How We Developed an Advanced Analytics Team to Solve Our Strategic Problems

David M. Wild, MDVice President, Performance ImprovementThe University of Kansas Health System

Session 6:

Chris Harper, MBAi, MPMVice President,

Applications & System DevelopmentThe University of Kansas Health System

Agenda• Learning objectives.

• Share our data story.

• Review how we built the team.

• Discuss results.

• Review key learnings and recommendations.

• Questions and answers.

Learning Objectives

Define the goals of an applied analytics

team.

Describe the key foundational cultural

and technology components to supporting an

analytics team.

Identify the political and strategic challenges in

implementing an advanced analytics

team.

Design a roadmap for implementing an advanced analytics

team in your organization.

University of Kansas Health SystemBuilding on a legacy of patient care that began in 1906, the University of Kansas Health System (UKH), based in Kansas City, Kansas, is a world-class academic medical center and destination for complex care and diagnosis.

• Leader in medical research and education. • 900-bed general medical and surgical facility.• FY18 volumes:

• 41,952 discharges.• 29,073 surgeries.• 60,207 emergency department visits.• 1,726,674 ambulatory encounters.

The University of Kansas Physicians (UKP), the largest multi-specialty group practice in Kansas, are researchers and educators expanding the boundaries of medical knowledge.

Without data you’re just another person with an opinion.

-W. Edwards Deming

The Problem: The Time-Value Curve

The Problem: The Data Quotient Formula

Our Data Story

Back to 2013 Recognized the needto change.

Conducted a current state assessment.

Identified a solution.

Implemented a phased

approach.

Reporting in 2013

Primary Reporting Teams

Business strategic development

Organizational improvement

EMR reporting

Finance reporting

HR reporting

Marketing

Pharmacy reporting

7+

Primary Reporting Tools

8+

Business intelligence tools

SQL reporting

Adhoc query tool

Resource utilization reporting

UHC interface

Primary Reporting Source

15+

EMR #1

HRIS system

ERP system

Laboratory system

Practice management system

Research database

ECG database

EVS and transport database

Diagnostic system

EMR #2

Cost accounting system

Relational database for budgets

OR scheduling system

Report requests, 60%

Data processing,

17%

EMR reporttesting conversion,

7%

Sustaining direct labor,

16%

Recurring requests, 4%

Other report related

requests, 10%

Enhancement change

requests, 6%

Data management –

modeling, marts, and ETL,

31%

Ad hoc/new report requests,

8%

Category 1

Challenged by Continued Growth

Growth Problem

1,153 1,065 1,065 1,065 1,065

194434 543

6721,153

1,259

1,4991,608

1,7371,860

0

500

1,000

1,500

2,000

2012 2013 2014 2015 2016

Volu

me

Report Limits Report Gap Demand Report Limits

0.7FTE’sshort

1.2FTE’sshort

1.4FTE’sshort

1.7FTE’sshort

2.0FTE’sshort

Database Challenges

Database Problem. Desktop databases

60,0000+and growing.

Identifying Solution Requirements

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10

Labo

r Res

ourc

es

Report Volume

Unmet Demand

Report Development

Analysis & Consulting

Solution Requirements• Accommodate increasing report demand with

platform approach.

• Provide more value-add activities such as data analysis and consulting with stakeholders.

• Introduce a scalable reporting platform that uses centralized and clearly defined standardized data elements while supporting business unit reporting autonomy.

• Speeds up the delivery of more dynamic information to decision makers.

• Allow KPI’s and detailed information to flow all the way to the desktop level of the organization.

Changing the Productivity Curve

The Road MapFragmented Enterprise Perspective Advanced Analytics Big Data

BI architecture None or several point. Central infrastructure basics implemented.

BI core and self-service infrastructure in place.

Optimized infrastructure (e.g., data marts).

Data sources / data currency

Transaction application from one system or BI tool specific from limited number of internal source systems.

ETL established for primary data sources.

ETL established for secondary data sources.

Web, patients, genomics, and other external sources (e.g., UHC, eMeasure).

Types of analysis / use of analytics

Automated internal reporting (some).

Enterprise KPIs and automated external reporting.

Predictive and prescriptive analytics and evidence-based analytics.

Analytics combining multiple and complex data sources.

Data models Departmental. Common vocabulary, start schema, dimensional. Multiple data models. No schema.

Data governance Independent and departmental.

Common policies and standards, centrally managed KPIs, and security management.

Agreed-upon agenda and priorities, data normalization, and initiate source system change.

Owners and stewards of internal and external data, complex analysis review and delivery.

Tools Redundant and/or desktop toolsets.

Consolidated data management tools. Extended analytic capabilities. Specialized, targeted

capabilities.

SkillsSQL, Excel, database management, light data modeling, light visualization.

In-depth knowledge of physical and logical data modeling, light statistics and terminology standards.

In-depth knowledge of statistics and operations analysis, procedural programing.

NLP, genomics, and rules engine programing.

Culture / enterprise data literacy

Value of data under-appreciated and “good enough” decisions.

Champions emerging and growing emphasis on fact-based decisions.

Training on data literacy identifying BI opportunities, and change.

Engrained understanding of BI capabilities and limitations.

SQL = structured query language, ETL = extraction transformation loading, KPI = key performance indicator; ODS = operational data store; NLP = natural language processing The Advisory Board BI Maturity Model

YEAR 1 YEAR 2 YEAR 3

1

1

1

1

1

1

1

1

1 1

2

2

2

3

3

3

3

?

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?

?

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Poll Question #1

Do you have a centralized team responsible for establishing advanced analytic and modeling standards in your organization?

a) Yesb) Noc) Unsure or not applicable

Creating the Analytics FoundationD

ata

Foun

datio

nKUHS Analytic Platform

HAWK (Healthcare Analytics Warehouse & Knowledgebase)Data Sources

• PT demographics• Registration and

scheduling• Lab results• Medication administration

record (eMAR)• Clinician notes• Outpatient

• Cost accounting

• General ledger • Supply chain• Human resource

• Patient satisfaction

• Ambulatory • Professional billing

• Time card

• Other

Data Aggregation

Extract

Clean

Conform

Transform

Load

Data Stores Intelligence & Analytics Delivery

Information Users

Consolidate Data Model

Source Data Marts

Patient satisfaction

Patient population

Cost General ledger

Ambulatory practice Other

Readmission Regulatory

Surgery Other

Subject Area Data Marts

BI A

bstr

actio

n &

Dec

isio

n Su

ppor

t

Information Access

Performance management

Alerts, dashboards, scorecards,

reports, queries

Services

Advanced analytics and data

science tools

Org management

• Operational performance

• Finance performance• Quality performance

Care management

Care coordination

Clinical Service Lines

ACO Strategy

More +

• Registry• Workflow management.• Referrals management• Complex prediction

• Pre-visit planning• Care transition event• Referral management• Prevention gaps

• Cardiovascular• Oncology• Neuro and spine• Orthopedics

• Referral management• Workflow management• Care gap ID• Care transition event

Security & Access

Data Governance

Sample Skills Assessment

Phased Layered Analytics Capabilities

To Do This…

We Must Do This

Find the truth. Tell the truth. Face the truth.

• Nebulous in healthcare.

• Relative to your point of view.

• Tribalism trumps facts.

• Presentation matters.

• Diplomacy and sensitivity are key.

• Requires mastery, autonomy, and a clear sense of purpose.

• Requires humility and openness.

• Benefit for the community instead of the individual.

The Pitch: Analytics as a Competitive Advantage• Data is an asset.

• Analyzing data is a supporting function of strategy.

• Is strategy developed and sharpened in IT, finance, or organizational improvement?

• Analytics in support of strategy is better when it is centralized.- One format.

- Recreational data collection and reporting are limited.

- Prevents the problem of the report writers knowing our data but not the business and the operations team knowing the business but not what data to request.

The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.

-Stephen Hawking

Target Condition

Data InformationActionable Knowledge

The Pilot Challenge

Take the people and resources you think you need and put them together to prove we can quickly answer key strategic and business questions. Don’t create a new cost center or reporting structure, just go out and convince their managers you need them for a couple months for something special…

The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill. To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advances.

-Albert Einstein

“The List”

Looking for Impact

1

3

13

715

14

9

17

115

21

8

12

2

4

1016

1819

20

6

22

23

And We Ended Here…

Building the Team

Analysis

Economic Impact

Data Knowledge

This group has been assembled to help tell the “story” about the specific challenges/opportunities that the enterprise is facing and what are the potential ways of addressing those through the use of data.

Finding the Space

Poll Question #2On a scale of 1 to 5, how effective is your organization at creating actionable knowledge from data?

a) 1-Not at all effectiveb) 2-Somewhat effectivec) 3-Moderately effectived) 4-Very effectivee) 5-Extremely effectivef) Unsure or not applicable

Cost of CAUTI Analysis• Question: What is additional cost of treating

CAUTI patients when they are compared to like non-CAUTI patients?

• Overview: Mean direct cost comparison between CAUTI and matched non-CAUTI patients, using DRG and propensity score for matching.

• Results: On average, CAUTI costs were higher than non-CAUTI, but the difference was not statistically significant (low prevalence of CAUTI at UKH provided a small sample size during the study period).

- Direct cost variance: $2,387.

Identified 120+ potential CAUTI

risk factors based on

literature review.

Selected 50 risk factors for analysis.

Seven significant risk factors

identified from bivariate analysis. Generated

propensity score from logistic

regression model with seven risk

factors.

Inpatient End of Life Care• Question: How does UKH compare to other Association of American Medical Colleges (AMCC) in

terms of cost for inpatient end of life care, and where are opportunities for cost savings?

• Results: Mean direct cost of UKH was significantly lower compared to AAMC (p = 0.003) and mortality index for UKH patients (3.58) was higher compared to AAMC cohort (3.35).

• Overview: Analyzed benchmark data, using 2017 risk adjusted model, for all inpatient discharges with the disposition status of “expired” (all in-hospital deaths except for Medicare or CHAMPUS hospice patients) and compared the direct costs for that patient cohort.

Willingness to Travel• Question: What is our current market share for patients traveling to the Kansas City metro area

based on demographics, geography, and service line?

• Overview: Kansas and Missouri residents who traveled at least 35 miles for their inpatient care to the Kansas City metro area and were discharged from one of the 27 hospitals in that market.

Metric Competitors UKHPatient age (average) 57 52Distance traveled (average) 67.2 99.3Case mix index >3.0000 14% 21%Surgical market share 61% 39%

• Results: Compared to its competitors UKH has brought younger patients with more complex cases who traveled further for their inpatient care. UKH captured 39% of the inpatient surgical market, compared to its 26 competitors who accounted for 61% (all findings were statistically significant at alpha level of 0.05).

Population Health – Diabetic Multiple Visit Patients (MVP) Readmissions

• Question: What patient population should the hospital to home team focus on to help reduce the overall readmission rate at UKH?

• Overview: Assessed the risk of readmission for diabetic patients in the following areas: demographics, hospital/patient care associated variables, medications, diagnoses and medical history.

• Solution: Developed an analytical solution that stratifies patients low- to high-risk of readmission patient groups based off of the unique risk score developed from a statistical model. Hospital to home team will utilize the solution on a daily basis to help identify customized care plans that should be carried out for high risk readmission patients.

From the 23 chronic conditions patient

populations identified the diabetic cohort with

largest opportunity.

Analyzed 240+ risk factors in the model

development.

16 statistically significant risk factors were

identified.

12 risk factors were used to develop a

unique MVP readmission rank and

risk score.

Cost Variation Analysis

• Objective: Identify cost reduction opportunities within the heart transplant event.

• Analysis: Two box and whisker plots for heart transplant patients in FY17 and FY18 using direct one cost data (only includes supplies and direct care labor salaries) and length of stay to understand the overall performance for this patient population.

- Mean direct one cost in FY17 was at $181,190 versus $152,102 in FY18.

- Mean length of stay in FY17 was at 39 versus 18 in FY18.

Cost Variation Analysis Continued

Analysis: Overview of each heart transplant event (patient encounter) broken out by fiscal year and direct one cost to highlight the trend in a visual manner and identify specific outlier cases and understand what drove that performance.

Cost Variation Analysis Continued

Analysis: Overview of the direct one cost for each heart transplant event broken out by service type at the line level detail for individual patients. In this instance, the focus was on pharmacy, in particular the administration of Simulect (basiliximab) and anti-thymocyte globulin for low risk patients.

Where We Are Now

Applied AnalyticsPerformance Improvement

If we have data, lets look at data. If all we have are opinions, lets go with mine.

-Jim Barksdale

Lessons and Recommendations

Think it through. Do it your own way. Don’t forget the why.

Questions and Answers

dwild@kumc.edu

David Wild, MD

charper@kumc.edu

Chris Harper, MBAi, MPM