3. 講 演クラーク 教授

南カリフォルニア大学「 V Pを 活 用 し た 臨 床 推 論 教 育 に つ い て 」

Prof. Glenn Clark
University of Southern California“How well does a Virtual Patient work to improvea students' clinical reasoning skills?”
この講演資料 (ＰＤＦ、ビデオ）は以下でご覧いただけます昭和大学 歯学部 スペシャルニーズ口腔医学講座 歯学教育学部門 Web サイト

http://dedu.showa-u.ac.jp/project/report_ws_03.html

“Does a Virtual Patient experience improvestudents' clinical reasoning skills?
「仮想患者（VP）で学習することで、臨床推論能力は向上するか？」
Glenn Clark, DDS, MS
Director, Advanced Program in
Orofacial Pain and Oral Medicine
Professor, Ostrow School of Dentistry of
University of Southern California

略歴
Dr. Clark is Professor in the Division of Diagnostic Sciences, Herman Ostrow School of
Dentistry of USC. He is Director of the Center for Orofacial Pain and Oral Medicine andthe Founding Graduate Program Director of the dual certificate residency program in
Orofacial Pain and Oral Medicine at USC. Dr. Clark published over 145 research
articles, review papers, and chapters in textbooks. He is Past-President of the
Neuroscience Group of the International Association of Dental Research, the Southern
California Association of Dental Research, and the Association of University
Temporomandibular and Orofacial Pain programs. He is currently a member of theprestigious Council on Scientific Affairs of the American Dental Association. He has
been awarded membership as a Diplomate of the American Board of Orofacial Pain
(since 1996) and was voted alumnus of the Year of the UCLA School of Dentistry (1990).
Dr. Clark was awarded the Pierre Robin Award for Academic Excellence (2001) by the
Academy of Dental Sleep Medicine and the fellowship in the American Academy of Oral
Medicine (2004). In 2006, he was given a lifetime achievement award by the American
Academy of Orofacial Pain.

講演要旨
医学教育のトレンドとして患者シミュレーションについての論文発表は激増している。
患者シミュレーションの目的は、初心者をエキスパートにするために教育を行う事である。クラーク教授は 1988 年に VP 教育を始めた(Clark GT, Koyano K, Nivichanov A.
Case‐based learning for orofacial pain and temporomandibular disorders. J Dent
Educ.1993 Nov;57(11):815‐20.)。コンピューター症例ベースシステムで顎関節症症例における 5 症例の試験点数は症例を重ねる毎に上昇した。2003 年に UCLA から USCに転勤した。2003 年の時点で、多くの医療系大学で VP を採用していた。クラーク教
授は分岐型のアルゴリズムを用いた Web-based

VP システムを採用した。この第 2 世代システムで授業を行ったところ、VP 症例を完了させるまでの時間は症例を重ねると短縮したが、いわゆるマンネリ化して、あまり教育効果が見られなかった。そこで、第3 世代の Virtual World‐based VP System

すなわち３D 仮想現実内の３D

自律的 VP を開発した。この VP を用いて第 1 症例から第 4 症例学生と専門医における平均質問数を比較すると、第 4 症例では同一になった。しかし臨床推論の点数は専門医
は 70％だったが、学生は 50%であった。臨床見学の前後における臨床推論の成績は、見学前の平均が 67％、見学後の平均が 76％と見学後有意に上昇していた。VP による学習は、医学部学生に、どのように新患の医療面接と診断を行うかを教育するのに有効か？という問に対しては、模擬患者と同等であると評価された。また VP が模擬患者よりも優れている場面はあるか？という問に対しては状況に応じて模擬患者より優れている場合もあると考えられた。また VP で様々な診察結果と検査結果を総合して、診断をつける課題で、学生に時間制限を設けると誤答率が高くなることが証明された。医療面接の結果から立てた仮診断をより確かなものにするために、検査を実施することは診断精度を向上させるかという問いに対しては、追加の検査結果が医療面接から推論した診断と一致しない場合に診断の精度を向上させるという結果が得られた。仮説の検証を繰り返すことが診断能力の向上につながるかという問いに対しては、PBL よりも効果的であるという結果が得られた。
VP を用いて学習することで、学生の臨床推論能力は向上するかという問いに対しては、
臨床推論能力を向上させるという結果が得られた。将来的にはこの訓練が、医療過誤の減少、医療費の削減、医療職種の教育費削減に効果がある事を期待している。
The End! Thank you & remember“The future is ours to create!”
クラーク教授の講演ビデオは以下でご覧になれます。
http://dedu.showa-u.ac.jp/project/report_ws_03.html

“Does a Virtual Patient
experience improve students'
clinical reasoning skills?"

仮想患者（VP）で学習することで、学生の臨床推論能力は向上するか？

Dr. Glenn Clark
Dr. Luciano Nocera
Ostrow School of DentistryUniv. Southern Calif. [USC]

Dr. Glenn Clark
Dr. Luciano Nocera
Ostrow School of DentistryUniv. Southern Calif. [USC]

南カリフォルニア大学

University of Southern California

医学教育のトレンド（傾向）Trends in Medical Education

Medlineでの論文発表の変化 / ５年
5000

4500

4500

4000

4000

3500

3500

3000

教育ゲーム
Educational Games

3000

Educational Games

2500

Patient Simulation

2000
1500

論文数

Number of Publications

Change in Medline Publications/5 yrs
5000

2500

Patient Simulation

2000

患者シミュレーション

1500

?

1000
500
0

?

1000
500
0

1980‐1984

1985‐1989

1990‐1994

1995‐1999

2000‐20042005‐20091980‐1984

1985‐1989

1990‐1994

1995‐1999

2000‐20042005‐2009

Alternate Title:
How long for a Novice tobecome an Expert?

代行タイトル:
初心者がエキスパートとなるのに、どれだけの時間がかかるか？

? If ever!

? なれるのであれば…

Design#1: First Generation stand alone
Computer‐based VP System

Q#1: When did we begin tocreate Virtual Patients?
Project begun in 1988

Clark GT, Koyano K, Nivichanov A. Case‐based learning for
orofacial pain and temporomandibular disorders. J Dent Educ.
1993 Nov;57(11):815‐20.

Final Score on 5 Consecutive TMD Cases
using a Computer Case‐Based System(3rd Year DDS students [n = 65])

1st Design: Linear Interactive Patient Scenario
90

MedicalQuestionnaires

Medical History
Interview andExamination

Diagnostic Tests

Diagnoses &Treatment(with score based
feedback)

80

70

Percent of Maximum Score

1. User clicks question (pre‐determined)
2. Patient gives answers (data)
3. Data gathered is used to make decisions
4. Each decision = Yes/No result and Final Score.
5. Understand meaning of data = make better decisions.

60

50

40

30

20

10

0
1

2

3

TMD Cases: 1 to 545

10 yr delay: I moved in 2003!

コンピューター症例ベースシステム
顎関節症症例における5症例の試験点数(歯学部３年生 [n = 65])
90

Percent of Maximum Score

80

70

60

50

40

30

20

10

0
1

2

3

4

5

TMD Cases: 1 to 5

USC Got Games

USCはゲームが凄い！

June 27, 2010 10:35 AM
Video game design has definitely
entered the educational mainstream,
to the extent that the Princeton
Review has created a ranking of the
50 best universities in the field.

June 27, 2010 10:35 AM
ビデオゲーム制作の教育が、主流大学
教育として組み込まれるようになった。
プリンストン・レビューがこの分野でのランキングトップ５０を発表したほどだ。
USCはそのランキングで１位である。

Heading the best list is USC.

USC Game Pipe Laboratory

2 Basic Virtual
Patient Styles

USC ゲームパイプ・ラボ

仮想患者の２つのスタイルBranching, linked‐page
Non‐Autonomous
2D VPs’3D Autonomous
VPs in a 3D virtualworld

分岐的、ページリン
ク、非自律的２D仮想患者

３D仮想現実内の３D
自律的仮想患者

Pros:‐ Button or narrative input (or both).
‐ Faster to operate (usually no typing)‐ Based on commercial software.
‐ Easier to create and maintain!
Cons:‐ Still text/image on a screen (engaging?)‐ Does not force student to generate
their own questions (engaging?)!

Pros:‐ Makes student generate own Q’s‐ More Engaging and Immersive‐ All dialog is captured for analysis
Cons:‐ Question‐Answer mismatch (~20%)‐ Slower so students aren’t as thorough‐ Harder to create and maintain!
‐ Need C#, JAVA programmer/animator特徴:‐ ボタンまたは文章による入力（両方）‐ 操作時間が短い (タイプの必要無し)‐ 既製ソフトを使用‐ 作成とメンテナンスが容易
欠点:‐ 文章と静止画像のみ表示 (参加的?)‐ 学生自身で質問内容を考えさせることがない。 (参加的か?)!

特徴:‐ 学生自身が質問内容を考える‐ より魅力的で夢中にさせる‐ すべてのやり取りが記録・分析可能
欠点:‐ 質問と回答のミスマッチ(~20%)‐ 時間がかかり、学生は大雑把になる‐ 作成とメンテナンスが煩雑‐ C#、JAVA プログラマー・アニメーターが必要Q#2: In 2003, what VP systems
were being used elsewhere?

Q#2: どのような仮想患者システムがすでに開発され実用されているか

Dr. Uno Fors (Professor)

Botezatu M, Hult H, Tessma MK, Fors U. Virtual patient
simulation: knowledge gain or knowledge loss? Med Teach.
2010;32(7):562‐8.
Study: examine the value a VP vs SP on knowledge retention
1. RCT on early and delayed assessments of knowledge
2. Gp 1= learning with VP method; Gp 2= traditional SP method
3. 49 medical students exposed to both VPs and regular exams
immediately after and 4 months after course.
4. Topics were hematology or cardiology & scored on 0‐10 scale
Results:
1. Mean difference for early VP vs Traditional on both outcomes
assessed and found statistically higher scores with VP learning
2. Gp 1 scored higher (10‐22%) vs traditional learning Gp2.
CONCLUSION: Our results indicate better retention with VP
than with traditional learning methods.

Multiple Branching Interactive Scenarios ($$$)
1. Patient images, audio, text files accessed with branching‐tree structure.
2. These games are much longer because branching allows for great variation.
3. You could have the encounter show Sx progression with response to Tx.
4. Major drawback of branching scenarios is complexity and authoring effort.
5. Each branch causes a potential geometric increase in authoring effort.
More
Branches

Correct
Diagnostic
Test
MedicalQuestion‐nairesMedicalHistory
Interview
and Exam
Incorrect
Diagnostic
Test

Correct
Diagnosis +Correct Tx

ResultPositive

Correct
Diagnosis +Incorrect Tx

ResultNegative

Incorrect
Diagnosis +Correct Tx

ResultNegative

Incorrect
Diagnosis +Incorrect Tx

ResultNegative

More
Branches
More
Branches
More
Branches
More
Branches
More
Branches
More
Branches
More
Branches

Dr. Uno Fors (Professor)

Q#3: Since then how many new
VP systems have been created?

Med. College of Georgia
and Univ of Florida

Institute for Creative
Technology, USC

Univ. Minnessota andVitalSims Inc.

Texas A+M University& Breakaway, Inc

Duke University andApplied Res. Associates, Inc.

Stanford University
CliniSpace

2012: Pulse!! Game2012: HumanSim Game

More Features: but can they
be implemented on a tablet?

2. Dialogue manager lowers misrecognition;difficult to achieve; processor intensive!

1. Functioning Face Engine: more
realistic; processor intensive!

3. Speech‐to‐Text Engines
4. Text‐to‐Speech Engines‐ Both are nice features‐ Slows game play
‐ Processor intensive‐ Requires training

Q#4: What did I elect to do
in Virtual Patients at USC?

2012: CliniSpace Game

Dean’s decision: “Duh!”$70,000 (upgrades + IT support)$1,000 (durable + students buy!)

Q#4: USCで、VPを用いて何を実現したか?

Design#2: Next Generation Web‐based
VP System

Branching algorithm style VP

Branching
Web Pages

Not much improvement- Cases to complex- No chance of failure- No reward for passing

Q#5: How do we engage thestudent more?

Q#5: 生徒にどのように、より積極的に参加させるかDesign#3: 3rd Generation Virtual World‐based VP System

Clark GT, Suri A, Enciso R. Autonomous virtual patients in dentistry: systemaccuracy and expert versus novice comparison. J Dent Educ. 2012
Oct;76(10):1365‐70.
Study: examine performance of experts and novices on a virtual patient
1. VP designed as autonomous system using a NLP to recognized questions.
2. User interface was based in virtual world with movable avatars.
3. Experts were 10 boarded or board‐eligible experts (7M:3F; age 40±11 yrs).
4. Novices were 26 fourth‐year dental students (15M:11F; age 27±3 yrs).
5. 4 VP cases/user with an Orofacial Pain or Oral Mucosal/Salivary Disease.
6. After interview and mock exam users selected best DxTest, Dx, Tx and Rx.
Results:
1. Mean misrecognition rate for the NLP was between 13 and 19 percent.
2. Significant group difference existed for final total score achieved (Exp>Nov)
3. Significant group difference existed for # of DxTests ordered (Exp<Nov)
3. Significant group difference existed for # of Rx ordered (Exp<Nov)
4. Novices were very positive about the value of VP educational experience.
5. Groups asked almost the same questions and had similar encounter times.
Conclusions: While they spent equal time and asked almost same questions,experts consistently scored higher and ordered fewer diagnostic tests andmedications than the novices. This suggests clear difference inunderstanding of meaning of interview and mock exam data collected.

Clark GT, Suri A, Enciso R. Autonomous virtual patients in dentistry: systemaccuracy and expert versus novice comparison. J Dent Educ. 2012
Oct;76(10):1365‐70.
Study: examine performance of experts and novices on a virtual patient
1. VP designed as autonomous system using a NLP to recognized questions.
2. User interface was based in virtual world with movable avatars.
3. Experts were 10 boarded or board‐eligible experts (7M:3F; age 40±11 yrs).
4. Novices were 26 fourth‐year dental students (15M:11F; age 27±3 yrs).
5. 4 VP cases/user with an Orofacial Pain or Oral Mucosal/Salivary Disease.
6. After interview and mock exam users selected best DxTest, Dx, Tx and Rx.
Results:
1. Mean misrecognition rate for the NLP was between 13 and 19 percent.
2. Significant group difference existed for final total score achieved (Exp>Nov)
3. Significant group difference existed for # of DxTests ordered (Exp<Nov)
3. Significant group difference existed for # of Rx ordered (Exp<Nov)
4. Novices were very positive about the value of VP educational experience.
5. Groups asked almost the same questions and had similar encounter times.
Conclusions: While they spent equal time and asked almost same questions,experts consistently scored higher and ordered fewer diagnostic tests andmedications than the novices. This suggests clear difference inunderstanding of meaning of interview and mock exam data collected.

Q#6: What is the difference
between a novice and an
expert?

Q#6: 初心者とエキスパートの違いは何か?

versusversus第１から第４症例までの平均質問数（平均±１標準誤差）を初心者（学生）とOFP/OM専門医で比較

Average # of Interview Questions(mean ± 1 std. error) for 1st to 4th Case
Comparing Novices with OFP/OM Experts
20.00

20.00
18.00
16.00

14.00

14.00

12.00

Expert (n = 10)

10.00

Novice (n = 26)
8.00

6.00

Number of Questions

16.00

12.00

Novice (n = 26)
8.00
6.00
4.00

2.00

2.00

0.00

Expert (n = 10)

10.00

4.00

0.00

Case1

Case2

Case3

Case4

Case1

90.00%

80.00%

80.00%

70.00%

70.00%

60.00%

Expert (n = 10)
Novice (n = 26)

40.00%
30.00%
20.00%
10.00%

Percent of Maximum Score

90.00%

50.00%

Case2

Case3

Case4

第１から第４症例までの平均標準化された最終点数(平均± 1 標準誤差) を初心者（学生）とOFP/OM専門
医で比較

Average Normalized Final Choice Scores (mean ± 1
std. error) for 1st to 4th Case Comparing Novices
with OFP/OM Experts

Percent of Maximum Score

Number of Questions

18.00

60.00%
50.00%

Expert (n = 10)
Novice (n = 26)

40.00%
30.00%
20.00%*** (p<.05)

10.00%

0.00%

*

*

* (p<.05)

0.00%

Case1

Case 2

Case 3

Case 4

Design#4: Fourth Generation hosted in Unity 3D

Case1

Case 2

Case 3

Case 4

Q#7: What is the value ofclinical observation?

Q#7: 臨床見学の重要性とは？

Average Normalized Final Choice Scores (mean ± 1
std. error) for 1st to 5th Case Comparing Novices
with and without Clinical Rotation Experience in
OFP/OM (n = 50/gp)

第１から第５症例までの平均標準化最終点数（平均± 1 標準偏差）を初心者（学生）で
OFP/OMクリニックの臨床見学を経験した前後で比較 (n = 50/gp)

85%

85%
R² = 0.8007

R² = 0.8007
80%

75%
70%

Novices (after
rotation)

65%
60%
R² = 0.1482

Novices (before
rotation)

55%
50%

Percent of Maximum Score

Percent of Maximum Score

80%

75%
70%

Novices (after
rotation)

65%
60%
R² = 0.1482

Novices (before
rotation)55%
50%

1st case

2nd case

3rd Case

4th case

5th Case

Problem: What are they learning by
watching? (Some but not enough!)

Solution: Add VP cases to rotation.

1st case

2nd case

3rd Case

4th case

5th Case

問題点: 見る事でなにを学んでいるのか？ (多少、しかし不十分)

How about other
Virtual Patientresearch?

Q#8: 仮想患者による学習
は、医学部学生に、どのよ
うに新患の医療面接と診断を行うかを教育するのに有効か？

Q#8: Can Virtual Patients beused to successfully teachmedical students how tointerview and diagnose anew patient?
Answer: Yes and it was rated
equivalent to SP system

解答：はい、模擬患者と同等であると評価されました。

Stevens A, et al. The use of virtual patients to teach medical students history
taking and communication skills. Am J Surg. 2006 Jun;191 (6):806-11.
Study: assess virtual patient with acute abdominal pain as teaching model.
1. Goal: VP to teach medical students history-taking and communication.
2. Setting: Wall projection of VP in an examination room.
3. Context: student reviews patient information on tablet before encounter.
4. Task: student directed to take a history and develop differential Dx.
5. Method: 2 linked PCs, projector, 2 Webcams tracking head/hand motion
6. VP responds with specific answers/gestures after each question.
7. VP response were based on Standardized Pts responses to students.
8. Subjects: 20 students (after voice recognition training) asked questions.
9. Outcomes: Maastricht Simulated Patient Assessment method.
10. All participants had prior experience with real SPs.
Results:
1. The VP correctly recognized 60% of the student's questions.
2. Student rated VP favorable, particularly the feedback by virtual instructor.
3. Overall student rating was between 6.5±1.6 and 7.5±1.2 on 10 point scale.
4. Performance on VP compare favorably to a student scores on real SPs.
Conclusion: VP technology provided students with a controlled, secure,safe learning environment with the opportunity for repetitive practice.

Stevens A, et al. The use of virtual patients to teach medical students history
taking and communication skills. Am J Surg. 2006 Jun;191 (6):806-11.
Study: assess virtual patient with acute abdominal pain as teaching model.
1. Goal: VP to teach medical students history-taking and communication.
2. Setting: Wall projection of VP in an examination room.
3. Context: student reviews patient information on tablet before encounter.
4. Task: student directed to take a history and develop differential Dx.
5. Method: 2 linked PCs, projector, 2 Webcams tracking head/hand motion
6. VP responds with specific answers/gestures after each question.
7. VP response were based on Standardized Pts responses to students.
8. Subjects: 20 students (after voice recognition training) asked questions.
9. Outcomes: Maastricht Simulated Patient Assessment method.
10. All participants had prior experience with real SPs.
Results:
1. The VP correctly recognized 60% of the student's questions.
2. Student rated VP favorable, particularly the feedback by virtual instructor.
3. Overall student rating was between 6.5±1.6 and 7.5±1.2 on 10 point scale.
4. Performance on VP compare favorably to a student scores on real SPs.
Conclusion: VP technology provided students with a controlled, secure,safe learning environment with the opportunity for repetitive practice.

Q#9: Are there somesituations where a Virtual
Patient is better than aStandardized Patient?

Q#9: 仮想患者が模擬患者よりも優れている場面はあるか？

Answer: In one specific case
VP was clearly better but
probably not overall!

解答：ある特定の条件では仮想患者は明らかに模擬患者より優れていたが、恐らくすべての状況でそうとは言えないだろう。

Wendling AL, et al. Virtual humans versus standardized patients: which leadresidents to more correct diagnoses? Acad Med. 2011 Mar;86(3):384-8.
Study: compare prevalence of correct Dx using similar SP vs VP cases.
1. Case included Hx features of OSA (snoring, hypersomnolence, observed
apnea, hypertension, and obesity).
2. Three SPs (in 2008) and one VP (in 2009) were used in this study.
3. VP had physical appearance of OSA case (morbidly obese and large neck).
4. VP airway images showed redundant soft tissue, large tongue, and tonsils.
5. VP had 259 answers triggered by 849 questions available.
Results:
1. 5 of 21 residents (23.8%) suspected OSA after interviewing the SPs.
2. 11 of 13 residents (84.6%) suspected OSA after interviewing the VP.
3. Difference was statistically significant (OR: 17.6; 95% CI of 2.9-107).
Conclusions: Interviewing a physically appropriate VP versus a trained
but physically incorrect SP resulted a much higher accuracy rate in the
resulting diagnosis.

Wendling AL, et al. Virtual humans versus standardized patients: which leadresidents to more correct diagnoses? Acad Med. 2011 Mar;86(3):384-8.
Study: compare prevalence of correct Dx using similar SP vs VP cases.
1. Case included Hx features of OSA (snoring, hypersomnolence, observed
apnea, hypertension, and obesity).
2. Three SPs (in 2008) and one VP (in 2009) were used in this study.
3. VP had physical appearance of OSA case (morbidly obese and large neck).
4. VP airway images showed redundant soft tissue, large tongue, and tonsils.
5. VP had 259 answers triggered by 849 questions available.
Results:
1. 5 of 21 residents (23.8%) suspected OSA after interviewing the SPs.
2. 11 of 13 residents (84.6%) suspected OSA after interviewing the VP.
3. Difference was statistically significant (OR: 17.6; 95% CI of 2.9-107).
Conclusions: Interviewing a physically appropriate VP versus a trained
but physically incorrect SP resulted a much higher accuracy rate in the
resulting diagnosis.

Q#10: What is effect of time
constraints on diagnosis as
students try to integrate
clinical/lab data during avirtual patient work‐up?
Answer: time constraints fornovices caused higher errors!

Q#10:仮想患者の診査で、時間制限はどのような影響を及ぼすか解答: 初心者に制限時間を設けると、誤答の可能性が高くなるGunning WT, Fors UG. Virtual patients for assessment of medical student
ability to integrate clinical and laboratory data to develop differential
diagnoses: comparison of results of exams with/without time constraints. Med
Teach. 2012;34(4):e222-8.
Study: evaluate effect of time limitations on performance in VP-based exam.
1. Gp 1: 155 2nd yr medical students given unlimited 1 week assess to VP case.
2. Gp 2: 175 2nd yr medical students given same VP case with 3-hr time limit.
Results:
1. Gp 1 (without time constraints) spent 2x more time on VP case than Gp 2.
2. Gp 1 (without time constraints) asked 50% fewer questions than Gp 2.
3. Time constriant students used a "shotgun approach" to try to collect as many"required" inquiries as possible.
4. 91% of Gp 1 (without time constraint) were able to correctly diagnose case.
5. 31% of gp 2 (with time constraint) correctly diagnosed the case.
Conclusion: VP exams, administered with and without time constraint
resulted in very different performance. It is possible that time limitations
unravel student's ability to integrate the new data discovered during theprocess of progressive disclosure.

Gunning WT, Fors UG. Virtual patients for assessment of medical student
ability to integrate clinical and laboratory data to develop differential
diagnoses: comparison of results of exams with/without time constraints. Med
Teach. 2012;34(4):e222-8.
Study: evaluate effect of time limitations on performance in VP-based exam.
1. Gp 1: 155 2nd yr medical students given unlimited 1 week assess to VP case.
2. Gp 2: 175 2nd yr medical students given same VP case with 3-hr time limit.
Results:
1. Gp 1 (without time constraints) spent 2x more time on VP case than Gp 2.
2. Gp 1 (without time constraints) asked 50% fewer questions than Gp 2.
3. Time constriant students used a "shotgun approach" to try to collect as many"required" inquiries as possible.
4. 91% of Gp 1 (without time constraint) were able to correctly diagnose case.
5. 31% of gp 2 (with time constraint) correctly diagnosed the case.
Conclusion: VP exams, administered with and without time constraint
resulted in very different performance. It is possible that time limitations
unravel student's ability to integrate the new data discovered during theprocess of progressive disclosure.

Q#11: Does testing the
hypothesis derived from
history, as you perform exam,
improve diagnostic accuracy?

Q#11: 診査を行う上で、医療面接から得られた仮説を検証する事は診断の精度を向上させるか。

Answer: Yes! [and if additional
data has some inconsistencies
with initial history this may
improve diagnostic accuracy]

解答：はい。もし、追加のデータ
が医療面接と一致しない場合、診断の精度を向上させる。

Coderre S, et al. To think is good: querying an initial hypothesis reduces
diagnostic error in medical students. Acad Med. 2010 Jul;85(7):1125-9.
Study: examine diagnostic reasoning and effect of discordant data.
1. Subjects: 67 first-year medical students asked to diagnose 8 cases.
2. Data: All students got primary data and they formulated initial Diagnosis.
3. Gp-1: received additional data consistent with primary data.
4. Gp-2: received additional data inconsistent with primary data.
5. Students then formulated a final diagnosis.
6. Metric: (1) change in diagnosis (1st to final); (2) accuracy of diagnoses.
Results:
1. Gp-1 (concordant 2nd data): 1st to final stayed same in 84.2% of cases
2. Gp-1: changers equally likely be correct as incorrect (6.9% vs 8.9%)
3. Gp-2 (discordant 2nd data): 1st to final stayed same in 23.3% of cases.
4. Gp-2: changers were much more likely to be correct (76.3% vs 0.4%)
5. Overall, no difference existed in the accuracy of final diagnoses two groups.
Conclusion: These data suggest that querying an initial diagnostic
hypothesis does not harm a correct diagnosis but instead allows students
to rectify an incorrect diagnosis.

Q#12: Does formal training
in the process of“hypothesis testing”
improve diagnosis skills?

Q#12: 仮説を反復検証する
事を正式に訓練すれば、診断能力向上につながるか。

Answer: Yes, IHT better thanPBL trained students!

解答：はい、IHT（仮説の反復検証）はPBLよりも効果的である。

Kahl KG, et al. A randomized study of iterative hypothesis testing inundergraduate psychiatric education. Acta Psychiatr Scand. 2010
Oct;122(4):334-8.
Study: RCT created to compare problem-based learning vs IHT.
1. IHT is iterative hypothesis testing.
2. MCQ used to get baseline assessment of knowledge and skills
3. Subjects: 71 medical students taking class in psychiatry.
4. Two groups: (#1) received problem-based learning (#2) received IHT.
5. Final exams based on scores from MCQ and simulated patient encounter.
Results:
1. Gp 2: IHT-trained students recognized more diagnostic items, comorbid
psychiatric disorders and acute suicidal tendencies than PBL-trained group.
2. No group difference in acquisition of general psychiatric knowledge.
3. No group difference in global satisfaction with the course.
Conclusion: teaching IHT to medical students may enhance their clinicalability to recognize complex disease patterns in psychiatry.

Kahl KG, et al. A randomized study of iterative hypothesis testing inundergraduate psychiatric education. Acta Psychiatr Scand. 2010
Oct;122(4):334-8.
Study: RCT created to compare problem-based learning vs IHT.
1. IHT is iterative hypothesis testing.
2. MCQ used to get baseline assessment of knowledge and skills
3. Subjects: 71 medical students taking class in psychiatry.
4. Two groups: (#1) received problem-based learning (#2) received IHT.
5. Final exams based on scores from MCQ and simulated patient encounter.
Results:
1. Gp 2: IHT-trained students recognized more diagnostic items, comorbid
psychiatric disorders and acute suicidal tendencies than PBL-trained group.
2. No group difference in acquisition of general psychiatric knowledge.
3. No group difference in global satisfaction with the course.
Conclusion: teaching IHT to medical students may enhance their clinicalability to recognize complex disease patterns in psychiatry.

Q#13: How do we measureefficiency and performance?

Q#13: 効率とパフォーマンス
をどのようにして測るか?

Fourth Generation Web‐based + SQL‐DB VP System

Design#5: Fifth Generation Web‐based + SQL‐DB VP System

質問を聞くのに費やした時間
0:12:58

0:11:31

0:11:31

0:10:05

0:10:05

0:08:38

0:08:38

0:07:12

4th Year DDS students

0:05:46

PGY‐1 OFPOM Residents

Minutes

Minutes

Total Time Asking Questions
0:12:58

0:04:19

0:02:53

0:07:12

4th Year DDS students

0:05:46

PGY‐1 OFPOM Residents
0:04:19

Pilot Data!

0:02:53

0:01:26

0:01:26

0:00:00

0:00:00
1

2

3

1

4

2

1st to 4th Case4

全体的な効率スコア[症例の点数/HVQを聞くにかかった分数]

Overall Efficiency Score[Case Score/Min per HVQs]
0.45

0.45

0.40

0.40

0.35

0.35

0.30

0.30

0.25

4th Year DDS students

0.20

PGY‐1 OFPOM Residents
0.15

Efficency Score

Efficency Score

3

1st to 4th Case

0.25

4th Year DDS students

0.20

PGY‐1 OFPOM Residents
0.15

Pilot Data!
0.10

0.10

0.05

0.05

0.00

0.00
1

2

3

1st to 4th Case

4

1

2

3

1st to 4th Case4

Next Generation: Avatar + Web‐based + SQL‐DB VP System

2014: Can we makethe virtual patient
platform system evenbetter?

Design#6: Six Generation: NLP‐Avatar (TTS ??) +Improved Feedback & Case Authoring Features

Currently expanding # of cases insystem and will launch a tablet web‐based system with additional
feedback elements next year!

Final Question: Does a Virtual
Patient experience improvestudents' clinical reasoning
skills.

Linear VP interactions with branching and hypothesis testing

1. Uses modified NLP system accepting new Q’s and menu Q’s.
2. Provides criteria before and feedback after each choice.
3. Gives final screen providing individualized hints retry options(a) failing performance = give general advice & repeat case.
(b) low score: access Q’s asked & suggest new HVQ’s + repeat.
(c) medium score: access FC’s & give criteria for wrong FC’s.
4. Gather 1st & 2nd Dx‐Ho to see if user actively tested Dx‐Ho
Modified NLP
systemMedicalQuestionnaire
and photos

MedicalInterview andExamination

1st Diagnosis
Guess Recorded

2nd Diagnosis
Guess Recorded

Criteria & FBk

Criteria & FBk

Final Feedback(Fail = generaladvice, try again)

Diagnostic
Tests

Final Dx & Tx
Choices

Final Feedback(low score = HVQ
hints, try again)

3rd Dx Analysis: did user
actively test/adjust
hypotheses using data?

Final Feedback(medium score =Give FC hints &try again)

最後の質問:仮想患者（VP）で学習することで、学生の臨床推論能力は向上するか？

Absolutely!

● More VP training = improved case scores.
[would it improve faster with formal IHT training?]● More VP training = better user efficiency.
[need to identify, track + train how to find HVQ’s?]● VP training supplements clinical training?
[would interactive VP be >/= clinic observation?]● VP training can be more engaging.
[which game elements are most important?]---------------other theories which need testing--------------------● Ho1: lower medical mistakes.
● Ho2: reduce unneeded healthcare costs.
● Ho3: reduce cost of training MD/DDS/RN

The End! Thank you & remember“The future is ours to create!”

もちろん!
● VPトレーニング= 症例の点数を改善.
[正式なIHTの練習によりさらなる改善が望めるか?]● VPトレーニング= ユーザーの効率を改善.
[HVQを識別し、追跡する必要がある]● VPトレーニングは臨床訓練の補足となる.
[魅力的な仮想患者システムは臨床見学と同等か？]● VPトレーニングは生徒により魅力的である.
[ゲーム要素をより向上させる必要がある]● Ho: 医療過誤の減少.
● Ho: 不要な医療費の削減● Ho: 医師・歯科医師・看護師の教育費削減

ありがとうございました“未来は我々が創る!”