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Huff # 1
SOA at Intermountain Healthcare: New IDEAs and Progress Towards a New Platform
SOA in HealthcareJuly 13, 2011
Stanley M. Huff, MD
# 2
Acknowledgements
• Tom Oniki• Joey Coyle• Craig Parker• Yan Heras• Cessily Johnson• Roberto Rocha• Lee Min Lau• Alan James• Many, many, others…
Intermountain Medical Center
Outpatient Pavilion
Heart and Lung Hospital
Main Tower
Women’s and
Newborn
Cancer Center
Intermountain Healthcare
• 24 hospitals• 2,488 inpatient beds• 123,447 Acute admissions• 98,674 Ambulatory
surgeries• 160,306 Homecare visits• 502,327 Acute patient
days• 5,817,392 Outpatient
visits• 429,949 emergency room
visits• 38,103 inpatient surgeries
Homer Warner and HELP
• The first version of the HELP (Health Evaluation through Logical Processing) system was built in 1967
• From its inception, the HELP system was built primarily to provide advanced decision support
Dr. Homer Warner
Patient
Core Assumptions
‘The complexity of modern medicine exceeds the inherent limitations of the unaided human mind.’~ David M. Eddy, MD, Ph.D.
‘... man is not perfectible. There are limits to man’s capabilities as an information processor that assure the occurrence of random errors in his activities.’~ Clement J. McDonald, MD
Clinical System Approach
Intermountain can only provide the highest quality, lowest cost
health care with the use of advanced clinical decision
support systems integrated into frontline workflow
233
581567 569 567
437
477
355
271 271 280
89 90 91 92 93 94 95 96 97 98 99(3)
Year
0
100
200
300
400
500
600
0
100
200
300
400
500
600
To
tal #
mo
der
ate
+ se
vere
AD
Es
• Rates today (2008-9) at about 230 per year
• Generates >$1 million per year in net cost reductions at LDS Hospital alone
• Rates today (2008-9) at about 230 per year
• Generates >$1 million per year in net cost reductions at LDS Hospital alone
Adverse Drug Events
Neo-natal intensive care unit (NICU) admits by weeks gestation
6.66
3.36
2.47 2.65
3.44
4.26
37 38 39 40 41 42
Weeks gestation
0
2
4
6
8
10
Per
cen
t N
ICU
ad
mis
sio
ns
0
2
4
6
8
10
Deliveries w/o Complications, 2002 - 2003
8,001 18,988 33,185 19,601 4,505 258n =
Elective inductions < 39 weeks
5.55.1
6.6 6.3 65.3
8.2
5.4 5.76.6 6.6
7.9
6.4
7.6 7.6
4.6
3.5
4.55
26.726.9
29 29.2
25.3
27.6
20.4
19.1
16.5
15.2
8.4
10.7
8.1
6.85.9 6.1 6
5.1
6.3
Jan 01 MarMay Ju
lSep Nov
Jan 02 MarMay Ju
l
Jan 03 MarMay Ju
lSep Nov
Jan 04 MarMay Ju
l
0
5
10
15
20
25
30
% e
lect
ive
ind
uct
ion
s <
39 w
eeks
0
5
10
15
20
25
30
382372
490415
430435
422455
430382
356337
372366
455n = 423453
473476 512
475602
557667
564637
578541
573533
505501
474536
562545
535500
Labor & delivery variable cost
Jan 20
03 Feb Mar Apr
May Jun
Jul
Aug
Sep Oct
Nov
DecJa
n 2004 Feb Mar Apr
May
1000
1200
1400
1600
1800
2000
Ave
rag
e co
mb
ined
var
iab
le c
ost
($)
1000
1200
1400
1600
1800
2000
Expected maternal and fetal combined variable costGoal: hold increase to no more than 6.85%Actual combined variable cost
ARDS … evidence in action
ARDS ventilator management:
• Survival 9.5% � 44%
• ~$120,000 less cost per case!
Defining the best practice clinical protocol
Physician compliance
Decision Support Modules
• Antibiotic Assistant
• Ventilator weaning
• ARDS protocols
• Nosocomial infection monitoring
• MRSA monitoring and control
• Prevention of Deep Venous Thrombosis
• Infectious disease reporting to public health
• Diabetic care
• Pre-op antibiotics
• ICU glucose protocols
• Ventilator disconnect
• Infusion pump errors
• Lab alerts
• Blood ordering
• Order sets
• Patient worksheets
• Post MI discharge meds
Huff # 15
Infrastructure Vision• Rapid application development environment
• Dynamic workflow configuration
• Standard data models and terminology
• Decision support authoring and execution
• Knowledge asset management– Rules, alerts, protocols, reminders, reports
• End user preferences– Common lists, Hot text
• Service Oriented Architecture/Enterprise Service Bus
Strategic Goals
• Minimum goal: Be able to share applications, reports, alerts, protocols, and decision support with ALL GE customers
• Maximum goal: Be able to share applications, reports, alerts, protocols, and decision support with anyone in the WORLD
Order Entry API (adapted from Harold Solbrig)
MUMPSDatabase
Application
Service
Interface
Data
VA OrderEntry
VAOrder
Services
COS
Update Medication Order
Update PharmacyOrderWHERE orderNumber = “4674” …
Order Entry API – Different Client, Same Service (adapted from Harold Solbrig)
MUMPSDatabase
Application
Service
Interface
Data
Update Medication Order
Update PharmacyOrderWHERE orderNumber = “4674” …
Deptof
Defense
VAOrder
Services
COS
Deptof
Defense
Order Entry API – Different Server, Same Client (adapted from Harold Solbrig)
GERepository
OracleTables
Application
Service
Interface
Data
Update Medication Order
Update PharmacyOrderWHERE orderNumber = “4674” …
COS
GEServices
Order Entry API (adapted from Harold Solbrig)
Application
Service
Interface
Data
COS
. . .
What Is Needed to Create a New Paradigm?
• Standard set of detailed clinical data models coupled with…
• Standard coded terminology
• Standard API’s (Application Programmer Interfaces) for healthcare related services
• Open sharing of models, coded terms, and API’s
# 22
What are detailed clinical models?
Why do we need them?
# 23
A diagram of a simple clinical model
data 138 mmHg
quals
SystolicBPSystolicBPObs
data Right Arm
BodyLocationBodyLocation
data Sitting
PatientPositionPatientPosition
Clinical Element Model for Systolic Blood Pressure
# 24
Need for a standard model
•A stack of coded items is ambiguous (SNOMED CT)– Numbness of right arm and left leg
• Numbness (44077006)• Right (24028007)• Arm (40983000)• Left (7771000)• Leg (30021000)
– Numbness of left arm and right leg• Numbness (44077006)• Left (7771000)• Arm (40983000)• Right (24028007)• Leg (30021000)
# 25
70
What if there is no model?
Hct, manual:Site #1
%
Hct :Site #2
Manual%Auto
37
7037
70Hct, auto : %35
Estimated
HL7 V2.X Messages
• Site 1:OBX|1|CE|4545-0^Hct, manual ||37||%|OBX|1|CE|4544-3^Hct, auto ||35||%|
• Site 2:OBX|1|CE|20570-8^Hct ||37||%|….|manual|OBX|1|CE|20570-8^Hct ||35||%|….|auto|
# 27
Model fragment in XML
Pre-coordinated representation<observation><cd>Hct, manual (LOINC 4545-0 ) </cd><value>37 % </value>
</observation>Post-coordinated (compositional) representation<observation><cd>Hct (LOINC 20570-8) </cd><qualifier><cd>Method </cd><value>Manual </value>
<qualifier><value>37 % </value>
</observation>
# 28
Relational database implications
If the patient’s hematocrit is <= 35 then ….
Patient Identifier
Date and Time Observation Type Observation Value
Units
123456789 7/4/2005 Hct, manual 37 %
123456789 7/19/2005 Hct, auto 35 %
Patient Identifier
Date and Time Observation Type
Weight type Observation Value
Units
123456789 7/4/2005 Hct manual 37 %
123456789 7/19/2005 Hct auto 35 %
# 29
Isosemantic Models
data 37 %
HematocritManual (LOINC 4545-0)HematocritManualModel
data 37 %
quals
Hematocrit (LOINC 20570-8)HematocritModel
data Manual
Hematocrit MethodHematocritMethodModel
Precoordinated Model
Post coordinated Model (Storage Model)
# 30
More complicated items:
• Signs, symptoms
• Diagnoses
• Problem list
• Family History
• Use of negation – “No Family Hx of Cancer”
• Description of a heart murmur
• Description of breath sounds– “Rales in right and left upper lobes”
– “Rales, rhonchi, and egophony in right lower lobe”
What do we model?
• All data in the patient’s EMR, including:– Allergies– Problem lists– Laboratory results– Medication and diagnostic orders– Medication administration– Physical exam and clinical measurements– Signs, symptoms, diagnoses– Clinical documents– Procedures– Family history, medical history and review of symptoms
# 32
Model Subtypes Created
• Number of models created - 4384– Laboratory models – 2933– Evaluations – 210– Measurements – 353– Assertions – 143– Procedures – 87– Qualifiers, Modifiers, and Components
• Statuses – 26• Date/times – 27• Others – 400+
How are the models used?
• Data entry screens, flow sheets, reports, ad hoc queries– Basis for application access to clinical data
• Computer-to-Computer Interfaces– Creation of maps from departmental/foreign system models
to the standard database model
• Core data storage services– Validation of data as it is stored in the database
• Decision logic– Basis for referencing data in decision support logic
• Does NOT dictate physical storage strategy
# 34
Model Source Expression (CDL)
model BloodPressurePanel is panel{
key code(BloodPressurePanel_KEY_ECID);
statement SystolicBloodPressureMeas systolicBloodPressureMeas optionalsystolicBloodPressureMeas.methodDevice.conduct(methodDevice)systolicBloodPressureMeas.bodyLocationPrecoord.conduct(bodyLocationPrecoord)systolicBloodPressureMeas.bodyPosition.conduct(bodyPosition)systolicBloodPressureMeas.relativeTemporalContext.conduct(relativeTemporalContext)systolicBloodPressureMeas.subject.conduct(subject)systolicBloodPressureMeas.observed.conduct(observed)systolicBloodPressureMeas.reportedReceived.conduct(reportedReceived)systolicBloodPressureMeas.verified.conduct(verified);
statement DiastolicBloodPressureMeas diastolicBloodPressureMeas optional….
statement MeanArterialPressureMeas meanArterialPressureMeas optional….
qualifier MethodDevice methodDevice optional;md.code.domain(BloodPressureMeasurementDevice_DOMAIN_ECID);
qualifier BodyLocationPrecoord bodyLocationPrecoord optional;blp.code.domain(BloodPressureBodyLocationPrecoord_DOMAIN_ECID);
modifier Subject subject optional;
attribution Observed observed optional;attribution ReportedReceived reportedReceived optional;attribution Verified verified optional;
}
So that is the vision of the future, what is
happening right now?
Huff # 35
DataWarehouse
IHC
DataWarehouse
IHC
Care Flow(Outpatient)
GEHCMisys Lab
GE/AGFA Radiology
Tamtron Anatomic Pathology
McKesson Pharmacy
ARUP Blood Bank
Blood Gas Machines
Dictaphone
Varis Oncology
MRS Mammography
GE/Logicare ER
Computrition Dietary
HDM &Medrec
3M
HELP(Inpatient HIS)
3M
Health DataDictionary
3M
Health DataDictionary
3M
Billing &Financial
IHC
Registration,Scheduling
ADT, Billing,Case Mix
ADT, Billing
ADT, Orders, Results, Billing
ADT, Results,OrdersRegistration,Scheduling
Tuxedo
DataStage
CDRDatabase(HEMS) 3M
CDRDatabase(HEMS) 3M
eGateInterfaceEngineSun
ClinicalWorkstation
3M
ADT, Orders, Results, Billing
HELP2
CDIHC
HELPDatabase
HELPDatabase
Application Explosion• 4000+ applications in the organization• Applications being purchased or built include
approximately 80% redundancy of functionality
• All new applications require duplication of data• One example of data duplication
– 49 copies of patient registration data– 294 million patient records online– 288 million or 97% are duplicate copies– 125,000 registration updates/day or 6.1 million total data updates
daily
Data Explosion
Information Delivery Enterprise Architecture
Desktop Subcommittee
IDEA Governance StructureEARB
Chair: Chief Technology Officer
IDEA SubcommitteeChair: Enterprise Software Architect
MEMBERSPMO, HELP1, HELP2, Migration,
Financial, Tactical, eBusiness, Clinical Operations, Interfaces,
EDW, ECIS, Informatics, Security, Select Health, Operations, RIM,
KTMI, HWCIR
IDEA GOVERNANCE
Security Architecture
Subcommittee
Database Architecture
Subcommittee
RESPONSIBILITES
•Provides strategic oversight and guidance for IDEA
•Empowers IDEA subcommittee to specifying standards, practices,
guidelines and tools•Approval of IDEA subcommittee
proposals•Monitors and is accountable for adherence to approved standards,
practices, guidelines and tools
Fostering ExcellenceWorkgroup
Exception Handling
Workgroup
AuditingWorkgroup
RESPONSIBILITES
•Establish the Information Delivery Enterprise Architecture by specifying standards, practices,
and guidelines and tools•Build consensus and document software
standards, processes, tools, and infrastructure to be approved by IDEA Governance
•Provides stewardship for respective teams•Brings forth gaps, issues and solutions for
IDEA
A Picture
Something More Concise
IDEA Enterprise Service Bus
Intermountain
Application
New 3rd
Party
Application
New 3rd
Party
Data Repository
Intermountain
Central Data Rep
Nursing ‘Medication Charting’ workflow
Physician “Note Writing” workflow
Clinician ‘Data Review’ workflow
Huff # 45
Potential Benefits
• Low risk
• Incremental changes - no “big bang” changes
• Gradual implementation of ESB
• Transition based on particular modules– Results review, text documents, allergies,
documentation, order entry
• Slowly increasing use of the new database– Opportunity to tune performance
Huff # 46
Questions?