Cli linic nical al De Decis isio ion n Sup uppo port rt - - PowerPoint PPT Presentation

cli linic nical al de decis isio ion n sup uppo port rt
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Cli linic nical al De Decis isio ion n Sup uppo port rt - - PowerPoint PPT Presentation

Oppo Op portu rtunit nities ies for r Ev Evid idence nce-base based d Cli linic nical al De Decis isio ion n Sup uppo port rt Syst stems: ems: An An Ap Appl plic ication ation for r On Oncolo logy gy BPM Roundtable


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SLIDE 1

BPM Roundtable undtable Eind ndhov

  • ven

en Univ iver ersit ity of Technol hnology

  • gy

5/11/2012

Op Oppo portu rtunit nities ies for r Ev Evid idence nce-base based d Cli linic nical al De Decis isio ion n Sup uppo port rt Syst stems: ems: An An Ap Appl plic ication ation for r On Oncolo logy gy

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SLIDE 2
  • Dr. Yaron
  • n Denek

nekam amp

CMIO, Hospital Division, n, Clalit Health th Service ces, Tel Aviv Facul ulty ty of M Medicine ne, Techni hnion, Haifa

Europ ropean an BPM Round undtabl table Eind ndhov

  • ven

en Univ iver ersit ity of Technol hnology

  • gy

5/11/2012

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SLIDE 3
  • Specialist in Internal Medicine
  • Medical Informatics - fellowship and studies at

Harvard MIT division of Health Sciences and Technology in Boston

  • Chief Medical Informatics Officer

Hospital Division, Corporate Headquarters, Clalit Health Services, Tel Aviv

  • Faculty of Medicine, Technion, Haifa
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SLIDE 4
  • Largest health care provider in Israel
  • 4 Million Insured Members – over half of the population
  • Second largest organization of its type in the world
  • 14 hospitals, 1400 clinics
  • 36,000 employees, 10,000 physicians
  • Clinical IT systems
  • Community- one EMR system
  • Hospitals – mainly 2 EMR systems
  • Several BI systems

Clalit Health Services Group

Israel’s Leading Health Care Organization

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SLIDE 5

Hospitals across Israel

  • Community outreach
  • National centers of excellence
  • Children’s Hospital
  • Cancer center
  • Organ transplant center
  • Rehabilitation centers

5

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SLIDE 6

I.

Why do we need to support clinical decisions and processes?

II.

Types and examples of clinical decision support systems (CDSS)

III.

Opportunities and challenges of implementing CDSS

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SLIDE 7
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SLIDE 8
  • Preventable <> side effects
  • Medication – allergy, dosages, drug-drug

interactions etc.

  • Misdiagnosis
  • Treatment – medical and surgical
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SLIDE 9

Covell study of LA Internists, Ann Intern Med 1985

2 unanswered clinical questions for every 3 pts

  • Holds across PCPs and specialty care
  • Holds across urban and rural

Gorman, Medical Decision Making 1995, Gorman, Medinfo 2001

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SLIDE 10
  • 30% of questions were pursued
  • 70% of information needs were not-pursued!
  • Too busy, no immediate access to resources, or office

materials (books, journals, etc.) in disarray or out-of-date

Some outcomes - Overuse

  • 30% of children receive excessive antibiotics for ear infection
  • 20-40% of surgical procedures unnecessary
  • 50% of back pain x-rays unnecessary

Underuse

  • 50% of elderly patients don’t get the necessary immunizations

in winter

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SLIDE 11
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SLIDE 12
  • > 20,000 journals published
  • 17,000 new books per year
  • Size doubles every 10-15 years
  • 2 Million facts needed to practice
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SLIDE 13

‘‘Men are men; the best sometimes forget’’

Othello, 1605; Act II, Scene iii

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SLIDE 14

Medical School Conferences, CME, Lectures Clinical Reports Medical Record Books, Journals Guidelines Administrative Issues Decisions, Decisions, Decisions Curbside Consultation

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SLIDE 15
  • A constellation of psychological studies converging to a

description of human decision making under uncertainty

  • It was awarded the Nobel Prize of Economics (2002)
  • Main points:
  • People use a few simple heuristics when

making judgments under uncertainty

  • These heuristics sometimes are useful and
  • ther times lead to severe and systematic

errors

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SLIDE 16

“Information Technology must play a central role in the redesign of the health care system…”

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SLIDE 17
  • Current Approach
  • Professional autonomy drives variability
  • Decision making is based on training and

experience

  • New Rules
  • Knowledge is shared and information flows

freely

  • Decision making is evidence-based

Richardson, William C. IOM 2001 Crossing the Quality Chasm, pg. 71

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SLIDE 18

Active knowledge systems which use two or more

items of patient data to generate case-specific advice Support <> Replace link health observations with health knowledge to influence health decisions by clinicians for improved health care processes

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SLIDE 19

Evidenced Based Medicine

  • High quality level studies – ranking for quality of

research, level of evidence Clinical practice guidelines

  • Developed by professional organizations, HMO’s,

Ministry of health Problems –

  • Physicians are not aware to that knowledge
  • Time constraints to read and follow
  • Compliance
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SLIDE 20

Evidenced Based Medicine Clinical Actions

CDSS

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SLIDE 21
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  • Medications – prescribing, ordering
  • Treatments – most useful cost effective

alternatives

  • Diagnostic – complex multi step process
  • Institutional administrative processes – duplicated

testing, consultations, transferring labs specimens to the labs etc.

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SLIDE 23
  • Alerting - when a clinical data is abnormal
  • r a clinical guideline is not followed
  • Critiquing – when ordering a medication or a test
  • Reminders – reminding the clinician to follow

desired practice guidelines and policies

  • More challenging -
  • Computer-interpretable guidelines
  • Expert systems
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SLIDE 24

Drug-Drug Interaction Screening Dosages Renal and Hepatic function Duplicate Therapy Checking Drug Food Interactions Drug Allergy Checking Drug Indications Drug Side Effects Drug Disease Contraindication Checking Paediatric Precautions Patient Education

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SLIDE 25
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SLIDE 27

Supporting cost effectiveness Redundant test example

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SLIDE 28

Suporting the diagnostic process Alternate exam

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SLIDE 29

Lower cost Guideline for expensive medication

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SLIDE 30

Reminders

Physicians reminded to give flu shots do so twice often

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SLIDE 31

Inference methods

  • Algorithmic
  • Statistical
  • Pattern Matching
  • Rule-based

(Heuristic)

  • Fuzzy sets
  • Neural nets
  • Bayesian

Knowledge Base Inference Engine

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SLIDE 32
  • Still are not a success
  • Problem in having the probability data like for

symptoms, signs etc.

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SLIDE 33
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  • Awareness to the need, also from the perspective of claims and

risk management

  • Platforms – EMR systems are increasingly used including CPOE

cannot be a standalone system

  • Standards of clinical data and information

More standards are used for diagnoses, medications, clinical information model

  • Less reluctance of healthcare workers
  • More evidence that it works
  • Documentation of the processes in EMR systems enables

process mining to discover improved workflows

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SLIDE 35
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SLIDE 36
  • Workflow integration on top of the EMR system - naturally fit into the

process of care

  • Representing and maintaining medical knowledge and process

models

  • Complexity of modeling time oriented data
  • Need for flexibility – supporting process, not replacing the

professionals

  • Dealing with ambiguity
  • Mechanisms to avoid the alert fatigue phenomena
  • UI – simple interface directed by the user
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SLIDE 37
  • Improved patient safety
  • reduced medication errors and adverse events
  • improved medication and test ordering
  • Improved quality of care
  • increased application of clinical guidelines, facilitating the use
  • f up-to-date clinical evidence
  • Improved efficiency in health care delivery
  • reductions in test duplication
  • decreased adverse events
  • changed patterns of drug prescribing favoring cheaper but

equally effective generic brands

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SLIDE 38