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![Page 1: March 10, 2009: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 10, 2009 Division of General.](https://reader035.fdocuments.us/reader035/viewer/2022062408/56649ee05503460f94bf095a/html5/thumbnails/1.jpg)
March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Decision Support Systems
Ida Sim, MD, PhD
March 10, 2009
Division of General Internal Medicine, and the Center for Clinical and Translational Informatics
UCSF
Copyright Ida Sim, 2009. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition
– clinical versus research decision support
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness
• CDSS Adoption
![Page 3: March 10, 2009: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 10, 2009 Division of General.](https://reader035.fdocuments.us/reader035/viewer/2022062408/56649ee05503460f94bf095a/html5/thumbnails/3.jpg)
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
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March 10, 2009: 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?
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March 10, 2009: 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
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March 10, 2009: 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’
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7
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.
CTMS Decision Support Systems
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CTMS Decision Support• Clinical trial execution decision support system
– software that is designed to be a direct aid to decision-making in clinical trial execution
– in which the characteristics of an individual subject are matched to a computerized protocol
– and subject-specific assessments or recommendations are then presented to the study-nurse, etc. for a decision
• Examples of CTMS decisions to be supported?– determining eligibility
– protocol-defined procedures (e.g., if WBC < 2 then hold Drug)
– detecting and reporting adverse events
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Similarities/Differences
• customized to patient• identify applicable
guidelines, evidence• variable presentations and
contexts• wide clinical coverage• may include diagnostic
support• involves many team
members• usually one locale
• uniform treatment • identify applicable patients• narrower range of
presentations/contexts• narrower clinical coverage• more procedural, less
diagnostic support• smaller defined, more uniform
target staff• could be in multiple sites• more controlled
circumstances, regulatory overlay
Clin Trial Decision SupportClinical Decision Support
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
DSS is Not Knowledge Discovery
• CTMS decision support supports transactions in conducting clinical research
• Data mining, pattern recognition, machine learning, symbolic models, etc. is knowledge discovery in research
• Defer further discussion of research decision support to next class
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition– clinical versus research decision support
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Basic Decision Support Task
• Decision– action that consumes resources in the real world
• 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 starting data or reasoning procedure
• outcomes (e.g. prob. of adverse reaction)– often thinking about concepts, not numbers
• symbolic vs. quantitative computing
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March 10, 2009: 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
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March 10, 2009: 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
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March 10, 2009: 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 SLE? => are 4 or more ACR criteria satisfied?
– malar rash?– discoid rash?– skin photosensitivity? etc
• if 4 or more ACR criteria true => SLE– use if lots of data
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March 10, 2009: 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
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March 10, 2009: 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 (DSM-xx))• Rules may involve complex semantic relationships
– if NEPHROPATHY caused-by DIABETES• caused solely by? predominantly by?
– 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
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Sharing Rules
• Why not have libraries of rules?• Reusable, central upkeep, evidence-based...
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Medical Logic Modules (MLMs)
• help_amp_for_pneumonia - Ampicillin for Pneumonia
• maintenance:– title: Ampicillin for
Pneumonia;;– filename:
help_amp_for_pneumonia;; – version: 1.00;; – institution: LDS Hospital;; – author: Peter Haug, M.D.;
George Hripcsak, M.D.;; – specialist: ;; – date: 1991-05-28;;
• validation: testing;; • library:
– purpose: Recommend the use of ampicillin for pneumonia.;;
– explanation: If the patient has pneumonia, then suggest treatment with ampicillin unless there is a penicillin allergy.;;
• keywords: pneumonia; penicillin; ampicillin;;
• citations: 1. HELP Frame Manual, version 1.6. LDS Hospital, August 1989, p.81.;;
• For sharing forward chaining rules • Expressed in Arden Syntax (an ASTM standard)
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Sharing of MLMs: No Success• Work of reuse often greater than building from scratch
– rules are often outdated: need to check evidence base
– context is under-specified• is pneumonia rule inpatient or outpatient? in HIV patients?
– can be wrong for local context• resistance patterns vary in different locales
– definitional problems• your “pneumonia” is not my “pneumonia”
– curly braces problem• if {K+} > 5.5 => alert MD• need to interface to local clinical information system to access
value of K+, using interchange (HL7) and data standard (e.g., LOINC)
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March 10, 2009: 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 knowledg engineering
and clinical expertise • Rules are difficult to share• But remain most widely used method due to
simplicity for small problems
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition– clinical versus research decision support
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption
![Page 23: March 10, 2009: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 10, 2009 Division of General.](https://reader035.fdocuments.us/reader035/viewer/2022062408/56649ee05503460f94bf095a/html5/thumbnails/23.jpg)
March 10, 2009: 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”
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March 10, 2009: 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
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March 10, 2009: 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
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March 10, 2009: 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)
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems
– background, definition
– clinical versus research decision support• How decision support systems “think”
– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Is Decision Support Effective?
• Moderate benefit found in improving physician behavior (Garg, 2005)
– 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)
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March 10, 2009: 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
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
“Apples” 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
• match 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
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March 10, 2009: 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
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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
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March 10, 2009: 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)
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CPOE and Medication Safety
• 1998: CPOE reduced medication errors 55%1
• CPOE called for in IOM reports– To Err is Human, 2000
– Crossing the Quality Chasm, 2001
• 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.
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.
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
CDSS Effectiveness Summary• Current data suggests CDSSs can improve the
process of care and perhaps clinical outcomes– most effective at preventive care reminders– probably effective at reducing some medication errors– modest at best for drug dosing and active care– generally not helpful for improving diagnosis except with
trainees• Findings limited by
– methodological problems and design type of studies– insufficient appreciation of workflow component of CDSSs– insufficient appreciation of heterogeneity of systems
• Bottom line: only moderate evidence of benefit– limited generalizability of evidence
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Outline
• Decision support systems– background, definition– clinical versus research decision support
• How decision support systems “think”– rule-based systems
– neural networks
• CDSS Effectiveness• CDSS Adoption
![Page 37: March 10, 2009: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 10, 2009 Division of General.](https://reader035.fdocuments.us/reader035/viewer/2022062408/56649ee05503460f94bf095a/html5/thumbnails/37.jpg)
March 10, 2009: 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
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March 10, 2009: 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
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March 10, 2009: 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– insufficient standards for accessing and understanding
clinical information, e.g.,• K+ is easy to get from a lab system via HL7/LOINC
• but Past Medical History may not be – may not be a separate EHR field– may be entered in text only – no standard interchange format for parts of the EHR
• Expensive to customize each CDSS to each EHR– standards for representing the EHR are required
• e.g, openEHR, Continuity Care Record, HL7 Clinical Document Architecture v2
• none are widely adopted
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March 10, 2009: 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 incentives/rewards for better quality
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Fundamental Barrier
• 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
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Implications
• Clear trade-off between physician coding effort and “smarter” decision support
• 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)
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Fundamental Barrier
• 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
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Observation to Policy
• NIH Challenge topics, priority– 06-LM-102* Self-documenting encounters. Develop technologies, tools,
and processes to achieve rapid and comprehensive electronic documentation of encounters with patients/research subjects. Clinicians & clinical researchers spend considerable time and effort in documenting clinical encounters (including using text to describe findings that are seen or heard) - often after the fact and with little immediate benefit to care of patients and clinical research subjects. Technologies and tools that could fully automate the capture of encounters and update electronic health records in real-time would support more effective and efficient health care and clinical research. Contact: Dr. Hua-Chuan Sim, 301-594-4882, [email protected]
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March 10, 2009: I. Sim Decision Support SystemsMedical Informatics – Epi 206
Summary on Decision Support• Most CDSSs are rule-based• Moderate 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 and better interoperability
• richer, usable, standard clinical vocabulary
• standard EHR format• Need to be realistic on what decisions computers
can best support