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
1
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.
2
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. Develop a set of questions on a topic.
2. Experts give opinions on topics; suggest new ideas that were missed
3. Explore and evaluate inconsistencies uncovered in step 2
4. 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)
3
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
4
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
5
Some concepts included as “Ringers” Expected to have low rating
Concept
Rating
Rating
(Academia) (Industry)
Statistical Physics (e.g., Bose-Einstein
statistics; Fermi-Dirac statistics)
2.32
2.58
Statistical Physics (e.g., Partition function;
statistical representation of entropy;
population of states)
2.82
2.58
Comparative Genomics (e.g., ortholog and
paralog genes; gene fusion events)
2.50
2.94
Dynamical Instability and Chaos
2.59
3.11
Unsteady state mass diffusion equation (e.g.
Fick’s second law; production and
consumption; boundary conditions; different
geometries; multiple layers)
3.42
3.29
All except unsteady state mass diffusion equation met expectations
6
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)
2.29/2.66
3.22/3.68
Signal Processing to Reduce Noise (e.g.,
signal-to-noise ratio; signal averaging)
4.24/3.83
4.17/4.14
Properties of Systems (e.g., boundary,
surroundings, universe)
3.88/3.97
3.63/3.88
Electrochemical Potential, Nernst Potential,
Fick's Law
4.09/4.23
3.54/4.00
7
Results: Highest rated eng’g concepts – Academia
Orange concepts are from statistics and general engineering
Concept
Rating
Hypothesis Testing (e.g., paired and un-paired t-tests; chi-square test)
4.69
Principles of Statics (e.g., forces; moments; couples; torques; freebody diagrams)
4.68
Descriptive Statistics (e.g., mean, median, variance, std deviation)
4.63
Circuit Elements (e.g., resistors, capacitors, sources, diodes,
transistors, integrated circuits)
4.56
DC and AC circuit analyses (e.g., Ohm's and Kirchoff's laws)
4.56
Mathematical Descriptions of Physical Systems (e.g., functional
relationships, logarithmic, exponential, power-law; ODEs; PDEs)
4.54
Strength of Materials (e.g., stress, strain; models of material behavior)
4.53
Pressure-Flow Relations in Tubes and Networks (e.g., flow rate =
[change in pressure]/resistance; Poiseiulle relation; Starling resistor)
4.51
Measurement concepts (e.g. accuracy, precision, …
4.50
Regression analysis
4.49
Forces and pressures in fluids (e.g. shear, normal, surface tension…
4.49
8
Results: Highest rated eng’g concepts – Industry
Orange concepts are from statistics and general engineering
Concept
Rating
Descriptive Statistics (e.g., mean, median, variance, standard
deviation)
4.76
Measurement Concepts (e.g., accuracy, precision, sensitivity; error
analysis - sources, propagation of error)
4.71
Hypothesis Testing (e.g., paired and un-paired t-tests; chi-squared)
4.65
Probability Distributions (e.g., normal, Poisson, binomial)
4.62
Strength of Materials (e.g., stress, strain; models of material behavior)
4.57
Fundamental Properties of Polymers, Metals and Ceramics
4.50
Product Specification (e.g., requirements, design, reliability,
evolution/tracking of the product)
4.45
Principles of Statics (e.g., forces; moments; couples; torques; freebody diagrams)
4.43
Mechanical Properties of Biological Tissues (e.g., elastic; viscoelastic,
hysteresis, creep, stress relaxation)
4.43
Data Acquisition (e.g., sampling rates and analog-digital conversion;
Nyquist criterion; aliasing)
9
4.39
Results: lowest rated concepts
some from “Ringers”
Academia
Databases - Interfaces and Structures (e.g., MySQL, relational tables, simple
queries, PERL, CGI, DBI)
2.29
Statistical Physics (e.g., Bose-Einstein and Fermi-Dirac statistics)
2.32
Artificial Intelligence (e.g., artificial neural networks, fuzzy logic)
2.33
Analysis of Phylogenetic Trees, Molecular Evolution
2.47
Comparative Genomics (e.g., ortholog and paralog genes; gene fusion
events)
2.50
Structural Prediction and Molecular Design
2.53
Industry
Statistical Physics (e.g., Partition function; statistical representation of
entropy; population of states)
2.58
Statistical Physics (e.g., Bose-Einstein; Fermi-Dirac statistics)
2.58
Artificial Intelligence (e.g., artificial neural networks, fuzzy logic)
2.78
Storage Instruments and their properties (e.g., tape, disk, memory)
2.89
Comparative Genomics
2.94
Root Locus Plots (e.g., definition, properties, sketching)
2.95
10
Results: Industry - Academia agreement
Distribution of mean ratings of all concepts
5.00
Industry
4.00
3.00
Concept Ratings
2.00
2.00
2.50
3.00
3.50
4.00
Academia
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.
11
Results: Industry – Academia Agreement
Differences in means (A-I)
Differences in Mean Responses
0.75
0.50
0.25
0.00
-0.25
-0.50
-0.75
Design
-1.00
-1.25
0
50
100
150
Concept #
200
250
12
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)
Industry
Academia
Software and Process Design Considerations
Risk Analysis/Hazard Analysis
Computer-Aided Design Considerations
Human Factors Issues/FDA
1.00
2.00
3.00
4.00
5.00
Mean Ranking (all participants)
13
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, DBI)
Ringer
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)
Significant Deltas
Scaling and Dime ns ional Analys is
Nume rical Diffe re ntiation and Inte gration
Industry
Academia
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)
5.00
14
Rating of Concept - Industry
Results: Biology Domains
5.0
4.5
4.0
Biochemistry
3.5
Cell Biology
Molecular Biol.
Bioinformatics
3.0
Unity slope
2.5
2.5
3.0
3.5
4.0
4.5
5.0
Rating of Concept - Academia
 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
15
Results: Largest biology
discrepancies
Concepts
Academia
Industry
Academia
- Industry
Flow of Genetic Information (i.e., DNA to
RNA to Protein)
4.5
4.1
0.44
Methods for Determining
Macromolecular Structure (e.g., NMR...)
3.5
4.2
-0.70
DNA Microarrays
3.4
3.8
-0.42
Biological Networks (e.g., genetic
networks...)
3.2
3.7
-0.46
Structural Prediction and Molecular
Design (e.g., homology modeling and
prediction of macromolecular structures
and interactions)
2.5
3.3
-0.72
16
Results: Physiology (82 concepts)
 Generally good
agreement
 Cardiovascular, neural,
cellular physiology
concepts rated highly
 Digestive, renal, parts
of endocrine rated low
5.0
Rating of Concept - Industry
 Very large span within
domain
4.5
4.0
3.5
Physiology
Unity slope
3.0
2.5
2.5
3.0
3.5
4.0
4.5
5.0
Rating of Concept - Academia
17
Results: Largest physiology
discrepancies between academia and
industry
Concepts
Academia
Industry
Academia
- Industry
Cellular Anatomy (e.g...)
4.56
4.14
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
4.03
3.50
0.53
Water Balance and Urine Concentration
3.73
3.25
0.48
Platelets and Coagulation (e.g....)
3.21
3.75
-0.54
Sodium Balance and the Regulation of
ECF Volume (e.g....)
3.76
3.15
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)
Industry
Academia
Chemistry - Organic (Semester One)
Chemistry - General
Ordinary Differential Equations
Linear Algebra
Vector Calculus
Calculus - Differential, Integral and Multivariate
"NO"
"UNSURE"
"YES"
Agreement that second semester organic chemistry is not
universally required; some uncertainty about one semester
19
Universities represented in round one
of the survey
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
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.
SUNY – Stony Brook
21.
Tulane University*
22.
University of Akron*
23.
University of Cincinnati
24.
University of Illinois – UC*
25.
University of Iowa*
26.
University of Memphis
27.
University of Michigan
28.
University of Minnesota*
29.
University of Pittsburgh*
30.
University of Rochester*
31.
University of Texas – Austin*
32.
University of Toledo*
33.
Vanderbilt University*
34.
VCU*
*ABET Accredited – 21 of 37 Accredited Programs Participated
20
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
21
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
22
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
23