1 exploring quest mortality understanding the baseline data and using clinical advisor and quality...

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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

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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

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Index > 1 : Actual mortality is greater than predicted (opportunity)

Index < 1 :Actual mortality is less than predicted

ObservedActual

ExpectedPredicted

IndexO/E Ratio=

QUEST Mortality Measure

ExpectedPredicted

ObservedActual

=Expected

Predicted

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ClinicalPrincipal Diagnosis (terminal digit) Severity Weighted ComorbiditiesProceduresUrgency of AdmissionNeonatal Birth Weight

DemographicAge, GenderHousehold IncomeFacility TypeRaceDischarge Disposition

Referral and SelectionAdmission Source (e.g Transfer in)Payor ClassTravel DistanceFacility Type

CareScienceRisk Prediction

APR-DRGSeverity Classification

Base APR-DRGAgeGenderDischarge status DiagnosesProceduresBirth weight

4 Levels of:•Severity (resource demand)•Risk of mortality

Measuring Risk (alternatives)

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Aspect CareScience APR-DRG

Risk Scaling Continuous 4 Buckets in APDRGs

Specification (structure) Stratified Regressions Decision-Tree Logic

Variables Clinical/Demog/Selection Subset of CSI factors

Secondary Diagnoses CACR (complication adj) Selected SDx

Population Stratification Diagnosis DRG

Calibration Data All Payor State & Client Perspective (Client)

Statistical Inference Regression-based errors Cell means

Summary of Model Differences

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1 2 3 4

Continuous Severity Scale

APR-DRG severity buckets

CareScience continuum

Patient 1Patient 2 Patient 3

Patient 1Patient 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

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Baseline O/E Variation across Hospitals

▪ Baseline: 161 hospitals – 2006q3 to 2007q2▪ CareScience and APR-DRGs are very close (next slide)

CareSci APR-DRG

Mean 0.99 0.96

Median 0.95 0.90

Top quartile 0.82 0.77

▪ 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

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Baseline Comparison of O/E Ratios APR-DRG vs. CareScience

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4CareScience

AP

R-D

RG

Correlation = 94%

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Baseline Distribution of O/E Ratios

Distribution of O/E Ratios

0

5

10

15

20

25

30

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0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

O/E ratio

Fre

qu

ency

Smaller hospitals

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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.).

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Overall Trend over 8 Quarters

O/E Ratio Trend

0.6

0.7

0.8

0.9

1.0

1.1

1.2

2005 Q3 2005 Q4 2006 Q1 2006 Q2 2006 Q3 2006 Q4 2007 Q1 2007 Q2

Year and Quarter

O/E

Rat

io

Moving Avg

Mean O/E ratio has fallen about 12%

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Strong Mortality DeclinesStrong Mortality Declines

(larger hospitals)

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

05q3-06q1 05q3-06q2 05q4-06q3 06q1-06q4 06q2-07q1 06q3-07q2 06q4-07q2

Overlapping quarters

O/E

Mo

rtal

ity

Rat

io

Aurora Medical Center - Kenosha

NW Alabama Health Care Authority (Helen Keller)

Baptist Memorial Hospital-North Mississippi

St. Mary's Medical Center

Baltimore Washington Medical Center

North Mississippi Medical Center

Baptist Memorial Hospital-Memphis

Note Bapt Mem

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Trend Break ExampleTrend Break Example

Baptist Memorial - Memphis

0.4

0.6

0.8

1.0

1.2

1.4

1.6

2005 Q3 2005 Q4 2006 Q1 2006 Q2 2006 Q3 2006 Q4 2007 Q1 2007 Q2

Year and Quarter

Mo

rtal

ity

O/E

Rat

io

Baptist Mem Hosp4-quarter MA

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Distribution of Palliative Care Coding

Hospital Palliative Care RateCoding Variation

0

10

20

30

40

50

60

70

80

90

2 4 6 8 10 12 14 16 18 20 22 24 26 28

Palliative Care Rate per Thousand

Fre

qu

ency

Half of hospitals have less than 2 per thousand

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Palliative Care Mortality DistributionHospital Mortality Rates

for Patients under Palliative Care

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Mortality Rate

Fre

qu

ency

Mean = 53%

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QUEST Mortality Drill Down Report to be Released End of April

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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

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A Tale of Two Hospitals

▪ Two Sample Hospitals▫ Hospital 1: > 375 beds, non-teaching, urban, o/e < 1.00, 2nd Qrtle▫ Hospital 2: < 375 beds, non-teaching, urban, o/e > 1.00, 3rd 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

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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

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Hospital 1: Excess Deaths by Age Group – Aggregate Level

Possible special cause variation in patients over 84 years old


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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.


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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)


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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

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Hosp 2: Excess MortalityPareto 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

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Hospital 2: Excess Mortality- ED AdmissionsPareto 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

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Hospital 2: Excess Mortality – Transfer from hospPareto Analysis (partial) by Excess Deaths

Clinical Category

Sources of Transfer Patient mortality: ? End of life issues


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Hosp 2: Excess Mortality by ICD9 DX – ALL DxDx with more than 5 Excess Deaths – grouped by category (Xcess > 5 deaths)

Clinical Category


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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 interventionTimely 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 ProtocolsEarly transfer to ICU if needed

Rapid response team

Post operative resp care protocols

Early identification of patients

Potential PRIMARY DRIVERS

Potential SECONDARY DRIVERSGOAL

*Data mining to examine top drivers of mortality is currently in progress Appropriate setting: hospice v acute

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QUESTIONS?

Eugene A. KrochRichard A. Bankowitz

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Appendix

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Primary &SecondaryDiagnoses

IgnoreSecondaryDiagnoses

Associated withPrimary

Diagnosis

Identify RiskCodes forRemainingSecondaryDiagnoses

Ignore SecondaryDiagnoses Related

to Other Higher RiskSecondaryDiagnoses

Set MinimumRisk Level Basedon Age or Non-OR Procedure

Set Base Scoreto HighestSecondary

Diagnosis LevelAbove Score in

Step 2

Adjust Risk Code Based UponSecondary Diagnoses Severity

Codes

Final RiskCode

Compare Risk Code toPrimary Diagnosis Severity

Code

AssignAPR-DRG 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.

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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 + 1X1 + 2X2 + … + nXn

dependent variable independent variables / explanatory variables

= 0.074

From CS client base sample

CareScience Regression Model

Principal Dx – Pneumonia – one of 142 disease strata

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Distribution of Hospital O/E Trends(over 8 quarters)

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5

10

15

20

25

30

-40% -35% -30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20%

Trend (%change)

Fre

qu

ency

Trend Distribution across Hospitals

Mean = -12%

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Trend Volatility

Maximum Quarterly O/E Changeacross hospitals

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5

10

15

20

25

30

35

40

45

50

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

O/E Ratio Quarterly Change

Fre

qu

ency

Smaller hospitals(avg 25% of mean size)

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Lives Saved by Disease

ICD9_Diag Description038 Septicemia 95,153 18.6% 1266 13.3 11.7%518 Other Lung Dis. 65,418 17.1% 1107 16.9 10.3%428 Heart Failure 168,421 3.2% 694 4.1 6.4%

480 - 486 Pneumonia 155,669 3.5% 624 4.0 5.8%584 Renal Failure 63,125 5.1% 511 8.1 4.7%

433 - 434 Ischemic Stroke 96,826 4.0% 325 3.4 3.0%162 Lung Cancer 26,484 11.3% 302 11.4 2.8%

430 - 432 Hemorrhagic Stroke 22,166 24.5% 294 13.3 2.7%197 Metastatic Cancer 21,893 12.5% 260 11.9 2.4%410 AMI 109,696 6.3% 216 2.0 2.0%

CABG 59,513 2.9% 202 3.4 1.9%571 Chronic Liver Dis. 16,517 7.7% 176 10.7 1.6%427 Cardiac Dysrhythmias 121,310 2.1% 103 0.9 1.0%852 Head Trauma 12,772 11.1% 70 5.5 0.7%153 Colon Cancer 18,054 4.1% 68 3.8 0.6%

764 - 765 Premies 74,192 3.4% -38 -0.5 -0.4%Sub_Total 1,091,058 6.6% 5,804 5.3 53.8%Grand Total 6,132,358 2.0% 10,793 1.8 100%

Comparison Period: 2006q3-2007q2

CasesPercent of lives

Reference Period: 2005q3-2006q2Lives Saved

Lives/1000

Mortality Rate

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Lives Saved Rate vs. Mortality Rate

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2

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0% 5% 10% 15% 20% 25% 30%

Mortality Rate

Liv

es

Sa

ve

d p

er

10

00

Dis

ch

arg

es

Hemorrhagic Stroke

Head Trauma

AMI

Renal

Septicemia

Liver

Other Lung

Lung Cancer

Metastatic Cancer

Ischemic Stroke

Dysrhythmias

Pneumonia

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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.

1 exploring quest mortality understanding the baseline data and using clinical advisor and quality...

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