Squeezing Juice from Clinical Data Repositories: Information for Patient Management and ABF Revenue Susan Smith Cardiothoracic Surgical Clinical Information

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

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

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

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

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

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

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)


“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


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


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

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

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

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.

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)



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



EWMA Reoperation for Bleeding- All Cardiac Surgery

0.07UCL

0.06CL

0.04LCL

0.00

0.01

0.02

0.03

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0.05

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0.10

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Quarter, Years

Inci

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ce A

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ge

2. Analysis of Trends in Reoperation for Bleeding post-CABG.

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

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EWMA Reoperation for Bleeding - Isol CABG

UCL 0.05

CL 0.03

LCL 0.02

0.00

0.01

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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

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0.080

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EWMA (Exp)

UCL

LCL

EWMA (Obs)


Expected risk (green) with observed (blue) reoperation for bleeding following isolated CABG, 2002-2005.

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

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7 825 1689 2260 2817 3348 3752 4184 4573 4960 5356 5703 6031

Patient Number

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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

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ACS Pathwys introduced

Aprotinin withdrawn

TXA use reduced

TXA introduced

TXA Peak use

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?



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

Documents

Squeezing Juice from Clinical Data Repositories: Information for Patient Management and ABF Revenue Susan Smith Cardiothoracic Surgical Clinical Information