1. Squeezing Juice from Clinical Data Repositories: Information for Patient Management and ABF Revenue Susan Smith Cardiothoracic Surgical Clinical Information Service The Prince Charles Hospital, Queensland Health, Brisbane Ian Smith St Andrews Medical Institute St Andrews War Memorial Hospital, Uniting Healthcare, Brisbane

2. 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

3. 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

4. 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

5. For the process of secondary data use to support decision- making to occur we need to extract meaningful information from growing stores of data _________ _________Background

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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 Paretos Principle or Jurans observation of "vital few and trivial many": 80% of cost due to 20% errors 1. ABF Project

12. 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 cant double capture Still require Med Recs to correct coding Coding module Time delay /cant change after submission Cant audit everything Estimated Benefits: Estimated increased revenue identified Jul-Jan: at least $200,000 Audit feedback noticeably improves coding quality 1. ABF Project

13. 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.

14. 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.

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

18. Variable ORSig. 95% C.I. for OR z LowerUpper BMI0.9170.0040.8650.973-0.10721 Aborig/TSI4.3570.0031.66111.4301.475711 Diabetes0.4900.0380.2500.961-0.58104 Preop Resus within 1hr86.1980.0034.7231573.3274.537611 To MOT direct from Cath Lab 15.3380.0012.96479.3632.577087 Constant-0.81216 Multivariate regression Odds Ratios for predictors of reoperation for bleeding following isolated CABG, 2002-2005. 2. Analysis of Trends in Reoperation for Bleeding post-CABG 2. Analysis of Trends in Reoperation for Bleeding post-CABG. Expected risk (green) with observed (blue) reoperation for bleeding following isolated CABG, 2002-2005.

19. Elective Non-Elective

20. 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.

21. Registry collected data can support a variety of uses Requires a ppropriate tools, expertise and resources Can be shown to have tangible and intangible benefits Conclusions