Exploring quest mortality
This presentation is the property of its rightful owner.
Sponsored Links
1 / 37

Exploring QUEST Mortality PowerPoint PPT Presentation


  • 84 Views
  • Uploaded on
  • Presentation posted in: General

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. Topics.

Download Presentation

Exploring QUEST Mortality

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Exploring quest mortality

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


Topics

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


Quest mortality measure

QUEST Mortality Measure

Observed

Actual

Observed

Actual

Index

O/E Ratio

=

=

Expected

Predicted

Expected

Predicted

Expected

Predicted

Index > 1:

Actual mortality isgreaterthan predicted

(opportunity)

Index < 1:

Actual mortality islessthan predicted


Measuring risk alternatives

Measuring Risk (alternatives)

APR-DRG

Severity Classification

CareScience

Risk Prediction

  • 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

  • Base APR-DRG

    • Age

    • Gender

    • Discharge status

    • Diagnoses

    • Procedures

    • Birth weight

  • 4 Levels of:

  • Severity (resource demand)

  • Risk of mortality


Exploring quest mortality

Summary of Model Differences


Exploring quest mortality

1

2

3

4

Illustration of Precision

Under APR-DRGs patient 2 is lumped together with Patient 1, even though under continuous severity scaling patient 2 is more like patient 3.

APR-DRG severity buckets

Patient 1

Patient 3

Patient 2

CareScience continuum

Continuous Severity Scale

Patient 1

Patient 3

Patient 2


Baseline o e variation across hospitals

Baseline O/E Variation across Hospitals

  • Baseline: 161 hospitals – 2006q3 to 2007q2

  • CareScience and APR-DRGs are very close (next slide)

  • 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


Baseline comparison of o e ratios

Baseline Comparison of O/E Ratios

Correlation = 94%


Baseline distribution of o e ratios

Baseline Distribution of O/E Ratios

Smaller hospitals


O e trends

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.).


Overall trend over 8 quarters

Overall Trend over 8 Quarters

Moving Avg

Mean O/E ratio has fallen about 12%


Strong mortality declines

Strong Mortality Declines

Note Bapt Mem


Trend break example

Trend Break Example


Distribution of palliative care coding

Distribution of Palliative Care Coding

Half of hospitals have less than 2 per thousand


Palliative care mortality distribution

Palliative Care Mortality Distribution

Mean = 53%


Quest mortality drill down report to be released end of april

QUEST Mortality Drill Down Report to be Released End of April


Exploring drivers of mortality

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


A tale of two hospitals

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


Hospital 1 excess death by admit source aggregate

Hospital 1: Excess Death by Admit Source – Aggregate

Notice the hospital-wide o/e is < 1.00 and very close to TPT

NO Excess Deaths by any given admission source

No evidence of special cause variation at hospital-wide level

Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix


Hospital 1 excess deaths by age group aggregate level

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


Exploring quest mortality

Hospital 1: Excess Mortality by Primary Dx Hospital-Wide Excess Deaths (partial) sorted by excess deaths

Remember this hospital has an O/E = 0.88. However, there are still many pockets of opportunity.

Nine Excess Deaths with Sepsis as Primary Dx

Source: Clinical Advisory Quality Reports with Excess Deaths added - see Appendix


Exploring quest mortality

Hosp 1: Excess Mortality by ICD9 Secondary DxHotpital- Wide Excess Deaths (partial) – sorted by Excess Deaths

Expected Rate

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


Exploring quest mortality

Hosp 1: Excess Mortality by ICD9 Secondary DxHospital- 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


Hosp 2 excess mortality pareto analysis by admit source all admits

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


Hospital 2 excess mortality ed admissions pareto analysis partial by excess deaths

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


Hospital 2 excess mortality transfer from hosp pareto analysis partial by excess deaths

Hospital 2: Excess Mortality – Transfer from hospPareto 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


Exploring quest mortality

Hosp 2: Excess Mortality by ICD9 DX – ALL DxDx 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


Approaching drivers of mortality illustrative examples of potential secondary drivers

Approaching Drivers of Mortality *Illustrative Examples of Potential Secondary Drivers

Potential PRIMARY DRIVERS

Potential SECONDARY DRIVERS

GOAL

Early appropriate level of care (ICU)

Sepsis

Early recognition and intervention

Timely transfer toICU

Elderly and other high risk groups

Hospital – Level Risk Adjusted Mortality (O/E Ratio)

Respiratory Conditions

Early recognition of resp compromise

Avoidance of VAP

Post operative resp care protocols

Cardiac Related and Shock

Rapid response team

Adherence to ACC Protocols

Early transfer to ICU if needed

Improved use of cardiac monitors

End of Life Care

Early identification of patients

*Data mining to examine top drivers of mortality is currently in progress

Proper use of V667 palliative code

Appropriate setting: hospice v acute


Exploring quest mortality

QUESTIONS?

Eugene A. Kroch

Richard A. Bankowitz


Appendix

Appendix


Exploring quest mortality

APR-DRG Process Flow

Step 1

Step 2

Step 3

NB: Risk code is mapped into mortality risk based on the mortality rates from calibration data base.


Exploring quest mortality

 = 0.074

From CS client base sample

CareScience Regression Model

Principal Dx – Pneumonia –one of 142 disease strata

Outcome = age + sex + distance + proc + …...

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

age

Y = 0 + 1X1 + 2X2 + … + nXn

dependent variableindependent variables / explanatory variables


Trend distribution across hospitals

Trend Distribution across Hospitals

Mean = -12%


Trend volatility

Trend Volatility

Smaller hospitals

(avg 25% of mean size)


Lives saved by disease

Lives Saved by Disease


Lives saved rate vs mortality rate

Lives Saved Rate vs. Mortality Rate


Appendix how were the excess death tables made

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.


  • Login