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Clinical Decision Support Systems. Ida Sim, MD, PhD March 12, 2002 Division of General Internal Medicine, and the Graduate Group in Biological and Medical Informatics UCSF.

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clinical decision support systems

Clinical Decision Support Systems

Ida Sim, MD, PhD

March 12, 2002

Division of General Internal Medicine, and

the Graduate Group in Biological and Medical Informatics

UCSF

Copyright Ida Sim, 2002. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.

Clinical Decision Support Systems

Medical Informatics

it and quality
IT and Quality
  • Information technology touted to improve quality of care
  • Dimensions
    • information availability
      • chart, lab results, allergies; all legible
    • process efficiency
      • visit level coding, e-prescribing
    • intermediate measures
      • vaccination and screening rates
    • patient outcomes

Clinical Decision Support Systems

Medical Informatics

outline
Outline
  • Clinical decision support systems (CDSS)
    • definition
    • methods of reasoning
    • effectiveness at improving quality
  • Clinical research informatics
    • infrastructure for clinical research
    • systems for supporting clinical research

Clinical Decision Support Systems

Medical Informatics

what is a cdss
What is a 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)

Clinical Decision Support Systems

Medical Informatics

major objectives
Major Objectives
  • Diagnostic support
    • DxPlain, QMR
  • Drug dosing
    • aminoglycoside, theophylline, warfarin
  • Preventive care reminders
    • vaccinations, mammograms
  • Disease management
    • diabetes, hypertension, AIDS, asthma
  • Test ordering, drug prescription
    • reducing daily CBCs in hospital, allergy checking
  • Utilization
    • referral management, clinic followup

Clinical Decision Support Systems

Medical Informatics

how do cdsss think
How Do CDSSs “Think”?
  • Some systems use more than one method
    • rule-based
    • adhoc
      • non-math method of reasoning about probabilities
      • e.g., if high WBC AND cough AND fever AND abn. CXR then likelihood of pneumonia is 4 out of 5
        • e.g., DxPlain, QMR
    • bayesian network
      • formal probabilistic reasoning, extension of decision analysis
    • neural network
    • fuzzy logic, genetic algorithms, case-based reasoning, etc.

Clinical Decision Support Systems

Medical Informatics

rule based approaches
Rule-Based Approaches
  • Forward reasoning (data-driven)
    • start with data, execute applicable rules, see if new conclusions trigger other rules, and so on
    • use if sparse data
      • if high WBC AND cough AND fever AND abn. CXR => pneumonia
      • if pneumonia => give antibiotics, etc.
  • Backward reasoning (goal-driven)
    • start with “goal rule,” determine whether goal rule is true by evaluating the truth of each necessary premise
    • use if lots of data
      • patient with lots of findings and symptoms
      • is this lupus? => are 4 or more ACR criteria satisfied?

Clinical Decision Support Systems

Medical Informatics

mlms and arden
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.;;

MLMs and Arden
  • Medical Logic Modules (MLMs) in Arden Syntax (an international ASTM standard syntax) :
  • 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;;

Clinical Decision Support Systems

Medical Informatics

neural networks
Neural Networks
  • Example of a data-driven data mining method
  • 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”

Clinical Decision Support Systems

Medical Informatics

neural network for mi diagnosis

EKG findings

Acute MI

Rales

No Acute MI

JVD

Response to TNG

Neural Network for MI Diagnosis
  • Inputs (e.g., all patient characteristics in the EMR)
      • EKG findings (ST elevation, old Q’s)
      • rales (Yes, No)
      • JVD (in cm)
  • Outputs are the set of possible outcomes/diagnoses

Clinical Decision Support Systems

Medical Informatics

training the neural network
Training the Neural Network
  • Network gets “trained”
    • feed network many examples of known patients and their diagnoses
    • system iteratively adjusts the weights of the connections to find the network “pattern” that associates sets of input variables (patients) with the right output state (MI or not)
  • In Baxt’s MI neural network
    • training set: 130 pts with MI, 120 without
    • test set: 1070 ER patients with anterior chest pain

Clinical Decision Support Systems

Medical Informatics

baxt s acute mi neural net
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 isn’t this neural network used more widely?
    • “black box” nature limits explanatory ability and lessens acceptance
    • users have to input the variables manually
      • if EMRs more widely available, these types of systems may be more prevalent

Clinical Decision Support Systems

Medical Informatics

cdss methods
CDSS Methods
  • Vast majority of clinically-used CDSSs use rule-based reasoning
    • problem of combinatorial explosion of rules
  • Major limitations
    • how to represent some data (e.g., “looks sick”)
    • formal, reproducible methods for making clinical decisions
  • Other major limitation is source of input data
    • manual input of data by doctors will not work
    • EMR can enable a new era of CDSSs
  • But lots can be done with current technology

Clinical Decision Support Systems

Medical Informatics

outline14
Outline
  • Clinical decision support systems (CDSS)
    • definition
    • methods of reasoning
    • effectiveness at improving quality
  • Clinical research informatics
    • infrastructure for clinical research
    • systems for supporting clinical research

Clinical Decision Support Systems

Medical Informatics

cdss effectiveness
CDSS Effectiveness
  • In controlled trials, only occasional modest benefit found (Hunt, JAMA 1998; updated RB Haynes 2000)
    • diagnosis: 1/5 studies beneficial
    • drug dosing: 9/15
    • preventive care reminders: 19/26
  • Few studies looked at patient outcomes
    • 6 of 14 showed benefit

Clinical Decision Support Systems

Medical Informatics

shortcomings of literature
Shortcomings of Literature
  • Variable study quality
    • 35% rate >8 on 10 point quality scale (mean ~6.2)
    • more recent studies better quality
  • Low power
    • 5 of 8 studies of patient outcome had low power
  • Patients randomized to CDSS or not
    • physicians treated some patients with CDSS, and some without CDSS
    • this would tend to …. any effects of the CDSS
  • Probably publication bias

Clinical Decision Support Systems

Medical Informatics

shortcomings of approach 1
Shortcomings of Approach (1)
  • E.g., a hypertension treatment CDSS
  • Is RCT best design for determining effectiveness?
    • should randomize MDs, \usually low power
    • intervention is usually more than just the CDSS
      • e.g., “buy-in” sessions to HTN guideline underlying CDSS
    • limited generalizability
      • applies only to this particular CDSS
      • integration of CDSS into existing workflow often unique to study site
    • if CDSS shows no effect, standard RCT gives little insight into why

Clinical Decision Support Systems

Medical Informatics

shortcomings of approach 2
Shortcomings of Approach (2)
  • How would you improve on the Hunt systematic review?
    • CDSSs are very heterogeneous
    • does the heterogeneity explain any variation in benefit?
  • Example: preventive care reminder CDSS
    • A clerk routinely abstracts preventive care interventions from paper chart into a database. Before each clinic session, nurse runs the CDSS for patients coming in that day. Guideline-based recommendations are printed out on paper and attached to front of chart. Doctor orders preventive care in usual way using paper-based methods

Clinical Decision Support Systems

Medical Informatics

heterogeneity of cdsss
Heterogeneity of CDSSs
  • Hypertension treatment CDSS
    • Clinic has an EMR. During patient visit, CDSS notes that BP and trend is too high. Checks patient’s Cr, diabetes status, cardiac status, current meds and allergies and recommends drug therapy change according to JNC VI guidelines. Presents e-prescription for MD to verify. If verified, order is sent directly to pharmacy and medication list updated.
  • How to meaningfully characterize CDSSs?
    • target decision maker (MD, nurse, patient)
    • urgency of decision (stat result, outpatient screening)
    • method of delivery (paper, EMR, pager)
    • force of recommendation (suggestion, requirement) ...

Clinical Decision Support Systems

Medical Informatics

typology of cdsss

CONTEXT

  • Clinical decision
  • Target patient setting
  • Point of care
  • Question orientation
  • Workflow integration
  • OUTPUT
  • Action complexity
  • Action embedded
  • Compliance urgency
  • Force action recommendation
  • Decision focus
  • Form information generation
    • CDSS
  • Customization
  • Update mechanism
  • Unit of analysis
  • Clinical knowledge source
  • Mode of information generation
  • INPUT
  • Data source
  • Data source-system intermediary

System-user interface

System-user interface

OR

System user/ Processor/Target decision maker

Target decision maker

System user

Processor

Typology of CDSSs

Clinical Decision Support Systems

Medical Informatics

cdss effectiveness summary
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
    • choice of study design
    • insufficient appreciation of workflow component of CDSSs

Clinical Decision Support Systems

Medical Informatics

summary on cdsss
Summary on CDSSs
  • Intense interest in promise of CDSSs to improve health care quality
  • Evidence is equivocal but quite severely limited by methodological and other shortcomings
  • Top challenge currently is to apply current technology effectively to care processes
    • get physician buy-in, get an EMR, integrate CDSS with the EMR, incentivize organizations for buying and using CDSSs to improve quality…
  • Technical limitations on reasoning capability are not short-term barriers

Clinical Decision Support Systems

Medical Informatics

outline23
Outline
  • Clinical decision support systems (CDSS)
    • definition
    • methods of reasoning
    • effectiveness at improving quality
  • Clinical research informatics
    • infrastructure for clinical research
    • systems for supporting clinical research

Clinical Decision Support Systems

Medical Informatics

evidence adaptive cdsss
Evidence Adaptive CDSSs
  • CDSS recommendations should be evidence-based
    • should keep up-to-date with research findings
    • update mechanism should be semi-automatic
  • \ Health care computing infrastructure should be integrated
    • for clinical care and decision support
    • for clinical research

Clinical Decision Support Systems

Medical Informatics

need for informatics infrastructure
Need For Informatics Infrastructure
  • “A nationwide effort is needed to build a technology-based information infrastructure that would lead to the elimination of most handwritten clinical data within the next 10 years, the committee said. ...Without a national pledge to create and fund such a technological framework, progress to enhance quality of care will be painfully slow.”(IOM, Crossing the Quality Chasm, Mar 2001)
  • IOM reports asks Congress to spend $1 billion on health informatics
  • How do needs of clinical research and care dovetail?

Clinical Decision Support Systems

Medical Informatics

joint infrastructure for care and research

Administrative

Clinical Care

Research

Practice

Management

Systems

??

Electronic

Medical

Record

Medical Business

Data Model

Clinical Care

Data Model

??

Billing

Clinical

Standard Vocabulary

Standard Communications Protocols (e.g., HL-7)

Physical Networking

Joint Infrastructure for Care and Research

Clinical Decision Support Systems

Medical Informatics

research and care together

for clinical research

New Ideas

Design

Study

Clinical

Care

Utilize

Results

for basic research

for patient

care & policy

Findings

Protocol &

Funding

Conduct

Study

Activate

Study

Approval &

Preparation

Research and Care Together

Clinical Decision Support Systems

Medical Informatics

the clinical trial cycle per nci

Findings

Protocol &

Funding

The Clinical Trial Cycle (per NCI)

New Ideas

Utilize

Results

Design Trial

  • trial simulators
  • trial costing
  • protocol authoring
  • data analysis
  • reporting
  • data management
  • remote data entry
  • GCP compliance
  • IRB approval
  • CRF design

Conduct

Trial

Activate Trial

Approval &

Preparation

Clinical Decision Support Systems

Medical Informatics

infrastructure for clinical trials

Findings

Protocol &

Funding

Infrastructure for Clinical Trials

Design Trial

New Ideas

Utilize

Results

• a few companies

  • trial simulators
  • trial costing
  • protocol authoring
  • data analysis
  • reporting

• FDA electronic

submission

standards

  • data management
  • remote data entry
  • GCP compliance
  • IRB approval
  • CRF design

Conduct

Trial

Activate Trial

Approval &

Preparation

• a few companies

• many companies

Clinical Decision Support Systems

Medical Informatics

joint infrastructure for care and research30

Administrative

Clinical Care

Research

Practice

Management

Systems

Clinical Research

Management

Systems

Electronic

Medical

Record

Clinical Study

Data Models

Medical Business

Data Model

Clinical Care

Data Model

Billing

Clinical

Standard Vocabulary

Standard Communications Protocols (e.g., HL-7)

Physical Networking

Joint Infrastructure for Care and Research

Clinical Decision Support Systems

Medical Informatics

case clinical research informatics
Case: Clinical Research Informatics
  • You are planning on a study on infant jaundice...
  • What relevant studies have been completed on this topic?
  • What ongoing studies should you know about?
  • You’re interested in running your study over the web as much as possible.
    • what types of study activities can be done over the web?
    • how good is the technology for these activities?

Clinical Decision Support Systems

Medical Informatics

relevant trials completed
Relevant Trials: Completed
  • Medline
  • Cochrane Controlled Trials Register
    • ~327,700 records of controlled trials
    • manual logging of CCTs by hand searching journals
    • accessible from UCSF machine (IP address) only
      • can set up proxy access
  • metaRegister of Controlled Trials
    • 10,755 commercial and ongoing trials

Clinical Decision Support Systems

Medical Informatics

relevant trials ongoing
Relevant Trials: Ongoing
  • Non-profit/government
    • www.clinicaltrials.gov
      • 5700 trials, ~3000 open
      • NIH-supported and some commercial cancer and AIDS trials
    • cancertrials.nci.nih.gov
    • www.actis.org
      • AIDS Clinical Trials Information Service
    • www.trialscentral.org (from Cochrane people)
      • pointers to hundreds of clinical trial registries, by disease

Clinical Decision Support Systems

Medical Informatics

relevant trials ongoing34
Relevant Trials: Ongoing
  • Commercial: mostly for patient recruitment
    • www.centerwatch.com
    • www.ClinicalTrialFinder.com
    • www.controlled-trials.com
    • www.clinicaltrials.com
    • etc., etc., etc.
  • How to get better web searching results
    • check out Web Search 101
      • http://websearch.about.com/internet/websearch/library/weekly/aa011599.htm

Clinical Decision Support Systems

Medical Informatics

case clinical research informatics35
Case: Clinical Research Informatics
  • You are planning on a study on infant jaundice...
  • What relevant studies have been completed on this topic?
  • What ongoing studies should you know about?
  • You’re interested in running your study over the web as much as possible.
    • what types of study activities can be done over the web?
    • how good is the technology for these activities?

Clinical Decision Support Systems

Medical Informatics

clinical study tasks
Clinical Study Tasks
  • Project website
  • Subject recruitment
  • Eligibility determination
  • Protocol and forms distribution
  • Randomization
  • Data collection
    • adverse events tracking

Clinical Decision Support Systems

Medical Informatics

industry is the innovator
Industry is the Innovator
  • RCTs now a $3.6 billion business (C. Scott, 7/00)
    • in 1988, 95% of RCTs conducted by academics
    • now, over 80% conducted by industry
  • Ergo, much of the technology innovation in clinical research execution is going on in industry
    • Applied Clinical Trials software directory
      • http://www.pharmaportal.com/magazines/act/itsol/itsindex.cfm
  • What’s the global picture for research informatics technologies?

Clinical Decision Support Systems

Medical Informatics

project website
Project Website
  • Project website
    • GISSI website has summaries of trial protocols, results, references
    • HERS main results revised tables from JAMA report
  • Requirements
    • web server computer
      • use a web hosting service (see http://www.cnet.com)
      • or have a web server program (e.g., Apache)
    • pages of material
      • produce these using Word (save as HTML file)
      • or use a web editor (FrontPage, Dreamweaver)

Clinical Decision Support Systems

Medical Informatics

project website cont
Project Website (cont.)
  • Personnel
    • webmaster: handles the machine stuff
    • web designer: produces text & graphical content
    • database administrator/programmer: some databases (e.g., FilemakerPro, Access) can be exported to the web, but usually this involves moderate programming
  • Status: easily doable today

Clinical Decision Support Systems

Medical Informatics

jife client server model
JIFE Client/Server Model

Kaiser

Oakland

Kaiser

Santa Clara

Kaiser

San Diego

Internet

  • The “jaundice.ucsf.edu” computer has
  • web server software. It “serves” web pages
  • in response to http commands such as
  • http://jaundice.ucsf.edu/project-home.html

aol.com

pacbell.net

jaundice

ucsf.edu

itsa

LAN

dial-in to itsa.ucsf.edu via modem

at home

Clinical Decision Support Systems

Medical Informatics

automated eligibility determination

Eligibility Rule

Match

Eligible Patients

EMR

Automated Eligibility Determination
  • Study enrollment is big bottleneck
  • Eligible patients: patients whose characteristics match with eligibility criteria
  • For computerized matching, need to have computer-interpretable descriptions of
    • patient characteristics
    • the eligibility criteria

Clinical Decision Support Systems

Medical Informatics

eligibility example
Eligibility Example
  • Eligibility criterion: women who are 2 or fewer years post-menopause, as defined in NCI’s Common Data Elements set
  • Allowed values:

Above categories not applicable AND Age < 50

Above categories not applicable AND Age >=50

Post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy and not currently receiving therapy with LH-RH analogs [eg. Zolades])

Post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy)

Pre (<6 mo since LMP AND no prior bilateral ovariectomy, AND not on estrogen replacement)

Clinical Decision Support Systems

Medical Informatics

emr data needed
EMR Data Needed
  • Gender
  • Age
  • Time since LMP, whether
    • 6 or fewer months, or 12 or more months
  • Past surgical history
    • bilateral ovariectomy and/or hysterectomy
  • Therapy
    • LH-RH analogs, or
    • estrogen replacement

Clinical Decision Support Systems

Medical Informatics

computer interpretable eligibility rule
Computer-Interpretable Eligibility Rule
  • NCI working on common model for representing eligibility rules
  • Logical rules
    • (Prior bilateral ovariectomy) OR

(>12 mo since LMP ANDno prior hysterectomy)

    • first order logic is the best representation model for this
  • Temporal constraints
    • greater than 12 months since LMP...
    • representing time requires second-order logic
  • Can do simple cases with database rules and triggers

Clinical Decision Support Systems

Medical Informatics

promising but
Promising, but...
  • Richly detailed EMR not widely available or well integrated
  • Coding of eligibility rules is difficult
  • At present, can only expect computer to suggest potential subjects, then EMR can
    • prompt MD in real-time to refer patient to study, or
    • periodically batch notify MD of eligible patients, or
    • send letter of solicitation to patients
  • Similar problems bedevil automated identification of guideline eligibility

Clinical Decision Support Systems

Medical Informatics

protocol and forms distribution
Protocol and Forms Distribution
  • Allows for centralized forms management and storage through a project website
  • If expecting users to download, print, fill out and fax form back
    • need protocol and forms in electronic format (e.g.,Word or PDF)
      • scan it using a scanner ($100-$4000)
        • makes an image of the page (e.g., .gif or .jpeg)
      • optical character recognition (OCR) scanning
        • convert scanned text into an editable document (e.g., Word)
  • Status: easily doable today

Clinical Decision Support Systems

Medical Informatics

jife forms download
JIFE Forms Download

Kaiser

Oakland

Kaiser

Santa Clara

Kaiser

San Diego

Internet

  • “jaundice.ucsf.edu” “serves” forms such as
  • http://jaundice.ucsf.edu/case-form.pdf for
  • printing out

aol.com

pacbell.net

jaundice

ucsf.edu

itsa

LAN

dial-in to itsa.ucsf.edu via modem

at home

Clinical Decision Support Systems

Medical Informatics

protocol and forms distribution48
Protocol and Forms Distribution
  • If expecting users to enter data online over the web
    • need someone to design the forms and build them to be served over the web
      • e.g., using Access Visual Basic
    • need security mechanisms (e.g., user login)
    • need data validation checks built into forms entry
    • data forms must send data to a database
  • Status: doable with some programming

Clinical Decision Support Systems

Medical Informatics

infant jaundice online forms
Infant Jaundice Online Forms

Kaiser

Oakland

Kaiser

Santa Clara

Kaiser

San Diego

Internet

  • “jaundice.ucsf.edu” “serves” online entry forms
  • such as http://jaundice.ucsf.edu/case-form.asp.
  • Users enter data, which get checked at the client
  • side, and data is sent back to “jaundice.ucsf.edu.”

aol.com

pacbell.net

jaundice

ucsf.edu

itsa

LAN

dial-in to itsa.ucsf.edu via modem

at home

Clinical Decision Support Systems

Medical Informatics

web based randomization

patient info

randomization results

Enroller

Project Central

Web-based Randomization
  • Requirements
    • a web-based data collection form to collect patient information
    • programs to verify eligibility and randomize patient
    • program to generate a response to the enroller
    • security, privacy, and backup provisions
  • Some commercial systems do this for you
  • Status: doable with some programming

Clinical Decision Support Systems

Medical Informatics

electronic data capture
Electronic Data Capture
  • Fax
  • Voice
    • most systems about 95-99% accurate for restricted domains
    • can speak normally, but may need to train computer to your voice
  • Handheld Electronic
    • your favorite PDA and docking station for downloading to a database (over the web)
    • wireless PDAs can’t be far from a receiver node
      • radiofrequency: slow, prone to interference
      • infrared: requires line of sight between PDA and node

Clinical Decision Support Systems

Medical Informatics

issues in remote data capture
Issues in Remote Data Capture
  • Managing copies (local and central copies?)
  • Concurrent updates
    • what if 2 people want to update same record?
  • Merging data
  • Security and privacy
  • System downtime
  • System response time
  • Workflow issues

Clinical Decision Support Systems

Medical Informatics

adverse events monitoring
Adverse Events Monitoring
  • Dedicated systems for capturing this data for FDA requirements
    • built in checks for adhering to GCP (Good Clinical Practice)
  • Standard electronic data model for AEs pending
    • will simplify AE data exchange

Clinical Decision Support Systems

Medical Informatics

clinical research informatics summary
Clinical Research Informatics Summary
  • Project website
    • easily doable today
  • Protocol and forms distribution
    • easily doable today
  • Randomization
    • doable with some programming
  • Data collection
    • fax
    • voice
    • electronic handheld
  • Adverse events monitoring

Clinical Decision Support Systems

Medical Informatics

teaching points
Teaching Points
  • CDSSs are heterogeneous in design and use
  • Organizational challenges greater than technical
  • Evidence of effectiveness is equivocal but limited by design shortcomings
  • Clinical care informatics infrastructure should be integrated with clinical research infrastructure
  • Many aspects of clinical research can be done electronically, but in fragmented fashion
  • Industry very active in clinical research
    • business imperatives dominate infrastructure design

Clinical Decision Support Systems

Medical Informatics

references
References
  • See Applied Clinical Trials (http://www.pharmaportal.com/magazines/act/index.cfm) for clinical research informatics industry and products

Clinical Decision Support Systems

Medical Informatics