improving data accuracy with governance, …...how data is acquired, managed and analyzed. chaired...

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Improving Data Accuracy With Governance, Definitions and

Structure

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Housekeeping

• Moderator – Anthony Guerra, editor-in-chief, healthsystemCIO.com

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Agenda — Approximately 40 Minutes

• 30 minutes: Michele Zeigler, VP of IS/CIO, Summit Health

• 10 minutes: Q&A w/Michele Zeigler


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Improving Data Accuracy With Governance, Definitions and

Structure


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Presentation Objectives

1) Overview of Summit Health

2) IT Strategic Planning Process

3) Data & Analytics Committee Overview

4) Data Governance – Guiding Principles

5) Lessons Learned


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Summit Health Overview

•Summit Health is a not-for-profit organization that provides acute, sub-acute and ambulatory services to 170,000+ residents of Franklin County and surrounding communities in South Central Pennsylvania2 acute care hospitals,

A free standing ambulatory surgery center

3 urgent care/walk in locations

2 large ambulatory campuses, plus numerous smaller ones throughout the county

a large multispecialty/multi location employed physician practices(approximately 240 providers)


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IT Strategic Planning Process

• In the Spring of 2014 Summit Health began a multi-year IT Strategic assessment and planning process that was facilitated by an external vendor

• Over 90 individuals/groups were part of the input and assessment process, including patients and family members

• Using the input gathered and working closely with Senior Management, 6 major themes were identified

• IT Governance which included Data Governance and data analytics were identified as 2 of the 6 major themes and a focused outcome of the Summit Health IT Strategic Plan process

• Prior to this IT Strategic planning process, Summit Health had completed a selection of an enterprise business intelligence tool in the beginning of 2013 and had the BI tool and staff in place


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IT Strategic Planning ProcessFY 2015 FY 2016 FY 2017 FY 2017+

Engineering a securefoundation

Enhancing data access and integration

Create a seamless patient experience

Empowering stakeholders & extending

reach

Invest in Summit Infrastructure to support future needs of the organization – such as increased internet bandwidth, updated technology, etc.

Implement major upgrades to foundational systems

Meet regulatory requirements for MU stages 2 and 3

Focus on optimizations of existing systems to improve overall efficiency

Establish IT and Data governance

Address immediate data needs

Implement mobility solutions for patients and providers

Implement and optimize the use of incumbent analytics tools

Conduct core systems evaluation for the longer term

Improve quality and operational performance through standard reporting and monitoring

Enhance patient safety through access to clear, accurate information

Continue projects related to standardization across Summit Health

Work toward a single patient portal with access to Inpatient and Ambulatory information, online bill pay, online scheduling, secure messaging, etc.

Implement a single platform patient record. Focus on reducing redundancies from patient processes.

Optimize systems ensure smooth transitions of care for all patients.

Provide access and training on customizable reports to end-users

Continue to optimize work flows to support high quality, efficient care at a low cost

Implement targeted technology solutions to extend reach of Summit Health to patients and providers

Enhance enterprise wide mobility solutions (e.g. BYOD, core system functionality)


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IT Strategic Plan – New Governance

Executive IT Governance

Senior IT Steering

Committee

Ad hoc Advisory Groups (user

groups)

Data and Analytics

Governance

Problem Solving teams


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Data and Analytics CommitteePurpose, Roles and Responsibilities

Purpose: The Data and Analytics Committee established the policies and procedures defining how data is acquired, managed and analyzed.

Chaired by VP, Community Services,

Approves data definition standards

Determines the sources of truth for each data type

Approves processes to acquire, maintain and analyze data

Establishes the problem solving teams based on the Data and Analytics project portfolio

Reviews and prioritizes project requests by the problem solving teams

Selects projects for approval and forwards them to the Executive IT Governance Committee

Reviews requests for new analytic tools and makes recommendations to Executive IT Governance Committee

Monitors and publishes data quality reports

Interdisciplinary membership


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Data Governance – Guiding Principle

Data governance is

an organizational

discipline,

driven by the

business, but

enabled by IT.


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Data Governance Guiding Principle* creates time for analytics

• Each team acquires, manages and analyzes data needed to achieve its objectives

• Benefits • Sense of control

• Challenges• Redundant effort

• Errors

• Delays, cost overruns

• Time invested in data gathering, not analysis

• Little investment in long-term solutions to known problems

Future State: Enterprise Governance

• Each team builds on an enterprise data program

• Benefits• Data improves over time leaving more

time for analysis

• Data fixed once and leveraged across multiple projects

• Projects completed on time and budget

• Challenges• Requires investment of resources in

data management

• Longer project timelines in Year 1

Current State: Fragmented Governance


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1. VisionData Governance is

managed at the department level

Some Data Governance will be managed at the enterprise level

Data is governed and owned at the enterprise level

2. Data IntegrationData is integrated when

accessed by analytics tools Financial and clinical data are

integrated for high value programs

Internal and external data is integrated once for use by many

analytics tools

3.DataManagement

Data is validated and enhanced when analytics

are runSome data is validated at the source

Regularly used data is validated at the source

4. Analytics Access Standard reports Dashboards and self-service analytics Real-time predictive analytics

5.Confidence in the Data

Analysts spend 80% of their time collecting and

validating data

Selected analytics are trusted by some users

Clinical and financial data is perceived as timely and accurate

6. ControlsIT rather than the business manages access and quality

Governance has been established for selected clinical and financial data

An enterprise Data Governance program controls how data is

acquired, managed and analyzed

Proposed Data Governance – Guiding PrincipleFuture directionCurrent position


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Name of Indicator and type Name: ED Length of Stay type: KRID&A Governance Sponsor name of sponsorSubmitted by ED Director at Chambersburg Hospital

Status In progress or COMPLETE

Situation

Describe the KRI and why it needs to be measured.

Background

Provide context to help understand the need and how far reaching it is.

List competing business definitions and departments / contacts Department / level of the request Source of the context for measure Already in place? How measured? Trusted? Widely recognized etc..

Assessment

Describe the “why” as well as how urgently the KRI needs to be delivered

Process Owner candidates Describe line of sight from patient to Indicator Define the context Name the type of measure (financial, patient exp, safety, productivity etc.)

Recommendation

Provide a recommendation on how this KRI should be delivered

Complete definition here Name data elements and qualification (available, quality, timeliness, all

sources and recommend single source) Recommend Process Owner

Gating and Score Only when status is complete

Data Governance – Guiding Principle

SBAR


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Lessons Learned

• This is a long marathon not a sprint (data quality, metadata definition, new roles/responsibilities, accountabilities, processes)

• Automation of data collection processes and its impact

• Accountability and ownership of use and change

• Line of sight of key indicators to the work units or areas who can influence the indicator or goal

• Governance and senior leadership buy-in is a key component


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Lessons Learned (Continued)

• Establish clear goals and objective to use the data for improvement efforts as part of the request process

• Establish 30/60/90 day follow-up process once dashboard/analytic application has been published

• Train and support the transition from static reporting to an analytic tool and mindset

• Don’t let perfection stand in your way, PDSA (plan/do/study/act) processes often break up large analytic projects into phases to deliver results faster to the customer

• Document your improvements and celebrate your successes!


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Q&A

Click on the Q&A panel located in the lower right corner of your screen, type in your questions in the text field and hit send. Please keep the send to default as “All Panelists.”


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Thank You!

• Thanks to our featured speaker: Michele Zeigler!

• You will receive an email when our archive recording is ready. (Separate registration is required)

• CHIME CHCIO Credits – Attending our Webinars = 1 CEU

• Sponsorship opportunities: Nancy Wilcox [email protected]

• Questions/Comments: Anthony Guerra [email protected]

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improving data accuracy with governance, …...how data is acquired, managed and analyzed. chaired...

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