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®

© 2008 University HealthSystem Consortium

Steve Meurer PhD, MBA/MHSUniversity HealthSystem ConsortiumVice President, Clinical Data & InformaticsNovember 6, 2008

Data’s Role in Driving Sustained Change

©2008 University HealthSystem Consortium 2

University HealthSystem ConsortiumUniversity HealthSystem Consortium

• A member owned and governed consortium of academic medical centers

– This relationship is what makes us unique– Approximately 95% of all major not for profit academic medical

centers are UHC members (n = 102)– Affiliate hospitals are welcome and increasing in numbers (we

currently have over 190 associate member hospitals)

• UHC provides comparative databases, benchmarking and other associated services, a Group Purchasing Organization, and networking opportunities


The Difficulty of ChangeThe Difficulty of Change

Not an innate drive

Against current societal norms

Current healthcare environment where quality has become scrutinized


DrivenDriven

Drive to Learn and make sense of the world and ourselvesDrive to Acquire objects and experiences that improve our status relative to othersDrive to Bond with others in long-term relationships of mutual care and commitmentDrive to Defend ourselves, our loved ones, our beliefs, and our resources from harm

By Paul R Lawrence and Nitin Nohria


All improvement must result from a changeIt’s difficult to change without first admitting that you don’t know somethingThe community believes that physicians know what is needed to improve health, and they don’t understand variationPhysicians / Nurses are taught that they are the sole purveyors of clinical medicine and that they should never admit to not knowing

Earnest Codman, MDThe Dartmouth Atlas

The Bell Curve


Don’t KnowWhat You Don’t Know

You KnowWhat You Don’t Know

You KnowWhat You

Know

Don’t KnowWhat You

Know

Selfawarenessthroughexperience/persuasion

Formal or informal education

Practice

Learning/Changing Process

C.R.A.P. Detector


Other Improvement TipsOther Improvement TipsThe Power of One is as important as the support of Leadership!The fallacy of CQI, Six Sigma and LeanThe Tipping Point

The Law of the Few (Mavens, Connectors and Salesmen)

Paul Revere’s Ride

The Stickiness FactorTetanus Shots (small but significant changes – 3% to 30%)

The Power of ContextNew York City at night (Broken Windows theory)


Quality Data ConcernsQuality Data Concerns• You need a physician to talk to a physician

• Risk Adjustment

• Transparency

• Data Quality / Accuracy

• Clinical vs. Administrative Data

• Timeliness


Motivating a Clinician to ChangeMotivating a Clinician to Change• Data in quality and variation of care is now mature

enough to use for improvement, but its still not perfect

• This is not about physicians vs. others, rather its about the process of approaching a clinician with data

– Step 1 – Provide the data, an explanation and support in a non-threatening manner

– Step 2 - Answer all questions regarding the data– Step 3 – Wait. If no change, then Medical Staff

• A physician not trained in the data performing steps one and two are much more likely to dismiss variation when the answer is ‘My patient’s are sicker, and the methodology does not adequately adjust…’


12.6the data

the analysis

the information

the tools and resources

the relationship

the understanding& innovation

The Improvement5.3


• 80 year old female, 1 day LOS ED Visit• MSDRG 389 – GI Obstruction w/ CC (Gastroenterology)• Moderate ROM and expected value of 1.7%

Diagnoses – intestinal obstr nos; senile depressive; rheumatoid arthritis; esophageal reflux; depressive disorder;hypopotassemia; palliative care


1.7% may seem low on face value, but it is higher than the model group observed of 1.4%


Oncology Product Line Analysis

qtr3 ’07 through qtr

2 ’08Includes the

Gyn/Onc, Medical Oncand Surgical

OncProduct Lines

To a physician not trained in data management, two responses are likely: 1) what’s wrong with the Oncologists;

& 2) the data is bad


Mortality ReductionMortality Reduction• 100% monthly mortality review using

the case profiles• Begin with mortality in minor or moderate

risk of mortality• Determine if a patient’s death:

1. Can be explained2. Needs further coding analysis (low expected)3. Needs further documentation analysis (low

expected)4. Needs further analysis from a clinical

committee


Risk AdjustmentRisk Adjustment


Key QuestionsKey Questions• Do I feel comfortable with the following items:

– What is the model group being used?– Are the model groups homogeneous enough to

minimize the effect of outliers?– Are the model groups inclusive enough (e.g. only

MedPar vs. all payor)?– Are the sample sizes of the model groups

appropriate?Too low = no statistical significanceToo high = overfitting

– Do the models use the appropriate variables


UHC Risk AdjustmentUHC Risk Adjustment

Each Patient’sCPDF Data

3M MS-DRGGrouper Each Patient

Assigned to a MS-DRG

Each PatientAssigned a ROM / SOI

Literature

Member Feedback

Last 2 Years of Pts in the DRG

CovariatesDeterminedBy DRG

Multiple Regression LOS Model

Multiple Regression Cost ModelLogistic Regression Mortality Model

Expected LOSExpected CostExpected Mortality

Base MS DRGs = model groupUse only AMC patients


High % of patients with low severity & above expected on a few levels


61.6% 62.1%

90.4%

98.9%

3.6%0.3%0.1% 0.1%

30.0%

3.9%0.3% 0.7%0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1 2 3 4

Severity of Illness (SOI) class

Pred

icte

d m

orta

lity

% (r

ange

)Why not just use the APRWhy not just use the APR--DRG to predict mortality? DRG to predict mortality?

What does the UHC methodology add?What does the UHC methodology add?If we predicted mortality using only the SOI class, all patients within that class will get the same expected mortality (see red squares). UHC models refine this prediction by using additional variables in risk adjustment. Note the range of expected mortality within each class when UHC models are used.

CDB CY2006. DRG 127 for all UHC hospitals.


OverfittingOverfitting & Palliative Care& Palliative Care

1. Significant Variables and Coefficients 1. Sample Size2. Prevalence

1. In a model group of 1 million, shoe size will be statistically significant, but definitely not relevant

2. Palliative Care / Hospice Care as a variable


TransparencyTransparency

Who are you being compared against?

Remember same questions from risk adjustment regarding model groups


Cross Hairs are national norms for cost & LOS Bubbles are physicians


Build your own custom group from a number of hospital demographics or choose a pre defined group and automatically populate the CDB


Gastroenterology Mortality by Quarter and Year


Cardiology Product Line AnalysisCompared to US News Heart Honor Roll

qtr3 ’07 through qtr 2 ’08

Includes Cardiology and Cardiac Surgery Product Lines


Data Quality ReportsData Quality Reports

CPDF txto UHC

Is it ‘clean’?Does it meet the specs?Are there major errors?

PASS (comparable)ORFAIL & RETURN

Adm Reg Data

Billing Data

Medical Staff Data

CDBCRM

Core MeasuresManagement Reports

Q & A Study


• Will receive after each submission• Can update individual records online


• Tolerance report displays items of most concerned

• Someone at each hospital can go into the records and change these from this tool


Z Score Report

This submission has more patient’s race categorized as Other than Hospital X’s last year’s data


Clinical vs. Administrative DataClinical vs. Administrative Data• In theory, only specificity of certain comorbid

conditions is enhanced by clinical data– Currently, there are approximately 63 codes one can

use for diabetes and 100 for infections– ICD 10 will provide more even more specificity

• Who do you want to be abstracting data?– Coders have extensive training in understanding how

to place certain words and phrases into a code• Clinical data can improve accuracy of risk

models


More Timely DataMore Timely Data

CM Pt AdmittedCM Pt Discharged

CM Pt Chart CodedCM Pt Coded Data Sent to IntermediaryIntermediary Determines CM Pt to be abstractedCM Pt chart abstracted

Day14

35404145

CM Vendors Attempting to Close this Space

The Holy Grail for Timely Core Measures Data


More Timely Clinical Comparative DataMore Timely Clinical Comparative DataEMR, decision support

warehouse, automated pt intake

UHC

data sent when entered

Daily pt expected valuesUpdated comparisonsTransparent utilization

Flags on potential issues

©2008 University HealthSystem Consortium 35©2007 University HealthSystem Consortium 35

Don BerwickDon Berwick

Healthcare’s single most important issue is its inability to improve

One major hurdle to improvement is that very little quality data is perfect

Imperfect data can be very useful in providing direction for improvement efforts … only if you understand the imperfections


Steve [email protected]

CDP Info [email protected]

Use us like an extensionof your staff

Give us the opportunity to respond to an issue / inconsistency you may have or find

Contact us regularly

microsoft powerpoint - nyp talk.ppt [read-only]

Documents

data step

data management

quality data concerns

administrative data

model groupsappropriate

model groups homogeneous

mortality reduction

moderaterisk of mortality