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Scoping Exercise: Patient Flow Management and Metrics in Public Hospitals in Mexico

Final Report to Metrics for Management

Aceso Global 1400 16th Street NW, Suite 430 Washington, DC 20036 | www.acesoglobal.org

Jerry La Forgia, CTO Kiran Correa, Analyst

January 6th, 2016

TableofContents

AcronymsandAbbreviations 1

I.Introduction 2II.Background:WhyPatientFlowManagementandMetricsMatter? 3III.ObjectivesandResearchQuestions 3IV.Sample,ApproachandMethodology 4V.Manifestations 5VI.Impacts 9VII.Causes 10VIII.DataIssuesandOpportunities 11IX.TowardRelevantPatientFlowPerformanceMetricsinLow-ResourceSettings 15

Aceso Global Scoping Exercise: PF Management and Metrics in Public Hospitals in Mexico

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AcronymsandAbbreviationsALOS AverageLengthofStay ED EmergencyDepartment GH GeneralHospital GYNOB GynecologyandObstetrics ICU IntensiveCareUnit IW InpatientWard MCH Maternal-ChildHospital OP OutPatient OR OperatingRoom PACU Post-AnesthesiaCareUnitPF

PatientFlows

PFA PatientFlowAnalysis PFMPs PatientFlowManagementPractices PHC PrimaryHealthCare PPP Public-PrivatePartnership


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I.IntroductionAnalyzingandimprovingpatientflowmanagementpractices(PFMPs)hasbecomeakeycomponentofperformanceimprovementeffortsinhospitalsandhealthsystemsinhigh-incomecountries.Thishasnotbeenthecaseindevelopingcountries,wherePFMPsapplicationsarescarce.PFMPsconsistofasetofvaluable(andproven)toolsforhospitalsanddeliverysystemstoimprovequalityofcare,patientexperience,staffproductivityandoutcomeswhilecontainingcosts.Forexample,analysisofPFMPscanhelpfacilitieswiththefollowing:

• Assesstheirprocessesandeffectivenessindeliveringservicestopatientsinemergencydepartments,operatingtheatersandwards;

• Promotecollaborationandteambuildingbyfosteringgreatercoordinationacrosshospitaldepartmentsandwithambulatoryproviderslocatedinhospitalcatchmentareas;and

• Craftmetricsthatencourageanunderstandingofdataanditsrelevancetorunningthehospitalandengagingwithotherproviders.

Analyticalinputsarecriticaltoshapingpatientflowimprovementinterventions,monitoringtheireffectsonpatientflows,andmeasuringtheirimpactsintermsofbenefitstofinancial,qualityandpatientoutcomes.MeasuringandimprovingPFMPshastheadditionalvalueoftransformingheretofore“dataproducing”organizationsinto“information-driven”organizationsthatutilizeevidencetoinformdecision-makingandimproveperformanceandoutcomes.Apatientflowassessmentlaysthefoundationtoidentifyproblemswithinhospitalsrelatedtohowpatientsareadmitted,dischargedandtransferredamongunitsandtootherhospitalsandhowcareiscoordinatedwithambulatoryproviders,aswellastodevelopstrategiestoaddresstheseproblems.Inanidealworld,onlypatientsrequiringcomplexcarewouldaccessthehospital,andthehospitalwouldshepherdthemthroughthevariousadministrativeandservicedeliverypoints—registration,screening,diagnostics,admission,treatmentanddischarge—inatimelybutsteadymanner.Inotherwords,allpatientswouldreceivetherightcareattherighttime,intherightplace.However,theworldisrarelyideal.Whilepatientarrivalpatternscanvaryenormouslyandcontributetoflowbottlenecks,researchhasshownthatsuboptimalinternalprocessesformanagingthepeaksandvalleysofpatientflowsarealsotoblame,resultingindelays,cancellations,patientmisplacement,lowerqualitycare(e.g.increasedmedicalerrors)and,ultimately,undesirableoutcomes(LitvakandFineburg,2013;Bakeretal.,2009).Thisreportpresentsthefindingsofascopingexerciseorrapidassessmentofpatientflowmanagementpracticesandmetricsinlow-resourcehospitalsettingsbasedonasampleofpublichospitalsinMexico.Drawingontheliteraturefromhospitalsinhigh-incomecountries,Section2firstprovidesabriefoverviewofthebenefitsofgoodpatientflowmanagementpractices.Section3statestheobjectivesandresearchquestionsguidingthescopingexercise.Section4reviewsthesamplingframeworkandmethods.Sections5,6and7reportthefindings,namelythemanifestations,impacts,andcauses,respectively,ofpatientflowmismanagement.Notably,whilethefindingsarenumerousanddiverse,thereisconsiderableconsistencyacrossthesamplehospitalsintermsofflowproblems,theirimpactsandunderlyingcauses.Section8reviewsdataissuesandopportunitiesfordevelopingfeasiblepatientflowmetrics.Section9concludesthereportbyoutliningrelevantpatientflowmetricsforaproposedsecondphaseofwork.Asetofannexescontainingsupplementaryinformationandinstrumentsusedinthescopingexerciseaccompaniesthisreportasaseparatedocument.Annex1brieflyreviewstheexisting


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literatureonthebenefitsofpatientflowimprovements,drawingonexamplesfromhigh-incomecountries.Annex2providesbasicsummaryinformationanddatafromthesampledhospitals.TheinstrumentsusedbytheinvestigativeteamduringhospitalvisitstoguidefocusgroupinterviewsandfacilitatethecollectionofstandardizedinformationareprovidedinAnnexes3and4,respectively.II.Background:WhyPatientFlowManagementandMetricsMatter?Patientflowanalysis(PFA)involvesmeasuringandunderstandingwhereproblemsinpatientflowsexistinhealthcaresettings,includingtheircausesandimpacts.SuccessfulPFAprovidestheinputsnecessarytoformulateandimplementeffectivestrategies,front-linemanagementpracticesandmetricsforpatientflowimprovement,withthegoalofusingexistingresourcesmoreeffectivelyandinatimeliermannertoachievehigherqualitycare,greaterefficiencyandbetterpatientexperience.Researchfromhospitalsinhigh-incomecountriesshowsthatthesegainscanbemadebystreamliningpatientflowsandredesigningcarepracticeswithoutaddingmoreinfrastructureorstaff.Annex1providesasummaryoftheliteratureonthebenefitsofimprovedpatientflowmanagement,highlightingthefollowingmajorimprovementsforhospitals:

• Addedcapacitywithoutcapitalexpense:Throughshorterlengthsofstayinemergencydepartmentsandwardsandbettertimemanagementofoperatingrooms.

• Betterclinicaloutcomes:Byimprovingtimelinessofcareandstandardizingprocesses,bettermatchingpatientstotheappropriatelevelofnursingcare,diagnostics,specialists,andtreatments,andenhancingpost-dischargecoordinationwithprimarycareproviders.

• Betterpatientexperienceandfacilityreputation:Byreducingwaittimes,patientflightandperiodsinwhichpatientsareleftunattended.

• Improvedstaffproductivity:Byreducingpaperwork,streamliningprocessesandenhancingcommunicationamongstaff.

• Reducedcosts:Throughhigherpatientthroughput,eliminationofwasteandavoidedexpensivecapitalinvestments.

III.ObjectivesandResearchQuestionsTheobjectivesofthescopingexerciseincluded:(i)identifythemajorpatientflowproblems(andtheirrootcauses)inpublichospitalsthatmakehospitalslesseffectiveintermsoftimeliness,efficiencyandqualityofcare;(ii)determinehowpatientflowsarecurrentlymeasured;(iii)identifyopportunitiesandobstacles,includingavailabledataandmetrics,tomeasuringandaddressingpatientflowchallengesinlow-resourcesettings;and(iv)determinethedemandforbettermetricsandimprovedpatientflowmanagementamongstkeystakeholders,includinggovernmentofficials,hospitaldirectorsandstaff.Theassessmentexaminedthreepatientflowdimensions:

(i) Intra-hospital:Theexercisefocusedprimarilyoninternalhospitalflows.TheteamexaminedPFMPsinemergencydepartments(ED),operatingrooms(ORs)andinpatientwards(IWs)and,toalesserextent,inintensivecareunits(ICUs),post-anesthesiacareunits(PACUs)andoutpatientdepartments(OPs).

(ii) Inter-hospital:Thisdimensionencompassespatienttransfersbetweenhospitals.


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(iii) Linkagesbetweenhospitalsandprimarycareproviders:Thisdimensionincludesreferralandcounter-referralsystems,post-dischargecareandbroadercarecoordinationpracticesacrossproviderlevels.

TheresultsoftheinvestigationwillinformwhatneedstobedoneonthegroundtopavethewayforasubsequenteffortinMexicotoimprovethemeasurementofpatientflowsanddevelopmanagerialstrategiesandinterventionstoimprovepatientflowpractices.Tothisend,acomplementaryproducttothisreportisaproposaltofundthedevelopmentandimplementationofeffectivepatientflowmanagementpracticesandcorrespondingmetricsinlow-resourcesettingsinMexico,andelsewhere.1Investigativequestionsguidingthescopingexerciseareasfollows:

PrimaryQuestions:Whatarethemajorpatientflowissuesandbottlenecksfacingpublichospitals?Whatarerigorousyetfeasiblemetricsinthepublichospitalcontextthatcanhelpfacilitymanagersdiagnose,measure,understandandaddresstheidentifiedproblems?

SecondaryQuestions:Aretheresufficientincentivesanddemandforhospitalmanagersandstafftoimprovepatientflows?Howcanpatientflowmetricsbemeasuredinaneffectiveyetaffordableway?

IV.Sample,ApproachandMethodologyThescopingexerciseincludedninestate-operatedhospitalsinfourstates.2Annex2presentssummarycharacteristicsoftheninefacilities.Allweresecondarylevelhospitalsconsistingoftwotypes:five“generalhospitals”offeringcareinspecialtiessuchasgeneralsurgery,internalmedicine,pediatrics,gynecologyandobstetrics(GYNOB);andfourmaternal-childhospitalsofferingpediatrics,GYNOBandneonatology.Somehospitalsofferedadditionalspecialties.Facilitysizerangedfrom85to232bedsandallhospitalswerelocatedinurbanorperi-urbanareas,usuallysecondarycities,butservedasreferralfacilitiesforcatchmentareasconsistingofbothurbanandruralpopulations.Typicalofstate-operatedfacilitiesinMexico,nearlyallhospitalsinthesampleweredirectlyadministeredandsupervisedbytheStateHealthSecretariat.Hospitalmanagershadlimitedautonomyinbudgetandresourcemanagement,includingstaffandsupplies,andallresourceswereprocuredandmanagedcentrally.Onegeneralhospital,however,wasmanagedunderapublic-privatepartnership(PPP)arrangementinwhichstaffwascontractedthroughathirdparty,butmanagedbyfacilitydirectors,whowerecivilservants.Whilethemanagementteaminthishospitalexercisedconsiderableautonomyinhumanresourcemanagement,drugs,suppliesandequipmentwereprocuredthroughtheStateHealthSecretariat.Becauseofaspecialgovernancearrangement,anotherhospitalhadfullautonomyinhumanresourcemanagement.Mostofthehospitalsoutsourcedmaintenance,and,toalesserextent,specialtydiagnosticsandotherhightechnologyservicestothirdparties.Allofthefacilitiesservedlow-incomepopulationsaffiliatedwiththefederalgovernment’shealthinsuranceprogramknownasSeguroPopular,which1Seeaccompanyingproposal:“ImprovingpatientflowsinMexicanhospitalsthroughmetricdevelopmentandmanagementinterventions.”2StateswereselectedwiththesupportofformerofficialsoftheFederalHealthSecretariatandspecifichospitalswerechosenbyStateHealthSecretaries.Ingeneral,thesampleconsistedofhospitalsthatstateofficialsdeemedtosufferfromovercrowdingandwereeasilyaccessiblebygroundtransportation(withintwohoursoftheofficesoftheStateSecretariat).Bymutualagreementwithstateofficials,thenamesofstatesandsampledfacilitiesremainanonymous.


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primarilychanneledpaymentsthroughStateHealthSecretariats,whichinturnincorporatedthemintohospitalbudgets.However,mosthospitalsreceiveddirectpaymentsfromSeguroPopularforspecific“catastrophic”proceduresandtreatmentsforwhichtheyhadbeenpreviouslyaccreditedbytheinsuranceprogram.Theinvestigativeteamemployedafour-prongedapproachtoassesspatientflows:34

(i) Focusgroupsoftwotothreehourswithhospitalmanagersandstaff,usuallyconsistingofthemedicaldirectororsub-director,nursingdirector,departmentaldirectorsandheadnursesfromtheemergencydepartment,operatingtheatreandwards.Inmostcases,front-linephysiciansandnurseswerepresent.Theteamfollowedanopen-endedquestionnairetoguidethediscussion(seeAnnex3).

(ii) Hospitaltourstoobserveovercrowdingandbottlenecks,whichincludedshortconversationswithfront-linestaffonpatientflowsthroughserviceareas.

(iii) Applicationofachecklistsurveyinstrumentcompletedwithgroupsofmanagersandstafffromspecificdepartments:ED,OR,outpatientclinics,ICUandIWs(seeAnnex4).

(iv) In-depthassessmentinonehospitalthatinvolvedexaminingandcollectingdatafromregistries,informationsystemsandstatisticalreports.5GiventheunitarynatureofthepublichospitalsysteminMexico,nearlyallhospitalshavesimilartimelogs,patientregistries,informationsystems,andstatisticalreports,suggestingthatsimilarassessmentscouldbecarriedoutatotherfacilities.Theteamalsomappedoutflowsinspecificdepartmentsanddocumentedadministrativeproceduresforcertainprocesses(suchasadmissionsanddischarges).

V.ManifestationsThisisthefirstofthreesectionsreportingonthefindings.Itsummarizesthemanifestationsofpatientflowproblemsinspecificdepartments,inter-hospitaltransfersandlinkageswithprimarycarefacilities.Whererelevant,dataareprovidedtosupportthefindingsfromthefocusgroups.Figures1-4displaythemanifestationsofpatientflowproblemsintheED,OR,IWandICU/PACUforthesamplehospitalsasreportedbyhospitalmanagersandstaffworkinginthesedepartments.67Figure5depictspatientflowsintheEDofonehospital.Figure6displaysestimatesofunnecessarypatientflowstoallhospitals(i.e.lowacuitypatientswhocanbetreatedatlowercarelevels).Mostofthemanifestationsindicatedinthefigureswerereportedbyatleasthalfofthehospitals;generally,EDsandORsexperiencedahigherincidenceandfrequencyofpatientflowchallenges.3Theresearchteamalsometwithstatesecretariesofhealthtodiscusstheobjectivesofthescopingexercise.Inonestate,however,thepositionofhealthsecretarywasvacant,sotheteammetwiththeformerstatehealthsecretary.4Theteamcompletedthescopinginasinglevisittoeachfacilitylastingthreetofourhours.5Thein-depthassessmentrequiredtwovisitstotalingabout10hoursinthehospital.6Itisimportanttonotethattheseissuesaresimilartothoseobservedintheliteratureofhospitalslocatedinhigh-incomecountries(Litvak,2010;Sayahetal.,2016;Michtaliketal.,2013;Whiteetal.,2014).7Thehospitalswerecodedbytype:GHstandsforgeneralhospitalandMCHformaternal-childhospital.


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0 1 2 3 4 5 6 7 8 9

Waits & delays in service provision

Extended service times

Patient misplacement

Patient assessments rushed/incomplete

Patient assessments inaccurate/omitted

Patients leave hospital unattended

Patients placed in hallways

Figure 1: Number of Public Hospitals Reporting PF Problems in ED (N=9)

GHs MCHs

0 1 2 3 4 5 6 7 8 9

Long pre-op time

Much down time

Cancelations

Delays in start time

High volume of add-on cases

Work beyond scheduled time

OR unavailability

Long bed turnover time

Figure 2: Number of Public Hospitals Reporting PF Problems in OR (N=9)

GHs MCHs

0 1 2 3 4 5 6 7 8 9

Long waits for beds

Misplacement of patients in incorrrect wards

Patient boarded elsewhere

Long waits for test results

Refused transfers

Figure 3: Number of Public Hospitals Reporting PF Problems in Wards (N=9)

GHs MCHs 0 1 2 3 4 5 6 7 8 9

Lack of bed availability

Rejection of patients

Figure 4: Number of Public Hospitals Reporting PF Problems in ICU/PACU (N=9)

GHs MCHs

Emergency(ED):AllhospitalEDsinthesampleappliedatriagesystembywhicharrivingpatientsareclassified,usuallybynursesorresidents,intooneofthreeseveritygroups:(i)criticalorhighseverity(“codered”patients)inneedofurgentcare;theseareimmediatelyroutedintotheED;(ii)seriousormoderateseverity(“codeyellow”patients)requiringmoreorlessimmediatecarebutwithoutalife-threateningcondition;dependingonthehospital,thesepatientsaredirectedintoEDobservationorwaitingrooms,butreceivecarepromptly,usuallywithin30minutes;and(iii)lowseverity(“codegreen”patients),notrequiringimmediateattention.ThesepatientsrepresentthevastmajorityofEDarrivalsinallhospitalsandoftenarriveingreatvolumesinthemorning.Theyaregivenaqueuenumberanddirectedtowaitingroomsoroutsidewaitingareastoremainuntilcalledforaconsultation,whichcantakehours.Insomefacilities,asubsetofcodegreenpatientsmaybesenttotheOPforaconsultation.Physicians,supportedbythenursingcorps,provideallEDconsultations.Figure5mapsthetriagesystemandEDpatientflowsofonehospital.CoderedandyellowpatientsarefrequentlyplacedongurneysinhallwaysfortreatmentandobservationduringpeakutilizationperiodsandlongqueuesformatdifferentservicepointsacrosstheED(seeBox1).Somehospitalshaveconvertedconsultationsroomsandwaitingroomsintoboardingareaswhereoverflowpatientsareheldformanyhours,ifnotovernight,untilanED(ormorelikelyIW)bedbecomesavailable.Inonehospital,theteamobservedtwocrowdedoverflow

Box 1: Extended length of stay in theEDReportedaverage lengthof stay (ALOS)intheEDcanbedeceiving.Onehospitalreported an average wait of 5.5 hoursbetweenadmissiontoEDandsecuringabed in the IW, but the distributionrevealed a number of cases in whichpatients waited between 10 and 60hours. In another facility, it was notunusual for patients’ ED LOS to exceed15days, inpartdue tounavailabilityofIWbeds.Whileclinicalstaffattendedtothesepatientsduringtheseperiods,thecongestion and long waits resulted insuboptimalcare.


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rooms(forcodeyellowpatients)inwhichpatientshadtositupinchairsforupto36hours,usuallywaitingforanEDorIWbed.Patientmisplacement,orplacementinaninappropriateholdingorboardingareas,iscommon.Periodsofhighovercrowdingplaceenormouspressureonclinicalstaff,especiallynurses,tosimultaneouslyattendlargenumbersofpatients,someofwhomareincriticalcondition.AsshowninFigure1,hospitalsreportedthatpatientscanbeleftunattendedforundeterminedperiodsinhallwaysandboardingareasandthatthe“supersaturation”canresultinrushed,delayed,incompleteorinaccurateassessmentsanddiagnoses,includingfailuretofollowestablishedtreatmentprotocols.Inmosthospitals,delaysinorderingandperformingdiagnostictests,aswellasinreceivingtheresultsalsocontributetothetreatmentdelays.

Figure5:TriageSystemandEDPatientFlowsOperatingRoom(OR):MosthospitalsreportedsignificantbottlenecksintheOR(seeFigure2).Intermsofpatientflows,ORscanbestbedescribedasintermediateunitswhoseefficient(orinefficient)useinvariablyimpactspatientflowsinotherdepartments:ORpatientsoriginatefromEDandsurgicalIWs(pre-surgery)andaftersurgeryaretransferredtoPACUs,ICUsandIWs.ORbottleneckscontributetopatientbackupsandlongwaitingtimesintheEDandalsocompromiseplanninganduseofbedsinPACUs,ICUsandsurgicalIWs.ORshavetwoworkstreams:scheduledandunscheduledsurgeries.Theformerconsistsofelectiveandnon-electiveproceduresthatareprogrammedinadvance,whilethelatterismadeupofemergencycases(e.g.coderedoryellowpatients).Thesecasesaretypicallyaddedtoscheduledprocedureloads,resultingindelays,bumpingandreschedulingofscheduledprocedures.8Queuesforelectivesurgeriesmaybeweeksormonths.Inseveralhospitals,thefullstockofORswasnot

8Inonehospital,theratioofscheduledtounscheduledsurgerieswasapproximately1:3.


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operationaloronlyavailableonapart-timebasis,usuallyduetohumanresourcelimitationsrelatedtoschedulingarrangements.SeveralhospitalsreportedanapparentlyparadoxicalsituationoflowORproductivityandlongperiodsofORdowntimecombinedwithregularcancellationsofscheduledproceduresandextendedworkhours,suggestingartificially-generatedpatientflowbottlenecksorpoorprogrammingofORtime.ThelattermaybedrivenbyreportedlylongORturnovertimes(timebetweenprocedures)causedbydelaysinORcleaning,longpre-operationpreparationanddelaysinstarttimesforscheduledsurgeries.InpatientWards(IWs),IntensiveCareUnits(ICUs)andPost-AnesthesiaCareUnits(PACUs):HospitalsreportedpatientflowchallengesrelatedtomatchingdemandtoavailablebedsupplyinIWs(seeFigure3).Allocationofbedstospecialtiesmaynotmatchdemand,meaningsomewardsaresaturatedwithpatientswhilebedsgounutilizedinothers.Nearlyallhospitalsreportedlongwaitsforbedsandboardingofpatientselsewhere,oftenininappropriatewardsorrooms(e.g.surgicalpatientsplacedininternalmedicinewards).LackofavailablebedsoftenresultsinrefusedtransfersfromtheED,creatingbackupsandlongwaits.PatientflowsinICUsandPACUsappearedtobelessofanissue(seeFigure4).Whilehospitalsreportedsomepatientrejectionandlackofbedavailabilityintheseunits,patientsrequiringintensivecaremaybetemporarilyboardedinthePACUoratelemetryunittohelpalleviatecongestion.Intra-hospitaltransfers:Allstate-operatedhospitalsinMexicoaresafetynetfacilitiesandfollowa“zerorejection”governmentpolicythatmandatestheycannotrefuseanypatient,includingpatientsinsuredbythevarioussocialsecuritysystemsinMexico.Mostarealsoreferralfacilitiesforthepublicnetworkintheirjurisdictions.Whilemosthospitalshaveagreementsgoverningtransferarrangementswithotherhospitals,theyareusuallynotappliedandtransfersdependmostlyonpersonalrelationshipsacrosshospitalanddepartmentaldirectors.Nevertheless,hospitalmanagersdidnotidentifytransfersasamajorissue,thoughasubsetofhospitalsdidmentionthatprivateandpublicfacilitiessometimesdump(usuallycritical)patientswithoutadvisingthereceivinghospitalfirst.Linkagesbetweenhospitalsandprimarycareunits:Allhospitalsoperatewithinaspecifiedjurisdictionandserveasreferralfacilitiesforanextensivenetworkofprimarycare(PHC)units,andtoalesserextent,smallruralhospitals.Formallinkagesbetweenhospitalsandtheseunitsappearweak.Allsamplehospitalsidentifiedpatientswithnon-acuteconditionsnotrequiringhospital-basedcareasamajorpatientflowchallenge.AsFigure6shows,mosthospitalsestimatedthatbetween60and90percentofpatientsseekingcareintheEDandOPhaverelativelysimpleconditionsthatcould(andshould)beresolvedatthe 0% 20% 40% 60% 80% 100%

GH 1

GH 2

GH 3

GH 4

GH 5

MCH 1

MCH 2

MCH 3

MCH 4

Figure 6: Estimated Percent of Inappropriate Demand for Emergency & Outpatient Care by

Hospital (N=9)


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0 1 2 3 4 5 6 7 8 9

Financial Repercussions Poor outcomes

Poor quality - Readmissions Unnecessary variations in care

Diminished reputation of hospital Patient dissatisfaction Patient abandonment

Premature discharge of acute care patients Extended stays

Figure 7: Number of Public Hospitals Reporting Negative Effects of PF (N=9)

GHs MCHs

primarycarelevel.Hospitalsmaintainthatcateringtothesepatients—whorepresentthemajorityofEDarrivalsandOPappointmentseekers—isamaincontributortoovercrowding;furthermore,theydivertscarceresourcesawayfromtheseverelyillandpatientswithcomplexconditions.Thereferralandcounter-referralsystemalsoappearsdysfunctional.HospitalsmentionedthatPHCfacilitiesignoreexistingpatientreferralguidelines,insteadreferringpatientsunnecessarilyandforconditionsthatareinappropriateforhospitalcareandbesttreatedatalowerlevel.Hospitalsarenotblameless,however:uponpatientdischarge,hospitalssendmedicalrecordsandcareguidelinestothepatient’sPHCunit,butrarelyfollow-uptoconfirmwhethertheunitreceivedthedocumentation,understoodtheinstructionsandwereprovidingappropriatecare.VI.ImpactsHighlycongestedsystemscreateahectic,stressfulenvironmentforbothpatientsandstaff,compromisingqualityofcareandpatientsafetyandincreasingcostsandtheriskofadverseoutcomes.IntheED,forexample,boardingofpatients—usuallyinsuboptimalsettings—cancausetreatmentdelaysandbreachesintheapplicationofstandardsandprotocols(suchastimelymedicationadministrationandpainmonitoring,turningofpatients,etc.).Forexample,astheEDnursingcorpsscramblestoattendasteadyflowofnewarrivals,somepatientsmaysimplybeleftunattended,contributingtodeteriorationintheircondition.Asobservedinhospitalsinhigh-incomecountries,thesamplehospitalsreportedthatstaffoverloading(e.g.unsafeworkloads)hasresultedinmedicalerrorsandsuboptimaloutcomes,inturncontributingtolongerstays,readmissions,unnecessaryvariationsincare,and,insomefacilities,highermortality(seeFigure7).Longwaitingtimesalsoresultedinpatientdissatisfactionandflight.Mosthospitalsreportedthat“voluntarydischarges”(patientleavingthefacilityagainstmedicaladviceorbeforetreatmentisprovidedorconcluded)wereaseriousissue.Forexample,onehospitaltalliedanaverageof41and8voluntarydischargespermonthfromEDandIWs,respectively.9Thehospitalalsoreportedthatonaverage15patientspermonthabandontheEDafterregistrationwithoutreceivingcare.10

9Accordingtostatepolicy,avoluntarydischargeisregisteredbecausethepatientmustsignaformandhospitalsarerequiredtoreportthenumber.10Theseareusually“codegreen”patients.


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Accordingtohospitalofficialsandstateauthorities,congestedconditions,lackofprivacy,longwaitsforcareorsecuringabed,surgicalcancellationsandextendedlengthsofstaycompromisethereputationofhospitals,andthepublicdeliverysystemingeneral.Somefacilitiesalsoemphasizedstaffburnoutanddissatisfactionduetoheavyworkloadsresultinginlowmorale,staffturnover(e.g.EDnurses)andlowerlevelsofeffort.Finally,patientflowinefficienciescontributetohighercosts(e.g.readmissions,longerthannecessarylengthsofstays,surgicalcancellations,extendedhours)andlostrevenues(e.g.patientflight).11VII.CausesWhatreasonsdidhospitalmanagersandstaffgiveforthepatientflowproblemsanddelaysdetailedabove?Notunexpectedly,mosthospitaldirectorsfirstidentifiedinadequatecapacity,suchasinfrastructurelimitations12andstaffshortages,asthemaincauseofpatientflowproblems.However,acknowledgingthatfiscalconstraintswouldmakeithighlyunlikelyforstategovernmentstoinvestinhospitalexpansionorenhancehumanresourcebudgetsintheforeseeablefuture,uponfurtherprobingdirectorsidentifiedanumberofshortcomingsinpatientflowmanagementpractices.Allofthehospitalsexperiencedsignificantvariationsinpatientdemandvolumes:fluctuationsdependedonthetimeofdayanddayoftheweek.Assuggestedabove,thesamehospitalmayexperienceovercrowdingandcongestiononcertaindaysandtimes,andunder-utilizationanddowntimeonothers.Somehigh-volumedemandwaspredictableandcontributedtolongqueuesandcongestion(e.g.earlymorningweekdaydemandforEDandOP),butotherbottlenecksappearedartificialornon-randomandevenself-inflicted.Forexample,hospitalstaffidentifiedthefollowingpracticesthatcouldnegativelyimpactpatientflowsandextendwaittimesandlengthsofstay:

(i) Ordering,administeringandreceivingresultsofdiagnostictests,(ii)ProcessingthepaperworkforIWadmissionsanddischarges,(iii)Arrangingintra-hospitalconsultationsbyspecialists,(iv) DelaysinIWbedturnovertime(betweenadischargeandnewadmission)andOR

turnovertime(betweensurgeries),(v) Nostandardizedprocessforinter-departmentalpatienttransfers,(vii)ImprovisedbedmanagementinIWs.13

OtheradministrativecausesincludedpoorschedulingofprogrammedsurgeriesinORsandelectiveadmissionsinIWs.Forexample,surgeonsscheduledprogrammedsurgeriesaccordingtotheirassignedblocktime,whichcorrespondedtotheirpreferredavailabilityratherthanoptimalthroughput.Latearrivalsofsurgeonsorpatientsandinefficientpre-operativecareprocessescanalsocreateflowproblemsbydelayingstarttimes,leadingtobackupsandcancellationslaterinthe

11AllofthehospitalsreceiveddirectpaymentsfromSeguroPopularforoneormore“catastrophic”conditions.Whilethesepaymentsrepresentaminorportionofthehospitals’budget,theyareimportanttotheiroverallfinancialviability.12Allbutoneofthefacilitieswereconstructedorremodeledduringthelast10to20years.13Fora200-bedhospital,oneextraLOSdayreducesbedavailabilityby33beds/month.


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day.PoorplanningofORtimeresultedinemergencysurgeries“bumping”programmedsurgeries,causingdelaysandcancellations.Inwards,lackofIWbedmanagementandplanningpracticescontributedtoextendedstaysandbedunavailabilitywhichinturncontributedtorefusedtransfers,“off-service”boardingandlongbedwaitingtimesintheED.Anothercausehighlightedbyhospitalanddepartmentaldirectorsisrigiditiesinstaffmanagementthatrestrictedmanagersfromaddressingpeaksindemandvolume.Allstaffwereassignedtoaspecificshiftandserviceareaandcollectiveunionagreementsdisallowedreallocatingstaffacrossshifts.Withinashift,managementcouldredistributestaffacrossserviceareas(e.g.fromtheIWstotheED)onatemporarybasis,butonlyifthestaffmemberagreed.Somehospitaldirectorsclaimedthattheirhospitalhadsufficientstaff,buttheywerepoorlydistributedacrossshiftsandserviceareas.Theystatedthattheir“handsweretied”bythecollectiveagreements,however,preventingthemfromredistributingstafftomatchdemand.Severaldirectorsalsocitedlowproductivityofsomestaffasacontributortolowthroughput.Aspreviouslymentioned,allhospitalscomplainedofthefailureofprimarycarefacilitiesandthereferralsystemtomitigatedemandforavoidablehospitalcare(e.g.forlow-acuityconditionsthatcanbeappropriatelytreatedatPHCfacilities).Hospitalstaffcitedmultiplereasonsforthissituation,including:limitedconsultationhoursandappointmentslotsatPHCfacilities,leadingtolongwaittimeswithnoguaranteeofaphysicianconsultation;PHCfacilitieswerestaffedbymedicalstudentsorrecentmedicalschoolgraduateswithlimitedclinicalexperienceandlittlefamiliaritywithreferralnorms;highturnoverofPHCphysicians;restrictionsintheuseofdrugsatPHCfacilities;andlackoffocusonchronicconditions.Withafewexceptions,mosthospitalshavedonelittletoworkwithPHCproviderstorectifythesituation,inpartbecausePHCfacilitiesandhospitalsareoverseenbyseparateandvertically-manageddirectorshipsinStateHealthSecretariatsandcoordinationbetweentheseentitiesisoftenlacking.VIII.DataIssuesandOpportunitiesGiventhatpatientflowsarenotonpolicyorhospitalperformanceagendas,hospitalinformationsystemsdonotcapturedataspecificallyformonitoringandimprovingpatientflowmanagement.Therefore,mostofthesamplehospitalsdidnotmaintainasetofmetricsthatcouldprovideevidenceorinsightsonfacility-specificpatientflowproblemsandtheircauses.Nevertheless,thehospitalsdidcarryoutrelativelywide-rangingandregulardatacollectionthroughinformationsystemsandregisters.Informativeanalysisandpresentationofhospitals’owndatawouldbeafastandrelativelyfeasiblewaytoassesspatientflowissues,informcorrectivemeasuresandgainsupportfrommanagersandstafftoimprovepatientflowpatterns.OnewaytoframethecurrentdataandmetricsituationistoexaminethehospitalsintermsoftheAnalyticInfluenceModeldisplayedinFigure8.Thismodelgaugesorganizationsintermsofhowdataandanalyticsareusedtoinfluencechangeandinformimprovement.Thesampledfacilitiesarecurrentlyatthelowerendofthecontinuum:theytendtoproduceinformationon‘whathappened’and‘whatishappening’forstandardmonitoringandreporting.Addressingpatientflowissueswillrequireshiftingtheinformationcultureupward,however,towardproducinginformationofhighervaluetoinformactionstoimproveperformance.Thefirststepwillinvolvedevelopingmetricsbasedontimeseries,trendandin-depthanalyticstogaininsightandknowledgeintopatternsand


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causesofpatientflowfailures(‘whyithappened’).Afuturestepwillbetousethesametimeseries,trendandmonitoringdata(andmetrics)toprovideguidanceon‘whatislikelytohappen’throughsimplemodelingexercises.14Inshort,understandingthesourcesofvariationthroughtrendandtimeseriesanalysisoffersopportunitiestodevelopcorrectionsorchangestoprocessestoimprovepatientflowmanagement.State-runhospitalsinMexicocollectthreetypesofdatathatcanbeusedformeasuringpatientflows.

(i) Dataincludedininformationsystems:Theseareusedtogeneratestandardizedindicatorsthataretotaledoraveragedonabiannualandannualbasis.15TheseindicatorsarethenusedtopreparestatisticalabstractsandstandardreportssubmittedtoStateHealthSecretariats.

(ii) Datademandedbystateauthorities:Theseareformonitoringtheimplementationofselectedpoliciesandprograms,aswellastargetcompliance(e.g.reductioninC-sections,neonatalmortality,infectionrates,etc.).Togetherwithdatafrominformationsystems,thesedataareusefulformonitoringandtargetcompliancepurposes,butaregenerallynotusedtosystematicallyimprovequality,efficiencyoroutcomesatthefacilitylevel.Nevertheless,dataalreadycapturedbytheinformationsystemforreportingandtargetcompliancepurposes,suchasnumbersofadmissions,discharges,EDarrivals,adverseevents,andinfectionrates,amongothers,canbeusefulforpatientflowanalyses(seeSection9below).

14Modelingcanfacilitatebetterresourceplanning(e.g.staffandoverflowplanning),enablesidentificationandresponsetoincreasesinutilization,andpreventsbottlenecksduetopoorscheduling.Whilesophisticatedsoftwareexistsforpredictivemodeling,mostpatientflowmodelingcanbeperformedwithelectronicspreadsheets(suchasExcel)andrelativelyinexpensivedecisiontools(e.g.Excel“add-ons”).15Theseinclude:installedcapacity(e.g.numberofoperationalORs),staffingbyprofessionalcategory,bedcensuses,efficiencyindicators(occupancyrates,bedturnoverratesandALOSbyward,ED,ICU,PACU),infectionrates,births,mortalityrates,production(numberofadmissions,discharges,EDandOPconsultations,surgeries,diagnostictests,mealsserved,poundsoflaundrywashed,etc.),maincausesofmortalityandmorbidity(ED,OP,IWs),andnumberofreferralsandcounter-referrals.Statehealthauthoritiesirregularlycomparetrendsacrosshospitalsforasubsetofindicators.


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Figure 10: Number of Programmed vs. Non-Programmed Admissions, Weekdays Only (June-

August, 2016)

Non-Programmed Admissions Programmed Admissions

0 100 200 300 400 500 600 700 800 900

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Num

ber o

f Pat

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s

Number of Hours

Figure 9: Number of Hours Between Admission to ED and Hospitalization, August 2016

(iii) Rawdata:Regularlycollectedbutnotnecessarilycompiled(systematically,atleast)bythehospitalsorincludedinstandardreports,thesedatacanbeusedtodevelopmetricsthatcontributetoassessing(andimproving)patientflows.Forexample,mosthospitalskeepdailyandshift-basedtimelogsandpatientregistriesonORstartandendtimes,EDinandouttimes,EDandIWvoluntarydischarges,programmedandnon-programmedsurgeries,andadmissionsanddischarges.Somehospitalsmaintainthisdatainhandwrittenregisters,whileothersenterthedatainelectronicspreadsheets.Importantly,giventhatdailyandshift-basedregistersarealreadypartofdatacollectionsystemsinthehospitals,movingtowardmoretime-sensitivedatacollectionforpatientflowpurposes(e.g.hourlyregistersofvoluntarydischarges,programmedsurgeries,bedavailability,etc.)wouldbefeasible.

Figures9through12provideexamplesfromonehospitalofmetricsandcorrespondingtrendortimeseriesanalysesthatcanbeperformedusingavailabledata.16WhileaverageEDwaittimesbetweenEDadmissionandhospitalization(e.g.admissiontoabedinanIW)averagedabout5.5hoursinAugust,2016,Figure9showsthatmanyEDpatientshadtowaitupto50hourstosecureaninpatientbed.Graphingthedistributionofwaittimescaninformadeep-diveanalysistounravelthepatientflowbottlenecks(suchasIWadmissiondelays)thatcontributetooverlylongEDwaitingtimes. Applyingtimeseriesmetricstorun-charts(andShewhartcharts17)helpsmanagersvisualizeandanalyzevariationsindemandandproduction,identifyingtheday(andhour)theyoccur.TherunchartillustratedinFigure10showsthelargevariationsinprogrammedandnon-programmedIWadmissionsbydayoverathree-monthperiodinasamplehospital.Interestingly,thepeaksinprogrammedadmissionsappeartocorrespondwiththevalleysinnon-programmedadmissions.Therecouldbeinterplaybetweenthetwotypesofadmissions,suggestingthatsomevariationis“artificial”ornon-random(e.g.drivenbyschedulingarrangementsforprogrammedadmissions)18However,withfurtheranalysis,theexactnature(ifany)ofthisrelationshipcouldbedetermined.

16DataforFigures9through12wereobtainedfromanin-depthassessmentofpatientflowsinoneofthesampledhospitalsandwerepreparedbytheinvestigativeteam.17AShewhartchartisatypeofrun-chartwithtimeseriesdata,butincludesameanandstatisticallycalculatedupperandlowerlimits.18Non-programmedoremergencyadmissionsrespondto“truedemand”,whichcontainsrandomor“naturalvariation.”However,inmostcasesthisvariationisstableovertime(exceptforepidemics)andthereforepredictableandreceptivetosystemredesignmeasures.


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Figure 12: Total Ward Admissions by Day and Nurse Staffing Levels, July-Sept 2016, Weekdays Only

NurseStafXingLevel

Medicalerrors,readmissions,infections,overcrowding,mortality

Waste

Similarly,Figure11depictsthevariationsinthenumberofoccupiedbedsinsurgicalandinternalmedicinewardsoverathree-monthperiod.Whileoccupationratesforbothwardsaveraged80percent,thechartshowslargeswingsindailyoccupancyrates,resultinginperiodsofcongestion(e.g.bedunavailability)andunderuse(e.g.“deadbedtime”).Moreindepthanalysiswouldhelpinformbetterbedmanagementtosmooththepeaksandvalleysofbedoccupancy.AnotherwaytoexaminetimeseriesmetricsisdisplayedinFigure12,whichoverlaysnursestaffinglevelsontotaldailyIWadmissions.Nursestaffingandperpatientratiosareawell-establishedpredictorofcarequalityandpatientsafety(seeAnnex1).Whilestaffingratiosarefixed,patientflowsarehighlyvariable.Errorsarecommittedinovercrowdedandstressfulconditions,leadingtoreadmissions,adverseeventsandmortality.Ontheotherhand,lowbeduseleadstounderuseofscarceresources.Metricsandanalysisalongtheselineshelpsmanagersrecognizepeaksandvalleysinoccupancyanddevelopinterventionstosmoothvariationsormatchstaffingtodemand.

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Figure 11: Number of Patients in Surgical and Internal Medicine Wards, Weekdays (July-Sept 2016)

Surgical Wards IM Wards

InspiredbyLitvak,2016.


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IX.TowardRelevantPatientFlowPerformanceMetricsinLow-ResourceSettingsRobustpatientflowmetricsshouldallowhospitalmanagerstoansweranumberofquestionsrelatedtopatientflowproblems:Whatandwherearetheproblems?Howseriousarethey?Whendotheyoccurandhowdotheyvaryovertime?Whataretheircausesandimpacts?Whatcanbedonetoanticipateandimprovepatientflows?Whatarethemanagementoptionsandtrade-offs?GivenresourcelimitationsfacingpublichospitalsinMexicoandotherdevelopingcountries,addressingthesequestionsshouldnotrequiremajorinvestmentsininformationanddatacollectionsystems,softwareortechnologies.19Internationalliteraturesuggeststhatmuchcandonetostrengthenpatientflowmanagementpracticesthroughtheuseofreadilyavailabledata,applyingrelativelysimplemethods(e.g.timeseriesandtrendanalysis)andemployingbottom-upimplementationstrategiesbasedonmultidisciplinaryteamsandcross-departmentallearningcollaboratives20(Greeneetal.,2012;Etheredge,2007;Ryckman,etal.,2010;Kibleretal,2010;Weintraubetal.,2010;IHI,2003).Beforeproceedingwiththerecommendedmetrics,itisimportanttoframethediscussioninthebroaderinformationenvironmentofMexicanpublichospitals.Asmentionedintheprevioussection,adoptingrelevantbutrigorouspatientflowmetricswillentailshiftingthedatacultureofhospitalsfrominformationproductionforstandardreportingpurposestoanalyticstoproduceknowledgeandinsighttofacilitateorganizationallearningand,ultimately,provideguidanceforimprovementandplanningpurposes(seeFigure8).Toaccomplishthis,datademandandusewillneedtobecomemoreinternally—ratherthanexternally—driven.Importantly,thechangingincentiveenvironmentinpublichospitalsinMexicofavorsthisshift.Throughdecentralization,thetraditionalcommand-and-controlapproachhasdiminishedandmanyhospitalshavebeenencouragedtoinnovatethroughtheirowninitiativestoimproveperformance.Indeed,severalhaverecentlylaunchedpatientflowimprovementinitiativestostrengthenlinkageswithambulatoryproviderstoreducetheflowofpatientswithlowacuityconditions(seeBox2).MosthospitalsseektoexpanddirectpaymentsfromSeguroPopular,whichpaysasingle(andfixed)packagerateforcertainhighcomplexityprocedures,requiringhospitalstoprovidesuchcaremoreefficientlytoavoidexceedingtheestablishedrate(suchasby19Wehavealreadyseenthatimprovingpatientflowmanagementpracticesisanalternativetoconstructingnewwardsandaddingbedsandstaff.20Implementationstrategiesarenotasubjectofthisreport.

Box2:HospitalInitiativestoAddressPatientFlowChallengesBelowareexamplesofinitiativestakenbysomeofthesampledhospitalstoimprovepatientflows.• OnegeneralhospitalworkedwithPHCunitstoofferconsultations on Saturdays and Sundays. (Programwassubsequentlydiscontinued.)

• Groupofpediatriciansinonematernal-childhospitalformedsocialmedia“Chat”groupstoprovideadviceand respond to clinical problems raised by primarycarephysicians.

• Another general hospital is finalizing a manual forPHC facilities to guide referrals to OP and the EDfromthePHClevel,andthedocumentationandtestresultsthatshouldaccompanypatients.

• Withthepurposeofdecreasingtheflowofreferredpatients with chronic conditions, one large generalhospitalorganizedatelemedicineprogramwithPHCcenters and small rural hospitals to providespecialist e-consultations jointly with local generalpractitioners.


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reducingextendedstays,readmissionsandmedicalerrors).Morerecently,fiscalconstraintsstemmingfromthedropininternationaloilpriceshaveresultedinacross-the-boardbudgetarycuts,andarealsodrivinghospitalstoenhanceefficiency.Insum,thechangingincentiveenvironmentfavorsmoreefficientuseofresources,andbetterpatientflowmanagementisonewayforhospitalstokeepwithincurrentrevenuestreams.Hospitalsarecomplexsystemsandnosinglemeasureissufficienttoinformmanagementofpatientflowproblemsandeffectivecorrectivemeasures.Internationalliteraturesuggeststhatafamilyofmeasuresisneededtoaddresstheaforementionedquestions.Further,anymetricshouldberobustformultiplepurposes:assessingproblems,designingsolutions,monitoringprogressandevaluatingimpact.Thefollowingcriteriashouldbeconsideredwhenselectingspecificmetrics:

• Location:Differentmetricswillberequiredfordifferentunitsandlevelsofcare.Somewillbedepartmentalspecific,othershospital-wide,embracingtheentireorganization,andstillotherssystem-wide,comprisingthehospitalandfacilitiesinitscatchmentarea.Thelatterwillinvolve,forexample,linkageswithotherproviders(e.g.primarycarefacilities).

• Type:Patientflowmetricscanbebroadlygroupedintothreecategories,thoughsomeoverlapexists:

o Patientflowoutcomemeasures,whichareorientedtowardreducingwaittimes(includingboardingtime)andpatientflight,increasingthroughput,anddecreasingcongestion,cancellationsanddelaysinservicetimes.

o Measuresofflowvariation,whichconsistsoftakingasubsetofpatientflowoutcomemeasurestogetherwithothermetricsandexaminingthemovertime(dayofweek,timeofday)tounderstandpatientflowpatterns,analyzepeaksandvalleysindemandandthroughput,anddetectpossiblecausesofpatientflowfailures.Understandingvariability–whetheritisartificial(non-random)ornatural(random)—iskeytocraftingapropermanagementresponse.

o Impactmeasures,whichareimportanttoshowquantitativeimpactsof“good”and“bad”patientflowmanagementpracticesoncaredeliveryandpatientwell-being.Thiscanbeaccomplishedbylinkingpatientflowvariabilityandoutcomemetricstoimpactmeasuresrelatedtoquality,patientsafety,patientsatisfaction,spendingandoutcomes.Unfortunatelythesemeasuresmaynotbereadilyavailableatthedepartmentallevel.

• DataAvailabilityandMetricFeasibility:Mostinformativeanalysesofpatientflowproblems(andsolutions)canbebasedondataandmetricsalreadyavailableinhospitals’informationsystemsanddataregisters.Slightadjustmentstodatacollectionsystemswillallowforthegenerationofadditionalmetrics,butthedatawillhavetobecompliedonaregularbasis,placedinformsthatfacilitatetrendandotheranalyses(e.g.runcharts)andmetricdevelopment,andallowforsimplestatisticalanalysis,ifneeded.21Data-to-metricformationshouldbebothfeasibleandeasilyderived(andvalidated)fromtheregisters(e.g.doortotreatmenttimesintheED).Asubsetofmoretimesensitivemetricswillrequiresomeadditionaleffort(e.g.admissionsanddischargesbyhourofday),butcanbefeasiblycollected.

21Patientflowanalysestendtoinvolvesimpledescriptivestatistics,suchameans,ranges,standarddeviations,coefficientsofvariationandtestsforrandomness(i.e.,Poissondistributionwithchi-squaretest).Allareavailableinstandardspreadsheetsoftware.


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Table1presentsexamplesofrecommendedmetricstogaugeandimprovepatientflowmanagementpracticesbasedonthisscopingexercise.Thelistdoesnotincludemetricsthatarealreadyavailablefromhospitals’informationsystemsandcanbeusedforpatientflowanalysis(averagelengthofstayinED,IWs,ICUsandPACUs,bedoccupancyandturnoverratesinIWs,length(inminutes)ofsurgicalprocedures,voluntarydischargesinEDandIWs,etc.).However,someoftheseexistingmeasuresmayrequiremorefrequentandtime-sensitivedatacollection(e.g.bydayorshift).Themetricspresentedinthetablebuilduponmeasuresanddataalreadyavailableininformationsystemsandregisters.Alimitednumberarenewmetrics,butrequireddatacanbeeasilycollectedthroughcurrentregistersorwithminoradjustmentsthereof.Theideaisthattherecommendedmetricsbetestedduringasubsequent“intervention”phaseoftheproposedpatientflowproject.Itisworthrepeatingthatthedatacompilation,metricdevelopment,andanalyticsrecommendedherearequitefeasibleinthepublichospitalenvironmentinMexico.Theydonotrequiremajortraining,additionalstaff,theapplicationofcomplexmethods,orinvestmentsinsophisticatedsoftware.However,methodologicallyassessing,managingandimprovingpatientflows,includingmetricdevelopmentandanalysis,willrequirestrongbuy-infromstatehealthofficials,hospitalmanagersandfront-linestaff.


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Table1:ExamplesofFeasibleMetricstobeTestedinaPhase2“Intervention”ProjectinMexico,byLocation,TypeofMeasureandDataAvailability

Metric

Location

TypeofMeasure

DataAvailabilityPFOutcome

FlowVariability

Impact

DepartmentSpecificMeasuresNo.andALOS(hours)ofpatientsinoverflowunits,observationrooms,boardingareasandhallwayswaitingtobeadmittedtoIWbedsbydayandhour

ED X

Patientdataavailable;LOSrequiresadditionaldatacollection

No.ofpatientswholeftwithoutbeingseenbyday ED X Willrequireregistrationofarrival

timeAveragepatientwaitingtimebydayandshiftfromdoortotreatmentandfromtreatmenttorelease(orIWadmission)

ED X X Dataavailable;mayrequireregistrationofarrivaltime

No.ofinpatientsinoverflowunitsorplacedininappropriateIWbydayandshift

IW X X Dataavailable

No.ofadmissions(programmedandnon-programmed)anddischargesbyIWbyday/hour

IW X Dataavailablebyshift;hourlyregistrationrequired

Timebetweenorderandactualpatientdischargeandadmissionbydayandshift IW X Newmetric;feasiblecollection

No.ofdischargeswithin2hoursafterdeemedmedicallyreadybyday IW X

Newmetric;feasibledatacollection

TimefromdecisiontohaveemergencysurgerytoOR ED,OR

No.ofdelayed,refusedorcancelledsurgeriesbydayandshift OR X X

Dataavailable

Actualandscheduledstarttimesforelectivesurgicalcasesbydayandshift OR X Dataavailable

Hospital-wideMeasuresNo.oflengthofstayoutlierspermonth ED,IW,

ICU,PCUX Newmetric;feasibledatacollection

butrequiresoutlierdefinitionNo.ofhospitalacquiredinfectionsduringhighutilizationperiods ED,IW X Dataavailable;timesensitive

registrationrequiredatdept.levelNo.ofadverseeventduringhighutilizationperiods

ED,IW,OR X Dataavailable;timesensitive

registrationrequiredatdept.levelReadmissionswithin30daysofdischargebymonth IW X

Dataavailable

Patientsatisfaction/experiencescoresrelatedtowaitsanddelaysbyday

ED,IW,OP X Newmetric,requiringinstrument

developmentanddatacollectionWorkersatisfactionscoresrelatedtoworkload(byshift)

ED,IW,OP X Newmetric,requiringinstrument

developmentanddatacollectionHealthSystem-wideMeasures

No.oflow-acuitypatientstreatedandreleasedbyday ED,OP X Dataavailablebyshift;hourly

registrationrequiredNo.oflow-acuitypatientsarrivingwithreferralfromPHCfacilitybyday ED,OP X Dataavailablebyshift;hourly

registrationrequired

References:Baker,D.,Pronovost,P.,Morlock,L.,Geocadin,R.,andHolzmueller,C.“PatientFlowVariabilityandUnplannedReadmissionstoanIntensiveCareUnit.”CriticalCareMedicine,37,no.11(2009).2882-2887.Etheredge,L.M.,“ARapid-LearningHealthSystem.”HealthAffairs,26,no.2(2007).W107-w118.Greene,S.,Reid,R.,andLarson,E.“ImplementingtheLearningHealthSystem:FromConcepttoAction.”AnnalsofInternalMedicine,157,no.3(2012).207-210.Kibler,J.,Free,M.,Lee,M.,Rinella,K.,White,J.,Ellison,E.,Miller-Phipps,J.,andSchilling,L.“KaiserPermanenteSouthernCalifornia,KaiserFoundationHospitalinAnaheim:OptimizingPatientFlow.”InManagingPatientFlowinHospitals:StrategiesandSolutions,2ndEdition,editedbyLitvak,E.JointCommissionResources(2010).113-128.Litvak,E.,ed.ManagingPatientFlowinHospitals:StrategiesandSolutions,2ndEdition.JointCommissionResources,(2010).LitvakE.andFinebergH.V.“SmoothingtheWaytoHighQuality,Safety,andEconomy.”NewEnglandJournalofMedicine,369(2013).1581-1583.Michtalik,H.,Yeh,H-C.,Pronovost,P.,andBrotman,D.“ImpactofAttendingPhysicianWorkloadonPatientCare:ASurveyofHospitals.”JAMAInternalMedicine,173,no.5(2013).375-376.Ryckman,F.,Adler,E.,Anneken,A.,Bedinghouse,C.,Clayton,P.,Hays,K.,Lee,B.,Morillo-Delerme,J.,Schoettker,P.,Yelton,P.,andKotagal,U.“CincinnatiChildren’sHospitalMedicalCenter:RedesigningPerioperativeFlowUsingOperationsManagementToolstoImproveAccessandSafety.”InManagingPatientFlowinHospitals:StrategiesandSolutions,2ndEdition,editedbyLitvak,E.JointCommissionResources(2010).97-111.Sayah,A.,Lai-Becker,M.,Kingsley-Rocker,L.,Scott-Long,T.,O’Connor,K.,andLobon,L.F.“EmergencyDepartmentExpansionversusPatientFlowImprovement:ImpactonPatientExperienceofCare.”TheJournalofEmergencyMedicine,50,no.2(2016).339-348.“TheBreakthroughSeries:IHI’sCollaborativeModelforAchievingBreakthroughImprovement.”InstituteforHealthcareImprovement.InnovationSeries2003,(2003).Weintraub,B.,Jensen,K.,andColby,K.“ImprovingHospitalwidePatientFlowatNorthwestCommunityHospital.”InManagingPatientFlowinHospitals:StrategiesandSolutions,2ndEdition,editedbyLitvak,E.JointCommissionResources(2010).129-151.White,C.,Statile,A.,White,D.,Elkeeb,D.,Tucker,K.,Herzog,D.,Warrick,S.,Warrick,D.,Hausfeld,J.,Schondelmeyer,A.,Schoettker,P.,Kiessling,P.,Farrell,M.,Kotagal,U.andRyckman,F.BMJQuality&Safety,23,vol.1(2014).1-9.

scoping exercise: patient flow management and metrics in ... · ed emergency department gh general...

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