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2013 Clinical and Business Intelligence Symposium Session 2: Measure and Analyze Cynthia Davis, RN, FACHE, CIC Advisory Philip A. Smith, MD, MS, MedMorph, LLC

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Doesn’t it feel this way some days?

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Think of three (overly) simple stages 1 EMR (paper to electronic data) 2 Lots of data! Time to invest in and/or better use BI to make

sense of it. 3 Implement change/reform with BI discoveries and insights

Healthcare BI

Some health systems in stage 1, majority in stage 2, some advanced organizations in stage 3

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BI is complex

“Simplicity is the ultimate sophistication” —Leonardo DaVinci

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Information Organizing Interpreting

Knowledge Interpreting Integrating

Understanding

Wisdom Understanding

Applying Applying with compassion

Incr

easi

ng c

ompl

exity

Increasing interactions and interrelationships

Data Naming

Collecting Organizing

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– Regulatory – Payer VBP, ACA, ACO – Performance Improvement – Research

Who needs the data?

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Understanding your baseline data

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Clinical vs. Financial

The data questions –Where does it reside? –How is it structured? –Is it standardized? –How reliable is it? –What is the data set ? Big questions –Is it paper or electronic ?

Understand your baseline data

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Use case and priorities What is the puzzle you’re trying to solve?

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Physicians and Clinicians Application Analyst Project Manager Subject Matter Experts

The team

Initial team

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Get the most comprehensive and detailed requirements possible up front

–Who is the SME (subject matter expert)?

–What is being analyzed?

–Who will validate the reports?

–Who are the consumers of the data?

–How will the data be presented?

–What is the anticipated goal and outcome?

The Assessment—where to begin

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Example: “Why are the ED doctors not ordering

ECG’s for chest pain patients?”

Understanding the data

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What is being analyzed? What is the “source of the truth?” Our historical silos and disparate systems and definitions

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Goal: – Predict the likelihood that a patient with CHF will be admitted to the hospital or ED in the next 6 months Inclusion criteria: – CHF diagnosis code on a bill/claim or problem list source – Patient has had an E&M visit and/or procedure over the last 12 months – Patient is at least 18 years old Modeling samples – Training and test samples

What is being analyzed?

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Validate the data, ensure it contains accurate data and adequate features Provide baseline data to Primary Care Offices about their CHF population Report results of predictive modeling tool applied to this population, further identifying a cohort as “CHF priority” patients. Establish a workflow to utilize data

What are the steps?

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Validation is not done by report writers Must be done by those who know what the data reflects

Who will validate the reports?

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Presentation is everything Find a highly motivated Stakeholder to create an immediate win!

Who are the consumers of the data?

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Changing data into information

Printed reports

Auto-distribution (push) Ad hoc (pull)

Turn data into information and tell a story Style guide

Variable date ranges and populations

Dashboards Self-service reports

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Plan for training and tools in hands of analysts throughout the organization Having strong team with subject matter and analytics knowledge base it critical success factor—how you ask the question with the end users is key

Training, training, training

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Who understands the process?

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Dissatisfaction with medication reconciliation

From Making Computerized Provider Order Entry Work, PA Smith. Springer, UK, 2013

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Dissatisfaction with medication reconciliation

From Making Computerized Provider Order Entry Work, PA Smith. Springer, UK, 2013

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– Standard reporting – Scheduled reporting – Dashboards/visualization – Scorecards – Kind of like the car speedometer/gauge

Front end BI

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– Your data should be extracted into structured data marts so that reports can run quickly and efficiently.

– Choose and use the appropriate report type, frequency and media to communicate the results

– Dashboards should be available on any platform, from your local network, iPad or smartphone.

– Dashboards should include national or internal benchmarking data as well as display trends appropriate for the data.

Front end steps

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– Data aggregation (i.e. data warehouses, repositories, marts, models)

– Kind of like the car engine

Back end BI: like care engine (data integrity)

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– Data governance (i.e. data quality, accuracy)

– ETL (extract, transform, load)

Back end BI: like care engine (data integrity)

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– In-depth User Acceptance Testing after initial implementation

– Appropriate resources in Operational areas to support validation, implementation plan and ongoing improvement work

– Data is only as accurate as source data/ billing/coding data

Back end

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– Data dictionary’s, data stewardship and accountability is the leading practice

– Predictive modeling

– Evaluate the promising, yet expensive tools coming to market

Back end steps drilled down

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Key challenges

Many BI professionals are informally trained &/or in-house developed

Creating an integrated BI function is susceptible to healthcare’s historical trend toward silos

Build a knowledge and improvement culture poised to innovate

The right culture in place is critical to realizing the benefits of BI

1 2 3 4

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– Were the goals and needs met?

– Do workflow processes need to be re-evaluated?

– Is additional reporting training required?

– Are you capturing the required data elements needed for priorities, as well as for reportable quality measures?

– Do staff roles and responsibilities need adjustments?

– Did you document the procedures?

– Do you have the organizational leadership in place?

Ongoing evaluation

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Roadmap for BI design

Training/workflow support preparation/communication

Activation/Preparation/ Go Live

Define business requirements

Identify the SME

Identify the Data— “source(s) of truth”

Scrub and validate the data

validate preliminary report/ dashboard

produce final output

Governance and intake process

Conform to style guide

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– While Business Intelligence can improve your clinical and business operations…

– The care and feeding is a critical success factor

– Need to have a BI strategy and roadmap in place

– Governance

– Clear roles and responsibilities

– Turn data into “actionable information”

Key take aways

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Ongoing performance improvement is critical – You are never “done” – Establish a culture of accountability at all

levels of the organization

Key take aways