosts.Further, fewstudieshaveindicatedthatperformance-orientedchallenges mayalsoberelated tothemanagementofnodesinaproposed framework.Forexample,uncertainties ina framework’sperfor- mancemayalsorelatetothenumberofnodes,latencybetween nodes,andattacks(Hyla&Peja´s,2019).Inthesamelineofthought, Fanetal.(2018)alsosuggestthathighernumbersofnodesmay adverselyaffectsystemefﬁciency.

4.2.2. Assumptions

Theeffectivenessandefﬁciency oftheframeworksproposed inprior literaturearelimited bytheassumptions thattheyare basedon.Theseassumptionsarealsolikelytoimpactanaccurate assessmentofaframework’sperformance.Forexample,Uddinetal.

(2018)assumethatusers,i.e.patients,wouldutilizeasmartphone tocollectandstorerequiredmedicaldatafromsensors.However, alldatageneratedmaynotrequirestorage,andthedevicesmaynot haveawaytoauthenticatethatalegitimateownerhasuploaded thegenerateddata.SimilarconcernswereraisedbyRoehrsetal.

(2017),whoacknowledgethattheinabilitytoguaranteetheiden- tityorauthenticityofpersonsordevicesprovidingmedicalrecords isasigniﬁcantlimitation.Inanotherstudy,RahmadikaandRhee (2019proposedaframeworkforpreservingdataprivacyindecen- tralizedsharedstorageonablockchain networkand basetheir frameworkontheassumptionthatthesharedstoragewouldhave appropriatecapacitytosupporttheblockchainsystem.

4.2.3. Constraints

Researchershave acknowledgedconstraints inprior studies, which may be classiﬁed across four dimensions. These identi- ﬁeddimensionsimply thatsuchconstraintstranscendtechnical boundaries (pertaining to the costs of developing and deploy- ingblockchain-basedframeworks,analysisofdataforframework evaluation,andtheconstituentelementsoftheframeworks)and includesomesocietalaspectsas well(e.g.trust ingovernment, technologicalinfrastructureofthecountry).

4.2.3.1. Costs. This groupof constraintsprimarilyrelatestothe resources,time,andmonetarycostsassociatedwithexecutinga blockchainframework.Forexample,Dwivedietal.(2019)referto theresourceconstraintsrelatedtoIoT,whereasZhangetal.(2018) acknowledgethecostsassociatedwithdeployingadecentralized appfordeployingblockchain.Additionally,othercostsindicatedas constraintsandlimitationsintheextantresearchincludethelin- earincreaseinprotocolcostscontingentuponthecharacteristics andattributesoftheinvolvedentities,suchaspatients(Guoetal., 2018),increasedoperationaloverhead(forthepatient)andaccess latency(fortherequester)(Shenetal.,2019),andthetransaction andexecutioncostsbasedonvariableinputsofstringlength&size (AlOmaretal.,2019).

Time-relatedissueshavealsobeendiscussedasaconstraint, includingthetimespentsearchingfortheglobalsmartcontracts (Dagher et al.,2018), increasedtime consumption (Islam etal., 2019),transmissiontiming(Griggsetal.,2018),timerequiredfor seekingtherequireddatainsharedstoragebythedatarecipient (Rahmadika&Rhee,2019),andhighertotalexecutiontime(Kuo etal.,2019a,b).

4.2.3.2. Data andanalysis. Somestudiesreportedlimitationsfor data,suchasnon-representativenessofsampledata(Quainietal., 2018;Firdausetal.2018),thelimitedavailabilityoftrainingdatafor runningsimulationsortests(Lee&Yang,2018),andthepossibility ofdataduplication(Roehrsetal.,2017).Otherreportedconstraints pertaintolackoftestingusingactualinter-institutionalmedical data(Quainietal.,2018),andthelackoftimefordynamicanalysis (Firdausetal.2018).Suchconstraintsmayaffectthecompletion orinitiationofprotocoltestsassociatedwithcommunicationand authenticationbetweenentities(Xiaetal.,2017),andaffecttheper- formanceevaluationforthedevelopedframework.Forexample, H.Lietal.(2018)reportedthattheperformanceoftheirframe- workcouldbeaffectedbyhavingasmallstructure/amountofdata thatwouldnotonlywastespace,butalsoaffectmultimediaimage contentrecognition.