Texas Health Resources (THR)

1. Texas Health Resources (THR) Guest Speakers: • Debbie Jowers • Administrative Director, Applications Texas Health Resources Information Services • Data warehousing and business intelligence initiative at THR • Discuss the use of ETL tools such as Cognos’ Business Intelligence Suite. • Ms. Jowers is on SIM DFW Chapter Board of Directors • Tony Keller • Director Enterprise Data Management Texas Health Resources Information Services

2. Enterprise Data Architecture Components • Corporate Information Road Map • Information Systems • Data Flow ( Enterprise, Application ) • Data Capture • User Process / Workflow • Ownership, Standards, Quality • Database Administration • Data Movement / Translation / Transformation • Data transmission standards ( HL7) • Common Vocabulary Engines • Interface Engine ( real-time transaction detail ) • Warehouse Tools ( historical batch detail ) • Data Analysis • Decision Support ( operational, retrospective ) • Metrics ( corporate, entities ) • Application Specific ( non-replicated, replicated ) • Data Warehouse Administration • Reporting Tools ( Statistical, Detailed, Summarized, Dash Board ) • Data Archive • Storage : making sure we ‘keep’ what we ‘need’ • Reasons: Legislative requirements, future patient care, historical reporting

3. Guiding Principles forData Management • Every data element must have a reason, a purpose for being collected. • Data should be managed as close to the originating source as possible. • There should be only one owner for the integrity of a given data element. • Data should be managed to insure the appropriate level of quality. • Data should be managed as a strategic asset to the organization. 1. Data Quality Data Accuracy - data is the correct value according to the field definition Data Comprehensiveness - required data is included Data Consistency - data is consistent across host applications (standardization) Data Currency - data is current for a specific time Data Granularity - attributes and values should be defined at the correct level of detail Data Relevancy - data is meaningful Data Timeliness - data is updated at meaningful times 1.

4. Data Quality • Data Accuracy • data is the correct value according to the field definition • Data Comprehensiveness • required data is included • Data Consistency • data is consistent across host applications (standardization) • Data Currency • data is current for a specific time • Data Granularity • attributes and values should be defined at the correct level of detail • Data Relevancy • data is meaningful • Data Timeliness • data is updated at meaningful times

5. Data Architecture Issues • Corporate Information Road Map • We have too many systems managing the same data across the enterprise • We don’t manage data at an Enterprise level • difficulties defining/assigning Enterprise data owners • Vendor application data management limitations • Data Capture • We have too many redundant data capture systems across the enterprise • ‘We can’t control the information you create in your department or it is in the wrong format or incomplete to meet our need’ • Model and build databases based upon application project requirements alone---without involvement of stakeholders outside of the project scope. • Leads to fragmented, non-integratable data models and non-sharable data bases • Most if not all clinical data is ‘paper based’ requiring chart abstraction • Data Movement / Translation / Transformation • We expect data standards to be managed within the interface infrastructure instead of at the source system • Data Analysis • Ambiguous, uncoordinated metric definition, strategy misalignment, variable data quality • Poorly defined requirements, multiple tool preferences, and control issues • Vendor application reporting environment and data replication mismanagement • Data Archive • Vendor limitations • Poorly managed user expectations

6. Administrative Data Enterprise Data Application Data Clinical Data Risk Data Patient Satisfaction Data Conceptual Enterprise Data Architecture OLAP Data - Produces Knowledge OLTP Data - Supports Operations DATA WAREHOUSE Load Feed 1 - Person Demographics - Physician Demographics - Payers - etc Load • TSI ( Billing / Cost ) • Advance GL • Advance AP • PeopleSoft Linked CDR • QAsys • HSM • Chart Abstracted • Apollo Feed Load - Administrative - Clinical - Ancillary - etc 1 Data Producers Data Users Data Quality Data Analysis : - Managed Care - Supply Chain - Human Resources - Strategic Planning - Quality Outcomes - Therapy Services 1 – Currently not in THR’s Data Warehouse

7. Radiology Pathology Results Coding Nursing Documentation Physician Documentation Pharmacy Medical Record Patient Acctng Encounter Orders Scheduling Hospital Care Plan Registration 1 1 Cost Acctng Reporting 2 2 Chart Abstraction Critical Data Needs : 3 3 Make sure we have the right Patient Make sure we have the right Physicians Make sure we have all the Diagnosis Codes and Procedures documented Clinical / Quality Reporting Data Capture / Process Flow Patient

8. Application Data Flow THR Radiology Registration Meditech ARL Advance Talk Tech MRS Results Vital Works SWDIC 1 2 2 ADT Meditech MiSYS ADT / Orders / Results Adac ADT / Orders / Results Fuji Pacs MRS SWDIC Fuji Pacs SWDIC MiSYS Advance Rad ( HSW ) Dictaphone Adac OCF Cerner Millennium Radiology HSW, HNW, STP, WAL Advance ADT / Orders E-Gate MiSYS HAR, HEB PHP Orders / Results / Billing Orders / OSU / Results ADT / Orders ADT / Orders Dictaphone Orders / Results / Billing HNW, STP, WAL Results / Transcriptions Results (HSW) ADT / Orders PHD, PHA Adac Orders / OSU / Results ADT / Orders Invision ADT Orders Orders / OSU / Results Orders / Results ADT / Orders Results Results ADT / Orders / Results ADT / Orders / Results ADT / Results OSU – Order Status Update 1 – PHD Only 2 – MiSYS billing file for HAR, HEB does not go through E-Gate however, orders and results do go through E-Gate Web PowerChart

9. Application Data Flow Future THR Radiology Meditech ARL E-Gate ADT / Orders ADT / Orders Advance MiSYS Orders / Results / Billing Results ADT, Orders, OSU Results, Billing Invision ADT / Orders Orders / Results / Billing / OSU ADT / Orders Orders Vital Works SWDIC MRS EPIC ADT Results Talk Tech ADT, Orders, Results 1 2 1 Orders Image Cerner OCF Lanvision Results Results Results Fuji Pacs CT, MRI, CR Workstations ADT – Patient registration data OSU – Order Status Update Results – Patient Exam Reports 1 – Includes SWDIC 2 – MiSYS billing file for HAR, HEB does not go through E-Gate however, orders and results do go through E-Gate

10. Data Analysis Pyramid Dash Board - Intuitive Presentation - Web based - Appropriate Data Granularity Executive User 2% Direct Data Access - Meta Data Layer - Easy Report creation - Powerful report capabilities - Easy access to multiple data environments IT Requirements - Easy Report creation - Powerful report capabilities - Production Scheduling - Multiple dissemination options - Easy Meta Data Management - Strong integration layer - Strong performance - Easily manageable - Strong security management - Easy access to multiple data environments Data Savvy User 8% OLAP - Intuitive Presentation - Web based - Appropriate Data Granularity Data Analysis User 20% Standard Reports - Web based report selection - Parameter driven - online viewing - Multiple “save as” options - Fast - Report subscription Operational User 70%

11. Enterprise Data Management Data Architecture Data Standardization • Reporting Workbench • KB SQL • Chronicles Adhoc • Cache Script Real Time Standard Reports development Oracle Active Clinical Data Repository SAS MainFrame Environment Cognos E-GATE Data Archive Data Marts Oracle Sagent Open Link SAS : PC, EG Cognos PowerPlay 1 OLTP THR’s Clinical Data Repository THR Systems EPIC System • Inpatient • - Orders Mngmt • - Clin Doc • - Identity • Ambulatory • Pharmacy • ED • HIM • Oncology • My Chart - Patient Registration -Advance -Invision - Laboratory -Cerner - Radiology -Mysis - Others Chronicles Real-time Shadow Repository Clarity Console Validation Tool kit 2 OLAP Nightly Clarity THR Data • TSI Cost Accounting • Supply Chain • SALT • JCAHO / CMS • Therapy Services • PeopleSoft • HR Exit Interview • HSM Surgical Sagent ETL Marts Ad hoc Reports Standard Reports Crystal Reports Sagent SAS EG CE EPIC Crystal Reports Crystal Enterprise • OLTP data - Supports business operations • OLAP data - Supports reporting/analysis

12. Sources of Variation in the Clinical Process Doctor Hospital Patient 1. Appropriateness - Medical Necessity of Care Applicability of specific services Proper location of care Acceptable duration of care 2. Efficiency - Measuring care on overall use and cost - Comparable to peer groups and standards 3. Effectiveness - Technical : refers to clinical outcomes - Perceived : relates to patient perception of the treatment results - History • Diagnostic Services • Therapeutic Services • Monitoring Services • Diagnostic Capability • Urgency of Action • Management Plan Clinical Process and Outcomes Measures of Results “Quality can be continuously improved by studying outcomes data, Evaluating the clinical processes that produce the outcomes, And making the required changes.”

13. Enterprise Performance Management Collaboration & Feedback Measurement & Monitoring Strategy Flag metric under-performers Drill down and evaluate via analytics Link targets & metrics to goals Top down vs. Bottom up Communication & Alignment Mission Vision Execution Results Automate data management process