developing a centralized repository strategy: the top three success factors
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John Sharp, MSSA, PMP, FHIMSSResearch Informatics
Developing a Centralized Repository Strategy:
The Top Three Success Factors
Introduction
• As healthcare organizations seek ways to better predict the true transformation of healthcare, centralized data repositories hold the keys to unlocking the potential for predicting the impact of quality on patient care.
• Learning Objectives:– Determine your healthcare quality challenge for leveraging data
– Identify the top three critical success factors for a centralized repository strategy
Current State of Quality Reporting –Multiple Demands
• Joint Commission
• CMS
• Get with the Guidelines – AHA
• Meaningful Use
• Core Measures
Current State of the Data
Claims data
EMR data – new for many health systems and reporting systems emerging
Other clinical systems
Public health, population data
Reference data
Genomic data
Three Success Factors of a Centralize Data Repository
Buy in and governance
Organizing Data
Displaying data in a meaningful way
BUY-IN AND GOVERNANCE
Buy-in and Governance
Hazards of Decentralized approach– Many copies of the data – storage issues
– Different data definitions
– Confusion over public reporting
– Are all data repositories up-to-date?
– Multiple teams with different degrees of expertise
– Different ways of transforming the data
– Taxing systems which data is extracted from
Buy-in and Governance
Complaints about a centralized approach
• Give up control
• Too IT centric
• Slow response – queue is too long
• Can’t get my request prioritized
Buy-in and Governance
Solution
• Involve key stakeholders from the beginning
• Include institution leadership and clinical leadership
• Include data stewards
• Define end users of the data broadly
Buy-in and Governance
Data Stewards
• Develop consistent definitions and interpretations of data and data concepts so that information can be consistently interpreted
• Document where data originates and the processes that act on it
• Help ensure data quality, accuracy, consistency, timeliness, validity, and completeness
• Define appropriate controls to address data security and privacy requirements
• http://himss.files.cms-plus.com/HIMSSorg/Content/files/201304_DATA_GOVERNANCE_FINAL.pdf
Buy-in and Governance
Governance Committee Role
Increasing consistency and confidence in decision making
Decreasing the risk of regulatory fines
Improving data security
Maximizing the income generation potential of data
Designating accountability for information quality
Enable better planning by supervisory staff
Minimizing or eliminating re-work
Buy-in and Governance
Governance Committee tasks
• Standards for defining – definitions and taxonomies
• Processes for managing – data quality, change management
• Organizational responsibilities – oversight, prioritization
• Technologies for managing – data dictionaries, data discovery tools
Organizing Data
Aggregate
Normalize
Analyze
DA
TA
DataFlux Data Governance Maturity Model
http://www.dataflux.com/DataFlux-Approach/Data-Governance-Maturity-Model.aspx
Organizing Data – Data Quality
• Data definitions – making sure that data are well understood across the organization
• Data lineage – documenting how data are created, transformed and intermingled
• Data accuracy – making sure that data accurately reflect the clinical and business transactions and activities of the organization
HIMSS Clinical & Business Intelligence: Data Management – A Foundation for Analytics
Data Quality (2)
• Data consistency – making sure that data within and across data stores provide a consistent representation facts
• Data accessibility / availability – making sure those who need data to perform their job function have access to relevant data
• Data security – making sure that data access is restricted to those who have a legitimate and legally allowable need for the data
Data Quality - Definitions
How do you define a hospital admission?
• > 24 hours
• >= 24 hours
• Include time in the ED?
• What about direct admits after a procedure?
• What if the patient dies in a hospital bed in less than 24 hours
• Does CMS and private insurers have the same definition?
Data Definitions
• ICD-9, ICD-10
• SNOMED-CT
• LOINC
• RxNorm
• National Quality Forum measure – definitions– Numerators/Denominators
DISPLAYING DATA IN A MEANINGFUL WAY
Basic Reports
Simplest solution for many requirements
Rows and columns
Detail, summary or both
Daily, weekly, monthly
Must know source of data
Validation
Basic Graphs
Bar, line, area – picking the appropriate display for the data
Comparing trends – YTD, previous year, nursing units, product lines
Making data actionable – how will this graph change what I am doing?
Big Data Visualization
Assist in understanding greater degrees of complexity
May be 3 dimensional
Geocoding of data into maps
Network diagrams
Layers of data
Must work with customers to find most effective display which is actionable
Disease prevalence and socioeconomic status across the US
Network Diagram showing relationships between concepts
Heatmaps
Future of Clinical Data Repositories
Become familiar with big data tools, such as, Hadoop, NOSQL, and use in unstructured data
Understand data growth – how big will your data be a year from now?
Begin to do real-time or near real-time reporting to impact quality
Stay on top of changing definitions
Three Success Factors of a Centralize Data Repository
Buy in and governance
Organizing Data
Displaying data in a meaningful way