Data Warehousing & Data Warehousing & Analytics in the VHAAnalytics in the VHA
Jack BatesJack BatesVHA Office of InformationVHA Office of Information
Corporate Data Warehouse GroupCorporate Data Warehouse Group
IHS Technology ConferenceIHS Technology ConferenceJune, 2005June, 2005
AgendaAgenda
What is Data Warehousing?What is Data Warehousing?Brief HistoryBrief HistoryPrinciplesPrinciplesCase StudiesCase StudiesOutlook for next 12 – 18 Outlook for next 12 – 18
monthsmonthsQ&AQ&A
““A structured repository of A structured repository of validated, integrated, and validated, integrated, and current data accessible to current data accessible to business people to provide the business people to provide the basis for both tactical and basis for both tactical and strategic business decisions.”strategic business decisions.”
What is a Data Warehouse?What is a Data Warehouse?What is a Data Warehouse?What is a Data Warehouse?
HRHR
Corporate Data Warehouse ConceptCorporate Data Warehouse Concept
Centralized extract and stagingCentralized extract and staging
Separate from operational Separate from operational systemsystem
Structured for analysisStructured for analysis
Current with sufficient historyCurrent with sufficient history
ADRADR
PATSPATS
HDRHDR
PFSSPFSS
FMSFMS
LogisticsLogistics
NPCDNPCD
CorporateCorporateData Data
WarehouseWarehouse
Operational SystemsOperational SystemsOperational SystemsOperational Systems
History of Data Warehousing in VHAHistory of Data Warehousing in VHA First instances were created at the regional First instances were created at the regional
levellevel First appeared in early FY98 First appeared in early FY98 Grass roots effortGrass roots effort Due to regional management (instead of Medical Due to regional management (instead of Medical
Center) centric management structureCenter) centric management structure No central data/information capabilityNo central data/information capability Spread slowly due to new technology issues Spread slowly due to new technology issues
(expertise was in M)(expertise was in M) Now technology is widespreadNow technology is widespread
Exists in every Region (more or less)Exists in every Region (more or less) VHA wide data marts now exist (PBM, Prosthetics)VHA wide data marts now exist (PBM, Prosthetics) VHA wide data warehouse strategy now existsVHA wide data warehouse strategy now exists VHA wide Corporate Data Warehouse in Phase II VHA wide Corporate Data Warehouse in Phase II
of construction phaseof construction phase
25 Years of VistA25 Years of VistA(July 25, 2005)(July 25, 2005)
Secrets of VistA’s SuccessSecrets of VistA’s Success Incremental EvolutionIncremental Evolution Built by the subject matter experts who Built by the subject matter experts who
were passionate about their productwere passionate about their product Best of Breed approachBest of Breed approach ModularModular Common infrastructureCommon infrastructure Consistent and evolving interfaceConsistent and evolving interface Provision for new ideasProvision for new ideas
Corporate Data Warehouse PrinciplesCorporate Data Warehouse Principles
Corporate Data WarehousingCorporate Data Warehousing is business-orientedis business-oriented will treat information as an will treat information as an
assetasset will be developed using will be developed using
standards-based and standards-based and architecturally-sound architecturally-sound principlesprinciples
will maximize leveraging, will maximize leveraging, collaboration, and reuse (and collaboration, and reuse (and Enterprise Agreements)Enterprise Agreements)
Rationale for Establishing aRationale for Establishing aCorporate Data Warehouse (CDW)Corporate Data Warehouse (CDW)
VHA needs a strategic corporate capability to:VHA needs a strategic corporate capability to: Manage cost, workload, utilization, quality, satisfaction, Manage cost, workload, utilization, quality, satisfaction,
and performanceand performance Perform effective budgeting and forecastingPerform effective budgeting and forecasting Enhance patient safetyEnhance patient safety Manage suppliersManage suppliers Facilitate disease managementFacilitate disease management Facilitate researchFacilitate research Facilitate business transformation from Facilitate business transformation from ”just in case” to ”just in case” to
“just in time”“just in time” Coordinate responses during national emergenciesCoordinate responses during national emergencies
Performance Measurement:Performance Measurement:Setting the U.S. Benchmark for 18 Setting the U.S. Benchmark for 18
Comparable IndicatorsComparable IndicatorsClinical Indicator VA 2002 VA 2003 Medicare 03 Best Not VA or Medicare
Advised Tobacco Cessation (VA x3, others x1) 69 75 62 68 (NCQA 2002)
Beta Blocker after MI 97 98 93 94 (NCQA 2002)
Breast Cancer Screening 80 84 75 75 (NCQA 2002)
Cervical Cancer Screening 89 90 62 81 (NCQA 2002)
Cholesterol Screening (all pts) 91 91 NA 73 (BRFSS 2001)
Cholesterol Screening (post MI) 92 94 78 79 (NCQA 2002)
LDL Cholesterol <130 post MI 74 78 62 61 (NCQA 2002)
Colorectal Cancer Screening 64 67 NA 49 (BRFSS 2002)
Diabetes Hgb A1c checked past year 94 94 85 83 (NCQA 2002)
Diabetes Hgb A1c > 9.5 (lower is better) 17 15 NA 34 (NCQA 2002)
Diabetes LDL Measured 94 95 88 85 (NCQA 2002)
Diabetes LDL < 130 70 77 63 55 (NCQA 2002)
Diabetes Eye Exam 72 75 68 52 (NCQA 2002)
Diabetes Kidney Function 78 70 57 52 (NCQA 2002)
Hypertension: BP < 140/90 55 68 57 58 (NCQA 2002)
Influenza Immunization 74 76 P 68 (BRFSS 2002)
Pneumocooccal Immunization 87 90 P 63 (BRFSS 2002)
Mental Health F/U 30 D post D/C 81 77 61 74 (NCQA 2002)
VHA Business Intelligence Road Map
VHA Business Intelligence Capability Maturity Model
A 5-7 year roadmap
Functional/Departmental QueriesPredefined QueriesKey Performance IndicatorsBalanced Score CardCorporate Alerts
Sophisticated OLAPAd hoc AnalysisPerformance DashboardsPortal Based ReportingStatistical AnalysisSpatial Analysis
Cross-Functional/Strategic AnalysisData VisualizationClick Stream AnalysisBehavior Profiling
Data MiningContent ManagementKnowledge ManagementText MiningClosed-loop Decision Processing
Pervasive and Personalized IntelligenceCollaborative and Real-time AnalysisWorkflow IntegrationBG and GG integrationCRM Contact Mgt. IntegrationMobile and Location Based Analysis
Level 1
Level 5
Level 4
Level 3
Level 2
Strategy, Program Planning,
Sponsorship, Governance, Communications,
Metadata, Data Standardization
WaitWaitTimesTimes
DataDataWarehouseWarehouseVHAVHAaa
VHAVHAcc
SourceSourceSystemsSystems
DiabetesDiabetes
AcquireAcquire Populate Populate Create Create Access Access Data Data WarehouseWarehouse MartsMarts Information Information
11 33 44
CommonCommonQuery, Reporting,Query, Reporting,
Analysis, and Analysis, and Data MiningData Mining
ToolsTools
OPOP
PBMPBM
22
OtherOther
VHA Corporate Data Warehouse Visual VHA Corporate Data Warehouse Visual ArchitectureArchitecture
VistAVistAHDRHDR
NPCDNPCDDSSDSSADRADR
VAVADoDDoDCMSCMS
ConformedConformedDimensionsDimensions
Prog OfficeProg OfficeData MartsData Marts
Ext
ract
, Tra
nsf
orm
, Lo
adE
xtra
ct, T
ran
sfo
rm, L
oad
VISNVISNWarehousesWarehouses
VHAVHAcc – VHA clinical data systems – VHA clinical data systemsVHAVHAaa – VHA administrative data systems – VHA administrative data systems
Program OfficesProgram Offices•Pharmacy BenefitsPharmacy Benefits•ProstheticsProsthetics•DentalDental
Closed Loop Information SystemClosed Loop Information System
(VISN Support Service Center)(VISN Support Service Center)(VHA Office of Information)(VHA Office of Information)
DataDataConsultantsConsultants
VSSCVSSCProfessionalProfessional
ServicesServices
ResearchResearchData MartsData Marts
VSSC ManagedVSSC ManagedVSSC ManagedVSSC Managed
Program Office ManagedProgram Office ManagedProgram Office ManagedProgram Office Managed
Value Added DataValue Added Data
Current Subject AreasCurrent Subject Areas OutpatientOutpatient
AppointmentsAppointments EncountersEncounters Primary Care PanelsPrimary Care Panels
InpatientInpatient MovementMovement DischargesDischarges
Lab (53 tests)Lab (53 tests) RadiologyRadiology PharmacyPharmacy ProstheticsProsthetics Non VA CareNon VA Care Human ResourcesHuman Resources Financial AccountingFinancial Accounting
•NursingNursing•DentalDental•PurchasingPurchasing•Outpatient EncountersOutpatient Encounters•Health Data RepositoryHealth Data Repository
•Vital signsVital signs•AllergiesAllergies•Lab testsLab tests•PharmacyPharmacy
Planned Additions:Planned Additions:
VHA Corporate Data WarehouseVHA Corporate Data WarehouseDimensional ArchitectureDimensional Architecture
Conformed Dimensions
Patien
t
Provid
er
Facilit
y
Stop
Code
Bed S
ectio
n
CPTIC
D9BOC
Time
Con
form
ed F
acts
Fact: Contains one/more measures or facts. E.g., Bed Days of Care per 1000 patients, Cost Per Veteran Per Month. These facts are numeric and additive. A fact table has a multi-part primary key made up of two or more foreign keys, and expresses a many to many relationship.
Dimension: Contains descriptive textual information. Dimension attributes form thesource for constraints for data warehouse/data mart queries. In essence, they providethe context/perspective for analysis. E.g., Drug, ICD9, DRG, Patient. Each dimensiontable has a single-part primary key that corresponds exactly to one of the componentsof the multi-part key in the fact table.
VHA > VISN > Facility > Division > Service > Clinic > Clinic Location
DimCPT
CPTID varchar(5)
CPTName varchar(30)CPTCategory varchar(75)CPTClass varchar(40)Inactive varchar(8)WorkRVU numeric(10,2)PrExpRVU numeric(10,2)MalPrRVU numeric(10,2)TotalRVU numeric(10,2)
DimDRG
DRGID smallint
DRGName varchar(70)DRGCategory varchar(70)DRGClass varchar(7)DRGType char(3)ValidYear int
DimICD9
ICD9ID int
ICD9Txt varchar(9)ICD9Prefix varchar(5)ICD9Name varchar(50)ICD9Category varchar(50)ICD9Class varchar(100)ICD9SubClass varchar(100)
Clinical
DimAge
AgeID smallint
AgeGrp2 varchar(15)AgeGrp4 varchar(15)AgeGrp10 varchar(15)AgeGrp14 varchar(15)AgeGrpARC varchar(15)
DimPatient
ScrNumID int
ScrSSN varchar(9)PtSSN varchar(9)ICN char(10)DoB smalldatetimeDoD smalldatetimeDoDSource varchar(10)Gender varchar(1)Race varchar(25)EnrZip intHmFIPS intPreferredVISN intPreferedSta3n intPreferredSta6a varchar(6)CFYVERAClass char(3)PFYVERAClass char(3)Eligibility char(3)EnrPriority varchar(3)EnrStatus varchar(50)PCProvider char(10)Diabetes char(1)Hypertensive char(1)
Demographic
DimARCTrtLoc
TrtLocID char(3)
TrtLocName varchar(100)TrtLocCategory varchar(100)
DimARCTrtType
TrtCodeID varchar(5)
TrtCodeName varchar(50)
DimALBAcct
AccountID smallint
AcctDesc varchar(50)
DimCostCtr
CostCtrID smallint
CCName varchar(100)CCCategory varchar(50)CCClass varchar(50)
DimALBProductionUnit
ProdUnitID char(2)
PUName varchar(125)PUCategory varchar(50)PUClass varchar(50)
DimARCDxClass
DXClassID smallint
DXClassName varchar(50)DXClassARC nvarchar(2)
DimARCEligibility
EligibilityID char(2)
EligibilityName varchar(100)EligibilityCategory varchar(100)
DimARCIncome
IncomeID smallint
IncomeGroup varchar(50)
Financial
DimBOC
BOCID smallint
BOCName varchar(50)BOCCategory varchar(50)BOCClass varchar(50)
Geographic
DimVISN
VISNID smallint
VISNTxt char(3)VISNName varchar(50)City varchar(75)State char(2)Zip intFIPS intFTE smallintBudget money
DimFacility3n
Sta3nID smallint
Sta3nName varchar(100)VISN smallintCity varchar(50)State varchar(2)Zip intFIPS intMCG smallintFTE smallint
DimFacility6a
Sta6aID varchar(6)
Sta6aName varchar(100)Sta6aType varchar(50)Sta3n smallintVISN smallintVASTID smallintSta6aCity varchar(100)Sta6aState char(2)Sta6aZip intSta6aFIPS intAdminParentCode intAdminParentCity varchar(100)AdminParentState char(2)AdminMCG smallint
DimFIPS
FIPSID int
State char(2)County varchar(30)VISN smallintVISNCARES smallintCARESMkt varchar(100)VetPop int
DimZip
ZipID int
State char(2)City varchar(100)
Hospital Location
DimStop
StopID smallint
StopName varchar(50)Category varchar(50)Class varchar(50)Type char(10)
DimBedsection
BedsectionID smallint
BedsectionName varchar(50)Category varchar(50)Class varchar(50)Type char(10)
Time
DimDate
DateID int
MDate smalldatetimeFYr char(4)FQtr char(6)CMth char(5)CDay varchar(30)FP smallintDoW char(3)DoM smallint
FCDM Dimensional Map
• Standardized•Type/Size conventions•Naming conventions
• Verified•“Gold” standard• Business rules
• Optimized•Primary keys•Indexed
• Refreshed
VHA Dimension MapVHA Dimension Map
VHA Corporate Data WarehouseVHA Corporate Data WarehouseDimensional ArchitectureDimensional Architecture
Conformed Dimensions
Patien
t
Provid
er
Facilit
y
Stop
Code
Bed S
ectio
n
CPTIC
D9BOC
Time
HDR.Vitals
ADR.Enroll
NPCD.Enc
PFSS.Bills
Pros.Actions
Sch.Appts
Con
form
ed F
acts
Business Process Transactions
12345, 34567, 598GA, 323, 5/1/05, BP, 140, 90
12345, 598GA, 5/1/05, 65, M, SC100, POW
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05, A123, $99
12345, 34567, 598GA, 323, 5/1/05, Wheelchair, $500
12345, 34567, 598GA, 323, 5/1/05, 6/1/05
VHA Corporate Data WarehouseVHA Corporate Data WarehouseDimensional ArchitectureDimensional Architecture
Conformed Dimensions
Patien
t
Provid
er
Facilit
y
Stop
Code
Bed S
ectio
n
CPTIC
D9BOC
Time
Business Process Transactions
HDR.Vitals 12345, 34567, 598GA, 323, 5/1/05, BP, 140, 90
ADR.Enroll
NPCD.Enc
12345, 598GA, 5/1/05, 65, M, SC100, POW
PFSS.Bills
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05
Pros.Actions
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05, A123, $99
12345, 34567, 598GA, 323, 5/1/05, Wheelchair, $500
Sch.Appts 12345, 34567, 598GA, 323, 5/1/05, 6/1/05
Con
form
ed F
acts
VHA Corporate Data WarehouseVHA Corporate Data WarehouseDimensional ArchitectureDimensional Architecture
Conformed Dimensions
Patien
t
Provid
er
Facilit
y
Stop
Code
Bed S
ectio
n
CPTIC
D9BOC
Time
Business Process Transactions
HDR.Vitals 12345, 34567, 598GA, 323, 5/1/05, BP, 140, 90
ADR.Enroll
NPCD.Enc
12345, 598GA, 5/1/05, 65, M, SC100, POW
PFSS.Bills
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05
Pros.Actions
12345, 34567, 598GA, 323, 73456, 250.01, 5/1/05, A123, $99
12345, 34567, 598GA, 323, 5/1/05, Wheelchair, $500
Sch.Appts 12345, 34567, 598GA, 323, 5/1/05, 6/1/05
Con
form
ed F
acts
Dia
bet
es D
MD
iab
etes
DM
Dia
bet
es D
MD
iab
etes
DM
SC
I D
MS
CI
DM
SC
I D
MS
CI
DM
PB
M D
MP
BM
DM
PB
M D
MP
BM
DM
Hig
h C
ost
DM
Hig
h C
ost
DM
Hig
h C
ost
DM
Hig
h C
ost
DM
DM – Data MartDM – Data MartDM – Data MartDM – Data Mart
Data Warehouse User ProfilesData Warehouse User Profiles
Information Information ConsumersConsumers
Information Information ExplorersExplorers
5-10% of users5-10% of users
15-25% of users15-25% of users
65-80% of users65-80% of users
AnalystsAnalysts
Robust analysis tool•Sophisticated reporting•Predictive analytics•Ranking, forecasting
Simple analysis tool•Data visualization•Simple filter/sort•Easy publishing
Simple analysis tool•Data visualization•Simple filter/sort•Easy publishing
Robust reporting tool•Access to reports•Report subscriptions •Browse/Search/View
Robust reporting tool•Access to reports•Report subscriptions •Browse/Search/View
Analytical & Reporting ToolsAnalytical & Reporting Tools
• Easy to learn and use• Latest technology• Variety• Complimentary• Standard access methods
Web Reporting
Spatial AnalysisStatistical Analysis
Systolic
Systolic
Fre
qu
en
cy
20000
10000
0
Std. Dev = 20.67
Mean = 137.4
N = 87199.00
Current Software SuiteCurrent Software Suite Operating SystemOperating System
MS Windows Server 2003 Enterprise Edition ServerMS Windows Server 2003 Enterprise Edition Server ETL EngineETL Engine
MS Data Transformation ServicesMS Data Transformation Services Database EngineDatabase Engine
MS SQL Server 2000MS SQL Server 2000 OLAP EngineOLAP Engine
MS Analysis ServicesMS Analysis Services Reporting EngineReporting Engine
MS Reporting ServicesMS Reporting Services Presentation ToolsPresentation Tools
ProClarity Analytics SuiteProClarity Analytics Suite Microsoft MapPointMicrosoft MapPoint SAS/SPSSSAS/SPSS
Portal EnginePortal Engine Microsoft SharePointMicrosoft SharePoint
Use cases…Use cases…
ProClarity ProfessionalProClarity Professional(Analyst, Information Explorer)(Analyst, Information Explorer)
Key Capabilities:•Effective ad-hoc analytic capabilities •Powerful calculation and query functionality •Advanced data visualization and navigation views
•Decomposition Tree •Perspective View •Performance Map
•Free form analytics
Key Capabilities:•Excel add-in to leverage Excel knowledge•Create high-quality, formatted business reports •Refresh information daily, weekly or whenever required •Eliminate the confusion and overhead in keeping spreadsheets current•Ties your Excel reports to a central data source
Business Reporter for ExcelBusiness Reporter for Excel(Information Explorer, Information Consumer)(Information Explorer, Information Consumer)
Web StandardWeb Standard(Information Consumer)(Information Consumer)
Key Capabilities:• Rapidly deploy analytics over the web• True zero-footprint client requiring no downloading or
installation on a user desktop • Users can consume pre-built analyses, or perform ad-hoc
analysis • Provides “Guided Analytics” to user community
DashboardDashboard(Information Consumer)(Information Consumer)
Key Capabilities:•Users can assemble KPIs and views that promote best practices for a given subject area•Groups can create personalized dashboards focusing directly on their specific KPIs and other information they need to monitor their specific area of interest•Users move quickly from monitoring KPIs into ad-hoc query mode. •Dashboards may be deployed alone or integrated within an enterprise portal solution such as Microsoft® SharePoint Portal Server•Add external content such as links to relational reports, web-based content or websites
DRG Root Cause Analysis: DRG 430 (Psychoses) is our highest volume discharge DRG. How do the medical centers in our Region compare to all other medical centers in volume and average cost/day of care for FY04?
CBOC Profile: We need to create a CBOC clinic profile that allows us to analyze cost and workload activity at each of the CBOC’s in our Region. We would also like the ability to look at cost and workload for specific disease entities.
National Program Office Issue: How many mammograms are we doing per month and what are the age ranges of the patients? Can I also look at CT and MRI procedures?
Non VA Care High Volume/High Cost Issues:What are the Regions high-cost/high volume DRGs? Where do the Non-VA Care patients live? Compare Avg. Cost/BDOC (Fee vs. VA) to assist in make-buy decisions.
Fee Inpatient Origin by County for DRGs 107 & 109Fee Inpatient Origin by County for DRGs 107 & 109
Prosthetic Issues:What percent of my prosthetics budget is spent on surgical implants as compared to the other Regions?
VHA Diabetic Patients by CountyVHA Diabetic Patients by County (One or more Inpatient or Outpatient ICD9 = 250.xx)(One or more Inpatient or Outpatient ICD9 = 250.xx)
Pts by County
10,000 to 99,999
1,000 to 9,999
100 to 999
10 to 99
1 to 9
Region 21 Diabetic Patient CostRegion 21 Diabetic Patient Cost(VA + Non VA)(VA + Non VA)
VISN 21 FY04 Diabetic Cost Statistics
23184 23184 23184
0 0 0
$3,224 $506 $3,731
$1,236 $0 $1,304
$0 $0 $28
$8,122 $5,117 $10,237
-$116 $0 -$62
$219,175 $474,498 $525,643
$599 $0 $625
$1,236 $0 $1,304
$2,697 $0 $2,923
Valid
Missing
N
Mean
Median
Mode
Std. Deviation
Minimum
Maximum
25
50
75
Percentiles
VACost FeeCost TotCost
CostPt by County
$3,724.00 to $48,109.00
$2,450.00 to $3,723.00
$1,650.00 to $2,449.00
$1,012.00 to $1,649.00
$620.00 to $1,011.00
$288.00 to $619.00
$108.00 to $287.00
$1.00 to $107.00
Human Resource Issues:What percentage of my nurses are eligible to retire? How does that compare with other Regions?
Quality Control:Quality Control:Data Completeness ReportData Completeness Report
Highlightmissing or
inconsistentsource data
Next steps…Next steps…
Longitudinal Data Mart Longitudinal Data Mart DevelopmentDevelopment
Inpatient Discharg
es
Outpatien
t Encoun
ters
Radiolog
y Pro
cedures
Account Level B
udgeting
Inpatient Tre
ating S
pecialty
Lab R
esults
Pha
rmacy F
ills
Non V
A C
are
830 R
eport
887 R
eport
Em
ployee Surveys
PA
ID
Natu
re of Action
Long T
erm C
are (R
AI/M
DS
)
Patient Demographics
Facility Demographics
Chart of Accounts
ICD9 Reference
Time
Workload Non VA Finance HR Other
Composite Inpatient/Outpatient Data Mart
Disease Specific Data Marts (Diabetes, Heart Disease, Mental Health)
Cohort Specific Data Marts (OEF/OIF)
Performance Measure Data Mart
Composite Inpatient and OutpatientComposite Inpatient and Outpatient
Diabetic Population AnalysisDiabetic Population Analysis
High Risk Patients
Details of the 20 Denver patients(1) Last A1C > 9
(2) Last Glucose => 140
Age Range
High Risk Diabetic AnalysisHigh Risk Diabetic Analysis
Personal Delivery of InformationPersonal Delivery of Information
•Performance Measures•Special Measures (MI)•Exception reporting•Clinical reminders/alerts•Event reporting•Designed to display on mobile devices (e.g.Blackberry)
Geographic Information SystemsGeographic Information Systems
Legend:
VA HospitalVA Clinic
Additional:• Puerto Rico• Hawaii• Alaska• Philippines
CustomizedBI
Portal
Ultimate Goal: Centralized BI Ultimate Goal: Centralized BI PortalPortal
ResourceResourceManagementManagementResourceResourceManagementManagement
ExceptionExceptionReportsReports
ExceptionExceptionReportsReports
VA/VHAVA/VHANewsNews
VA/VHAVA/VHANewsNews
PerformancePerformanceManagementManagement
ScorecardScorecardReportsReports
ScorecardScorecardReportsReports
Policies &Policies &ProceduresProceduresPolicies &Policies &
ProceduresProcedures
DiseaseDiseaseManagementManagementDiseaseDiseaseManagementManagement
Government & Government & Industry NewsIndustry NewsGovernment & Government & Industry NewsIndustry News
Current ChallengesCurrent Challenges
ScaleScale Subject area isolationSubject area isolation Tool availabilityTool availability Cube architectsCube architects Organizational culture (tipping point?)Organizational culture (tipping point?) Source data formatSource data format SSN level access and securitySSN level access and security Data qualityData quality Timeliness of dataTimeliness of data TrainingTraining
Next Six MonthsNext Six Months
Roll out ProClarity EARoll out ProClarity EA User Education and Training (w/ Analysis)User Education and Training (w/ Analysis) Continue Refining ProcessesContinue Refining Processes Continue Product Creation and Refinement Continue Product Creation and Refinement
(primarily cubes)(primarily cubes) Clinical Data Marts (Diabetes, Mental Health)Clinical Data Marts (Diabetes, Mental Health) Cultivate Existing Partnerships (DSS,ARC, Cultivate Existing Partnerships (DSS,ARC,
OQP, etc.)OQP, etc.) Establish Knowledge Sharing Framework (e.g. Establish Knowledge Sharing Framework (e.g.
SharePoint)SharePoint) Data Quality ControlData Quality Control
Next Eighteen MonthsNext Eighteen Months VISTA Reengineering ProjectsVISTA Reengineering Projects
Health Data RepositoryHealth Data Repository SchedulingScheduling BillingBilling
Common BI InterfaceCommon BI Interface SharePoint basedSharePoint based
ProClarity Web web partsProClarity Web web parts ProClarity Dashboard web partsProClarity Dashboard web parts Reporting Services web partsReporting Services web parts GIS web partsGIS web parts
Expanded Statistical AnalysisExpanded Statistical Analysis Predictive modelingPredictive modeling
Direct Query AccessDirect Query Access Power usersPower users Research communityResearch community
Questions?Questions?