Transcript Document
Biomedical Engineering Key Content Survey - Results from Round One of a Delphi Study
David W. Gatchell and Robert A. Linsenmeier VaNTH ERC for Bioengineering Educational Technologies and Northwestern University Whitaker Foundation Biomedical Engineering Educational Summit March, 2005 Supported by NSF EEC 9876363
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Why conduct a BME key content survey? Motivation and potential benefits
Motivation
Establish an identity for undergraduate Biomedical Engineers
Improve communication between academic BME programs and industry
Academia – Inform industry of the knowledge, skills and training of BMEs
Industry – Inform academia of the knowledge, skills and training expected
Benefits
More industrial positions for BMEs
Each graduate does not have to explain curriculum Recognition that BME degree is ideal preparation for at least some industrial positions.
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Delphi study - Overview
In General:
An iterative process for collecting knowledge from, and disseminating
results to, a group of experts Four steps (repeat steps #2 and #3 to attempt to reach consensus)
1.
2.
Develop a set of questions on a topic. Experts give opinions on topics; suggest new ideas that were missed 3.
4.
Explore and evaluate inconsistencies uncovered in step 2 Disseminate findings, or revise questions and go back to 2 Key point is that experts remain anonymous
Our Study: A set of three surveys
Round 0:
Select concepts from VaNTH taxonomies; reviewed by domain
experts
Round 1:
Survey BME industrial representatives and faculty. Asked
participants to rate relevance of concepts important for
ALL undergrads in BME
, and make suggestions of concepts missed
Round 2
:
Refine and update list of concepts and resubmit to the above groups for further evaluation
Round 3:
Question proficiencies expected (e.g., using Bloom’s Taxonomy)
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Overview of the key content survey, round one
Utilized an online survey tool to query ~274 concepts:
Eleven bioengineering domains (including design) Physiology, cellular biology, molecular biology and genetics, biochemistry
Mathematical modeling, statistics, general engineering skills (e.g., computer programming)
Survey divided in two parts, each with half the domains:
Total number of participants, n = 136
Part one: Academia – 42, Industry – 25 Part two: Academia – 35, Industry – 23
Participants were asked to:
Provide demographic information
Employer, Job Title, Responsibilities, Years of Experience
Self-assess level of expertise in each domain (e.g., Biomechanics) Rate the importance/relevance of each concept to a BME core curriculum
Suggest concepts that had not been included
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Overview of the key content survey
Concepts rated on 5 point Likert Scale
1- very unimportant for all BMEs 5 – very important for all BMEs
Mean ratings across concepts similar for industry and academia
Academia (n=77) mean and SD rating: 3.71 ± 0.52
Industry (n=48) mean and SD rating: 3.75 ± 0.41
Domains Investigated:
Bioinformatics, bioinstrumentation, biomaterials, biomechanics, biooptics, biosignals and systems, medical imaging, thermodynamics, transport (fluid, heat, mass)
Cell biology, biochemistry, molecular biology and genetics, physiology
Statistics, general engineering
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Some concepts included as “Ringers” Expected to have low rating
Concept Statistical Physics (e.g., Bose-Einstein statistics; Fermi-Dirac statistics) Statistical Physics (e.g., Partition function; statistical representation of entropy; population of states) Comparative Genomics (e.g., ortholog and paralog genes; gene fusion events) Dynamical Instability and Chaos Unsteady state mass diffusion equation (e.g. Fick’s second law; production and consumption; boundary conditions; different geometries; multiple layers) Rating (Academia) 2.32
Rating (Industry) 2.58
2.82
2.50
2.59
3.42
2.58
2.94
3.11
3.29
All except unsteady state mass diffusion equation met expectations
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Some concepts included in more than one domain to check consistency of response
Two values shown are ratings when concepts are included in different domains Generally good agreement, but rating sometimes depended on context Concept Ratings (Academia) Ratings (Industry) Databases - Interfaces and Structures (e.g., MySQL, relational tables, simple queries, PERL, CGI, DBI) Signal Processing to Reduce Noise (e.g., signal-to-noise ratio; signal averaging) Properties of Systems (e.g., boundary, surroundings, universe) Electrochemical Potential, Nernst Potential, Fick's Law 2.29/2.66
4.24/3.83
3.88/3.97
4.09/4.23
3.22/3.68
4.17/4.14
3.63/3.88
3.54/4.00
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Results: Highest rated eng’g concepts – Academia Orange concepts are from statistics and general engineering Concept Hypothesis Testing (e.g., paired and un-paired t-tests; chi-square test) Rating 4.69
4.68
Principles of Statics (e.g., forces; moments; couples; torques; free body diagrams) Descriptive Statistics (e.g., mean, median, variance, std deviation) Circuit Elements (e.g., resistors, capacitors, sources, diodes, transistors, integrated circuits) DC and AC circuit analyses (e.g., Ohm's and Kirchoff's laws) Mathematical Descriptions of Physical Systems (e.g., functional relationships, logarithmic, exponential, power-law; ODEs; PDEs) Strength of Materials (e.g., stress, strain; models of material behavior) Pressure-Flow Relations in Tubes and Networks (e.g., flow rate = [change in pressure]/resistance; Poiseiulle relation; Starling resistor) Measurement concepts (e.g. accuracy, precision, … Regression analysis Forces and pressures in fluids (e.g. shear, normal, surface tension… 4.63
4.56
4.56
4.54
4.53
4.51
4.50
4.49
4.49
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Results: Highest rated eng’g concepts – Industry Orange concepts are from statistics and general engineering Rating Concept Descriptive Statistics (e.g., mean, median, variance, standard deviation) Measurement Concepts (e.g., accuracy, precision, sensitivity; error analysis - sources, propagation of error) Hypothesis Testing (e.g., paired and un-paired t-tests; chi-squared) Probability Distributions (e.g., normal, Poisson, binomial) 4.76
4.71
4.65
4.62
Strength of Materials (e.g., stress, strain; models of material behavior) 4.57
Fundamental Properties of Polymers, Metals and Ceramics Product Specification (e.g., requirements, design, reliability, evolution/tracking of the product) Principles of Statics (e.g., forces; moments; couples; torques; free body diagrams) Mechanical Properties of Biological Tissues (e.g., elastic; viscoelastic, hysteresis, creep, stress relaxation) Data Acquisition (e.g., sampling rates and analog-digital conversion; Nyquist criterion; aliasing) 4.50
4.45
4.43
4.43
4.39
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Results: lowest rated concepts
some from “Ringers”
Academia
Databases - Interfaces and Structures (e.g., MySQL, relational tables, simple queries, PERL, CGI, DBI) Statistical Physics (e.g., Bose-Einstein and Fermi-Dirac statistics) Artificial Intelligence (e.g., artificial neural networks, fuzzy logic) Analysis of Phylogenetic Trees, Molecular Evolution Comparative Genomics (e.g., ortholog and paralog genes; gene fusion events) Structural Prediction and Molecular Design
Industry
Statistical Physics (e.g., Partition function; statistical representation of entropy; population of states) Statistical Physics (e.g., Bose-Einstein; Fermi-Dirac statistics) Artificial Intelligence (e.g., artificial neural networks, fuzzy logic) Storage Instruments and their properties (e.g., tape, disk, memory) Comparative Genomics Root Locus Plots (e.g., definition, properties, sketching) 2.29
2.32
2.33 2.47
2.50
2.53
2.58
2.58
2.78
2.89
2.94
2.95
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Results: Industry - Academia agreement
Distribution of mean ratings of all concepts 5.00
4.00
3.00
Concept Ratings 2.00
2.00
2.50
3.00
3.50
Academia 4.00
4.50
5.00
Most concepts rated highly. Few ringers in survey.
All traditional domains had some highly rated concepts. Cutoff level for inclusion in recommended undergrad curriculum still to be determined on basis of further analysis and round two.
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Results: Industry – Academia Agreement Differences in means (A-I)
0.75
0.50
0.25
0.00
-0.25
-0.50
-0.75
-1.00
-1.25
0
Design
50 100 150 Concept # 200 250
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Results: Discrepancies in design concepts
Rankings - BME Design Concepts (A comparison of opinions from Academia and Industry) Decision Matrix Approaches to Initial Design Design for Manufacturing and Assembly Software for Design and Project Management (e.g., flowcharting; Gannt and PERT charts) Software and Process Design Considerations Risk Analysis/Hazard Analysis Computer-Aided Design Considerations Human Factors Issues/FDA
1.00
Industry Academia 2.00
3.00
4.00
Mean Ranking (all participants)
5.00
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Results: A comparison of general engineering concepts
Databas e s - Inte rface s and Structure s (e .g., MySQL, re lational table s , s imple que rie s , PERL, CGI, DB I) Artificial Inte llige nce (e .g., artificial ne ural ne tworks , fuzzy logic, e tc.) Familiarity with Multiple Computing Platforms (e .g., Windows , Macintos h, LINUX, UNIX) Scaling and Dime ns ional Analys is
Ringer Significant Deltas
Nume rical Diffe re ntiation and Inte gration Ge ne ralize d Ohm's Law (i.e ., driving force -flow-re s is tance conce pt) Compe te ncy with (at le as t) One Programming Environme nt (e .g., Matlab, Mathe matica, C, C++, FORTRAN) Es timation and Orde r of Magnitude Calculations Me as ure me nt Conce pts (e .g., accuracy, pre cis ion, s e ns itivity; e rror analys is - s ource s , propagation of e rror)
1.00
2.00
3.00
4.00
Mean Rating (all participants)
Industry Academia 5.00
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Results: Biology Domains
5.0
4.5
4.0
3.5
3.0
Biochemistry Cell Biology Molecular Biol.
Bioinformatics Unity slope 2.5
2.5
3.0
3.5
4.0
4.5
Rating of Concept - Academia 5.0
Good agreement on the whole
All biology areas important, but industry sees molecular biology as being more important than academia
Bioinformatics generally scored low, but industry feels that it is more important than academia does
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Results: Largest biology discrepancies
Concepts Flow of Genetic Information (i.e., DNA to RNA to Protein) Methods for Determining Macromolecular Structure (e.g., NMR...) DNA Microarrays Biological Networks (e.g., genetic networks...) Structural Prediction and Molecular Design (e.g., homology modeling and prediction of macromolecular structures and interactions) Academia Industry Academia - Industry 4.5
4.1
0.44
3.5
3.4
3.2
2.5
4.2
3.8
3.7
3.3
-0.70
-0.42
-0.46
-0.72
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Results: Physiology (82 concepts)
Very large span within domain
Generally good agreement
Cardiovascular, neural, cellular physiology concepts rated highly
Digestive, renal, parts of endocrine rated low 5.0
4.5
4.0
3.5
3.0
2.5
2.5
Physiology Unity slope 3.0
3.5
4.0
4.5
Rating of Concept - Academia 5.0
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Results: Largest physiology discrepancies between academia and industry
Concepts Cellular Anatomy (e.g...) Academia 4.56
Industry 4.14
Academia - Industry 0.41
Membrane Dynamics (e.g....) 4.44
3.95
0.49
Processes of the Kidney (e.g....) 4.26
3.85
0.41
Renal Filtration (e.g...) 4.03
3.60
0.43
Homeostasis of Volume and Osmolarity Water Balance and Urine Concentration 4.03
3.73
3.50
3.25
0.53
0.48
Platelets and Coagulation (e.g....) Sodium Balance and the Regulation of ECF Volume (e.g....) 3.21
3.76
3.75
3.15
-0.54
0.61
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Results: Should the following foundational courses be required?
Comparison of Responses - Industry and Academia Physics - Waves and Optics Physics - Electricity and Magnetism Physics - Mechanics Chemistry - Organic (Semester Two) Chemistry - Organic (Semester One) Chemistry - General Ordinary Differential Equations Linear Algebra Vector Calculus Calculus - Differential, Integral and Multivariate "NO" "UNSURE" "YES" Industry Academia
Agreement that second semester organic chemistry is not universally required; some uncertainty about one semester
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Universities represented in round one of the survey
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Arizona State University* Binghampton University Boston University* Columbia University Devry Institute of Tech Duke University* Florida International University IIT Johns Hopkins University* Marquette University* Milwaukee SOE* MIT NJIT NC State University* Northwestern University* RPI* RHIT Stanford University Syracuse University* 20.
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SUNY – Stony Brook Tulane University* University of Akron* University of Cincinnati University of Illinois – UC* University of Iowa* University of Memphis University of Michigan University of Minnesota* University of Pittsburgh* University of Rochester* University of Texas – Austin* University of Toledo* Vanderbilt University* VCU* *ABET Accredited – 21 of 37 Accredited Programs Participated
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Companies and industrial expertise represented in round one of the survey
Companies Represented
Abbott Laboratories AstraZeneca Baxter Healthcare
Boston Scientific Cardiodynamics Cleveland Medical Devices
Datasciences, International Dentigenix, Inc.
Depuy, a Johnson and Johnson Co.
ESTECH Least Invasive Cardiac Surgery GE Healthcare Intel, Corp.
Materialise, Inc.
Medtronic, Inc. Tyco Healthcare Underwriter Laboratories
Areas of Expertise
Biomaterials
Biomechanics Bioinformatics Bioinstrumentation BioMEMS Biotransport Cellular Biomechanics Computational Modeling Control Systems Engineering Fluid Mechanics Medical Devices Medical Imaging Medical Optics Signal Processing
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Conclusions
More analysis is required to:
Investigate variation of opinions for individual topics Correlate ratings with expertise levels Eliminate contextual bias
Incorporate concepts omitted from first round
BUT, preliminary results have shown that:
“Consistency checks” validate data Generally good agreement between industry and academia Industry and academia disagree on a significant number of Design concepts
Industry highly values knowledge of statistics and probability
Core biology should include all domains assessed
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Conclusions
Remaining issues
Determine level of significance for deciding what concepts can be dropped from core curriculum
Determine significance of differences between industry and academia Launch second round – by summer
Full matrix of results by concept will be posted on www.vanth.org/curriculum
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