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1 Exploring QUEST Mortality Understanding the Baseline Data and Using Clinical Advisor and Quality Manger to Create Actionable Hypotheses for Intervention.

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Presentation on theme: "1 Exploring QUEST Mortality Understanding the Baseline Data and Using Clinical Advisor and Quality Manger to Create Actionable Hypotheses for Intervention."— Presentation transcript:

1 1 Exploring QUEST Mortality Understanding the Baseline Data and Using Clinical Advisor and Quality Manger to Create Actionable Hypotheses for Intervention Eugene Kroch, Ph.D., Vice President and Chief Scientist Richard A Bankowitz, MD MBA FACP, Vice President and Medical Director

2 2 Topics Baseline reports Model comparison Variation across hospitals Size effects Trending Two-year time frame General trends Trend ranges and volatility Palliative care patterns Exploring Potential Drivers of Mortality using Clinical Advisor or Quality Manager

3 3 Index > 1 : Actual mortality is greater than predicted (opportunity) Index < 1 : Actual mortality is less than predicted Observed Actual Expected Predicted Index O/E Ratio = QUEST Mortality Measure Expected Predicted Observed Actual = Expected Predicted

4 4 Clinical Principal Diagnosis (terminal digit) Severity Weighted Comorbidities Procedures Urgency of Admission Neonatal Birth Weight Demographic Age, Gender Household Income Facility Type Race Discharge Disposition Referral and Selection Admission Source (e.g Transfer in) Payor Class Travel Distance Facility Type CareScience Risk Prediction APR-DRG Severity Classification Base APR-DRG Age Gender Discharge status Diagnoses Procedures Birth weight 4 Levels of: Severity (resource demand) Risk of mortality Measuring Risk (alternatives)

5 5 AspectCareScienceAPR-DRG Risk ScalingContinuous 4 Buckets in APDRGs Specification (structure)Stratified RegressionsDecision-Tree Logic VariablesClinical/Demog/SelectionSubset of CSI factors Secondary DiagnosesCACR (complication adj)Selected SDx Population StratificationDiagnosisDRG Calibration DataAll Payor State & ClientPerspective (Client) Statistical InferenceRegression-based errorsCell means Summary of Model Differences

6 6 1 234 Continuous Severity Scale APR-DRG severity buckets CareScience continuum Patient 1 Patient 2 Patient 3 Patient 1 Patient 2 Patient 3 Under APR-DRGs patient 2 is lumped together with Patient 1, even though under continuous severity scaling patient 2 is more like patient 3. Illustration of Precision

7 7 Baseline O/E Variation across Hospitals Baseline: 161 hospitals – 2006q3 to 2007q2 CareScience and APR-DRGs are very close (next slide) CareSciAPR-DRG Mean 0.990.96 Median 0.950.90 Top quartile 0.820.77 Cross hospital range = 0.50 to 2.00 All 12 hospitals with O/E ratios > 1.35 are relatively small (smallest third in size) Not so for 16 hospitals with O/E ratios < 0.65

8 8 Baseline Comparison of O/E Ratios Correlation = 94%

9 9 Baseline Distribution of O/E Ratios Smaller hospitals

10 10 O/E Trends 8 quarters: 2005q3 to 2007q2 Overall pattern O/E ratio falls by about 12% over the 8 quarters Trend range For 4-quarter moving averages 40% decline to 20% increase Volatility Time volatility is inversely related to size (correlation is about -50%) Quarter-on-quarter O/E changes greater than 0.4 are concentrated in smaller hospitals (<1000 disch. per qtr.).

11 11 Overall Trend over 8 Quarters Moving Avg Mean O/E ratio has fallen about 12%

12 12 Strong Mortality Declines Note Bapt Mem

13 13 Trend Break Example

14 14 Distribution of Palliative Care Coding Half of hospitals have less than 2 per thousand

15 15 Palliative Care Mortality Distribution Mean = 53%

16 16 QUEST Mortality Drill Down Report to be Released End of April

17 17 Exploring Drivers of Mortality Goal Explore in-patient mortality by finding ACTIONABLE clusters – IE patient cohorts in which mortality rates might be improved with an intervention (Part of a PDCA cycle) »Common cause – systemic problems »Special cause – isolated but important causes Definition Excess Deaths = Total deaths in excess of predicted by the risk adjustment model = (obs % - exp %) * N patients Excess Deaths can be negative in this definition Therefore sum of all non-negative Excess Deaths over all patient subsets will be greater than hospital-wide results (hospital-wide obs – hospital-wide exp) * Total Discharges In other words, there are always pockets of opportunity Approach Use CA or QM to determine excess death by categories »Admission Source, Age, Principal Dx, APR-DRG or DRG, severity, other

18 18 A Tale of Two Hospitals Two Sample Hospitals Hospital 1: > 375 beds, non-teaching, urban, o/e < 1.00, 2 nd Qrtle Hospital 2: 1.00, 3 rd Qrtle Questions What conditions are associated with excess mortality across the entire hospital population? Conditions can be primary or secondary conditions (e.g., sepsis is not always coded as primary diagnosis) Is there evidence for special cause or common cause variation by common groupings? »Admission source, care progression, age, principal dx, etc. Goal Determine top three or four focus areas in which to implement PDCA cycles to improve in-patient mortality

19 19 Hospital 1: Excess Death by Admit Source – Aggregate NO Excess Deaths by any given admission source No evidence of special cause variation at hospital-wide level Notice the hospital-wide o/e is < 1.00 and very close to TPT Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix

20 20 Hospital 1: Excess Deaths by Age Group – Aggregate Level Possible special cause variation in patients over 84 years old Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix

21 21 Hospital 1: Excess Mortality by Primary Dx Hospital-Wide Excess Deaths (partial) sorted by excess deaths Nine Excess Deaths with Sepsis as Primary Dx Remember this hospital has an O/E = 0.88. However, there are still many pockets of opportunity. Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix

22 22 Expected Rate Hosp 1: Excess Mortality by ICD9 Secondary Dx Hotpital- Wide Excess Deaths (partial) – sorted by Excess Deaths Notice: 1) Observed and expected mortality for Palliative Care Notice: 2) Many other pockets of opportunity – (note these are not mutually exclusive patients) Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix

23 23 Hosp 1: Excess Mortality by ICD9 Secondary Dx Hospital- Wide Excess Deaths (partial) – sorted by Clinical Categories Notice: Grouping Excess Deaths into meaningful categories may help opportunities stand out Source: Clinical Advisory Quality Reports with Excess Deaths and Clinical Categories added, Clinical Categories are user defined- see Appendix

24 24 Hosp 2: Excess Mortality Pareto Analysis by Admit Source (all admits) Evidence of special cause variation in patients by admit source. Almost all Excess Deaths are from two sources Source: Clinical Advisory Quality Reports with Excess Deaths and Clinical Categories added, Clinical Categories are user defined- see Appendix

25 25 Hospital 2: Excess Mortality- ED Admissions Pareto Analysis (partial) by Excess Deaths Clinical Category Sources of ED mortality: Respiratory, Stroke, Renal, Sepsis, and Low Mortality Populations Source: Clinical Advisory Quality Reports with Excess Deaths and Clinical Categories added, Clinical Categories are user defined The category Low mortality population is based upon the APRDRG expected mortality. Low Mortality and End of Life Care are arbitrarily defined, not clinically determined, and are intended to aid analysis only- see Appendix

26 26 Hospital 2: Excess Mortality – Transfer from hosp Pareto Analysis (partial) by Excess Deaths Clinical Category Sources of Transfer Patient mortality: ? End of life issues Source: Clinical Advisory Quality Reports with Excess Deaths and Clinical Categories added, Clinical Categories are user defined The category Low mortality population is based upon the APRDRG expected mortality. Low Mortality and End of Life Care are arbitrarily defined, not clinically determined, and are intended to aid analysis only- see Appendix

27 27 Hosp 2: Excess Mortality by ICD9 DX – ALL Dx Dx with more than 5 Excess Deaths – grouped by category (Xcess > 5 deaths) Clinical Category Source: Clinical Advisory Quality Reports with Excess Deaths and Clinical Categories added, Clinical Categories are user defined The category Low mortality population is based upon the APRDRG expected mortality. Low Mortality and End of Life Care are arbitrarily defined, not clinically determined, and are intended to aid analysis only- see Appendix

28 28 Approaching Drivers of Mortality * Illustrative Examples of Potential Secondary Drivers Sepsis Hospital – Level Risk Adjusted Mortality (O/E Ratio) Respiratory Conditions Cardiac Related and Shock End of Life Care Early appropriate level of care (ICU) Elderly and other high risk groups Early recognition and intervention Timely transfer to ICU Avoidance of VAP Early recognition of resp compromise Proper use of V667 palliative code Improved use of cardiac monitors Adherence to ACC Protocols Early transfer to ICU if needed Rapid response team Post operative resp care protocols Early identification of patients Potential PRIMARY DRIVERS Potential SECONDARY DRIVERS GOAL *Data mining to examine top drivers of mortality is currently in progress Appropriate setting: hospice v acute

29 29 QUESTIONS? Eugene A. Kroch Richard A. Bankowitz

30 30 Appendix

31 31 Step 1 Step 2 Step 3 APR-DRG Process Flow NB: Risk code is mapped into mortality risk based on the mortality rates from calibration data base.

32 32 Outcome = age + sex + distance + proc + age * * * * * * * * * * * * * * * * 1.0 - 0.9 - 0.8 - 0.7 - 0.6 - 0.5 - 0.4 - 0.3 - 0.2 - 0.1 - | | | | | | | | | 10 20 30 40 50 60 70 80 90 Y = 0 + 1 X 1 + 2 X 2 + … + n X n dependent variableindependent variables / explanatory variables = 0.074 From CS client base sample CareScience Regression Model Principal Dx – Pneumonia – one of 142 disease strata

33 33 Trend Distribution across Hospitals Mean = -12%

34 34 Trend Volatility Smaller hospitals (avg 25% of mean size)

35 35 Lives Saved by Disease

36 36 Lives Saved Rate vs. Mortality Rate

37 37 Appendix: How were the Excess Death Tables Made? Hospital 1: Excess Death by Admit Source CA Quality Reports > Mortality Reports > Select QUEST time period > Select Patient Type Inpatient > Drill by Admit Source > Export to Excel Add column Excess Death (Mortality – Expected Mortality)* Cases Sort by Excess Death Hospital 1: Excess Death by Age Group CA Quality Reports > Mortality Reports > Select QUEST time period > Select Patient Type = Inpatient > Drill by Detailed Age Categories > Export to Excel Add column Excess Death (Mortality – Expected Mortality)* Cases Sort by Excess Death Hospital 1: Excess Death by Primary Dx CA Quality Reports > Mortality Reports > Select QUEST time period > Select Patient Type Inpatient Drill by Principal Dx > ICD9 > Export to Excel Add column Excess Death (Mortality – Expected Mortality)* Cases Sort by Excess Death Hospital 1 Excess Death by Secondary Dx – Sort by Excess Death CA Quality Reports > Mortality Reports > Select QUEST time period > Select Patient Type Inpatient Drill by Secondary Dx > ICD9 > Export to Excel Add column Excess Death (Mortality – Expected Mortality)* Cases Sort by Excess Death Hospital 1 Excess Death by Secondary Dx – Sort by Clinical Grouping CA Quality Reports > Mortality Reports > Select QUEST time period > Select Patient Type Inpatient Drill by Secondary Dx > ICD9 > Export to Excel Add column Excess Death (Mortality – Expected Mortality)* Cases Sort by Excess Death Assign categories to the top source of Excess Death – any grouping that is clinical useful will do Resort by the categories You may color if you like to enhance visual communication Note: All Clinical Categories are user defined and are arbitrary, The category Low mortality population is based upon the APRDRG expected mortality. Low Mortality and End of Life Care are arbitrarily defined, not clinically determined, and are intended to aid analysis only. They are not intended as a substitute for clinical judgment.


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