HaiderRaza1 ,DheerajRathee2 ,RenatoAmorim1 ,MariaFasli1
Abstract—TheUK’sNationalHealthService(NHS)confronts criticalchallengesinpatientreferralsamidstrapidlygrowing musculoskeletal(MSK)caredemands.Currentsystemscontributetoextendedwaitingtimes,incompletereferrals,fragmentedcare,andaccessdisparities.Toaddressthis,weproposed anAI-assistedSmartReferralSystem(SRS)thatenhances accuracy,efficiency,andequity.TheSRSintegratesapatient webportal,AItriageforreal-timerecommendations,anda digitalpathwayforseamlessdatahandling.Thesystemaims tostreamline,utilizingAIfordataanalysisandspecialist recommendations,potentiallyreducingwaitsandadministrative burdens.Inthisstudy,weexamineddataspanningfromAugust 2022toJuly2023,coveringaperiodof12months,toassess theinfluenceoftheSRSplatformonservicedelivery,costeffectiveness,andtimeefficiency.Theresultsshowedasignificant reductioninmissinginformation,coupledwithsubstantialtime andcostsavingsbothattheadministrativeandclinicallevels. IndexTerms—AI,Healthcare,Musculoskeletal,RecommendationEngine
I.INTRODUCTION
TheNationalHealthService(NHS)intheUKisfacing severalchallengeswhenitcomestopatientreferrals[1]. Oneofthehealthconditionssuchasmusculoskeletal(MSK) caredemandisrisingandsignificantlyimpactingindividuals, employers,theNHS,andtheeconomy[2].MSKcondition resultsinthelossofover30millionworkingdaysannually intheUK,constitutingupto30%ofGPconsultations[3]. Withanageingpopulation,thedemandforMSKservicesis expectedtorise,posingchallenges,especiallyinsocioeconomicallydisadvantagedareasandcertainethnicgroups[4].To managehugedemand,NHSreferralsinvolvedirectingpatients tospecialists,whichcomeswithseveralchallenges.Someof themainproblemsincludewaitingtimes:patientsmayhave towaitseveralweeksormonthstoseeaspecialist,whichcan leadtodelaysindiagnosisandtreatment[5].Thereareseveral reasonsbehindthisunwanteddelay:a)Inappropriatereferrals [6]:sometimes,patientsarereferredtospecialistswhoare notthebestfitfortheirspecificconditionorneeds,leading tofurtherdelaysorineffectivetreatment.b)Fragmentedcare [7]:Patientsmaybereferredtomultiplespecialistsorservices,
*ThisworkwassupportedbyInnovateUKFundingforKnowledge PartnershipwithProvideCommunityCIC.
1 H,Raza.,R.Amorim,andM,Fasli.arewiththeSchoolofComputer ScienceandElectronicsEngineering,UniversityofEssex,Colchester,CO4 3SQ,UnitedKingdom h.raza@essex.ac.uk
2 D.RatheeiswithProvideDigital,900TheCrescent,Colchester,CO4 9YQ,UnitedKingdom.
whichcanleadtoalackofcoordinationandcommunication betweenhealthcareproviders,andmayresultingapsincare. c)Disparitiesinaccess[8]:Patientsfromcertaindemographics orgeographicareasmayfacebarriersinaccessingspecialistservices,resultingininequitablehealthcareoutcomes.d) Administrativeburden[9]:Referralprocessescanbetimeconsumingandcomplex,placinganadministrativeburden onhealthcareprovidersandpotentiallydelayingpatientcare. AnAI-assistedreferralsystemcouldhelpaddresssomeof thesechallengesbyimprovingtheaccuracyandefficiency ofreferrals,ensuringthatpatientsarereferredtoappropriate specialistsandservices,andreducingadministrativeburdens onhealthcareproviders.
Around100millionappointmentsinEnglandalonewere dedicatedtoanMSKcomplaint–allofwhichcouldbefreed upifpatientsweregiventhechoiceofaphysiotherapistas theirfirstpointofcontact[10].Thereisaspecificoperational andclinicalneedbasedondatathatrevealedittookoverthree monthsforapatienttoaccesstreatment,mainlybecausea GPreferralformwentstraighttothecentralprocessingunit andcontainedminimalinformation[2].Exploringissuesinhouse,ourcliniciansreportedachallengethatvariousformats ofreferralformsarebeingusedtoreferpatientsfroma varietyofsources,fromdifferentGPsurgeries.Thislackof consistencyoftencreatesbackwardsandforwardsbetween thecentralprocessingunitandthepersonreferringtoget theinformationneededsurroundingtheinjury,andthento determinetheurgencyofthetriage.Duetothistedious process,eachpatienttakesapproximately2-3hourstotriage. Becausethewaitinglistwassolong,somepatientshadoften self-healedbeforetheappointmentbutwerestilltakingup aplaceinthesystem.Moreover,cliniciansdiscoveredthat thereferralformsfrequentlylackedcomprehensivedetails regardingtheonsetoftheproblemorinjury.Asaresult, theywereoftenrequiredtodedicatesignificantportionsofthe initialappointmenttopiecingtogetherbackgroundinformation fromthepatient,evenmonthsaftertheincidentoccurred.The demandforamorestreamlinedsystemforphysioservices camefromcliniciansthemselves,whosemaindriverswere aredefinedreferralpathway,animprovedpatientexperience andincreasedstaffefficiency.AnAI-assistedreferralsystem couldhelpstreamlinethisprocessandimprovetheefficiency andaccuracyofreferrals[11].Byanalyzingpatientdata,such asmedicalrecordsandtestresults,andapplyingmachine learningalgorithms,thesystemcouldidentifypatternsand
makerecommendationsforthemostappropriatespecialistor servicetoreferthepatientto.Thiscouldpotentiallyreduce waitingtimesforpatients,improvepatientoutcomes,and reducetheburdenonhealthcareproviders.Additionally,an AI-assistedreferralsystemcouldalsohelpaddressdisparities inhealthcareaccessandoutcomesbyensuringthatpatientsare referredtoappropriateservicesregardlessoftheirlocation, ethnicity,orsocioeconomicstatus.Itcouldalsoprovidea standardizedandtransparentprocessformakingreferrals, whichcouldhelpimprovetrustandconfidenceintheNHS amongpatientsandhealthcareprovidersalike.
Inaddressingtheaforementionedchallenges,weproposean AI-assistedSmartReferralSystem(SRS).TheSRScomprises essentialcomponents:i)apatientwebportalforself-referral, ii)anAI-assistedtriageapplicationofferingreal-timerecommendationstopatientsandtheclinicalteambasedondatadriveninsights,andiii)adigitalpathwayfordatacollection andreferraltriage.TheSRSsystemisdeployedwithinthe MSKserviceofProvideCommunity,aCommunityInterest Company(socialenterprise)deliveringhealthcareservicesfor NHSacrossEastAnglia,DorsetandnorthernEngland.This studyexaminesdataspanningfromAugust2022toJuly2023, coveringaperiodof12months,toassesstheinfluenceofthe SRSplatformonservicedelivery,cost-effectiveness,andtime efficiency.
Thispaperisorganizedasfollows:SectionIIpresentsthe proposedAI-assistedSmartReferralSystem(SRS).SectionIII containstheresultandimpact,whichincludesalltheexperimentalresultsandacomparisonwiththetraditionalsystem. SectionIVcontainsdiscussionsandconclusionsregardingthe experiment.
II.P ROPOSED S YSTEM
A.ProblemStatement
ThehighvolumeofreferralswithintheMSKservice causespressureontheexistingworkflow.Thisresultedin severalchallengesforhealthcareorganisationssuchasi)data inconsistency:incompleteandinconsistentdatafromdiverse referralsourcesledtodelayedtriagetimes,non-standardized decision-making,andloweroverallservicedeliveryquality; ii)variedreferralsources:referralsreceivedfromdifferent resources,complicatingthecoordinationandconsolidationof information;iii)datacollectionchallenges:carenavigatorsare compelledtochasereferrersandpatientsfordatacollection, consumingadditionaltimeandresources.iv)triagetime burden:cliniciansspendextensivetimeontriageduetothe needtogatherandreviewcollateddata.v)extendedwaiting times:patientsexperienceprolongedwaitingtimesforreferral triageoutcomes,impactingoverallserviceefficiency.
B.DevelopedSolutionandAnalysisMethodology
WehavedesignedanddevelopedanAI-assistedSmart ReferralSystem(SRS)-adigitalplatformtotacklethe challengesmentionedinSectionII-A.Fig.1illustratessystemarchitecturecomponentsfortheSRSplatform.Thekey componentsofthedigitalplatformconsistofawebportal
Fig.1:Systemarchitecturediagramofthedeveloped:AIassistedSmartReferralSystem(SRS)pathwayfortheMusculoskeletalServices(MSK).
forpatientstocompleteaself-referral,anAI-assistedtriage applicationtoprovidereal-timeinputs/recommendationsto thepatientsandclinicalteambyusingdata-driveninsights fromvarioussourcesofdata,andadigitalpathwayfor datacollectionandreferraltriage.TheAIengineunderwent trainingusingelectronichealthrecordssourcedfromboththe localserverandmedicalrecordsstoredinthecloud.More precisely,weemployedcollaborativefilteringwithk-nearest Neighborsandcontent-basedfilteringalgorithms[12].This trainingaimedtoequiptheAIenginewiththecapabilityto providerecommendationstobothcarenavigatorsandMSK clinicians.
TheSRSdigitalplatformhasbeendeployedwithinthe MSKserviceofahealthcareorganisationdeliveryservice forNHSEnglandintheMidandSouthEssexregion.We gathereddatarelatedtoservicedelivery,cost-effectiveness, andtimeefficiencycoveringaperiodof12months(between August2022toJuly2023)toevaluatetheimpactoftheSRS digitalplatform.Duringthistimeperiod,theMSKservice receivedatotalof9149referralsfromvarioussources(i.e., viaemail,ElectronicHealthRecords,andtheSRSplatform). Outofthese,2671referrals(approx.29%ofthetotal)were receivedthroughtheSRSplatform.Infurthersectionsofthe manuscript,wecalledthesereferrals: SRSreferrals andthe newAIsystem-drivenprocess: SRSpathway.Similarly,the
restofthereferrals(i.e.,6478)arecollectivelycalled: nonSRSreferrals andtheoldprocess: non-SRSpathway. ToevaluatetheimpactcreatedbytheSRSpathway,we focusedonthetimeandcostsavingsgeneratedandperformed fourstepsofanalysis.Firstly,weconsideredseveralkey impactfactors(KIFs)thatarerelevanttothetime-saving comparisonbetweenthetwopathwaysandamongthetwo stakeholders(i.e.,CareNavigatorstaffandMSKclinician staff).Secondly,weestimatedthepercentage(%)andactual numberofreferralsforeachKIF.Here,weestimatedthe numbersbyconsideringonlytheSRSreferralstoshowthe impactgeneratedbythecurrentuptakeofthepathway.Table IprovidedthelistofselectedKIFsforthestudy,thepercentage(%)ofreferralsassociatedwiththeseKIFs,andtheiractual numbersconsideringthetotal2671SRSreferrals.Thirdly,we estimatedthetimespent(onasinglereferralandoverall)by eachstakeholderfortherelevantKIFs.Lastly,weusethetime spentinminutesandcostinGBPperunit[13]toestimatethe overalltimesavingsforbothstakeholdersagainsttheKIFs. Furthermore,thecostsavingsareestimatedfordifferentuptake percentages(30%,50%,70%,100%)toshowcasethepossible impactcreatedinfuture.
III.R ESULTSAND I MPACT
ToassesstheimpactoftheSRSpathwayoncurrentservice delivery,wehaveconductedanevaluationbasedonthe2671 SRSreferrals,reflectingthecurrentutilizationoftheSRS pathway.TableIIprovidescomprehensivedataoncostand timesavingsforcarenavigatorstaff,encompassingthreeKey ImpactFactors(KIF-01,KIF-02,andKIF-03).Theaggregated annualtimeandcostsavingsacrossallthreeKIFsforcare navigatorstaffamountto24,580minutesand£14,010.60, respectively.Additionally,TableIIIoutlinesthecostandtimesavingmetricsforMSKclinicianstaff,coveringfourKIFs (KIF-03,KIF-04,KIF-05,andKIF-06).Thetotalannualtime andcostsavingsacrossallfourKIFsforcarenavigatorstaff are126,681minutesand£105,145.23,respectively.
ThecomparativeanalysisinFig.2and3revealsdistinct trendsintherelationshipbetweenkeyvariables.Fig.2,depicting"CareNavigatorTime"and"MSKClinicianTime"against "%uptakeofSRSpathway".Additionally,Fig.3illustrates "CareNavigatorCost"and"MSKClinicianCost"against "%uptakeofSRSpathway",whereweobservedaclear correlationbetweencostandfuturesavingsforboththeCare NavigatorandMSKClinician,whichelucidatesadifferent dynamic.Asfuturesavingsincrease,thereisacorresponding upwardtrendincostsincurredbybothentities.Theplot illustratesthatasfuturesavingsrise,thetimeinvestedby boththeCareNavigatorandMSKClinicianfollowsdistinct trajectories.Analyzingthesefiguressidebysideprovidesa comprehensiveunderstandingofhowfuturesavingsimpact bothcostandtime,offeringvaluableinsightsintotheefficiencydynamicsoftheCareNavigatorandMSKClinician Staffprocesses.
Fig.2:Possiblefuturetimesavingswith50%,75%and100% uptakeofSRSpathway.
Fig.3:PossiblefuturecostsavingsinGBPwith50%,75% and100%uptakeofSRSpathway.
IV.D ISCUSSION
Currenthealthcaresystemsareunderhugepressurewith increasingdemandandlimitedresources.AI-drivenclinical pathwayscanplayahugeroleinsupportinghealthcare providersbycreatingvalueandefficiency.Thisstudyshowed significantlyhighefficienciescanbecreatedintermsofstaff timeandcost.Moreover,thenewpathwayalsodelivered severalotherpositiveimpactsi.e.,theSelf-referralalgorithm allowedpatientstorefertotheservicedirectlyandeffectively signpostedbasedontheirconditioninreal-timethusreducing theneedforGPappointment.Thisitselfleadstohugesavings fortheNHSintermsoffreeingtheGPtime,traveland timecostforpatientstoattendGPappointments,andtotheir overwell-beingandtimeofrecovery.Thedatashowedthat theMSK-HQscore(akeydeterminantofhealthrecovery outcome)[14]forSRSpathwaypatientsincreasedby18.7 pointsascomparedto16.9pointsfornon-SRSpathway patientsthusdeliveringbetterhealthrecoveryoutcomes. Thefeedbackcollectedfrompatientsforthenewpathway
TABLEI:KeyfactorsselectedfortheimpactanalysisofSRSpathway
KIFID KeyImpactFactors(KIFs)
KIF-01
LackofKeeleSTaRTQuestionnaireScoreinnon-SRSpathway
KIF-02 LackofMusculoskeletalHealthQuestionnaire(MSK-HQ)Scoreinnon-SRSpathway 100%
KIF-03 Lackofanyothercriticalinformationrequiredforthetriageprocessinnon-SRSpathway 12%
KIF-04 Processingofreferralsmeetingexclusioncriterionforservicedelivery 9.4%
KIF-05 AdditionalprocessingtimespentbytheMSKcliniciansforthenon-SRSreferralscomparedto theSRSreferralswithcompleteinformation
KIF-06 AdditionalappointmenttimespentbytheMSKcliniciansforthenon-SRSpathwaypatientsas comparedtotheSRSpathwaypatients
TABLEII:TimeandcostsavingsforCare-navigators
KIFID
KIFID #referrals
TABLEIII:TimeandcostsavingsforMSKclinicians.
servesasatestamenttotheircontentmentwiththeexpeditious servicedeliveryfacilitatedbytheAI-assistedSRSplatform. Notably,thesystemhasdemonstratedasignificantadvancementinoperationalefficiency,exemplifiedbyaremarkable reductioninadministrativeprocessingtime.Physiotherapists havereportedsubstantialtimesavings,withanotablereductionofonepatientappointmentforeachreferral.This efficiencygainisattributedtotheimmediateavailability ofcomprehensivebackgroundinformationduringtheinitial patientappointment.Thesystem’sabilitytoprovideaswift reviewofpatienthistoryempowersphysiotherapiststoconductmoreexpeditiousassessments,contributingtoanoverall enhancementintheefficiencyandeffectivenessofpatientcare.
Thedualperspectivecollectedfrombothpatientfeedback andprofessionalinsightsunderscoresthemultifacetedpositiveimpactoftheimplementedsystem.Patientsexperience quickerandmoretransparentservicedelivery,whilehealthcare professionalsbenefitfromstreamlinedworkflows,leading tosubstantialvaluegeneration.Thefutureprojectionswith higheruptakeoftheAI-drivenpathwayshowamassiveimpact intermsofstaffcostandtimeandadditionalbenefitsfor allkeystakeholdersi.e.patients,cliniciansandcare-provider organisationswillcreateasignificantimpact.
V.C ONCLUSION
Inconclusion,theSRSplatform,comprisingapatientweb portal,anAI-assistedtriageapplication,andadigitalpath-
way,provestobeinstrumentalinaddressingkeychallenges identifiedintheMSKservicereferralprocess.Thesignificant reductioninmissinginformation,coupledwiththepotential forsubstantialtimeandcostsavings,positionstheSRSasa transformativesolutioninhealthcareservicedelivery.Asthe systemcontinuestoevolve,ongoingresearchandcontinuous evaluationwillplayapivotalroleinrefiningitsfunctionalities andensuringitsseamlessintegrationintothebroaderhealthcarelandscape.Inconclusion,theAI-assistedSmartReferral Systemstandsasabeaconforoptimizedandpatient-centric MSKreferralpathways.
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