Documentation errors are probably driving this data - X

January 16, 2018 | Author: Anonymous | Category: Math, Statistics And Probability, Statistics
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So many questions, So much data, So little time A Journey with Surgical Outcome Data

Alison M. Knight, P.E. IIE 2014 National Conference

©2014 MFMER | slide-1

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Agenda • Tutorial in quality databases • Background information on project

• Strategy and process for report creation • Example of report and metrics chosen

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Quick Tutorial in Surgical Quality Databases The dirty secrets THEY don’t tell you

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Lesson #1: Quality databases are NOT created equal Clinical Abstracted vs Administrative Data

Outcome Definition

Database Attributes

Data Audit Process

Sample vs. Population ©2014 MFMER | slide-4

Lesson #2: Use all databases, but choose metrics carefully Example #1: Mortality & Length of Stay

Administrative Data & All Cases

Example #2: Surgical Site Infection & Urinary Tract Infection

Clinically Abstracted Data & Sample of Cases ©2014 MFMER | slide-5

Lesson #3: Data denial will happen, Be ready! Five Stages of Data Denial* 1. The data is wrong

2. The data is old 3. We have changed already 4. This is a different place 5. We tried that already *Borrowed from Dr. Brian Postl, CEO of the Winnipeg Regional Health Authority Presentation ©2014 MFMER | slide-6

Let’s set the stage

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The surgical practice is frustrated New costs

• Each surgical specialty pays a portion No perceived value • Most surgical specialties believed this quality data provided little value Data is not trustworthy • Surgical specialties didn’t believe the data ©2014 MFMER | slide-8

Some background facts…

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What is NSQIP? • American College of Surgeons (ACS)’s National Surgical Quality Improvement Program • History • Started in the Veterans Administration(1994)* • Open to other hospitals (2004)* • Mayo Clinic joined in 2006 • Clinically abstracted random sample of cases *ACS NSQIP website: ©2014 MFMER | slide-10

What types of information is included and how is it collected? Uniform Operational Definitions 135 Patient Variables 30 day Post-op Assessment Audit Process ©2014 MFMER | slide-11

Data Delivery to the Practice • Twice a year the practice chair received a “canned report” from NSQIP • Each specialty had risk-adjusted odds ratios • Ranged in granularity depending on specialty • Highlighted the specialty’s decile rankings (1-10) • Decile*: divides the distribution of the variable into ten groups having equal frequencies.


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The Project

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Project Goals • Educate providers on database facts Educate • Work through the stages of denial • Provide data at more detailed level Provide • Point providers to “hot spots” • Aid specialty in developing datadriven quality projects Change ©2014 MFMER | slide-14

Identified the Challenges NSQIP costs money, value not recognized “Why should I pay for NSQIP if I am not using it?” Lack of understanding of data collection process: “Documentation errors are probably driving this data” Decile rankings are the focus “These rankings can’t possibly be right, our patients are sicker and we do more complicated procedures” Specialty practices only were recognized for negative quality “Leadership just doesn’t understand our practice” ©2014 MFMER | slide-15

The Plan to Create a Meaningful Dashboard Practice Input Use in Research

Dispel Myths

Make Data Actionable


Focus on Good & Bad

Address Concerns

De-emphasize Rankings ©2014 MFMER | slide-16

The process of development: PDSA-Like Cycle • One specialty at a time


• Analysis done by hand

• Review report for completeness • Obtain feedback from each specialty for improvements




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A Sample Report This data has been created for example purposes only and does not reflect real Mayo Clinic data.

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Components of the Report • Generic information from NSQIP • Data collection and risk-adjustment information • Specialty specific information • Without risk adjustment • Example research articles using NSQIP data

• Recommendations to practice

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Demographic Data Example*

*Data is fictitious and does not reflect Mayo Clinic performance.

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Volume Analysis by CPT Code Example*

*Data is fictitious and does not reflect Mayo Clinic performance.

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Occurrences: Raw Rates Example*

*Data is fictitious and does not reflect Mayo Clinic performance.

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Case Volume versus Occurrence Volume*

*Data is fictitious and does not reflect Mayo Clinic performance.

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Rankings Example*

*Data is fictitious and does not reflect Mayo Clinic performance.

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Recommendations and Next Steps • Based on the data, Outcome #1 and Outcome #3 should be addressed. • CPT Code Category #2 shows opportunity for improvement • Additional detailed information by request • All case data pulled for specific CPT code categories and/or cases with specific outcomes • Logistic regression analysis to recognize factors that contribute certain outcomes ©2014 MFMER | slide-25

Current State and Next Steps

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Where are we now? • Stable report that practices find helpful • Worked through 5 stages of data denial

• Creating automated process to provide information in a timely manner • Creating processes for follow-up and practice accountability

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Summary • All databases are not created equal • Use the correct type of database depending on the information needed • Address data denial • De-emphasize rankings

• Create a report that magnifies the “hot spots” for practices to launch quality projects

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Questions & Discussion

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