session 4.01. physicians & physician organizations emerging initiatives to put clinical...
TRANSCRIPT
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Session 4.01.Session 4.01. Physicians & Physician Physicians & Physician
OrganizationsOrganizations
Emerging Initiatives
to Put Clinical Guidelines
at the Point of Care
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Panelists
Nick Beard, MD IDX Systems Corp.
Stan Huff, MD Intermountain Health Care
Bob Greenes, MD, PhD Brigham & Women’s Hospital,
Harvard Medical School
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Importance of decision support
• Error prevention/ patient safety
• Encourage best practicesQualityReduced variability,
disparity• Efficiency• Cost-effectiveness
A key motivation for the EHR!
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We know how to do this Computerized alerts
– Reduced errors– Faster response to problems
Reminders– Improved compliance with guidelines
CPOE– medication error & ADE reduction – cost savings
ADE detection and monitoring… etc.
So, why is use not more widespread?
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Goal of this presentation is to explore that question
Three case studies– Focus on lessons learned
Generalization of experience– Key challenges– Recommendations
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Example: Partners Healthcare System
Integrated healthcare delivery network in Eastern Massachusetts
Founded in 1995 Includes:
– Mass. General Hospital– Brigham & Women’s Hospital– Dana Farber Cancer Institute– several community hospitals– many practice groups
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Long tradition of computer-based decision support
e..g, Brigham system (BICS): Order entry
– Drug-drug, drug-lab interaction checks– Redundancy/appropriateness checks– Dose ranges, contraindications, allergies, age, renal function– Order sets
Alerts Reminders Lab result interpretation Adverse event detection Guideline recomendations
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Cost-effective
55% decrease in serious medication errors– Bates, JAMA 1998
Decreased redundant labs– Bates, Am J Med, 1997
More appropriate renal dosing
No reduction in inappropriate x-rays– Harpole, JAMIA, 1997
Minimal effect of charge display– Bates, Archives of Internal
Medicine, 1995 More appropriate dosing,
substitutions accepted – Teich, Archives of Internal
Medicine, 2000 Decreased vancomycin
use– Sojania, JAMIA, 1998
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CDM Modeling
Decision Systems Group R&D– Data mining/predictive modeling– Technology assessment– Guideline modeling (GLIF)– Expression language development (GELLO)
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So what’s broken?
Gap between models and practice Generic slowness of technology diffusion Specific issues relating to our
environment
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Converting research to care
Publication
Bibliographic databases
Submission
Reviews, guidelines, textbook
Negative results
variable
0.3 year
6. 0 - 13.0 years50%
46%
18%
35%
0.6 year
0.5 year
9.3 years
Dickersin, 1987
Koren, 1989
Balas, 1995
Poynard, 1985
Kumar, 1992
Kumar, 1992
Poyer, 1982
Antman, 1992
Negative results
Lack of numbers
ExpertExpertopinionopinion
Inconsistentindexing
17:14
Original research
Acceptance
Patient Care
Balas EA, Boren SA. Managing clinical knowledge for health care improvement. Yrbk of Med Informatics 2000; 65-70
17 years to apply 14% of research knowledge
to patient care!
17 years to apply 14% of research knowledge
to patient care!
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Knowledge Inventory Study
Conducted spring/summer, 2002 Findings: KI Report
– Many PHSIS apps/subsystems use embedded knowledge for decision support
• If…then rulesIF labtest_result_type < value AND medication_class THEN send
textpage• Tabular data
(Drug_a, drug_b, interaction_type)– can be thought of as if…then rules
• Knowledge-Element Groupings (“KEGs”)Order sets, structured documents, data entry forms, …
• Other…
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Major findings
Multiple systems/application w/ CDS– Multi-vendor environment– Many apps as result of academic projects
• Main goal to demonstrate effectiveness
• One-of-a-kind implementations
– Not standards-based– Knowledge embedded in systems
• Difficult to extract, generalize, replicate
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Rules knowledge, as example: Widely used:
– Alerting• Drug-lab interactions• Panic lab alerts
– CPOE• Order-entry rules• Drug dictionary (incl. interactions, Gerios, Nephros)• Order sets• Relevant labs when ordering medications• Redundant tests• Use and impact
– Adverse event monitor– LMR Outpatient reminders– LMR Result manager– P-CAPE (guideline implementation)
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Varied authoring approaches
Direct encoding in host language – e.g., MUMPS
Creation of tables Application-specific authoring tools &
DBs Representation varied accordingly Also apps have counterparts
– e.g., CPOE
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Common rules engine feasibility study
Explore requirements for KM– Externalizing the knowledge from the application– Making it transparent
Particular focus on rules knowledge– Feasibility of a common representation– Implications for authoring/updating and execution
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Rules intRules intRules int.
Rules development and management (extant process)
Rule authoring or editing (human readable)
QM / QI committees identify rules (typically for an app/class)
Encoded for app (computer interpretable, interfaced)
Recoded for other versions of app
Periodic review
Update
Export
Evidence
Externalrules
Recoded for other versions of app
Recoded for other versions of app
manually
manually
manually
Rules Rules int.
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Rules development and management (goal process)
Rule authoring, editing, and update
Rules engine format (used by all apps)
“auto” convert
auto import
QM / QI committees identify rules (general or app-oriented)
Evidence
Externalrules
authoring tool/templates
Rules execution thruapp interfaces
Rules corpus, human-readable format
export
periodicreview
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Main findings
Parsimony– Hundreds of rules, used in many apps– Yet only 13 data classes represented
• Mappable to HL7 RIM
– Only 41 unique primitive expression types– Few action types
• Mainly types of notification or scheduling
Common representation feasible Limited touch points with applications Template/wizard-based authoring feasible
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Next steps (now ongoing)
Focus on front-end of knowledge authoring/ knowledge management process– transition from reference knowledge to executable if…then format– Common repository / portal– Ability to locate related or similar knowledge– Version control, update control
Expansion beyond rules knowledge– knowledge element groups (“KEGs”)
• order sets, reports, forms, …
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Intermountain Health Care (IHC)
Not for profit corporation
22 Hospitals– 500 to 25 beds– ~ 1.8 million
patients/members Ambulatory Clinics 14 Urgent Care
Centers Health Plans
Division (Insurance) Physician’s Division
(~450 employed physicians)
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Clinical Info Systems at IHC(Roberto Rocha)
HELP System– Comprehensive HIS with extensive collection of
decision support modules (“frames”)• Operational for the past 30+ years
• 13,382 unique users (Aug 2004)
HELP2 System– New EMR (replace core HELP functions)
• Operational for the past 5+ years (initial outpatient focus)
• 5,224 (Web) + 2,519 (CW) unique users (Aug 2004)
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HELP System (frames) – 1/2
Laboratory– Critical lab and blood gases 2
Pharmacy– Drug dosing checking 100+– Drug-food and drug-lab 17– Drug-drug interaction (FDB source) 1– Allergies 1– Duplicated therapy 1– Drug monitoring 3– Drug route 4
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HELP System (frames) – 2/2
Protocols 7
– Ventilator, ARDS, TICU, Pressure ulcer, etc.
Infectious diseases 22
– Antibiotic assistant, Pre-op, positive cultures, etc.
ADE 10
Nurse charting 8
Nutrition (TPN and nutritional value) 2
Others 9– Blood ordering, ER drug cards, Apache scores, etc.
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HELP2 System (rule sets)
Protocols 6– Chronic anticoagulation (live)– Pediatric ventilator weaning (live)– Post Liver transplant management (live)– Neonatal Bilirubin management (live)– Possible ADE based on Creatinine (live)– Glucose management (dev)
Care Process models 2– Outpatient Community Acquired Pneumonia (dev)– Abnormal Uterine Bleeding (dev)
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HELP2 System (ordering)
Outpatient medication orders – 750+ users– Drug-drug interactions (FDB) (live)
Inpatient Order sets (live) 88– 30+ MDs using POE (pilot phase)
Neonatal dosing calculations (dev) 13 Allergies (dev) Nursing Order sets (dev) 193
– 60+ RN care standards
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July 2004: 4,926 unique logons
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“Infobuttons” only
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v1v1
AuthoringAuthoring ReviewReview Clinical useClinical use
KATKATKnowledge Authoring ToolKnowledge Authoring Tool
KROKROKnowledge Review OnlineKnowledge Review Online
HELPHELP22
Different modulesDifferent modules
Reviewer Feedback
User Feedback
Publish Activate
v2v2
NewNew ViewView ClinicalClinicalSystemSystem
Version 1 of the document
Version 2 of the document
Document under review
Content available forclinical use
Enable clinicians to create and maintain knowledge content
Establish an open review process - users and authors collaborate to refine content
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What are the issues? People
– NIH syndrome (not invented here) Commercial knowledge bases Integration with workflow
– Expert systems - not in clinical use– Community Acquired Pneumonia Protocol
• Different environment in different clinicals– EHR functions
• Alerts• Flowsheets• Data drive, time drive, “ask drive”
The “Curly braces” problem– Al Pryor and George Hripcsak experiment
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Too many ways to say the same thing (2)
A single name/code and value– Weight at birth is 3500 g
Combination of two names/codes and values– Weight is 3500 g
• Weight circumstance is at birth
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Relational database implicationsPatient Id DateAndTime Weight Units Circumstance
1234567 1/22/01 01:15:00 AM 3500 g Birth
1234567 1/24/01 10:20:00 AM 3650 g Discharge
Patient Id DateAndTimeBirth Weight
Discharge Weight
Units
1234567 1/22/01 10:20:00 AM 3500 3650 g
How would you calculate the weight gain during the hospital stay?
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SAGE experience
Nick Beard to present
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Conclusions & Recommendations - Greenes
Three principal foci needed1. Accelerate standardization of CDS
components in HL7• Expression language, data model, vocabulary
model, process/flow representation, guideline modeling
2. Adopt common knowledge management & dissemination approach
• Content, tools, examples, other resources
3. Encapsulate key functionality as services• Expression evaluation, data model instantiation,
action invocation, …
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Conclusions & Recommendations - Huff
Three suggestions1. Accelerate standardization of CDS
components in HL7• NLM contract to link CHI vocabularies to HL7
data models and messages
2. Establish EHR content and infrastructure• Data entry, interfaces, data drive, time drive
3. We don’t need “artificial intelligence” (A little natural intelligence would be a good start!)
• Reports, order sets, alerts, reminders
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Conclusions & recommendations - Beard
To be added