cyber learning: theory and application

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NYU School of Medicine | Medical Informatics | Advanced Educational Systems Cyber learning: Theory and application Marc M. Triola, M.D. Adina L. Kalet, M.D., M.P.H. http://informatics.med.nyu.edu http://aes.med.nyu.edu NYU School of Medicine | Medical Informatics | Advanced Educational Systems Why change? NYU School of Medicine | Medical Informatics | Advanced Educational Systems Medical Education Our fundamental challenge is timeless and, hence, unchanged: Prepare future physicians to meet medicine!s existing and predictable challenges -Jordan Cohen, MD NYU School of Medicine | Medical Informatics | Advanced Educational Systems MedEd Challenges Rapid knowledge growth Reliance on memorization rather than problem solving Reliance on lecture method Passive recipients vs active

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Page 1: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Cyber learning: Theory

and applicationMarc M. Triola, M.D.

Adina L. Kalet, M.D., M.P.H.

http://informatics.med.nyu.edu

http://aes.med.nyu.edu

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Why change?

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Medical Education

Our fundamental challenge is timeless and,

hence, unchanged:

Prepare future physicians to meet

medicine!s existing" and predictable

challenges

-Jordan Cohen, MD

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

MedEd Challenges

• Rapid knowledge growth

• Reliance on memorization rather than

problem solving

• Reliance on lecture method

• Passive recipients vs active

Page 2: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

MedEd Challenges

• Medical evaluations are increasingly performed

in the ambulatory setting

• Hospital stays are shortening

• Physicians have increasing clinical obligations

• Schools are moving towards competency-

based assessment

• Students have diminishing access to patients

and to teachers

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Shifting Paradigms in

Health Care• The individual The community

• Cure of disease

• Episodic care

• Physician provider

• Paternalism

• Provider centered

• Anecdotal care

• In-patient focused

• Individual accountability

Preservation of health

Continuous care

Teams of providers

Partnership with patients

Patient/family centered

Evidenced-based medicine

Ambulatory/home centered

System accountability

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Shifting Paradigms in

Med Ed• Passive “spoon feeding” Active, student directed

• Rote learning

• Regurgitate facts

• Departmental courses

• Curriculum structure

• Etiology of disease

• Physical examination

Curiosity driven, PBL

Demonstrate competence

Interdisciplinary segments

Learning objectives

Determinants of illness

Comprehensive clinical skills

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Computer Aided

Instruction (CAI)

Page 3: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Roles of computers in

medical education

• Provide facts and information

• Teach strategies for applying knowledge appropriately in medical situations

• Encourage the development of lifelong learning skills

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Overview

The Web…

• Does it make medical education better?

• Does it make medical education different?

• Different and better?

Examples

• Remote teaching

• Two types of virtual patients

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

History of CAI

• Pioneering research in the 1960!s

• Ohio State: Tutorial evaluation system

• Octo Barnett MGH 1970

• Simulated patient encounters

• Mathematical modeling of physiology

and effects of drugs (Warfarin, insulin)

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

History of CAI• Initial installations site-limited

• Subsequent modem dial-up

• Proliferation of medical CAI, CME

development entities

• Development of the internet

• Initial material bandwidth limited

• Increasing use of streaming video

Page 4: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Rationale for CAI

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Advantages of CAI

• Computer can augment, enhance or replace

traditional teaching methods

• Rapid access to body of information

• Data

• Images

• Immersive interfaces

• Any time, any place, any pace

• Simulated clinical situation

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Advantages of CAI

• Interactive learning"

• Active vs. passive solving

• Immediate learner specific feedback

• Correct vs. incorrect, tailored response

• Tailored instruction

• Focus on areas of weakness

• Request help in interpretation

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Advantages of CAI

• Objective testing

• Permits standardized testing

• Self-evaluation

• Fun!

Page 5: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Humanism/ Doctor-Patient Relationship / Ethics

Cholesterol

Gall

Bladder

Carotid

Stenosis

Adrenal

Tumor

Colon

Cancer

Genetic predisposition, testing, techniques

stone formation

atherosclerosis

steroid biosynthesis

Informed consent Breaking bad news

Genetic testing

Reusable, integrated content

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Experimentation

• Safe exploration of what-if in a well

done scenario

• You can do things with simulated

patients you can!t do with real ones

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Case variety

• The ability to experience disease scenarios

one otherwise wouldn!t see

• Simple: diabetes

• Complex: multiple disease, multiple

medications

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Evidence Based Medical

Education • Specific

• Do students who experience a course more comprehensively exhibit higher levels of learning in the OR?

• Do students who participate in an online discussion exhibit improved clinical reasoning skills in the OSCE differential?

• Broad

• What instructional methods are worth the investment in time, money, effort?

• Impact on patient outcomes?

Page 6: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Time

• Asynchronous learning and communication

• Compress or expand time of clinical cases

• Manage diseases as they evolve over time

• Rapidly evolving problems

• Chronic diseases

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Pedagogical aspects

of CAI

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Pedagogical truths

• Classroom strategies do not directly

translate

• Goal is to create an active, self-directed

learner

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Behaviorism

• Store knowledge and maintain it with

repetition and practice

• Three phases:

• Cognitive knowledge acquisition

• Associative skill acquisition

• Autonomous performance

Page 7: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Modes of CAI

• Drill and practice

• Material presented with immediate testing

• Grading and progress or loop back

• Poor students benefit

• Didactic

• Lecture with the advantage of time and place independence

• No questions

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Formative Feedback• Feedback

• Correct vs. incorrect

• Summaries

• References

• Guidance

• Tailored feedback

• Hints

• Interactive help

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Constructivism

• Knowledge is a large integrated body

• New knowledge fits into existing cognitive

structures

• Context and attitude facilitate learning

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Interactivity

• Constrained vs. unconstrained response

• Student may have a pre-selected set

of possible response (learn to answer

questions)

• Student may be able to probe system

using natural language

• locus of control

Page 8: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

The Surgery Clerkship

A Cognitive Apprenticeship

Modeling

Anchoring

Scaffolding

Integration

Extension

Articulation

AssimilationApplication

Coaching and Fading

Graded independence*Collins A, Brown JS, Newman SE. , 1989

Taylor KL, Care WD, .1999

TRIGGER Clinical

reasoningEXERCISE

Clinical

Activities

SP Exam

Shelf exam

EXTENDED

EXPOSURE

The Clerkship

Tutorial

Cyber Classroom

Skills Lab

WISE MD

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Intelligent tutoring

• Sophisticated systems can

• Intervene if a student goes down an

unproductive path

• Gets stuck

• Appears to misunderstand a detail

• Mixed initiative systems

• Coaching vs. tutoring

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Example Systems

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

CAI as a solution

• Fewer patients are in hospitals

• The range of disease is narrow

• Teaching physicians have less time

• There is a lot more to learn

• Students and faculty are spread out

• Teachers want to create cases

themselves

Page 9: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Current SPs

• Use actors to simulate patients in a realistic

clinical situation

• Excellent for teaching, assessment

• Four cases used in our full-day CME

workshop

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Definition of “Virtual

Patient”

"Virtual Patients are interactive computer

programs that simulate real-life clinical

scenarios in which the learner acts as a health

care professional obtaining a history and

physical exam and making diagnostic and

therapeutic decisions.

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Advantages to VP!s:

Patient Safety

• Mitigation of time and chance

• Acceleration of expertise curve

• Practice without risk

• More measurement

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Advantages to VP!s:

Pedagogy

readily available

realistic

up-to-date

individualized

standardized

Problem-based learning

learner-centered

contextual assessment

distance learning

efficiency

cost-effectivenessWeb-based delivery

interactive

multimedia

universal

automated

Page 10: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Virtual SP

• Developed four virtual patients based live

CME cases

• Web-based

Page 11: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Virtual SP

Case Introduction:Instructions and Case

context

VP Interview Differential Diagnosis

Faculty Discussant Feedback: Interview Content Feedback: Differential Diagnosis

and Screening Criteria

-Analysis of differential diagnoses and correct diagnosis-Presentation of correct screening for this case

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Virtual SP

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Virtual SP

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Randomized Trial

Control Group Intervention Group

32 Participants assigned to usual workshop and

4 live SP cases

55 Participants who attended CME workshop in Tampa, Florida

23 Participants assigned to usual workshop and

2 live + 2 virtual SP cases

Randomization

Pre-workshop survey and assessment

Large-group bioterrorism simulation exercise

Didactic lecture

Live SP cases:PTSD

Sub-diagnostic distressASD

Bereavment

Virtual SP cases:PTSD

Sub-diagnostic distressLive SP cases:

ASDBereavment

Post-workshop survey and assessment

Risk communication exercise

Page 12: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Performance by CaseControl N=32

%Correct

Virtual N=23

%Correct

PTSD Pre 72 81

Post 72 83

% Improvement 0 (-22,22) 2 (-25,21)

Sub-diagnostic distress Pre 38 27

Post 63 83

% Improvement 25 (1,48) 56 (31,80)

Acute Stress Disorder Pre 28 41

Post 59 83

% Improvement 31 (8,54) 42 (16,67)

Bereavement Pre 78 86

Post 84 87

% Improvement 6 (-17,23) 1 (-21,19)

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

• Encounters using Virtual Patients had equivalent impact

on learners when compared to those exposed to live

cases.

• Objective measures of performance, knowledge, and

diagnostic abilities were equivalent between live and

virtual standardize patients

• The VP may be superior in certain specific applications.

• An escalating series of simulations from Virtual > actor >

live clinical setting may be most appropriate

Conclusion

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Surgery Problem• Environment: Face-to-face small group teaching sessions held

weekly as part of 8-week surgical clerkship

• Problems:

• Faculty didn!t get to know students enough to assess knowledge

and clinical reasoning ability with such limited interaction

• Current health care delivery environment limited:

• Students! ability to observe a full breadth of cases along the

continuum

• Students! opportunity to participate in case discussions and

work collaboratively to develop a differential diagnosis

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Surgery Solution: CC

• On-line structured chat room to facilitate discussion

• Among students

• Between students and tutorial leaders

• Blended learning

• Goal for students

• Demonstrate their clinical reasoning

• Gain exposure to wider variety of cases

• Work within a collaborative work environment

Page 13: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Surgery CC

• One classroom per tutorial group

• Faculty member and 8-10 students

• Students assigned roles

• Discussion leader posts case and leads discussion

• Summarizer wraps up discussion and becomes the next!s week!s discussion leader

• Participants post twice per week (ideally)

• Faculty member facilitates but does not lead discussion

• Notified by e-mail whenever a student posts

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Discussion Leader

talks with Faculty

Facilitator about

case

Discussion Leader

posts case

Students discuss

case

Summarizer

wraps up

discussion

Friday-Saturday Sunday Monday-Friday Saturday

At least 2 posts

per student

Faculty facilitator contributes

to correct mistakes, post

stimulating questions and

encourage participation

Cyber Classroom Structure

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Surgery CC• What we!ve learned

• Students cut and paste if not strictly

directed not to do so and reminded

• Structure is important

• Must oversee or it doesn!t happen

• Competitive vs. Collaborative

• Some students perceive the classroom

as a place for a few to show off and

dominate, leaving little room for others to

participate

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

I’ve heard the exact opposite, that any inguinal hernia should

be repaired unless the patient isn’t a surgical candidate for

other reasons. Since we’ve hear 2 completely different

answers, I decided to do a lit search…the review showed the

risk of strangulation was greatest just after the onset of the

hernia…

In clinic, many patients present with hernia and ask

if they need them repaired. I’d be interested to hear

what others have heard regarding this but the

answer I’ve learned is – not really. Any thoughts?

In response to the topic Steve and Molly addressed, I’ve heard

that both perspectives should be explained to the patient and the

patient should make the decision about whether or not to undergo

surgery, without the physician really making a strong

recommendation either way.

The definitive treatment for all hernias is

surgical repair…the risk/benefit ratio favors

surgery for the vast majority of patients.

Student 1

Student 4

Student 3

Student 2

Page 14: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Cyber Classroom Evaluation

• Pattern of discussion

• Student-centered vs. faculty-centered

discussion

• Evidence that the students are working

collaboratively

• A Handbook for Group Discussion Leaders: Alternatives to

Lecturing Medical Students to Death, Neal Whitman and

Thomas Schwenk; University of Utah School of Medicine,

Salt Lake Utah 1983

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Preliminary Results

• Looked at 19 classrooms over 6 rotations

• Weeks 3 and 6 for each classroom

• Quality Scores

• Pattern Scores

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

C

B

E

G

1

2

45

3

6

A

D

8

F

7

H

9

A

= Quality Level 1

= Quality Level 1.5

= Quality Level 2

= Quality Level 2.5

= Quality Level 3

= Quality Level 3.5

= Quality Level 4

= Participant

= Discussion Leader

= Faculty

= Summarizer

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

G

D

E

D

1

2

4 5 3

6

C

H

A

F

10

8

7 9

C

B A11

3

C

E13

12

= Quality Level 1

= Quality Level 1.5

= Quality Level 2

= Quality Level 2.5

= Quality Level 3

= Quality Level 3.5

= Quality Level 4

= Participant

= Discussion Leader

= Faculty

= Summarizer

Page 15: Cyber learning: Theory and application

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU Cyber Classroom

Student Assessment• Quantity and quality of student posts, as

evaluated by Faculty Facilitator

• Considered in conjunction with

participation in small group teaching

session

• Tutorial and cyber classroom are 15% of the

students! grades

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU Faculty Feedback

• In a survey of 10 faculty facilitators:

• More than half stated that the classroom improved the

quantity and quality of conversations among students and

between students and faculty

• More than half stated the classroom met its goals to:

• Expose students to a wider variety of cases

• Increase students! opportunity to learn collaboratively and

function within a collaborative work environment

• BUT

• More than half did not think the cyber classroom had an

impact on students! clinical reasoning skills

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

NYU Student Feedback• In focus group with 8 medical students:

• Half felt the cyber classroom was a good

way to get to know their tutorial leader

• Half felt the classroom did not foster

conversation and was just a way for some

students to show off

• All felt that faculty participation was critical

• All wished the time on cyber classroom

could instead be used for more face to

face meetings with faculty

NYU School of Medicine | Medical Informatics | Advanced Educational Systems

Future Directions