CDW Data Lifecycle

Report
(Closet Skeletons Version)
Richard Pham
Enterprise Architect
OI&T Corporate Data Warehouse – Architecture
[email protected]
VHA Corporate Data Warehouse Visual Architecture
Closed Loop Information System
Source
Systems
VistA
HDR
Metadata
Repository
Conformed
Dimensions
Outpatient
Encounters
VHAc
NPCD
DSS
ADR
VHAa
VA
DoD
CMS
Extract, Transform, Load
Diabetes
Common
Query, Reporting,
Analysis, and
Data Mining
Tools
Wait
Times
Data
Warehouse
Other
Research
Data Marts
1
Acquire
Data
VISN
Warehouses
2
Prog Office
Data Marts
Value Added Data
Populate
Warehouse
VHAc – VHA clinical systems
VHAa – VHA administrative and financial systems
Pharmacy
Benefits
Management
3
Create
Marts
Program Offices
•Pharmacy Benefits
•Prosthetics
•Dental
4
Access
Information
CDW Informatics and Analytics Ecosystem
REGION 2
REGION 1
REGION 4
V1
V12
V20
RPC
Farm
V19
RPC
Farm
V15
V18
V23
V5
V16
V4
REGION 3
V6
BI Farm
CDW – Corporate Data Warehouse
RDW – Regional Data Warehouse
V3
V17
V22
• SQL Server Data Center Build
(Engine, SSIS, SSAS, SSRS)
• Excel Services
• SharePoint/PerformancePoint
Services
• Team Foundation Services
• SAS
• Stata
• TreeAge
V2
RDW
RDW
RDW
V21
RPC
Farm
RPC
Farm
SAS
Grid
RPC
Farm
V7
VINC
I
CDW
Ana
GIS
RDW
V11
Apps
V8
V10
V9
ePM
Hardware Stats
• 411 Servers
• 4 PB Storage
• 54 Racks
Some Things Never Change
 VHA and OI&T have a tense/unhappy relationship
 OI&T project management bureaucracy is onerous
 The use and oversight of contractors is problematic
 Pharmacy knows what they are doing (more so than
OI&T)
In the beginning, there were files
(early 70s)…
There were problems…
 How do I maintain each file?
 If I change one file, what happens to the other files?
 How do I control growth of the files?
Then came databases…(late 70s)
And there were more problems…
 How can the databases share common elements like
patient?
 What if some idiot changes one table structure that
collapses everything else?
 Who remembers how this database was designed?
This is only two packages, think of
the 100+ that are in VistA
 Now, try extrapolating those trends in your head
 Have a picture in your mind?
Did That Picture Look Like This?! (~7% of VistA as of 2010)
One more extension, let’s try to
analyze this data…
This Is What Happens With Extracts
(90s)
Even more problems….
 Is my data timely (Extract to production system time
lag)?
 Are the extracts one-time? Are they repeatable?
 Who manages all these extracts?
 No seriously, this becomes a really ugly problem
Why Am I Giving This
Presentation?
 Quite simply, feedback on:
 “I don’t understand what you mean when you say “File”
or “Pointer.”
 “Where does the data come from?”
 “How does the data get to CDW?”
 Also, while you are using the CDW to prepare your
work, it really helps if you know the origins of where
the data comes from…
DHCP/VistA/CPRS/HealthEVet
 VistA – Veterans Health Information Systems and Technology
Architecture – 2nd Generation Architecture. Refers both to the
architecture and the database which the architecture supports
 DHCP - Decentralized Hospital Computer Program – The DOS
(Unix-like) system where many of VistA’s non-clinical entries
take place
 CPRS - Computerized Provider Record System – A user-friendly
GUI providing access to clinical order entry functions
 HealthEVet – 3rd Generation of VA’s EMR. Planned inclusions
are patient-facing applications, better alignment with coding
standards, and MDS compliance.
The Health Care Process Is More Complicated
Than We Think
Objectives
 The main objective is to understand the data lifecycle
of VA’s VistA/CPRS and the user experience of
VistA/CPRS
 A high-level overview of VistA Internals
 Learn about data structures and outputs in VistA
 Learn where data enters and travels throughout the VA
 Try to make sense of data resources within the VA and
how they are accessed
The VA Data Lifecycle
The VA Data Lifecycle
Core Patient Care Functionality
 VistA is first and foremost an Electronic Medical
Record. The architecture design supports veteran
health care.
Core Patient Care Functionality
 VistA Internals
 DHCP
 CPRS
VistA Internals 101
 MUMPS
 Server and Operating System
 Kernel
 “Three Wise Men (Managers)”
 TaskMan
 MailMan
 FileMan
 Modules
Why Is Med Safety at Ann Arbor?
To Best Care Anywhere…
Massachusetts General Hospital Utility
Multi-Programming System (MUMPS or M)
 My definition in English
 M is a programming language designed for hierarchical
databases that is convenient for medical applications or
anything else where speed and data storage upkeep are a
problem and programmer intelligence/organization is not
 My technical definition
 M is a Turing-complete, low and high-level, imperative,
machine-compiled (no longer interpreted) programming
language utilizing a hierarchical global array file structure
 Used commonly in healthcare and financial industry
settings
Structure of The Veterans Administration
Data Efforts (Late 1970s)
VHA Ancestor
Department of Medicine
and Surgery (DMAS)
VHA-OI Ancestor
Computer Assisted System
Staff (CASS)
OI&T Ancestor
Office of Data
Management &
Telecommunications
(ODM&T)
Comparing The Two Offices
CASS
ODM&T
 Decentralized design
 Centralized design
philosophy
 Rapid, agile development
philosophy
 Bureaucratic, process-focused
development
 SME-involved development
 Development without SME’s
Highlights of ODM&T Development
 Took 6 years to deploy APPLES Pharmacy at 10 sites
 A 1980 paper detailing ODM&T’s transactional patient
treatment file (PTF) system promised an interactive
national solution by 1990.
 Navigating the mandated 17 steps between system
specification and deployment alone is said to have
required at least 3 years.
Beginnings of DHCP
 There were subject matter experts that believed that
they could put out useful applications faster than the
ODM&T sloth
 Development of the testing and principles was done
unofficially throughout the early to late 1970s
Original DHCP Design Principles
 A commitment to rapid prototype development
 All use ANSI MUMPS
 Modular Design
 Actively Maintained Data Dictionary
 Code Sharing/Portability
 Involve the SME’s
DHCP Kernel
 Functions as both an operating system for VistA
applications and an M virtual machine
 Kernel shields DHCP modules from needing to know
hardware and OS configurations on the server
 Isolates M to the ANSI standard (1995)
 Provides a toolbox of standard functions for most
programmers
MUMPS Classic Database
 One Data Type
 String (Text)
 Other types
 Cardinal Numbers
 Float Numbers
 $H Dates
 One Data Storage Type
 Multidimensional Array aka Globals
 Dynamic (duck) typing
VistA Data Organization
 Namespace
 File

Field
 Record
 654 (VAMC Reno)
 File 120.5 (GMR Vitals)

Field 0.1 (DATE/TIME VITALS TAKEN)
 IEN-1, BP, 140/90
 Most Files have an entry at the 0.001 Field called “IEN” or
“Internal Entry Number” as an identity key to mark the
record as unique
From The Beginning - Entry
 An entry is a “piece” of data
 Richard – First Name
 Pham – Last Name
 05/03/1983 – Date of Birth
Record (Row)
 A group of related data
 Richard Pham
M
 05/03/1983
Field
 A group of related data
 Richard – First Name
 Pham – Last Name
 05/03/1983 – Date of Birth
File
 A group of related fields and the records that we have
 File 200 – NEW PERSON
 Richard – First Name – 200
 Pham – Last Name – 200
 Date of Birth -
File Relationships
 One-to-One - Pointer
 One-to-Many (Subfile, Multiple)
 Self-referential (Recursive)
 Reverse Recursive (Past Records)
 Forward Recursive (Replace Records)
 Pointer with Logic – Multiple POinter
File Relationships - Pointers
 When two files share a common field with each other, this
is called a pointer
 There are three major types
 Pointer - One record in one file matches to one record in
another file
 Self-Referential – One record in one file matches to one
record in the same file (in the past or the future)
 Multiple – One record in one file matches to many records in
one file (parent-child)
 Variable – One record and some logic matches to one file
Pointers
File 52
PRESCRIPTION
Field 2
Patient
One-to-one
File 2
PATIENT
All fields
Self-Referential Pointer
Warning – DO NOT $o these fields without programmer
assistance! You will bring down DHCP this way!!!
File 100
OE/RR
Field 9
Replaced Order
Present-to-Past
File 100
OE/RR
Field 9.1
Replaced Order
Present-to-Future
File 100
OE/RR
(Past Order)
File 100
OE/RR
(Future Order)
Multiple Subfile
File 52
PRESCRIPTION
Field 52
Refill Subfile
One-to-many
File 52.1
REFILL
All fields
Multiple Subfile
One-to-many files
File 120.8
PATIENT ALLERGIES
Field 1
GMR Allergy
File 50.605
DRUG
CLASS
File 50
DRUG
File 50.6
NATIONA
L
DRUG
120.2
GMR
ALLERGIE
S
File 50.416
DRUG
INGREDIE
NT
Computed/MCode
 A placeholder that does not contain any stored
information
 Calculated ad hoc when you look up the value
 Warning – For this reason, the value ALWAYS has the
possibility of changing
How Complicated Is The Pharmacy
Package?
 440 files in the File 50 Series
 3,175 fields
 527 Pointers
 310 External References
VistA to Relational Database
Terminology
VistA (Example)
Relational Database (Example)
Namespace (VHAFRE)
Database (VA Fresno)
“Package” – Not hardcoded
Schema (RxOutpatient)
File (50.68 – VA PRODUCT)
Table (NationalDrug)
Field (.01 – NAME)
Column (DrugNameWithDose)
Domain (cardinal/decimal, setofcodes,
freetext/wordprocessing)
Field Type (numeric, boolean, varchar)
Internal Entry Number (IEN or .001)
~Key (9722)
Record
Tuple/Row (ISOSORBIDE
MONONITRATE 120MG TAB,SA)
Pointer (IEN)
Foreign Key (VAClassIEN)
Multiple Pointer
No equivalent
Computed/MCode Field
Trigger (Age Trigger)
Upside of Using Globals
 Faster - No joins
 Faster – All parameter pointers built in
 Faster – Direct and planned programmatic access to
database (Look at SQL execution plans)
 Less Data Storage Overhead and faster paging – If the
data point does not exist in the array, there does not
need to be a fixed point like in relational
Downside of Using Globals
 No Intrinsic Structure and No Enforcement* - M
believes whatever you put into the globals (most M
programmers view this as an advantage while
relational programmers have an MI)
 ACID-compliance not mandated
 (Il)logical data structures guaranteed – There are
many interesting* ways that the M programmers
modeled the data that does not make sense to later
viewers
MUMPS Quirks
 Whitespace (Space) matters
 Requires knowledge of kernel and sometimes lower-
level concepts
 Programming Without Type or Structure Enforcement
 VA programming standards and conventions
The Three Wise Men (Managers)
 TaskMan – The man(anger) that schedules tasks to the
kernel
 MailMan – The man(anger) that messages between
the user, TaskMan, and any other two-way
communication between packages
 FileMan – The man(anager) that controls internal file
(data structure) interactions
TaskMan
 TaskMan handles application processing:
 Creation of application processing tasks
 Scheduling these tasks
 Monitoring health/statistics of these tasks
 If kernel is the brain, then TaskMan is the body of the
operation
 If programming, NEVER EVER use the TaskMan global.
This subverts TaskMan’s scheduling queue, and can cause a
system memory leak. Use the calls instead…
MailMan
 VistA needs a way to pass and receive data from the database to
other areas
 MailMan fulfills this function in the pre-TCP/IP days
 “Electronic mail” doesn’t mean just email
 Practically any message between the database and anyone else (the
end-user, another site, or application, etc.) can be moved this way
 Gives programmers methods to both receive and return data to
the database
 MailMan is its own protocol, but will use HL7 when
communicating with non-DHCP programs
FileMan
 A higher-level method to access the VistA database
without exposing a programmer interface
 Mostly menu-driven
 One can use limited programming
 Serves as the model for all other modules that interact
with the VistA database
ODM&T Initial Action Plan To DHCP
Development (1980)
 Ordered that development stop
 Fired the developers
 Removed the hardware
 Cut the DMAS budget so it would never happen
again…
The official history
Development Goes Underground
 Developers that survived the ODM&T purge
continued their work as a black project in DMAS
 During 1980 and 1981, the survivors (Underground
Railroad) continued work on developing modules for
system integration
Modules
 Modules are programmed to interact with the VistA
database
 Most use FileMan as a model for programming
Some of the Many Modules
Medicine
Surgery
Dentistry
Nursing
Pharmacy
Laboratory
Care
Management
Patient Care
Encounters
ADT
Mental Health
EDIS
Oncology
Nutrition and
Food Service
Imaging/PACS Prosthetics
Not really in the scope of this presentation to cover each
module .
Try the VistA Documentation Library:
http://www4.va.gov/vdl/
Or
VHA eHealth University (VeHU): http://www.vehu.va.gov/
Acceptance and DHCP 1.0
 Once there was a critical mass of packages that were
shown to be useful, the tide turned and the project was
blessed…
 Initial testing/installation done in 1980-83
 1.0 installation was in 1985
 Most of the underlying packages can still be
recognized by the original programmers
Computerized Patient Record
System (CPRS)
 A Real-Time Order Checking System that alerts clinicians during the ordering session
that a possible problem could exist if the order is processed
 A Notification System that immediately alerts clinicians about clinically significant
events
 A Patient Posting System, displayed on every CPRS screen, that alerts clinicians to issues
related specifically to the patient, including crisis notes, warning, adverse reactions, and
advance directives
 The Clinical Reminder System, which allows caregivers to track and improve preventive
health care for patients and ensure timely clinical interventions are initiated
 Remote Data View functionality that allows clinicians to view a patient’s medical history
from other VA facilities to ensure the clinician has access to all clinically relevant data
available at VA facilities
CPRS Internals
 Written in Embarcadero Delphi (NOT in MUMPS)
 Connects from the Graphic User Interface to the VistA
database using a Remote Procedure Call (RPC) Broker
 This Remote Procedure Call Broker translates
instruction sets from other languages into M
Present State of VistA
 Large MUMPS database
 Over 50+ Main Clinical Packages
 Over 10,000 + Tables
 Each medical center runs somewhere between 2-4 TB
worth of data over 30 years (mostly imaging)
 Many processes
 300+ MB of running executable at any given time
 Over 20,000 subroutines (VDL)
 Many simultaneous users
Analytic Coursework
 \\r01scrdwh65.r01.med.va.gov\vadatalifecycle\sql
 SQL
 T-SQL dialect is for VHA
 PL\SQL dialect is for VBA
 SQL Server Reporting Services
 SQL Server Analysis Services
 Statistical Analysis Programs
 SAS
 Stata (preferred)
 TreeAge
Next Class
 SQL
 Basic query – Optional introduction lecture on basic
computer science (algorithms, heaps, sorts, data
structures). Two 50 minute lectures for five weeks
 Basic Reporting - Two 50 minute lectures for five weeks
 Advanced Programming – One 50 minute lecture every
other week
 Class is placed on the site
 Current version has the DBZ Abridged Disclaimer
The VA Data Lifecycle
National Analytic Systems
 A list of systems that support policy, planning, and
congressional needs
 There are more extracts than this, but I have chosen
the most common ones…
Systems to Support Planning
 Decision Support System (DSS)
 Supports accounting and costing for the OIG, GAO,
CBO, and other auditing agencies
 Allocation Resource Center
 Supports personnel and resource allocation at the
medical center level
 Workload capture, resource allocation
 Basis for the VERA (VA’s Fund Control Point) Model
Systems to Support Planning and
Research
 National Patient Care Database
 An integrated set of data that captures a patient’s care
encounter with the VA
 Corporate Data Warehouse –
 A near real-time accumulation of much of the same data
 The result of the Health Data Repository process
Local VistA
Installations
Local VistA
File
Site 1
Local VistA
File
Site N
Host
Location
DSS – Austin, Tx
PBM – Hines, IL
NPCD – Austin, Tx
Etc.
Local Extract
Software
Site 1
VistA Extracts
Load and
Translate
Software
Local Extract
Software
Site N
VistA Extracts
DSS Extract Software
PBM Extract Software
NPCD Extract Software
Etc.
DSS Extract Files
PBM Extract Files
NPCD Extract Files
Etc.
Diagram of Data
Sources Available to
VA Researchers
DSS Production Database
PBM Database
NPCD Database
Etc.
National
Database
DSS Build Software
PBM Build Software
NPCD Build Software
Etc.
Build SAS
Datasets
DSS NDE SAS Datasets
Medical SAS Datasets
Etc.
Custom
Extract By
Database
Owner
Custom
Extract
SAS Datasets
Research
Database
PBM Custom Extract
Medicare Data
National Death Index
Etc.
External
Data
Researchers
78
NPCD Processing
UNIX
Daily
Data
Loading
Flat files are indexed
and loaded into the
database daily
Oracle on Unix
NPCD
DSS
data
extracted
z900 (MAINFRAME)
Master
Extract File
(MEF)
SAS
Data is checked
for duplicates bimonthly
WINDOWS
VSSC/
KLF Menu
Data is extracted
and filtered for
reporting twice a
month
NPCD Data Flow Diagram
VistA
MailMan
•NPCD data is sent from the facilities to the AAC via
MailMan messaging
•Once a message reaches the AAC MailMan server,
It automatically moves to the Data Management
Interface System (DMI)
•NPCD and other applications retrieve their respective
data from DMI for use
• Acknowledgement messages are sent to facilities
z900
SD
P R O LI A N T
80 00
Data
Data
extracted
extracted
&
backed
by application
up nightly M-F
ESC
Austin
MailMan
Server
Data Stream
DMI
SD
D LT
NPCD
Acknowledgement
message
Data received
in DMI 24x7
Acknowledgement
message
HL7 data to Oracle DB
Secrets of the VA Data Universe
 This was an extremely brief introduction to a
complicated area
 I have another presentation on the availability of
databases in the VA and how to access them for
operations and/or research
The VA Data Lifecycle
Regional Remote Data Processing
Center Shadow Systems
 A offsite backup process to ensure continuity of
operations for VistA Patient Care
Regional Data Processing Centers
(RDPCs)
 Started as backups
 Read only backup VistA systems are set up to take journaling
files
 When a record is written or altered to a local medical center’s
VistA, a journal file with that entry is prepared and sent to a
Regional Data Processing Center
 This maintains an active backup in case the local medical
center’s VistA goes down
 Nowadays, even the production systems work from there
 Region I and IV fully
 (? On status) Region I and III
Regions and RDPCs
 Region I RDPC – Sacramento (SAC) and Denver (DEN)
 Region II RDPC - Little Rock (LIT)
 Region III RDPC – Durham (DUR) and Augusta
 Region IV RDPC – Philadelphia (PHI) and Brooklyn
RDPC Denver and Brooklyn
The VA Data Lifecycle
Business Intelligence
Business Intelligence in the VA –
Making the Data Work For Us
 VistA has a wealth of clinical and administrative data
available
 In the past, giving a value-added, timely VistA dataset
was hard
 Querying the active system with minimal impact
 Needed an interface between M and analyst languages
(SAS, SQL, etc.)
 Easy to read reports was hard to build
BISL Informatics and Analytics Ecosystem
REGION 2
REGION 1
REGION 4
V1
V12
V20
RPC
Farm
V19
RPC
Farm
V15
V18
V23
V5
V16
V4
REGION 3
V6
BI SharePoint (MOSS) Farm
CDW – Corporate Data Warehouse
RDW – Regional Data Warehouse
V3
V17
V22
• Performance Point Services
•Excel Services
• Reporting Services
• Analysis Services
• SharePoint Services
• Team Foundation Services
V2
RDW
RDW
RDW
V21
RPC
Farm
RPC
Farm
SAS
Grid
RPC
Farm
V7
VINC
I
CDW
Ana
GIS
RDW
V11
Apps
V8
V10
V9
ePM
Hardware Stats
• 411 Servers
• 1.5PB Storage
• 54 Racks
Different Ways To Access DHCP
Data
 Direct Methods
 FileMan – Individual methods
 M Routines – Not favored (permanent moratorium in
Region I)
 CPRS Injection – MDWS (this is HI2’s major method)
 Cache Direct – HDR Extractor (CDW Method)
 VDEF – VistA Data Extraction Framework
 Indirect Methods
 Journal Reader (CDW method)
MDR Extractor
Shadow Servers
Corporate/Regional Data
Warehouse
 Takes a copy of the journal file that goes into the backup
shadow system
 Translated from the M array to a relational database format
using Intersystems Cache’s class mapping program
 Staged in a Feeder-Collector system for collection
 Indexed and value-added columns produced and loaded to
an VISN RDW Server
CDW Governance
VHA Business
Owners/SME’s
Communicates
Organizational
Priorities
CDW Governance Board
Organizes SMEs
and Data
Stewards
Sets and monitors
domain, work priorities,
and timelines for
completion.
OI&T
VHA-OI
Data Quality
10N, OIA, VBA
Provides
Documentation
and
Clarification of
Business Logic
Corporate Data
Warehouse
CDW Governance Is In VHA’s Hands
 Ordered By VHA
 Domain and Work Prioritization By CDW Governance Board
 Chair – KLF (OIA)
 Vice-Chair – Larry Mole (Public Health SHG)
 Monitored and Accountable To VHA
 Project management provided by John Quinn (National Data
Systems) and KLF (OIA)
 Supported By VHA
 OI Data Quality
 Business Owners
 PBM’s Data Steward is Rob Silverman
“As the number of eyes goes up,
the number of bugs goes down.”
 Writing documentation about the business logic of the
files and fields
 Answering end user questions about the data
 Data validation
 Preferably before
 Inpatient Pharmacy
 ADR/Allergy Package
1st category models are simple –
V Health Factor
FMFile
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
V HEALTH FACTORS
Source Mapping
FMField
ResolveFld DWTableName
HEALTH FACTOR
HealthFactor
HEALTH FACTOR
0.01 HealthFactor
PATIENT NAME
HealthFactor
EVENT DATE AND TIME
HealthFactor
VISIT
0.01 HealthFactor
VISIT
0.01 HealthFactor
LEVEL/SEVERITY
HealthFactor
VISIT
HealthFactor
ENCOUNTER PROVIDER
HealthFactor
COMMENTS
HealthFactor
DWFieldName
HealthFactorTypeIEN
HealthFactorType
PatientIEN
EventDateTime
VisitVistaDate
VisitDateTime
LevelSeverity
VisitIEN
EncounterStaffIEN
Comments
2nd category models require
transformation – Prescription
Prescription
and 1st fill
Refill
Partial
Fill
Fileman
Prescription
Only
All Fills
Data Warehouse
3rd category models not usable
without transformation - PCMM
Levels of Data
 National – Corporate Data Warehouse (CDW)
 Region – Regional Data Warehouse (RDW)
 VISN – VISN Data Warehouse (VDW)
 Medical Center – Local Data
Entities Who Produce Business
Intelligence Products
 National – VSSC, PSSG, DMDC, HEC, ARC, DSS, BIPL, OQP, PCS, PBM
 Region – Regional BISL Teams
 VISN – VISN Data Warehouse, VISN PBM
 Local – DSS
 Bolded are ones that have substantial resources in clinical business
intelligence
 PSSG handles much of the GIS and Statistical Demography for the VA
Data Access
 VISN and Station Level – Contact Your VISN Database
Manager
 Regional/Corporate Access – Contact NDS for the 9957
Permissions
Operational Challenges of VistA
 System Resources
 $8 Billion investment over 20 years
 New needs for new domains
 MUMPS Programmers must be internally trained (and many of
them are retiring or dying)
 Communication with Other Systems
 HIMISS compliance with data interchange
 E-functions (billing, prescribing, verification)
 Interagency Cooperation – DoD and NHIN
 Business Intelligence
 Closing the data lifecycle and bringing back clinical data for
knowledge discovery
Challenges CDW Faces
 Finding personnel who are able and willing to help us
define the data
 PCMM
 Giving analytic advice and documentation
 What date should I use….?
 Where is this data….?
 Building Advanced Tier II products
 Multifact table cubes
 Syndromic Surveillance monitoring models with high
dimensionality scoring
Acknowledgments
 Kernel – Jack Schram (Oakland OIFO)
 SQLI – Ellen Zufall (SF IRMS)
 FileMan/History of Production System – Chuck Cobalis
 RPC Broker and MUMPS coding – Perry Richmond (VISN
18 BI)
 Regional Data Process – Vincent Bui and Ken Koenig
(Region I SQL Back Office Team)
Acknowledgments
 OI&T Business Intelligence Product Line (BISL)
 Jack Bates – Manager, OI&T BIPL
 Stephen Anderson – Lead Data Architect
 Mike Baker – Lead ETL Architect
 Denver Griffith/Ken Fuchsel – Server Administrators
 Dave Fackler
 Ron Talmage
 Dan Hardan, Jeff King, Jeff Price
Questions
Further Information On The
Background
 For the VA Base M Training
 http://vaww.vistau.med.va.gov/VistaU/MTraining/Def
ault.htm
 For the VA Programming Standards and Conventions
 http://vista.med.va.gov/sacc/
 For the VA Document Library
 http://vista.med.va.gov/vdl/
Resources for Further Information
 VA Information Resource Center (ViREC)
 http://www.virec.research.va.gov
 National Patient Care Database –
 (Internal) http://vaww.aac.va.gov/npcd/
 National Data Systems (NDS)
 (Internal) http://vaww4.va.gov/NDS/DataAccess.asp
Reading for Fun – Official History
 VistA*/U.S. Department of Veterans Affairs national-
scale HIS
 Steven H. Brown, Michael J. Lincoln, Peter J. Groen,
Robert M. Kolodner
 International Journal of Medical Informatics 69 (2003)
135/156
 VistA Document Library (VDL)
 www4.va.gov/vdl

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