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Squeezing Juice from Clinical Data Repositories:
Information for Patient Management and
ABF Revenue
Susan SmithCardiothoracic Surgical Clinical Information ServiceThe Prince Charles Hospital, Queensland Health, BrisbaneIan SmithSt Andrews Medical InstituteSt Andrews War Memorial Hospital, Uniting Healthcare, Brisbane
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Background
A variety of Clinical Information Systems (CIS) now exist • Operational & patient management systems eg
– Medical imaging, diagnostics– Pathology– EMR – ED, Anaesthetics, Operating Theatre, Oncology,
GP/Community, etc• Managerial/tactical
– Bookings/referral systems• Strategic
– Registries, vital statistics– Research databases
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Increasing pressure for secondary use of clinical information to support decision-making due to a number
of drivers: • Health System Reform, Restructure & Transformation• Information Revolution• ‘Evidence-Based’ paradigm• Accountability & Performance monitoring• Q&S• Multidisciplinary Research Activities• New Analytical tools
Background
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Increasing Development of analytical tools/technology eg• Data Integration
– Data Warehousing– In-line memory– Hadoop
• Business/Clinical Intelligence– Interactive reports– Dashboards
• Analytics– Statistical Process Control for Healthcare– Geospatial Analytics– Visual Analytics– Data Mining– Predictive statistics
Background
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For the process of secondary data use to support decision-making to occur we need to extract meaningful information from growing stores of data
DATA DATA --> INFORMATION > INFORMATION --> KNOWLEDGE > KNOWLEDGE --> WISDOM > WISDOM --> PRACTICE/POLICY> PRACTICE/POLICY
Relationship of Data and Knowledge:Relationship of Data and Knowledge:
Source: L Ryan, Source: L Ryan, HIC 2007HIC 2007
= Evidence Based = Evidence Based HealthcareHealthcare
= Evidence Based = Evidence Based Service Service ManagementManagement
= Evidence Based = Evidence Based Decision MakingDecision Making
_________Background
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Purpose of CSCIS• Provide accurate and reliable, clinically actionable information, from
data available relating to Cardiothoracic surgical practice, to support/facilitate the best patient outcomes
• Primary functions relate to:– Outcomes Data Acquisition, – CTSx Morbidity & Mortality Peer Review Reporting/Support, – Clinical Audit Reporting, – Supporting Clinical Research cohort definition,– Support retrospective observational analyses
Cardiothoracic Surgery Clinical Information Service
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To perform these duties CSCIS have:• Data Registry Database & Ancilliary data repositories, DLU• Tools – Access, Excel, SPSS, QI Macros• Clinical Informatics
– Clinical Knowledge & experience (RN x3, Hosp scientist)PLUS– Health Informatics knowledge and experience (HIMOx2, MHlth
Sci (CDM), M Epi)PLUS– Public Health/Epidemiology/Biostatistics skills (Outcomes /Audit/
analysis reporting, SPSS training)
Cardiothoracic Surgery Clinical Information Service
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“Succeeding with … analytics requires a database and information infrastructure that supports it, plus a culture that bridges the gap between DBAs and analysts”
Wayne Eckerson , Director of Research for The Data Warehousing Institute
- Assuming that the gap between analysts and clinicians is also bridged!
- Socio-technical and cultural issue
Cardiothoracic Surgery Clinical Information Service
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Two examples of extending the use of registry-based information:
• Activity Based Funding DRG coding Audit against Clinical Data• Analysis of Trends in Reoperation for Bleeding post CABG
Cardiothoracic Surgery Clinical Information Service
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Data Audit and exchange with Medical Records FACT group to optimise accuracy of DRG allocation
Levels of crosscheck for data capture• Cardiac Surgery Level-
– Referencing against Clinical CTSx Register data, crosscheck DRG allocations to identify any inconsistent with cardiac surgery codes
– eg cost difference: from $1,750 - $40,960• Procedure Level -
– confirm Valve, CABG, Other CTSx eg all concomitant procedures, complexity of procedure, Other CardThor related DRG appropriate
– eg cost difference: from $1,750 - $40,960• Complication Comorbidity Levels (CCLs) -
– Sort Clinical Data records by Euroscore Risk Score (Clinical Severity index)– Cross check Clinical data with DRG coding for records with Euroscore >8%– If high risk score cases not coded to appropriate DRG codes, check for capture of
comorbidities• Invasive investigations Level
– eg sort Clinical data for inpatient preop coronary angiogram procedure, crosscheck against DRG allocation
– eg cost difference: up to $13,456
1. ABF Project
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This could be done by audit of DRG output against charts, however this would be more costly and by use of the clinical data we can target the critical procedures, rather than review all cases.
ie Pareto’s Principle or Juran’s observation of "vital few and trivial many":
80% of cost due to 20% errors
1. ABF Project
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Requirements:• Clinical Data repository + DRG expertise + Clinical DM expertise• Good relationship with Med Recs• Time allocation – approx 2 hrs per month
(for 1350 cases (1200 discharges) pa of CTSx complexity)
Limitations:• Nb some complications are inherent to particular DRGs ie can’t
double capture• Still require Med Recs to correct coding Coding module• Time delay /can’t change after submission• Can’t audit everything
Estimated Benefits:• Estimated increased revenue identified Jul-Jan: at least $200,000• Audit feedback noticeably improves coding quality
1. ABF Project
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Bleeding is a significant consequence of Cardiac Surgery. • Reported rates vary from 2-8%• TPCH identified increasing rate over 2002-2010 through regular
peer review M&M meetings
How do we use our data to investigate this?
• Highly complex mechanism with many predictors and confounders
– Physiological patient factors– Procedural factors– Care management factors
2. Analysis of Trends in
Reoperation for Bleeding post-CABG.
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Data Issues:– Some good quality data
• eg primary outcome: reoperation– Incomplete data on potential modifiers of bleeding
rates eg• Preop antiplatelets therapy (poor documentation)• Use of antifibrinolytics – aprotinin, aminocaproic acid, TXA
(not in CTSx Register – imprest stock)• Other system modifiers such as use of clinical pathway not
captured (eg ACS)
2. Analysis of Trends in
Reoperation for Bleeding post-CABG.
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Analysis methodology options– Traditional multivariate regression does not reveal
factors that explain the increasing trend, difficult to discern trends for different procedures, etc
– Statistical Process Control• Much used in industrial and engineering processes• Being adopted more widely in healthcare• Exponentially Weighted Moving Average (EWMA)
statistically robust and clinically intuitively interpretable
2. Analysis of Trends in
Reoperation for Bleeding post-CABG.
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EWMA Reoperation for Bleeding- All Cardiac Surgery
0.07UCL
0.06CL
0.04LCL
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
1Q02
2Q02
3Q02
4Q02
1Q03
2Q03
3Q03
4Q03
1Q04
2Q04
3Q04
4Q04
1Q05
2Q05
3Q05
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
Quarter, Years
Inci
den
ce A
vera
ge
2. Analysis of Trends in Reoperation for Bleeding post-CABG.
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EWMA Reoperation for Bleeding - All Non-CABG
0.12UCL
0.08CL
0.04LCL
0.00
0.02
0.04
0.06
0.08
0.10
0.12
1Q02
2Q02
3Q02
4Q02
1Q03
2Q03
3Q03
4Q03
1Q04
2Q04
3Q04
4Q04
1Q05
2Q05
3Q05
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
Quarter, Year
Inci
den
ce A
vera
ge
EWMA Reoperation for Bleeding - Isol CABG
UCL 0.05
CL 0.03
LCL 0.02
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
1Q02
2Q02
3Q02
4Q02
1Q03
2Q03
3Q03
4Q03
1Q04
2Q04
3Q04
4Q04
1Q05
2Q05
3Q05
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
Quarter, Year
Inci
den
ce A
vera
ge
2. Analysis of Trends in Reoperation for Bleeding post-CABG.
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VariableOR Sig.
95% C.I. for OR
zLower Upper
BMI 0.917 0.004 0.865 0.973 -0.10721
Aborig/TSI 4.357 0.003 1.661 11.430 1.475711
Diabetes 0.490 0.038 0.250 0.961 -0.58104
Preop Resus within 1hr 86.198 0.003 4.723 1573.327 4.537611
To MOT direct from Cath Lab
15.338 0.001 2.964 79.363 2.577087
Constant -0.81216
Multivariate regression Odds Ratios for predictors of reoperation for bleeding following isolated CABG, 2002-2005.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.080
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
4200
4400
4600
4800
5000
5200
5400
5600
5800
6000
6200
Patient Number
Reo
per
atio
n R
ate
EWMA (Exp)
UCL
LCL
EWMA (Obs)
2. Analysis of Trends in Reoperation for Bleeding post-CABG.
Expected risk (green) with observed (blue) reoperation for bleeding following isolated CABG, 2002-2005.
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Elective
Non-ElectiveReturn to MOT For Non-Elective Vs Elective CABG Only Cases
0.029
0.024
0.020
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0.050
7 825 1689 2260 2817 3348 3752 4184 4573 4960 5356 5703 6031
Patient Number
Av
era
ge
in
cid
en
ce
EWMA Reoperation for Bleeding - Isol CABG
UCL
0.05
CL 0.03
LCL
0.02
0.00
0.01
0.02
0.03
0.04
0.05
0.06
1Q02
2Q02
3Q02
4Q02
1Q03
2Q03
3Q03
4Q03
1Q04
2Q04
3Q04
4Q04
1Q05
2Q05
3Q05
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
Quarter, Year
Incid
en
ce A
vera
ge
ACS Pathwys introduced
Aprotinin withdrawn
TXA use reduced
TXA introduced
TXA Peak use
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Requirements– Analytical tools: Excel, SPSS, QI Macros – Expertise: Clinical Data Management, Epidemiological, Statistical
Process Control methodology– Resources: fte, financial & clinical support
Limitations: – Indirect /circumstantial evidence– Caveats re data quality
Benefit: – How can this enhance clinical decision-making?– How can this direct further work?
2. Analysis of Trends in
Reoperation for Bleeding post-CABG.
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Registry collected data can support a variety of uses
Requires appropriate tools, expertise and resources
Can be shown to have tangible and intangible benefits
Conclusions