Using data to drive change

39 Pages·2012·3.31 MB·English

Using data to drive change
and improve patient
outcomes
Dr. Graeme Duke
MBBS MD FCICM FANZCA GradDipEmergHealth
Intensive Care Specialist, Box Hill Hospital, Eastern Health
Thursday, 6 December 12
1. Problem?
2. Goal?
3. Audience?
4. Data?
5. Methodology?
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Data
powerful ammunition
ambiguous, amoral, agnostic
take care!
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Case Example 1
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Case Example 1
•
Problem? Preventable trauma deaths in-hosp.
•
Goal? Improve trauma care in Victoria.
•
Audience? Ambulance, Clinicians, DoHV, etc.
•
Data? High quality, large quantity.
•
Methodology? Detailed peer review deaths.
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Case Example 1


TRAUMA AUDIT METHODOLOGY

FRANCIS T. MCDERMOTT, STEPHEN M. CORDNER, ANN B. TREMAYNE AND THE CONSULTATIVE

COMMITTEE ON ROAD TRAFFIC FATALITIES IN VICTORIA*

Aust. N.Z. J. Surg. (2000) 70, 710–721

In-depth review
 !"#$% &'$&(()*()!($&
250 consecutive road trauma deaths
30+ personnel over 5-years
high rate of errors of care and potentially preventable deaths.

 ( !"#
++' *$$
++! 
++,&%&
Thursday, 6 December 12
(!% )-$($&. **&$$ (!
(//&'($(-&$& 
&-0
Case Example 1
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Death Preventability

Although the combined preventable/potentially preventable death rate reduced
significantly from 1997/1998 as compared with 2002/2003 this significant reduction has
not been mirrored in the time period of this project (2003-2005) (Figure). However, lower
death rates have been maintained. This lack of significant reduction in
preventable/potentially preventable deaths is due at least in part to inadequate
Case Example 1
compliance with accepted recommendations/guidelines/protocols.

Death Preventability Assessment
100%
90%
*
76%
80%
68% 69%
70% 64%
60%
50%
36%
40%
32% * 31%
30% 24%
20%
10%
0%
1997/1998 2001/2003 2002/2003 2003/2004
Non preventable Preventable/Potentially preventable

*P<0.05

The present situation requires improvement. The key factors required for
improvement include audit of compliance with guidelines/protocols (including the
development of statewide protocols and minimal standards), the development and
use of prompting systems, the appointment of a Director for each trauma service
receiving major trauma patients and better education. The appointment of such
Directors with responsibility for education, compliance with guidelines and protocols,
audit and educational feedback is essential. This is essential for effective
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accountability and successful implementation of progressive improvements in
process and outcomes. All these factors are covered in the summary of
recommendations and in the sections detailing the recommendations, comments and
implementation status. These sections are included within this report.

At the final meeting between the CCRTF and MTS to discuss all relevant findings it
was agreed that the responsibility for implementation and compliance with the
consensus recommendations lies with the Directors of Trauma Services for their
individual hospitals and with the State Trauma Committee for Victoria wide
compliance. It was also recommended that the resource requirements for the MTS
6 of 22
Case Example 2
•
Problem? Quality of medical care @ TNH?
•
Goal? Maximal clinical ‘bang’ for the audit ‘$’
•
Audience? Medical staff
•
Data? Comprehensive, high quality
•
Methodology? Directed effective peer review
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Stevensetal.
Case Example 2
usingthe‘socialdeterminantsofhealth’. 16 HaileyD,OhinmaaA,RoineR.Study validation.JChronicDis1987;40:
AustJRuralHealth2000;8:254–60. qualityandevidenceofbenefitinrecent 373–83.
12 TurrellG,MathersC.Socio-economic assessmentsoftelemedicine.JTelemed 21 HallW,RamachandranR,NarayanS,
1. Hospital Outcinoequmalitieesi nPall-rcaeusedanidction EquaTelteciaroe2n004 ;S10t:3a18t–e24. mortality JbanieA,nVijcayhakummaraS.rAkne lectronic
specific-causemortalityinAustralia: 17 MoffattJJ,EleyDS.Thereported applicationforrapidlycalculating
1985–1987and1995–1997.IntJ benefitsoftelehealthforrural Charlsoncomorbidityscore.BMCCancer
Epidemiol2001;30:231–9. Australians.AustHealthRev2010;34: 2004;4:94.
13 RobsonB,PurdieG,CormackD. 276–81. 22 KaneB,LuzS.Multidisciplinarymedical
UnequalImpactII:Ma¯oriandNon-Ma¯ori 18 StevensG,StevensW,PurchuriS,Kolbe teammeetings:ananalysisofcollabora-
CancerStatisticsbyDeprivationand J,CoxB.Radiotherapyutilisationin tiveworkingwithspecialattentionto
InternalMedicineJournal3R9ur(2a0l–0U9r)b2a8n3S–t2a8tu9s2002–2006. lungcancerinNewZealand:disparities timingandteleconferencing.Comput
Wellington:MinistryofHealth;2010. withoptimalratesexplained.NZMedJ SupportCoopWork2006;15:501–35.
14 RoineR,OhinmaaA,HaileyD. 2009;122:43–54. 23 KaneB,LuzS.Probingtheuseand
ORIGINAL ARTICLE
Assessingtelemedicine:asystematic 19 VinodS,BartonM.Actualversus valueofvideoformulti-disciplinary
reviewoftheliterature.CMAJ2001; optimalutilizationofradiotherapyin medicalteamsinteleconference.CBMS,
Validation of the hospital outcome prediction equation (HOPE)
165:765–71. lungcancer:whereistheshortfall?Asia pp.518-523,19thIEEESymposiumon
15 HershWR,HickamDH,SeveranceSM, PacJClinOncol2007;3:30–6. Computer-BasedMedicalSystems
model for monitoring clinical performance
DanaTL,PyleKragesK,HelfandM. 20 CharlsonM,PompeiP,AlesK, (CBMS′06),2006.[cited2006Jun22].
imj_1676283..289
Diagnosis,accessandoutcomes:update MackenzieC.Anewmethodof AvailablefromURL:http://doi.
G. J. Duke,1 M. Graco,2 J. Santamaria3 and F. Shann4
ofasystematicreviewoftelemedicine classifyingprognosticcomorbidityin ieeecomputersociety.org/10.1109/CBMS.
services.JTelemedTelecare2006;12 longitudinalstudies:developmentand 2006.131
1CriticalCareDepartment,TheNorthernHospitaland2NorthernClinicalResearchCentre,NorthernHealth,Epping,and3IntensiveCareDepartment,
(Suppl.2):3–31.
StVincent’sHospitaland4IntensiveCareUnit,RoyalChildren’sHospital,Melbourne,Victoria,Australia
Recent trends in Victorian risk-adjusted in-hospital mortality:
1999–2009
Key words G. Duke,1 A. Barker,2AJ.bSsatnratacmt aria3 and M. Graco4
clinicalindicators,hospitalperformance,risk
adjustment,health-care1NoourttchoemrneHs.ealth,2CentreofRBesaecakrcghrEoxcuenlledn:ceTihnePataieimntSoaffettyh,MisosntaushdUyniwvearssityt,o3SvtaVlinidceantte’saHorsipsikta-l,aMdejulbsotuerdneh;TohsepUitnaivlersityofMelbourne
and4NorthernClinicalResearcohuCtecnotmre,eMperlbeoduircntei,oVnicetoqriua,aAtiuostnra(liHa OPE)usingastatewideadministrativedataset.
Correspondence Methods:Retrospectiveobservationalstudyusingmultivariatelogisticregres-
GraemeJ.Duke,CriticaKlCeayrewDoerdpsartment,The sion modelling. CAablibstrraaticotn and discrimination were assessed by standardized
NorthernHospital,185hCoosoppitearl,Smtroeretat,litEyp,pteinmgp,oraltrend,
mortality ratio (SMR), area under the receiver operating characteristic plot
Background: The background of the study is a comparison of risk-adjusted mortality
Vic.3076,Australia. epidemiology.
(ROC AUC), Hosmer–Lemeshow contingency tables and goodness-of-fit sta-
across hospitals from different jurisdictions is now common worldwide.
Email:[email protected]
Correspondence tistic in an indepeAnidme:nTtodaextaasmeitn,eantedmipnoraalllt2re3nidmspinorrtisakn-atdtjeursttieadrym,omrteatlritoypionliVtaicntoria over the last
Received30NovemberG2r0ae0m7;eaDccuekpet,e1d855CooperStraenetd,Eprpeignigo,nViacl. hosdpeictaadles.. The dependent variable was in-hospital death. All
February2008. 3076,Australia. consecutiveadultMheotshpoidtasl:sReeptarorasptieocntivsebceothwoertesntu1dyJuolfy62.8090m4ilalinodn3ad0uJltu(n>e142y0e0a6rs,)patientepisodes
Email:[email protected] obstetrfircoman2d3mdaayjo-rcVasicetoorinalnypaudblmicihssoisopnitsa,lsfbreotmweaenll1a9c9u9teanhde2a0l0th9.Tsehre-primaryoutcome
doi:10.1111/j.1445-5994.2008.01676.x vices within the Swtaasteino-hfoVspicittaolrdiae,atAh.uTshtrraeleiamweaesruereisnwcleuredecdal.culated: the crude mortality rate, risk-
Received10November2011;accepted21 adjusted mortality rate and standardised mortality ratio (SMR). The Hospital Outcome
Results:Atotalof379676consecutiverecords(1July2004to30June2005)
March2012. Prediction Equation (HOPE) was applied to generate estimates of predicted mortality
was used to derive the HOPE model. Six variables (age, male sex, admission
thatwereusedtocomputetheSMRandrisk-adjustedmortalityrates.TheHOPEmodel
doi:10.1111/j.1445-5994.2012.0d27ia97g.nxosis, emergency admission, aged-care resident and inter-hospital trans-
includes 26 exogenous risk factors for which providers have no influence. The model
fer) were selected for inclusion in the final model. It was validated in the 384
was calibrated using the 2004–2005 data. Temporal mortality trends from 1999–2009
489 consecutivewreecreoredvsalufraotemd utshinegfnoellgoawtivinegbinyoemarial(1regJruelsysio2n0f0o5r ctroud3e0mJourtnaleity and SMR esti-
2006). The 95%mcoantefisdaenndcreanindotemr-vinatlefrocerptthheieSraMrcRhicwalalsog0i.s9ti8c–r1eg.0re2s,siaonndfofrorristkh-aedjusted mortality.
ROC AUC, 0.87–R0e.8su8l.tsD:isTchreimstuindaytpioonpualantidon(oinnceludoerdm84or4e2)3cina-lhiborsaptitiaolndecartihtesr(i1a.2%).Crudemor-
were achieved inta2li2ty(r9is6k%de)cloinfetdhfero2m31h.5o%spiinta2l0s.00to1.1%in2005–2009(incidencerateratio(IRR):
0.96; 95% confidence interval (CI): 0.95–0.97; P < 0.001). There were 1.39 million
Conclusion: The HOPE model is a simple risk-adjusted outcome prediction
episodes in the HOPE calibration cohort. Between 1999 and 2009, the SMR decreased
tool, based on six variables from data that are routinely collected for admin-
from 1.4 to 0.9 (IRR = 0.91; 95% CI: 0.90–0.97; P < 0.001) and adjusted mortality risk
istrative purposes and appears to be a reliable predictor of hospital outcome.
Thursday, 6 December 12 declinedfrom2.1%to0.9%(oddsratio=0.94,95%CI:0.94–0.94,P<0.001).Declining
mortality trends were evident in the tertiary, metropolitan and regional peer groups (P
< 0.001).
Conclusion: Analysis of in-hospital risk-adjusted mortality trends using the HOPE
Introduction modpelerinfodricmataenscsiegnairfiecdanetsiirmabprleovbeumteanrteinlespsatrieeandtiolyutacvomaielasbilne,thine State of Victoria
overptahretpbaestcaduecsaedoe.f the complexities of data capture, storage
Monitoring of clinical performance may be undertaken
and analysis.1,2
by reviewing discrete cases, groups of similar cases or the Systemwide monitoring must also take into account ©2012TheAuthors
care of all patient1s21in8 a health-care system. Individual InternalMedicineJournal©2012RoyalAustralasianCollegeofPhysicians
those factors, such as casemix and illness severity, that
casereviewandgroupanalysismaybeeasiertocarryout
are outside the influence of the hospital or system being
and more precise than a systemic review but they may
monitored.3,4 Risk adjustment is crucial. The input vari-
not necessarily reflect the overall quality of care within
ablesofavalidriskadjustmenttoolmustbeindependent
the hospital or system. Global measures of health-care
of the therapy provided, otherwise the monitoring
processcannotassessthequalityofthatcare.Thedepen-
dent variable (outcome) being monitored, such as major
morbidity or unexpected death, must be clinically
Funding: Research partly funded by Department of Human
relevant and reflect the quality of care delivered. Each
Services, Victoria, Australia.
Potential conflicts of interest: None definition must be simple and unambiguous to minimize
©2009TheAuthors
Journalcompilation©2009RoyalAustralasianCollegeofPhysicians 283