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