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March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision Support Systems
Ida Sim, MD, PhD
March 1, 2011
Division of General Internal Medicine, and the Center for Clinical and Translational Informatics
UCSF
Copyright Ida Sim, 2011. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness
• CDSS Adoption
• CDSS in the Age of Watson
3
Big Picture of Health Informatics
Virtual Patient
Transactions
Raw data
Medical knowledge
Clinical research
transactions
Raw research
data
Dec
isio
n su
ppor
t
Med
ical
logi
c
PATIENT CARE / WELLNES RESEARCH
Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.
Clinical Decision Support Systems
EHR
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
What is a “Decision”? “Logic”?
• An action that consumes resources in the real world• Logic
– Oxford English Dictionary• reasoning conducted or assessed according to strict principles
of validity
– Merriam Webster• interrelation or sequence of facts or events when seen as
inevitable or predictable
• something that forces a decision apart from or in opposition to reason
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision or Logic?
Decision Logic
Diabetics with hypertension should be started on ACEI, ARB, or other
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision or Logic?
Decision Logic
Diabetics with hypertension should be started on ACEI, ARB, or other
X
I prescribe lisinopril for Mrs. Chan (diabetic, BP 156/92)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision or Logic?
Decision Logic
Diabetics with hypertension should be started on ACEI, ARB, or other
X
I prescribe lisinopril for Mrs. Chan (diabetic, BP 156/92) X
I prescribe amlodipine for Mrs. Chan (diabetic, BP 156/92)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision or Logic?
Decision Logic
Diabetics with hypertension should be started on ACEI, ARB, or other
X
I prescribe lisinopril for Mrs. Chan (diabetic, BP 156/92) X
I prescribe amlodipine for Mrs. Chan (diabetic, BP 156/92) X
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Clinical Decision Support
• Clinical decision support system (CDSS)– software that is designed to be a direct aid to clinical decision-
making
– in which the characteristics of an individual patient are matched to a computerized clinical knowledge base
– and patient-specific assessments or recommendations are then presented to the clinician and/or the patient for a decision (Sim et al, JAMIA, 2001)
• Examples of clinical decisions to be supported?
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Major Target Tasks of CDSSs• Diagnostic support
– DxPlain, QMR• Drug dosing
– aminoglycoside, theophylline, warfarin• Preventive care
– reminders for vaccinations, mammograms• Disease management
– diabetes, hypertension, AIDS, asthma• Test ordering, drug prescription
– reducing daily CBCs in hospital, drug allergy checking• Utilization
– referral management, clinic followup
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
What Isn’t a CDSS
• Medline• UpToDate• Static guideline repositories
– www.guideline.gov (National Guideline Clearinghouse)
• Online laboratory data, test results, chart notes
• Retrospective quality improvement reports– how your vaccination rates compare to your
colleagues’
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
A CDSS?
• Chief complaint: “Symptoms for $400 please”• Symptom: Chest pain and shortness of breath
• Dr. Watson: What is pulmonary embolism!
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption• CDSS in the Age of Watson
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Basic Decision Support Task
• Decision support– given starting conditions and a defined set of action choices,
recommend or rank action choices for user• Requires some “thinking” to recommend or rank
– strictly deterministic thinking– thinking with fuzziness and probabilistic features
• in the starting data or the reasoning procedure
• in the outcomes (e.g. prob. of adverse reaction)– often involves thinking about concepts (e.g., “abnormal”) as
well as numbers• symbolic vs. quantitative computing
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision Support “Thinking”• Strictly deterministic, e.g.,
– first-order logic rule-based systems
– adhoc rule-based systems (non-mathemetical reasoning about probability)
• e.g., if high WBC AND cough AND fever AND abn. CXR then likelihood of pneumonia is 4 out of 5
• Probabilistic/fuzzy, e.g.,
– bayesian networks• formal probabilistic reasoning, extension of decision analysis
– neural networks
– fuzzy logic, genetic algorithms, case-based reasoning, etc., or hybrids of these
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Forward Chaining Rules
• Forward chaining/reasoning (data-driven)– start with data, execute applicable rules, see if
new conclusions trigger other rules, and so on– example
• if HIGH-WBC and COUGH and FEVER and ABN-CXR => PNEUMONIA
• if PNEUMONIA => GIVE-ANTIBIOTICS• if GIVE-ANTIBIOTICS => CHECK-ALLERGIES• if PNEUMONIA and GIVE-ANTIBIOTICS and NOT
(ALLERGIC-DOXYCYCLINE) => GIVE-DOXYCYCLINE
– use if sparse data
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Backward Chaining Rules
• Backward chaining/reasoning (goal-driven) – start with “goal rule,” determine whether goal rule
is true by evaluating the truth of each necessary premise
– example • patient with lots of findings and symptoms• is this lupus? => are 4 or more ACR criteria satisfied?
– malar rash?– discoid rash?– skin photosensitivity? etc
• if 4 or more ACR criteria true => systemic lupus– use if lots of data
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Rule Reasoning Problems
• Combinatorial explosion of rules– need rule for each contingency
• if MOD-WBC and COUGH and FEVER and ABN-CXR => PNEUMONIA
• Rules may be contradictory– if COUGH and ABN-CXR => INTERSTITIAL-LUNG-DZ
• Rules may be circular
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Representational Challenges
• Need to use standard vocabulary terms– need to manage evolution of vocabularies (e.g., changing
terminologies in psychiatry: no Asperger’s in new DSM-V)• Rules may involve complex semantic relationships
– if NEPHROPATHY caused-by DIABETES• caused solely by? predominantly by?
• what if I’m not sure? 20% sure? 80% sure?
– if SINUSITIS greater than 6 months• representing temporal relationships requires 2nd order logic
• Need knowledge engineering and clinical expertise to build and maintain the knowledge base over time– need to keep rules up-to-date with latest evidence
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Sharing Rules
• Why not have libraries of rules?• Reusable, central upkeep, evidence-based...• AHRQ funding library of e-recommendations
– see structured logic statement for mammography screening
• Morningside initiative1
– public private initiative to define organizational and technical approach to sharing CDS rules
– includes VA, Kaiser, DoD, AMIA, Partners, Intermountain, ASU, etc.
1http://www.tatrc.org/docs/2010-8-6-Morningside-Article.pdf
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Summary of Rule-Based Systems
• Deterministic, relatively simple reasoning• Combinatorial explosion even for small domains• Requires extensive knowledge engineering and
clinical expertise • Rules are difficult to share• But remain most widely used method due to
simplicity for small problems
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption• CDSS in the Age of Watson
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Neural Networks• Finds a non-linear relationship between input parameters
and output state• Structure of network
– usually input, output, and 1-2 hidden fully connected layers
– each connection has a “weight”
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
NN for MI Diagnosis• Inputs (e.g., all patient characteristics in the EHR)
• EKG findings (ST elevation, old Q’s)
• rales (Yes, No)
• JVD (in cm)
• Outputs are the set of possible outcomes/diagnoses
EKG findings
Rales
JVD
Response to TNG
Acute MI
No Acute MI
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Training the Neural Network• Network gets “trained”
– give examples of known patients and diagnoses• can handle missing data
– system iteratively adjusts connection weights to find the network “pattern” that associates sets of input variables (patients) with right output state (MI or not)
• Test accuracy on another set of patients• In Baxt’s MI neural network
– training set: 130 pts with MI, 120 without– test set: 1070 UCSD ER patients with anterior chest
pain
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Baxt’s Acute MI Neural Net• Evaluation results: prevalence of MI 7% (Lancet, 1996)
• Results were driven by non-standard predictors– rales, jugular venous distention
• Why wasn’t this neural network used more widely?– “black box” nature limits explanatory ability and lessens
acceptance– users have to input the variables manually
• interfacing to EHRs would increase adoption– need to define and code “rales” and other input terms
Sensitivity Specificity
Physicians 73.3% (63.3-83.3) 81.1% (78.7 – 83.5)
Neural Net 96.0% (91.2 – 100) 96.0% (94.8 – 97.2)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems
– background, definition• How decision support systems “think”
– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption• CDSS in the Age of Watson
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CPOE and Medication Safety• 1998: CPOE reduced medication errors 55%1
• 2005: Qualitative study found 22 error types promoted by CPOE, quite common2
• 2008: Systematic review of 10/543 citations, no RCTs3 – 5 studies (P <= .05) for ADE reduction, 5 n.s.
• 2011: CPOE part of Meaningful Use criteria– “more than 30% of patients with at least one
medication on their medication list have at least one medication ordered through CPOE”
1Bates JAMA 1998;280:1311-1316.2Koppel JAMA. 2005 Mar 9;293(10):1197-2033Wolfstadt J Gen Intern Med. 2008 Apr;23(4):451-8.
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Is Decision Support Effective?
• 2005 systematic review of CDSS effectiveness1
– diagnosis: 4/10 (40%) studies beneficial
– reminder systems: 16/21 (76%)
– disease management systems: 23/37 (62%)
– drug dosing: 19/29 (66%)
– few studies improved patient outcomes: 7/52 (13%)
• Counted the number of systems in each category that were “effective” (p>0.05)– but CDSS not all the same! (apples and oranges)
1Garg et al. JAMA 2005 293(10):1223-1238
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CDSS Running Example
• Hypertension treatment Clinical Decision Support System (CDSS)– Clinic has an EHR
– During patient visit, CDSS notes that BP and trend is too high
– CDSS checks patient’s Cr, diabetes status, cardiac status, current meds and allergies and recommends drug therapy change according to JNC VII guidelines and insurance coverage
– Presents e-prescription for MD to verify. If verified, order is sent directly to pharmacy and medication list updated
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Apples” HTN CDSS• Clinical Decision Support Systems (CDSSs)
– software designed to directly aid clinical decision-making• helps clinician to prescribe anti-hypertensive
– in which the characteristics of an individual patient are matched to a computerized knowledge base
• matches EHR and other data to computable guideline
– and patient-specific assessments or recommendations are presented to the clinician and/or patient for a decision
• recommends drug according to clinical, guideline, and insurance information
• provides clinician with decision choice to prescribe or not prescribe
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Oranges” HTN CDSS• Clinical Decision Support Systems (CDSSs)
– software designed to directly aid clinical decision-making• help clinician to prescribe anti-hypertensive
– in which the characteristics of an individual patient are matched to a computerized knowledge base
• clerk routinely abstracts current BP, A1C, meds, allergies and insurance status from paper chart into a database
• computer runs pt information against computerized guideline
• computer outputs a piece of paper with recommendation
– and patient-specific assessments or recommendations are presented to the clinician and/or patient for a decision
• MD given piece of paper with individualized drug recommendation
• MD writes prescription in usual paper-based way
Taxonomy of CDSSs
OR
INFORMATION DELIVERY•Delivery format•Delivery mode•Action integration•Delivery interactivity/explanation availability
System user/Target decision
maker
DECISION SUPPORT•Reasoning method•Clinical urgency•Recommendation explicitness•Logistical complexity•Response requirement
CONTEXT•Target decision maker•Clinical setting•Clinical task•Unit of optimization•Relation to point of care•Potential external barriers to action
WORKFLOW•Degree of workflow integration
System user/Output
intermediary [ ]
Target decision maker
KNOWLEDGE/DATA SOURCEClinical knowledge source [ ]Patient data source [ ]Data source intermediary [ ]Degree of customizationUpdate mechanism
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CDSS Characteristics• Using taxonomy, reviewed and classified 42 RCT-
evaluated CDSSs• Tremendous variation in decision-maker/context, how
recommendation delivered, staff needed to make system run, complexity of recommended actions– 45% targeted to clinician, 55% patient, 5% both– 62% based on national guidelines or literature– 69% “pushed” recommendation to decision maker– 43% collected data directly from the EHR
• 45% required data input intermediary (11% MD)
– 26% required an output intermediary
• Generalizing successes from literature is difficult
(Berlin, Sim, 2006)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CDSS Effectiveness Summary
• Systematic review of systematic reviews on “Impact of eHealth on Quality & Safety”– “…many of the clinical claims made about the most
commonly deployed eHealth technologies cannot be substantiated by the empirical evidence.”1
• Findings limited by– methodological problems and design type of studies
– insufficient appreciation of workflow component of CDSSs
– insufficient appreciation of heterogeneity of systems
1Black et al, PLoS Med 2011 8(1):e1000387
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption• CDSS in the Age of Watson
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Low CDSS Adoption
• Adoption of CDSSs beyond simple reminders– < 10% of those with EHRs
• Reasons – informatics
– technical
– organizational / financial
– fundamental conundrum
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Informatics Barriers• Requires computation across Data, Information,
Knowledge– data is often qualitative, fuzzy
• how to represent “looks sick,” “severe pneumonia”
– information (meta-data) often not easily available• e.g., seen in another ER last week for same problem
– lots of tacit vs. explicit knowledge required• Most CDSSs are rule-based systems
– combinatorial explosion, rules not shared, updated...– inability to handle probabilistic outcomes, values
• Computer best at data-intensive simplistic deterministic decisions (augmenting intelligence) vs. knowledge-intensive, probabilistic, value-based decisions
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Technical Barriers
• CDSS has to interface to local data systems– manual double-entry input is a no-go
– Meaningful Use establishes CCD1 as standard EHR exchange format
• e.g., Problem List, Allergies, K+ value
• new industry-wide “green” CCD standard due May 2011
• Exchange standard may not be “granular” enough for CDSS– e.g., Allergies as free text, vs. med and reaction
• Need standardized (i.e., coded) input data – e.g., what’s in the Past Medical History field?
1http://www.hl7.org/implement/standards/cda.cfm
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Organizational Barriers
• CDSSs are complex workflow interventions– high requirement for complementary innovations
– requires organizational change leadership and expertise
• Lack of or uncertain incentives/rewards for better quality– e.g., accountable care organization rules
“imminent” (Berwick D, CMS Administrator, Feb 24, 2011)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems
– background, definition• How decision support systems “think”
– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption• CDSS in the Age of Watson
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
A Huge Advance in Computing
• Watson winning Jeopardy! is like Deep Blue beating chess grandmaster Gary Kasparov (1997)– but Deep Blue less successful than expected at
solving protein folding problems
• First industry that IBM wants to apply Watson to is health care,1 partnering with– Nuance voice recognition company– Columbia University on clinical decision support– U. Maryland on imaging
1http://www-03.ibm.com/press/us/en/pressrelease/33726.wss
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
AI Background• “Artificial intelligence” holy grail
– chess is a highly structured game with defined rules and solutions (just a lot of them)
– Jeopardy! also a narrow game (a “question answering” game) but played using “natural language” requiring “processing” (=NLP)
• “Factoid” question answering is a specific kind of “intelligence”– "The antagonist of Stevenson's Treasure Island” -- “Who is
Long-John Silver?,” vs.
– “What triggered the revolution in Egypt?” “What causes Chronic Fatigue Syndrome?” vs.
– Book the cheapest, most convenient transportation for my family for this 4 city trip to Spain in July
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Similarities/Differences
• very large scope
• natural language full of puns, ambiguities
• corpus is free text only • all fact based• there exists one and only
one right answer• right answer is in the corpus
somewhere, requiring only syntactic (ie grammatical) processing to get at
• is “one shot”
• very large scope• clinical notes and literature highly
idiosyncratic natural language• corpus includes text, numbers,
images (MRIs), video (eg echo) • not only facts (should pt. be on
warfarin to prevent stroke?)• often no single right answer, best
answer requires semantic (I.e., meaning) processing
– e.g., “azithromycin,” critical appraisal of literature
• often requires back and forth (e.g., to clarify context, values, constraints)
Clin Decision SupportJeopardy!
Watson in the Big Picture
Virtual Patient
Transactions
Raw data
Medical knowledge
Clinical research
transactions
Raw research
data
Dec
isio
n su
ppor
t
Med
ical
logi
c
PATIENT CARE / WELLNES RESEARCH
Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.
Watson
Nuance voice recognition
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
The Jeopardy!/Medical Logic Problem
• Voice recognition (picking out words from speech)– Watson: was given questions in written text
– Clinical: Dragon Dictate etc moderately good for restricted domains (e.g., radiology)
• Understand the sentence/question– Watson: “The antagonist of Stevenson’s Treasure Island”
– Clinical: “What antibiotics treat pertussis?”• Go look for candidate answers in the corpus of knowledge
– Watson: free text Project Gutenberg, wikipedia, dictionaries, encyclopedias, newspaper articles, etc.
– Clinical: EHR, PubMed, UpToDate, all of Internet? free text, images, numbers, video, data streams (eg GPS, ICU data)
• Score answers for likely “correctness”• Give best answer (or rank answers and be able to explain why)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Example Jeopardy! Process
• http://blog.reddit.com/2011/02/ibm-watson-research-team-answers-your.html
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Data + Watson”
. .
Doc
Computer: Ms. Lee has had paroxysmal cough
for 2 weeks, with emesis.
Adult pertussis is a strong possibility.
Symptom inquiry, diagnosis using
neural network or rule-based system
. .
Doc
What is the current incidence of pertussis?
17.8 cases / 100,000 in S.F. county Jan thru December
2010
Question answering: public health
reports, data, culture results, etc.
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Data + Watson”
Your patient is 4 months post-partum. I suggest treating presumptively for
pertussis.*
Rule-based checking of EHR
. .
Doc
I agree. Don’t macrolides treat pertussis?
Yes, erytho, clarithro and azithromycin are the preferred antibiotics. Bactrim is second-
line.
Question answering: reference sources, literature
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Data + Watson”
I would suggest azithromycin 500 mg on Day 1 and 250 mg on Days 2 to 5.*
*CDC guidelines 2005, local cultures uniformly sensitive to azithro, pt not allergic, azithro covered by insurance
Question answering and rule-based checking of allergies, insurance, local sensitivities
. .
Doc
Make it so!
CPOEAPEX
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Some Clinical AI Systems
• Question Answering– askHermes http://www.askhermes.org/
• Diagnostic support– http://dxplain.org/dxp/dxp.pl
– http://www.isabelhealthcare.com/home/default
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Fundamental CDSS Conundrum
• Better quality care <-- better decision support• Better decision support <-- “smarter” systems
– “know” more about the patient, evidence, context• “Smarter” systems <-- more richly coded D-I-K
– for EHR: SNOMED, standard EMR structure– for knowledge: coding, structures for guidelines,
RCTs…• Coded data <-- Coding of data entry• Coding of data entry <-- Greater physician time• Greater physician time --> no play --> no gain
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Implications• Clear trade-off between physician coding effort and “smarter”
decision support– can NLP help? do we really need to “code” if we have Watson-like
abilities to understand (un)natural language• For now, don’t expect more decision support than coding allows
– generally successful decision support• preventive care: age, last mammogram, etc.
• allergies: Yes/No on specific drugs
• drug dosing: weight, height, creatinine, age
– generally unsuccessful decision support• diagnostic assistance
• complicated therapies (e.g., management of hypertension, treatment of depression)
March 1, 2011: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Summary on Decision Support
• Most CDSSs are rule-based• Equivocal evidence of benefit
– workflow/organizational inputs underappreciated• Fundamental trade-off between
– effort of coding data and quality of decision support• Greater decision support adoption will require
– wider EHR use, better interoperability, more coding or far more powerful NLP
• Need to be realistic on what decisions computers can best support
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