Data Warehousing Data Mining. Why Providers Struggle With Data Warehousing. Interviews with Vendors Claiming to Sell Data Warehousing HIMSS 2002

Transcription

1 Data Warehousing Data Mining HIMSS 2002 Homer Warner Jr. Dale Sanders Lynn Brookshire Wiley Thomas Why Providers Struggle With Data Warehousing Data collection and interface costs are high Not a high priority for institution Data is not normalized Ad hoc queries are difficult Process takes a long time and requires teamwork Demands change over time Interviews with Vendors Claiming to Sell Data Warehousing Majority sell a clinical database but not a true warehouse Most focus on more on financial and administrative data rather than clinical Most promote third party query tools rather than propriety solutions All expect that data warehousing will eventually be hot - just not sure when. 1

2 Interviews with Healthcare Provider Organizations: Negative: We have lots of data, but no one is asking for it. We have the data we just don t know how to use it. Positive: We have high performance operational teams that use warehouse data extensively. Warehousing will help us get to six sigma performance levels. Success Factors Commitment from business and physician leaders to be data driven Leaders understand that biggest costs will be in: collecting connecting normalizing Return on investment cannot be expected until after infrastructure is in place Panel Introductions Dale Sanders Lynn Brookshire Wiley Thomas 2

3 Overview of the IHC Enterprise Data Warehouse Dale Sanders August 23, 2001 Intermountain Health Care Non-profit, Integrated Delivery System Utah, Idaho $2.5B per year 23,000 employees 22 hospitals, 2061 beds 85+ clinics 1 million Health Plans members 400 employed doctors Best IDS in 2000 Modern Health Care magazine Systems Supplying the EDW Hospital casemix Accounts payable Clinical Data Repository Problems list, CV Discharge Meds, STS, NRMI, NICU/VOX, HNA IDX ambulatory casemix Health Plans claims Health Plans SAS extracts State of Utah outpatient data Postal service Medical Staff Services Storkbytes Tamtron (Pathology) Radiology Pharmacy Cancer Registry Enterprise Master Patient Index Health Data Dictionary Health Needs Appraisal Mammography Sunquest (Laboratory) Health Data Management In planning Blood services In planning 3

4 Political and Technical Challenges Political The production systems perception of threat Data quality finger pointing Funding and ROI Technical Oracle complexities Load-time windows Data warehousing is different Extracting from the Clinical Data Repository Future Plans EDW Alerting Rules engine? OLAP Logic Modules? Data mining Pattern recognition in relational data Genetic data Relate clinical outcomes to genetic codes Familial relationships Natural Language Processing (NLP) Extracting meaningful information from text Speech-based queries? XML OLAP and Java OLAP Retrieving data objects, rather than rows Useful for loading data from operational systems Query By Image Content (QBIC) Population-based image analysis Top down reporting requirements and data model For Example Oncology Data Integration Strategy An Integrated Reporting Model of Cancer Patient s Data Disparate Sources Connected to the Bus Architecture Cancer Registry Mammography Radiology Pathology Laboratory Continuing Care And Follow-Up Quality of Life Survey Radiation Therapy Acute Care Casemix Health Plans Claims Ambulatory Casemix 4

5 Development Flow Architectural Data Flow Diagram Analytical Reporting Requirements Logical Data Model ETL Requirements Data Processing Requirements Physical Data Model Source-to-Target Data Map and Logic ETL Programming Analytical Application Development Functional System End User Interface Options Key People CIO: Carvel Whiting CMIO: Paul Clayton EDW Director: Dale Sanders EDW Sponsor: Greg Poulsen Chair, Analytical Services Council EDW IT Team: DBA, Data Architects, Programmers Data Analysts in business units Costs Operations: ~$1.4M in 2001 ~17 FTEs Capital: ~$250K in 2001 Total investment over last four years Operations: ~$3.8M Capital: ~$1.5M 5

6 Successes Numerous business stories more than I know about Cardiovascular Discharge Meds: 400 lives per year Materials Management: Hundreds of thousands of dollars saved Diabetes: Exemplary Health Care Award Physicians Division: 83 days to 41 days average on days outstanding Fair, metrics based negotiations with United Health Total health care costs of Oncology patients Lessons Learned Adjust your technical strategy to fit your business environment Top down vs. Bottom up Don t start until you have a business, clinical and technical vision in mind The ROI is buried in the feedback loop Changing behavior Manage as two separate projects Employee satisfaction is number one Must have for customer satisfaction Lessons Learned Allow users as much access as possible Err on the side of too much empowerment Leverage off-the-shelf tools! Data quality belongs to the source systems We are a tool for data quality, not the solution Data Quality = Completeness x Validity Manage project risk, proactively Risk = Frequency x Consequence 6

7 Lessons Learned: Strategy Maintain data structure familiarity, initially Give end users what they are used to Separate business logic from data structures Avoid overly complex load processes Address data quality from the production system Avoid data cleansing in the EDW; improve data quality in the source systems first Data marts first, data warehouse second Have a grand vision of the future, but realize it is one step at a time Work on the infrastructure first Hardware, software, tools, processes, data Opening the doors to business before you are ready, and you will lose customers forever Lessons Learned: Architecture Don t be a schema bigot Star, Relational, Snowflake, whatever Do what makes sense in your environment Our approach: Start traditional, then denormalize Turn off RI in the EDW That s the job of the OLTP systems Organize and name your schemas around your business Schemas should contain similar data, functionally or operationally Organize and name your roles around the types of users Define standard types and naming for core dimensions E.g., patient, provider, SSN, etc. Naming, indexing and partitioning standards/rules-ofthumb Publish these early and use them! Lessons Learned: Architecture IBM/SP2: Very scaleable, very complex Prone to failures High system overhead: Traffic between nodes; multiple O/S and database instances High TCO: DBA skills Performance is tough to tune RS6000/S80 SMP is working great Disk storage RAID5 works great 3:1 overhead ratio to real data Tape backup One tape drive per 144G of data Real time feeds: Buffer the warehouse Until RDBMS snapshot error problem can be fixed Development environment: Not as important as I thought it would be Oracle: Better than it was in data warehousing, but very high TCO Customer service has declined Microsoft SQL Server/Windows2000 data center is looking better 7

8 Charleston Area Medical Center Lynn Brookshire The largest hospital in West Virginia 919 bed non-profit, regional tertiary referral center with over 4,200 employees and 692 Medical Staff 33,855 inpatient discharges in 2000 and 412,739 outpatient visits Affiliated with two rural hospitals Plateau Medical Center (91 beds) and Braxton County Memorial Hospital (A critical access hospital with 40 beds) Charleston Area Medical Center The largest provider of uncompensated care in West Virginia The largest Medicaid provider in West Virginia One of the top five largest cardiac programs in the United States Level III Neonatal Intensive Care Unit Level I Trauma Center Primary medical safety net provider of women and children s and trauma services in central and southern West Virginia Systems Self developed, Oracle-based data warehouse established in 1994 Siemens [SMS] core patient management, order communication, results, and billing systems Adjunct Siemens Novius warehouse 8

9 CAMC Warehouse Source Systems SMS Patient Management Patient Accounting SMS Data Warehouse SoftMed TSI ALB CCM DCM DeRoyal OR Cath Lab Solucient Medical Manager Cactus CAMC Warehouse Source Systems Key People Previous CIO established Information Center and home grown Decision Support System Director of Information Center Vice President for Quality and Care Management Decision Support Analysts Data Subgroup 9

10 Costs Dedicated resources for startup 7 FTEs in 1996 Limited capital investment Continuous hardware upgrades, storage requirements, and interfaces $ 100,000 to $ 250,000 annually Oracle, SAS, and Sagent licenses Still seven dedicated resources today in the Information Center, distributed subject matter experts including six sigma blackbelts Political Challenges Information shopping and data source issues Who owns the data Reduction in funding for 2002 when requirements are growing Technical Challenges Greater demands for real-time information Clinical decision support The capture and management of data not traditionally designed in our legacy hospital information systems Integration of process improvement metrics and the ability to monitor sustained change Focus on statistical analysis in support of high performance improvement teams using six sigma methodology 10

11 Typical Successes Ability to get a unified picture of a patient visit that includes utilization, cost, and research data Central source for decision support Capability to drill down within data sets and look for specific trends CAMC Successes Early adopters Subject matter experts Acceptance and weighted importance with hospital operations Care Management Institute Integration with six sigma and High Performance Teams Physician champion asking for more! Product of The Information Center Most Recent Discharge Statistics-CAMC 1500 SelectArea /14 10/15 10/16 10/17 10/18 10/19 10/20 10/21 10/22 10/23 Inpatients Outpatients Emergency Most Recent Admission Statistics-CAMC Monthly Census Occupancy %-CAMC /14 10/15 10/16 10/17 10/18 10/19 10/20 10/21 10/22 10/23 Inpatients Outpatients Emergency Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Copyright: Information Center 11

12 CAMC Decision Support Admit, Discharge, and Transfer CDL Report Daily Census Daily Census Summary DRG Management DRG Financial Reporting HR Reports Medicare DRG Payor Medicaid DRG Payor Patient Accounting Peripheral Vascular Surgery Quality LOS and Cost Reductions Medicare ALOS to GMLOS By Unit Admitting Unit-M5S /2000 2/2000 3/2000 4/2000 5/2000 6/2000 7/2000 8/2000 9/ / / /2000 1/2001 2/2001 3/2001 ALOS GMLOS A B C D E F G H I J K L M N O P 1 ALOS To AGMLOS 2 Admitting Unit: M5S 3 4 Admitting Unit Month Visits ALOS GMLOS 5 M5S 1/1/ M5S 2/1/ M5S 3/1/ M5S 4/1/ M5S 5/1/ M5S 6/1/ M5S 7/1/ M5S 8/1/ M5S 9/1/ M5S 10/1/ M5S 11/1/ M5S 12/1/ M5S 1/1/ M5S 2/1/ M5S 3/1/ Maryland Indicator Report 12

13 Lessons Learned Physicians respond positively to datadriven change and can champion initiatives given the right information Timing is a difficult concept for nontechnical constituents People shop for data that meets their needs or expectations Questioning data integrity is an excellent tactic to stall change initiatives Future Plans Increased opportunity for research on clinical outcomes and quality of care. Refine data models around utilization so that areas of opportunity related to best practice are easily identified Creating an environment where data required to sustained improvement is concurrently collected in a production system and available in the warehouse. Otherwise creating an easy way to capture and incorporate data with a high confidence interval entered directly into the warehouse. Future Plans On-line physician decision support including quality of care, utilization, and outcomes as benchmarked against their peers. Improved presentation of our Decision Support system including alerts and reminders. Report cards and dashboards including control limits and statistical monitoring. 13

14 Good Shepherd Medical Center Located in Longview, Texas (100 miles east of Dallas) 350 bed not-for-profit hospital with 2,000 employees and a Medical Staff of rural area clinics serving 6 counties in a 50 mile radius 19,000 inpatient admissions and 165,000 outpatient visits annually Affiliated with ASC Systems Meditech system installed in 1987 Currently running all modules except Nursing and Pharmacy (Pharmacy is outsourced) Medical Manager practice management system used by all owned and managed clinics MIDAS Care Management GSMC Warehouse Source Systems Wiley Thomas Meditech Financial Clinical (Lab, Radiology) Medical Manager MIDAS GSMC Developed Applications Intraoperative Information (Clinical Events, Supply utilization) 14

15 Political and Technical Challenges Competition for Capital Integration of internal data sources Increased demand for information Real-time data Reconciliation of benchmark data Key People CFO Care Management Director Information Systems department DBA Vendor The Shams Group, Inc. Costs Infrastructure Upgrades Initial network expansion Ongoing cost of storage Training Personnel 15

16 Successes Care Management early champion Finance Ability to look outside the Meditech box Lessons Learned Physicians initially question and criticize data They will respond and champion change when given meaningful information Future Plans Increase information on clinical outcomes Drive current practice with real-time clinical data Personalized dashboards for more users managers and clinicians 16

17 ORTHOPEDIC SERVICE LINE Study Period: 7/1/00 to 12/31/00 Study Period: 7/1/00 to 12/31/00 DRG Volume By Severity Major Joint & Limb Reattachment Proc of Lower Extremity V o l u m e % 12% 14 10% 61 44% 52 40% 51 39% 64 46% Previous Period Current Period No Score Available Extreme Major Moderate Minor Previous Period: 7/1/99 12/31/99 Current Period: 7/1/00 12/31/00 DRG 209 Variances From Targets Major Joint & Limb Reattachment Proc of Lower Extremity Higher Cost/Lower LOS $1,500 $1,200 $900 $600 $300 #00712 $ $ # $600 -$900 -$1,200 -$1,500 Lower Cost/Lower LOS #00146 Higher Cost/Higher LOS #00818 Only physicians with DRG Volume >4 are displayed. #00080 #01500 #00823 Lower Cost/Higher LOS 17

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