Clinical decision support, medical knowledge

representation and workflow technology

A seminar in clinical informatics – a subfield of biomedical informatics

Vojtech Huser MD PhD

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Agenda Clinical Informatics

Knowledge representation in medicine Clinical Decision Support

Workflow Technology (WT) overview Application of WT (my research)

http://www.linkedin.com/in/vojtechhuser

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General research interest Research in: Clinical Informatics

Biomedical informatics (Medical Informatics)

Lifetime Electronic Health Record (EHR) Computable knowledge representation

Clinical Decision Support systems Quality improvement Medical research using retrospective data

http://clinicalinformatics.stanford.edu/scci_seminars http://en.wikipedia.org/wiki/Clinical_informatics

informatician (vs. informaticist)

1/3: Clinical informatics

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Biomedical Informatics Medical Informatics + Genomics (bioinformatics) = BMI

BMI definition: Scientific field that deals with biomedical

information, data and knowledge – their storage, retrieval, and optimal use for problem solving and decision making.

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Spectrum of fields

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Clinical Informatics

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Key CI journals Journal of the American Medical

Informatics Association Methods of Information in Medicine Journal of Biomedical Informatics Journal of the International Medical

Informatics Association

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CI diagram

Browse the article

2/3: Knowledge representation in medicine

(sub-topic within clinical informatics)

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Medicine Extremely complex field (“black box”)

(vs. law)

Sub-specialties

Represent general human biology or anatomy (declarative)

there are 2 types of lymphocytes: T and B

clinical knowledge (how to diagnose, treat, deal with the patient) (procedural)

untreated diabetic keto-acidosis leads to death for severe asthma – the treatment plan needs to include

maintenance medication and emergency medication

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Knowledge representation Knowledge (about a human)

Ejection fraction (EF) is the fraction of the end-diastolic volume that is ejected with each heart beat.

Facts (about a particular patient)

John Smith’s EF changed from 63% (43 old) to 39% (74 old). John Smith’s had pace maker implanted at age 65 and a period of uncontrolled hypertension (48-64).

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Three KR domains 1. Terminology

Kidney is an organ in abdomen Possible kidney diseases

2. Facts John Smith’s “coded” medical history

3. Knowledge in Clinical Guidelines Diagnosis and management of patients with

cough

Common challenge: computable knowledge representation is difficult

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1. Medical terminologies Clinical:

SNOMED-CT, LOINC, ICD9-CM

Administrative: CPT, DRG

Research: UMLS, GO, MeSH, NCI Thesaurus

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2. Facts Knowledge about patients

Data only research on electronic health record no intervention, only existing data De-identification of data IRB approval (waiver of consent)

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Clinical dataAdditional terminologies

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3. Knowledge in Clinical Guidelines Why?

Physician’s mind capacity is limited

What is it? Executable representation format which is

managed/authored by physicians

Challenge: Treatment concepts, therapeutic steps linkage to EHR data (different data

granularities)

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Examples On each encounter, check compliance with

medications, take asthma history, record peak flow, look at asthma diary, check inhaler technique, and assess asthma state. For patients taking short-acting β2 agonists and low-dose steroid inhalers, if asthma is not under control, consider stepping up to a medium-dose of steroid or adding a long-acting β2 agonist.

Patients in the alternative regimen group should receive Adriamycin 60mg/m2 IV every 21 days for 4 cycles, along with Cytoxan 600mg/m2 IV every 21 days for 4 cycles. Patients who are estrogen-receptor positive will receive tamoxifen PO for 5 years. Delay administration of Adriamycin and Cytoxan if there exists > grade 1 granulocytopenia on day 1.

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Executable Guidelines Clinician’s decisions (goal, alternatives) Temporal dimensions of actions and data Abstractions (granulocytopenia) Degree of uncertainty Targeted recipient (MD, RN, patient) Normal case and exceptions Medical knowledge vs. events/actions (+participant)

Two example medical knowledge representation standards ARDEN Syntax Glif

Tu (1999)

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Arden Syntax Text based format

Clinician-friendly syntax

ANSI approved standard Several versions Existing vendor implementations

repository of DSS modules at Columbia U 283 DSS modules

browse MLM file

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GLIF Developed by InferMed collaboration Task-network model Protégé as the guideline editor

2 types of graph Action map Decision map

Rule-in, rule-out criteria Has important similarities with EON, SAGE Engine component (experimental vs. actual use)

Wang, (1999, 2004)

Boxwala, (2004)

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GLIF guideline

(task-network model)

Wang, (1999, 2004)

Boxwala, (2004)

End of part 1 and 2

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Transition slide Ultra-short questions? Summary of part 1 and 2 How is this relevant to the CIBM program?

Bioinformatics (sequence analysis) (health inf. systems)

Computer Science (intro to data structures)

Biology (organic chemistry)

Focus on clinical medicine (last year’s patients) Current knowledge: Improve care Research settings: Find patients (to collect samples, apply results) (type 2 translation, CTSA, bench to bed)

The “human clinome project” continuously changing library of declarative and procedural knowledge that is

computable (can be accessed by computatational means) Y.Shahar (Methods of Information Medicine, 2008/4)

3/3: Workflow Technology (WT) (overview and my

research)

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Publications

http://healthcareworkflow.wordpress.com

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Automating Workflow Workflow Management Coalition (WfMC)

www.wfmc.org Terminology and Glossary

http://www.wfmc.org/standards/docs/TC-1011_term_glossary_v3.pdf

Workflow The automation of a business process, in whole

or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules.

WfMS = Workflow Management System

BPM = Business Process Management BPMS = Business Process Management System

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Minimum componentslanguage edito

r

execution engine

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eComm (worklist handler)

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Examples of WT use in healthcare

Bed managementInfections control (MRSA)

J. Emanuele and L. Koetter, "Workflow Opportunities and Challenges in Healthcare," in 2007 BPM & Workflow Handbook, 2007.

L. Koetter, "MRSA infection control with workflow technology," Spring AMIA Conference, Orlando, FL, 2007.

R. Hess, "The Chester County Hospital: Case Study," in 2007 Excellence in Practice: Moving the Goalposts., 2007.

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Stroke guideline (WfMS)

Quaglini, Panzarasa, et al. (2000,2001,2003,2007)

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GLIF guideline

(task-network model)

Wang, (1999, 2004)

Boxwala, (2004)

Examples of workflow editors and engines

http://healthcareworkflow.wordpress.com

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WT software components Core components

Editor Engine

Additional components Administration application (deplay, terminate, versioning)

Worklist handler User management (LDAP, MS, other)

Organizational roles Monitoring/Analytical application Simulation tools Workflow mining

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Interesting analyses Improving processes

Allocation of tasks Push (human decides)/pull (machine) strategy

(push to all or to one and then escalate) Earliest due date, first-in first-out Rules: (1)let a resource practice its specialty; (2) do similar

task in succession; (3) flexibility of staff (“save the generalist”)

Bottlenecks Number of cases in progress Case completion time Level of service (customers)

BPR = business process re-engineering

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Workflow mining Traditional approach

model your process, pilot, deploy

Alternative Take existing event data Mine process definition

Delta analysis Discovered process (current) vs. Human modelled process

(goal, dream design) Migration strategy

www.processmining.org ProM (SourceForge)

http://healthcareworkflow.wordpress.com

3/3: Application of WT (my research)

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HL Scenario

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Clinical Guidelines Quality Improvement measures

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RetroGuide (RG): key concepts

Works with retrospective data Flowchart layer + code layer (applications) Set of RG external applications (RGEAs) Single patient execution model (DSS)

Works with time ordered chart Concept of current position in EHR

Resembles manual chart review Use of variables to remember data Procedural modeling approach (rather then

declarative)

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Application areas Research

Hodgkin's lymphoma and pregnancy Hepatitis C

Decision support Computerized glucose management protocol Adverse Drug Events (naloxone, respiratory failure)

Quality improvement Osteoporosis Cholesterol management Blood pressure control in diabetics

Additional areas of interest (analysis) perinatal care, pneumonia, course of care for diabetes, AMI, Barrett’s

esophagus, critical labs alerting, chronic kidney disease

engine

Reports generated by RetroGuide

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1. Summary report (population)

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2. Detailed report (execution trace)

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3. Individual patient view

More RetroGuide information

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RetroGuide Evaluation Comparison of RG vs. SQL

Non-expert analyst as subjects (18 participants)

A) Quantitative results Statistically significant difference in scores

paired t-test, 2-sided RG: 11.1± 1.8 vs. SQL: 6.3 ± 2.1 (p<<0.0001)

B) Qualitative results Which technology do you prefer (SQL or RG)?

94% of participants preferred RG

and why do you prefer it? 1. easy to learn/use/understand 2. temporal modeling capabilities 3. more intuitive/natural/logical

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WT, guidelines, RetroGuide Goal: Use of WT within an EHR system Why?

Clinicians are willing to review a flowchart Knowledge management should by done by clinicians

Retrospective mode Search EHR data without any intervention Identifying opportunities Refining the logic Demonstrating the potential value to consumers,

administrators, physicians Prospective mode

Intervention at the point of care

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Clinical informatics context Query EHR data (.sql, .sas, .java --> flowchart)

Working with lifetime EHR data Discovering limitations of this data

Model decision support Sharing problem (flowchart, cross-industry technology)

Other knowledge domains: Quality improvement Clinical research

Knowledge management (and acquisition) Clinician-friendly technology

Examples

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RetroGuide

Data Integration Process Execution Report ReviewProcess design

EDW

3TBengine

RGEAs+

Summary report

Detailed report

Individual patient view

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http://healthcareworkflow.wordpress.com

Examples using Marshfield Clinic Data

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Conclusion Summary

Clinical Informatics

Knowledge representation Executable Clinical Guidelines

Workflow technology and my research Retrospective analysis of data Plans for prospective implementation

Questions ?