Improving Data Accuracy With Governance, Definitions and Structure

Transcription

1 Improving Data Accuracy With Governance, Definitions and Structure A Complimentary Webinar From healthsystemcio.com Your Line Will Be Silent Until Our Event Begins at 12:00 ET Thank You!

2 Housekeeping Moderator Anthony Guerra, editor-in-chief, healthsystemcio.com Ask A Question We will be holding a Q&A session after the formal presentations. You may submit your questions at any time by clicking on the QA 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. Download the Deck Go to Download today's deck at: Shortened URL at bottom of all slides View the Archive You will receive an when our archive recording is ready. Separate registration is required.

3 Agenda Approximately 40 Minutes 30 minutes: Michele Zeigler, VP of IS/CIO, Summit Health 10 minutes: Q&A w/michele Zeigler

4 Improving Data Accuracy With Governance, Definitions and Structure

5 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

6 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 Pennsylvania 2 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)

7 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

8 IT Strategic Planning Process FY 2015 FY 2016 FY 2017 FY Engineering a secure foundation 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 endusers 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)

9 IT Strategic Plan New Governance Executive IT Governance Senior IT Steering Committee Ad hoc Advisory Groups (user groups) Data and Analytics Governanc e Problem Solving teams

10 Data and Analytics Committee Purpose, 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

11 Data Governance Guiding Principle Data governance is an organizational discipline, driven by the business, but enabled by IT.

12 Data Governance Guiding Principle * creates time for analytics Current State: Fragmented Governance 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 Page 12

13 Proposed Data Governance Guiding Principle Current position Future direction 1. Vision Data 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 Integration Data 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. Data Management Data is validated and enhanced when analytics are run Some 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. Controls IT 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

14 Data Governance Guiding Principle SBAR Name of Indicator and type D&A Governance Sponsor Submitted by Status 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. Assessment Describe the why as well as how urgently the KRI needs to be delivered Recommendation Provide a recommendation on how this KRI should be delivered Gating and Score Name: ED Length of Stay type: KRI name of sponsor ED Director at Chambersburg Hospital In progress or COMPLETE 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.. 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.) Complete definition here Name data elements and qualification (available, quality, timeliness, all sources and recommend single source) Recommend Process Owner Only when status is complete

15 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

16 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!

17 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.

18 Thank You! Thanks to our featured speaker: Michele Zeigler! You will receive an when our archive recording is ready. (Separate registration is required) CHIME CHCIO Credits Attending our Webinars = 1 CEU Sponsorship opportunities: Nancy Wilcox nwilcox@healthsystemcio.com Questions/Comments: Anthony Guerra aguerra@healthsystemcio.com Go to to view our upcoming schedule and see the last 12 months of archived events.