March 11, 2008: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 11, 2008 Division of General

March 11, 2008: I. Sim Decision Support SystemsMedical Informatics – Epi 206

Decision Support Systems

Ida Sim, MD, PhD

March 11, 2008

Division of General Internal Medicine, and the Center for Clinical and Translational Informatics

UCSF

Copyright Ida Sim, 2008. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


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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PATIENT CARE / WELLNES RESEARCH

Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.

Clinical Decision Support Systems


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?


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


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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Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

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ppor

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Med

ical

logi

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PATIENT CARE / WELLNES RESEARCH

Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.

CTMS Decision Support Systems


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


Similarities/Differences

• customized to patient• identify applicable

guidelines, evidence• variable presentations and

contexts• wide clinical coverage• may include diagnostic

support• involves many team

members• 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


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


Outline

• Decision support systems– background, definition– clinical versus research decision support


– neural networks

• CDSS Effectiveness• CDSS Adoption


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


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


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


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


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


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


Sharing Rules

• Why not have libraries of rules?• Reusable, central upkeep, evidence-based...


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)


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)


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


Outline



– neural networks



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”


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


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


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)


Outline

• Decision support systems

– background, definition

– clinical versus research decision support• How decision support systems “think”

– rule-based systems

– neural networks



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)


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.


“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


“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


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)


CDSS Effectiveness Summary• Current data suggests CDSSs can improve the

process of care and perhaps clinical outcomes– most effective at preventive care reminders– 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


Outline



– neural networks



Low CDSS Adoption

• Adoption of CDSSs beyond simple reminders– < 10% of those with EHRs

• Reasons – informatics

– technical

– organizational / financial

– fundamental


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


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


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


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


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)


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

Documents

March 11, 2008: I. Sim Decision Support Systems Medical Informatics – Epi 206 Decision Support Systems Ida Sim, MD, PhD March 11, 2008 Division of General