1
00:00:00,389 --> 00:00:03,480
Today on Problem Solved, we're
speaking with the recipient of
2
00:00:03,519 --> 00:00:07,579
IISE's highest honor, the Frank
and Lillian Gilbreth Award.
3
00:00:07,840 --> 00:00:12,109
Over the course of his career, Dr. Carter
has helped pioneer healthcare systems
4
00:00:12,149 --> 00:00:16,209
engineering in Canada and mentored
hundreds of students along the way.
5
00:00:16,670 --> 00:00:20,149
In this conversation, we talk about
how he found his way into industrial
6
00:00:20,149 --> 00:00:24,229
engineering, why healthcare became
his life's work, the future of AI in
7
00:00:24,229 --> 00:00:28,670
healthcare systems, and what receiving the
Gilbreth Award means to him personally.
8
00:00:29,139 --> 00:00:31,659
Here's our conversation
with Dr. Michael Carter.
9
00:00:35,229 --> 00:00:40,479
Today we are joined by Dr. Michael
Carter, a professor in mechanical and
10
00:00:40,479 --> 00:00:45,359
industrial engineering at the University
of Toronto, where he has been since 1981.
11
00:00:45,700 --> 00:00:49,609
Over the course of his career, he has
become a leading voice in healthcare
12
00:00:49,609 --> 00:00:54,710
systems engineering, with decades of work
focused on healthcare resource modeling
13
00:00:54,710 --> 00:00:56,730
and improving how care is delivered.
14
00:00:56,810 --> 00:01:00,919
He is the founding director of the
Center for Healthcare Engineering and
15
00:01:00,919 --> 00:01:04,550
has mentored hundreds of students,
many of whom are now working
16
00:01:04,550 --> 00:01:06,520
directly in healthcare systems today.
17
00:01:07,079 --> 00:01:12,320
His impact spans research, education, and
real-world implementation, contributing
18
00:01:12,320 --> 00:01:14,520
to healthcare in Canada and beyond.
19
00:01:15,149 --> 00:01:20,329
Dr. Carter is a fellow of the Canadian
Academy of Engineering, the Canadian
20
00:01:20,329 --> 00:01:27,280
Academy of Health Sciences, INFORMS, and
IISE, and most recently, he has been named
21
00:01:27,280 --> 00:01:33,360
the recipient of the Frank and Lillian
Gilbreth Award, IISE's highest honor.
22
00:01:33,780 --> 00:01:37,660
So Dr. Carter, thank you so
much for joining us today.
23
00:01:38,299 --> 00:01:39,320
Well, thank you for having me.
24
00:01:39,849 --> 00:01:43,669
Well, let's start at the beginning
of your journey, if you don't
25
00:01:43,669 --> 00:01:45,869
mind taking us back a little bit.
26
00:01:46,610 --> 00:01:51,229
can you tell us a little bit about
how did you first find your way into
27
00:01:51,269 --> 00:01:56,010
industrial engineering and ultimately
into a career as a professor?
28
00:01:56,760 --> 00:01:57,380
That's right.
29
00:01:57,380 --> 00:02:04,389
Though it's, it's interesting that, when
I started out, now in 1960s, I decided
30
00:02:04,389 --> 00:02:06,139
I wanted to be a computer programmer.
31
00:02:06,720 --> 00:02:11,050
I wanted to learn about programming,
and so I enrolled in computer science
32
00:02:11,050 --> 00:02:12,719
at the University of Waterloo.
33
00:02:13,100 --> 00:02:18,209
Waterloo at that time was the only
university in Canada where undergraduates
34
00:02:18,209 --> 00:02:19,809
were allowed access to computers.
35
00:02:20,649 --> 00:02:26,600
And, so I went there, and, around
in my third year, I took a course
36
00:02:26,600 --> 00:02:30,779
in operations research, and it sort
of changed the direction of my life.
37
00:02:31,410 --> 00:02:32,360
I really enjoyed it.
38
00:02:32,360 --> 00:02:35,200
I really liked it, and I wound
up, I decided I wanted to
39
00:02:35,680 --> 00:02:37,869
specialize in operations research.
40
00:02:38,349 --> 00:02:43,919
I switched, specialties from
computer science to combinatorics
41
00:02:43,919 --> 00:02:47,860
and optimization, sort of very
theoretical operations research.
42
00:02:48,240 --> 00:02:52,869
And, I wound up, when I graduated, I
actually got a job at the University
43
00:02:52,869 --> 00:02:55,169
of Waterloo as a systems analyst.
44
00:02:55,169 --> 00:02:59,509
I worked there for seven years,
and I decided I wanted to do
45
00:02:59,509 --> 00:03:03,119
graduate work to, just to find out
more about operations research.
46
00:03:03,810 --> 00:03:08,329
So I did a master's and part-time,
and then I started a PhD
47
00:03:08,329 --> 00:03:09,799
just to pick up more courses.
48
00:03:10,170 --> 00:03:14,460
I really had no idea about,
being a professor or going on.
49
00:03:15,160 --> 00:03:20,660
And, my joke was, during my PhD,
they wanted me to do theory.
50
00:03:20,820 --> 00:03:24,329
They wanted me to solve
theoretical, theoretical problems.
51
00:03:24,410 --> 00:03:25,969
I wanted to solve real problems.
52
00:03:26,719 --> 00:03:30,560
And I should've been in industrial
engineering all the time, but, frankly,
53
00:03:30,560 --> 00:03:33,729
because of the name, I didn't know
this, that they were really the
54
00:03:33,729 --> 00:03:35,999
application of operations research.
55
00:03:37,579 --> 00:03:42,149
In 1981, I got hired as assistant
professor in industrial engineering
56
00:03:42,149 --> 00:03:45,850
at the University of Toronto, and I
ended up where I should have been.
57
00:03:46,590 --> 00:03:49,539
So it kinda took you a while
to, to find your way there, huh?
58
00:03:49,679 --> 00:03:50,649
Exactly.
59
00:03:51,170 --> 00:03:51,460
Yeah.
60
00:03:51,840 --> 00:03:52,619
You know, I, I know.
61
00:03:52,619 --> 00:03:56,600
I meet, undergraduate students today
who have decided, you know, "I'm
62
00:03:56,600 --> 00:03:58,799
gonna do a PhD and be a professor."
63
00:03:58,799 --> 00:03:59,549
That was not me.
64
00:04:00,210 --> 00:04:00,429
Hmm.
65
00:04:00,689 --> 00:04:00,960
Yeah.
66
00:04:01,660 --> 00:04:04,009
So at some point along the
way, though, your work began
67
00:04:04,009 --> 00:04:05,439
to kind of focus on healthcare.
68
00:04:05,439 --> 00:04:05,499
Yeah.
69
00:04:05,499 --> 00:04:08,589
So when... Can you kinda tell us
when that transition happened?
70
00:04:08,589 --> 00:04:11,850
Was there something, like, in particular
that made you realize it could re- you
71
00:04:11,850 --> 00:04:13,699
could really make an impact in that field?
72
00:04:14,014 --> 00:04:14,635
Exactly.
73
00:04:14,635 --> 00:04:19,955
Like it's-- And, and, I spent, well,
sort of the first ten years of my, of
74
00:04:19,955 --> 00:04:26,204
my, academic career working on scheduling
problems with, with companies, you
75
00:04:26,204 --> 00:04:30,925
know, looking at, process optimization,
operations research applications.
76
00:04:31,475 --> 00:04:36,964
And then, in around nineteen
eighty-five, I, I got, hired to
77
00:04:36,964 --> 00:04:41,255
do a small consulting project with
one of the hospitals in Toronto.
78
00:04:41,884 --> 00:04:47,565
And the problem they had was that nurses
in Ontario at that time, the standard
79
00:04:47,565 --> 00:04:49,704
workday was eight and a half hours.
80
00:04:50,475 --> 00:04:52,894
And the reason it was eight and a
half hours is there was a half an
81
00:04:52,894 --> 00:04:56,114
hour for, for turnover, for handoff.
82
00:04:56,765 --> 00:04:59,954
You know, the, the nurse from the
previous shift could talk to the
83
00:04:59,954 --> 00:05:03,434
nurse coming on, and they spent a
half an hour talking about patients.
84
00:05:03,914 --> 00:05:08,675
And in nineteen eighty-five, the
nursing union in Ontario, they
85
00:05:08,675 --> 00:05:12,524
changed the-- they, they, got an
agreement to do eight-hour work.
86
00:05:13,315 --> 00:05:16,754
And now it's-- it has created
handoff problems that continue
87
00:05:16,754 --> 00:05:18,225
today, but that's another story.
88
00:05:18,994 --> 00:05:23,824
Anyway, one of the hospitals came to
me, and they had, they had, operating
89
00:05:23,824 --> 00:05:29,604
room nurses, fifty-three operating room
nurses, and, they said, "Well, we're going
90
00:05:29,604 --> 00:05:31,525
from eight and a half to eight hours."
91
00:05:32,125 --> 00:05:36,644
The nurses were basically working
from Monday to Friday from,
92
00:05:36,765 --> 00:05:38,155
seven thirty to four o'clock.
93
00:05:39,025 --> 00:05:42,135
And, they're going eight-- they're
losing a half an hour per nurse.
94
00:05:42,705 --> 00:05:47,869
So they figured they probably need to hire
three more nurses to fill the gap So they
95
00:05:47,869 --> 00:05:52,420
asked me to look at the problem, and, they
gave me two months' worth of schedules.
96
00:05:52,990 --> 00:05:56,809
And what I quickly realized was
that the nurses were showing up at
97
00:05:56,809 --> 00:06:00,240
seven thirty in the morning, spend
a half an hour prepping the room.
98
00:06:00,669 --> 00:06:02,339
The room was ready to go at eight o'clock.
99
00:06:02,990 --> 00:06:06,670
The doctors were showing up at
nine and ten, and then the doctors
100
00:06:06,670 --> 00:06:10,580
were sometimes working overtime and
going till six or seven at night.
101
00:06:10,939 --> 00:06:12,369
The nurses were working overtime.
102
00:06:13,160 --> 00:06:17,639
And not only that, but on Friday
afternoon, nothing was happening.
103
00:06:18,020 --> 00:06:22,169
They had virtually, virtually no
operations on Friday afternoons.
104
00:06:22,849 --> 00:06:24,910
So the nurses were sitting
around with nothing to do.
105
00:06:25,590 --> 00:06:27,919
So I looked at it and I said, "Well,
you know, really what you need
106
00:06:27,919 --> 00:06:29,779
to do is you really need to do...
107
00:06:29,779 --> 00:06:31,700
You know, this, this
doesn't make any sense.
108
00:06:32,209 --> 00:06:35,849
So what we should have is we should
have staggered start times for the
109
00:06:35,849 --> 00:06:41,070
nurses and probably longer days Monday
to Thursday, shorter days on Friday."
110
00:06:41,799 --> 00:06:45,770
And, and so I made this report and
I said, "You don't need new nurses.
111
00:06:45,770 --> 00:06:47,989
You can just redistribute
the work you've got.
112
00:06:48,340 --> 00:06:53,499
Makes perfect sense." And, and I gave it
to them, and they were furious because
113
00:06:53,499 --> 00:06:57,770
what they wanted was they wanted Professor
Carter to write a report to say they
114
00:06:57,770 --> 00:07:01,689
needed to hire three nurses to go to
the board, and that's not what I said.
115
00:07:01,689 --> 00:07:04,869
Mm. And I, and I realized,
like, the inefficiency of it
116
00:07:04,869 --> 00:07:07,170
all, it was, it was crazy.
117
00:07:07,170 --> 00:07:11,740
Now, the doctors in Ontario typically
do not work for the hospital.
118
00:07:12,299 --> 00:07:13,539
The doctors are private.
119
00:07:13,539 --> 00:07:17,759
The doctors are-- work on their own,
and they are paid fee for service,
120
00:07:17,759 --> 00:07:19,460
so they get paid for what they do.
121
00:07:19,990 --> 00:07:21,200
So they really didn't care.
122
00:07:22,340 --> 00:07:26,609
anyway, so... And then, then four
years later, I got involved in a
123
00:07:26,609 --> 00:07:30,749
large project, major project, looking
at operating room scheduling- Mm-hmm
124
00:07:31,649 --> 00:07:36,109
and, a simulation project with
five large hospitals in Toronto.
125
00:07:36,880 --> 00:07:42,079
And I realized that the hospitals were
incredibly inefficient, not just with the
126
00:07:42,079 --> 00:07:44,299
doctors' time, but all over the place.
127
00:07:44,329 --> 00:07:45,560
There were no standards.
128
00:07:45,960 --> 00:07:47,469
There was no data collection.
129
00:07:47,990 --> 00:07:50,609
There were no performance
metrics nor error tracking.
130
00:07:51,100 --> 00:07:54,899
Each physician was running their
own show at their convenience.
131
00:07:55,379 --> 00:07:57,340
No one was allowed to question the doctor.
132
00:07:57,340 --> 00:07:57,399
Mm.
133
00:07:57,849 --> 00:07:58,509
There,
134
00:07:58,709 --> 00:08:00,800
there were no appointments
on Friday afternoon.
135
00:08:01,300 --> 00:08:04,110
Surgery started late,
finished early, finished late.
136
00:08:04,170 --> 00:08:04,869
They didn't care.
137
00:08:05,340 --> 00:08:05,369
Mm-hmm.
138
00:08:05,720 --> 00:08:09,519
There was no concept of, of
patient flow or process flow.
139
00:08:10,050 --> 00:08:12,289
Every part of healthcare
was working in silos.
140
00:08:12,940 --> 00:08:16,550
I mean, if, if you ran a company like
this, it would fold in, in weeks.
141
00:08:17,009 --> 00:08:18,800
And I just-- And I looked at
it and I just... You know,
142
00:08:18,800 --> 00:08:22,489
from an industrial engineering
perspective, it was a disaster.
143
00:08:22,899 --> 00:08:24,869
I mean, things are quite a bit better now.
144
00:08:24,869 --> 00:08:28,209
There-- We've still got a lot of
issues, but, things have changed.
145
00:08:28,739 --> 00:08:35,879
And nobody was doing anything, and in,
in Canada, very little in the US, and,
146
00:08:37,019 --> 00:08:38,759
and I felt something had to be done
147
00:08:39,529 --> 00:08:39,809
And
148
00:08:39,899 --> 00:08:43,959
I decided the only way that, Mike
Carter, I could influence, assistant
149
00:08:43,959 --> 00:08:48,150
professor, that I could influence
the industry, I had to create a small
150
00:08:48,150 --> 00:08:50,909
army of IEs working in healthcare.
151
00:08:51,869 --> 00:08:56,510
And so I've spent the past 38 years
trying to convince engineers to work in
152
00:08:56,510 --> 00:09:00,669
healthcare, and I also had to convince
healthcare they needed engineers.
153
00:09:01,290 --> 00:09:03,139
So this became my passion.
154
00:09:03,659 --> 00:09:05,139
This is-- I wanted to make a difference.
155
00:09:05,139 --> 00:09:07,870
And th-this isn't a, a research project.
156
00:09:07,870 --> 00:09:10,210
This has been a, a full-time passion.
157
00:09:10,450 --> 00:09:14,829
I'm, I'm, I'm, I'm pushing 80 years of
age, and I'm still working full time.
158
00:09:15,419 --> 00:09:17,140
It's, it still needs me.
159
00:09:17,690 --> 00:09:21,120
You know, but-- And one of the things
that I realized was that there were
160
00:09:21,120 --> 00:09:27,039
many people within the industry who
knew that things were bad . And they
161
00:09:27,039 --> 00:09:30,949
knew that change was necessary, and
but they didn't know how to do it.
162
00:09:30,949 --> 00:09:35,840
So my approach was to sort of dig
out these individuals and then match
163
00:09:35,840 --> 00:09:39,940
them up with student projects to
demonstrate the, you know, the obvious,
164
00:09:41,390 --> 00:09:46,779
advantages of an engineering approach
to a wider audience and demonstrate the
165
00:09:46,779 --> 00:09:48,810
value in process improvement methods.
166
00:09:49,480 --> 00:09:53,599
And so over the years, my net- my
network, I just been networking.
167
00:09:54,980 --> 00:10:00,160
networking, that's been my, basic
method, has been trying to network
168
00:10:00,160 --> 00:10:01,389
with people within healthcare.
169
00:10:02,389 --> 00:10:07,429
Well, when you look back over the,
the past of your career and the way
170
00:10:07,429 --> 00:10:11,160
that the field has evolved, and then
when you look at the future, like, we
171
00:10:11,160 --> 00:10:14,740
can't talk about the future without
at least talking about AI a little bit
172
00:10:14,740 --> 00:10:16,249
and things like advanced technology.
173
00:10:16,249 --> 00:10:16,310
Yeah.
174
00:10:16,679 --> 00:10:20,740
So how do you see the way-- how do
you see AI and advanced technology?
175
00:10:20,740 --> 00:10:24,980
How do you see that changing the way
that healthcare modeling is going to
176
00:10:24,980 --> 00:10:27,399
be taught and practiced in the future?
177
00:10:28,249 --> 00:10:33,840
Well, it's, it's, it's funny, you
know, that, I-I'm a dinosaur . My--
178
00:10:33,840 --> 00:10:35,740
All of my students all wanna do AI.
179
00:10:36,300 --> 00:10:36,849
Mm-hmm.
180
00:10:37,550 --> 00:10:41,539
and, and I actually took a two-week
boot camp intro to AI a couple
181
00:10:41,539 --> 00:10:44,770
of years ago, so at least I would
know what they were talking about.
182
00:10:45,170 --> 00:10:45,539
Yeah, that's
183
00:10:45,539 --> 00:10:45,840
pretty
184
00:10:45,840 --> 00:10:46,170
good.
185
00:10:46,209 --> 00:10:46,630
You know?
186
00:10:46,660 --> 00:10:47,689
It's not bad, right?
187
00:10:48,209 --> 00:10:52,289
And, everybody in healthcare,
they all wanna do AI applications.
188
00:10:52,970 --> 00:10:59,920
And one of the, one of the major problems
with that is that, you know, AI typically
189
00:10:59,920 --> 00:11:03,700
requires large, comprehensive data sets.
190
00:11:04,439 --> 00:11:07,019
You know, like, it-- I wanna, I
wanna be able to predict what's gonna
191
00:11:07,019 --> 00:11:10,780
happen to these patients, and so I
need a data set with a few million
192
00:11:10,780 --> 00:11:12,390
patients and all their information.
193
00:11:12,959 --> 00:11:13,170
Mm-hmm.
194
00:11:13,170 --> 00:11:16,670
And, and typically in
healthcare, we don't do it.
195
00:11:16,670 --> 00:11:18,610
The-- People don't share data.
196
00:11:19,189 --> 00:11:23,159
So, you know, primary care docs
have electronic data, but they
197
00:11:23,159 --> 00:11:24,439
don't share with the hospitals.
198
00:11:24,439 --> 00:11:28,769
The hospitals don't share long-term
care, rehab, walk-in clinics.
199
00:11:29,179 --> 00:11:33,409
I think we're moving to a point
where, you know, researchers are,
200
00:11:33,409 --> 00:11:39,510
are beginning to get access to large
Comprehensive, de-identified dataset.
201
00:11:39,879 --> 00:11:41,380
It's a, it's a work in progress.
202
00:11:41,960 --> 00:11:45,650
And, I think it's a huge,
you know, huge opportunity.
203
00:11:45,679 --> 00:11:50,919
I have students doing work on natural
language processing to extract
204
00:11:50,919 --> 00:11:53,659
labeled data from doctors' notes.
205
00:11:53,710 --> 00:11:57,909
You know, doctors are recording their
notes now in machine-readable form,
206
00:11:58,270 --> 00:11:59,620
so we pull information out of it.
207
00:12:00,099 --> 00:12:05,470
I have students developing models to
predict adverse events before they happen.
208
00:12:05,499 --> 00:12:11,139
For, for example, for patients who are
on chemotherapy, they often wind up in
209
00:12:11,139 --> 00:12:15,159
the emergency department, and if we could
predict who might be doing that, we could
210
00:12:15,159 --> 00:12:17,220
maybe change their protocol a little bit.
211
00:12:18,060 --> 00:12:22,369
I tell, high school students about
industrial engineering and, in
212
00:12:22,369 --> 00:12:24,779
particular, information systems.
213
00:12:25,369 --> 00:12:29,709
And, I tell them that, you know,
like, computer science develops tools
214
00:12:29,709 --> 00:12:35,589
and computer engineers develop the
machines, and, industrial engineers help
215
00:12:35,619 --> 00:12:40,260
users apply these tools to make their
work easier, faster, more effective.
216
00:12:40,859 --> 00:12:43,539
You'd never send a computer
scientist to help a user.
217
00:12:43,539 --> 00:12:46,380
They would come up with
something inappropriate.
218
00:12:46,960 --> 00:12:51,329
And I think the same is true for AI,
that Indies really need to know about the
219
00:12:51,329 --> 00:12:56,899
tools and how to design systems to make
it easier for users to do their jobs.
220
00:12:57,469 --> 00:13:04,020
And I, and I think that's our, that's our
role, and, it's that, interface with the
221
00:13:04,729 --> 00:13:04,850
user.
222
00:13:04,850 --> 00:13:09,450
What's your big hope for the
field of industrial systems
223
00:13:09,450 --> 00:13:11,600
engineering in the future?
224
00:13:12,339 --> 00:13:15,710
Well, I th-- yeah, I think it's, it's--
In, in a sense, it hasn't changed, right?
225
00:13:15,710 --> 00:13:16,780
So you know- Yeah.
226
00:13:16,780 --> 00:13:21,450
-back to, back to, you know, the
Gilbreths, what they were trying to do
227
00:13:21,450 --> 00:13:27,180
was, you know, make it easier for people,
to do things and make it, you know, make
228
00:13:27,180 --> 00:13:28,930
people more efficient, more effective.
229
00:13:29,839 --> 00:13:34,880
industrial engineering, you know, in,
in my own area, in healthcare, we really
230
00:13:34,880 --> 00:13:39,259
didn't have industrial engineers, you
know, thirty-five years ago looking
231
00:13:39,259 --> 00:13:42,409
at, processes within healthcare.
232
00:13:42,890 --> 00:13:45,130
And, today they really are.
233
00:13:45,130 --> 00:13:50,059
You know, today we now have, in
every hospital that I know about,
234
00:13:50,570 --> 00:13:56,199
has, performance measurement and
pro-- improvement teams, reporting.
235
00:13:56,799 --> 00:14:02,409
they are tracking and, and,
measuring safety and quality metrics,
236
00:14:03,150 --> 00:14:06,540
optimization of, operational processes.
237
00:14:07,119 --> 00:14:12,550
So at this stage in your career,
what does receiving this award,
238
00:14:12,550 --> 00:14:15,510
the Frank and Lillian Gilbreth
Award, what does it mean to you?
239
00:14:16,210 --> 00:14:17,380
Oh, it's, it's huge.
240
00:14:17,380 --> 00:14:20,100
It's, like it, it, it is truly special.
241
00:14:20,100 --> 00:14:21,010
It was unexpected.
242
00:14:21,010 --> 00:14:21,119
Mm-hmm.
243
00:14:21,920 --> 00:14:26,210
You know, I think the, you know, the
Gilbreths were pioneers in the application
244
00:14:26,210 --> 00:14:30,349
of industrial engineering concepts,
and it's, it's a privilege and an honor
245
00:14:30,349 --> 00:14:32,310
to be counted among people like them.
246
00:14:33,070 --> 00:14:37,210
You know, I, I, I don't think of
myself as a pioneer like them, but it
247
00:14:37,210 --> 00:14:39,589
was sure up the old battle for years.
248
00:14:40,010 --> 00:14:45,760
I, I've had several awards from Canadian
engineering, and, and healthcare
249
00:14:46,130 --> 00:14:53,170
organizations, but, the- getting this
from, IISE, the, it- it's international.
250
00:14:53,920 --> 00:14:59,030
And, I, you know, I, I look around
at, colleagues in other areas and
251
00:14:59,030 --> 00:15:04,350
going like, "Why me?" And, like I say,
I was really surprised and honored.
252
00:15:04,959 --> 00:15:08,470
Well, you've, you've influenced
many people throughout your career,
253
00:15:08,679 --> 00:15:13,790
many students, and so your impact,
it's directly on your students,
254
00:15:13,790 --> 00:15:17,639
but then it's also through what
your students have gone on to do.
255
00:15:17,639 --> 00:15:22,620
So w- if you s- take a moment to stop
and reflect on that kind of ripple
256
00:15:22,620 --> 00:15:26,989
effect that you've had, I mean, how
does that make you feel when you stop
257
00:15:26,989 --> 00:15:28,260
to think about that for a minute?
258
00:15:28,669 --> 00:15:32,220
I mean, that, that's... You know, you
know, a lot of the th- the things that
259
00:15:32,809 --> 00:15:35,179
I have accomplished, I didn't really do.
260
00:15:35,319 --> 00:15:42,190
I mean, what I did was I, I, I encouraged
students to get out into healthcare.
261
00:15:43,120 --> 00:15:46,050
and you know, I've been doing this
for so long now, many of them have
262
00:15:46,410 --> 00:15:50,450
worked their way up the food chain,
and they are making a difference.
263
00:15:51,020 --> 00:15:54,540
And they are, they are changing things.
264
00:15:55,010 --> 00:15:59,480
And so I sort of... I've, I've
created a path for them to go
265
00:15:59,480 --> 00:16:00,830
out and do wonderful things.
266
00:16:01,249 --> 00:16:06,190
So I, to me, this is, this is
just, th- this is, this is amazing.
267
00:16:06,220 --> 00:16:10,059
I just sit back and watch them
and go, and go, "Wow," you know.
268
00:16:11,499 --> 00:16:15,750
Dr. Carter, congratulations on your very
well-deserved honor receiving the Frank
269
00:16:15,750 --> 00:16:19,890
and Lillian Gilbreth Award, and thank you
very, very much for talking with us today.
270
00:16:19,920 --> 00:16:20,650
I appreciate it.
271
00:16:21,175 --> 00:16:22,614
Thank you very much, Elizabeth.
272
00:16:23,044 --> 00:16:24,125
It's a pleasure.
273
00:16:26,105 --> 00:16:30,745
Dr. Michael Carter, recipient of the
2026 Frank and Lillian Gilbreth Award.
274
00:16:31,145 --> 00:16:34,445
And hearing Dr. Carter reflect
on the Gilbreths themselves
275
00:16:34,884 --> 00:16:36,365
raised another question for us.
276
00:16:36,844 --> 00:16:40,545
Who exactly were Frank and Lillian
Gilbreth beyond the names attached
277
00:16:40,545 --> 00:16:43,494
to the awards, scholarships,
and engineering history?
278
00:16:43,904 --> 00:16:47,764
Next time on Problem Solved, we take
a deeper look at the real people
279
00:16:47,764 --> 00:16:52,144
behind the legacy with Mike Farrelly
of the Montclair History Center.
280
00:16:54,015 --> 00:16:58,354
He gave Frank a job as a helper,
and he started paying attention to
281
00:16:58,354 --> 00:17:02,405
how each individual bricklayer did
their job and noticed that they
282
00:17:02,405 --> 00:17:04,015
didn't all do it the same way.
283
00:17:05,175 --> 00:17:11,545
And he began to start
thinking about the best way.
284
00:17:11,545 --> 00:17:13,655
Nothing deterred Lillian.
285
00:17:14,685 --> 00:17:17,925
She was interested in the
psychology of how things worked.
286
00:17:19,435 --> 00:17:22,115
That, by the way, is one of the
biggest differences between them
287
00:17:22,144 --> 00:17:27,034
and Frederick Taylor, is they
were most, Frank and Lillian were
288
00:17:27,034 --> 00:17:29,055
very interested in the individual.
289
00:17:30,424 --> 00:17:37,375
And in many ways, even though she was
the quiet partner, she was the dynamo
290
00:17:38,905 --> 00:17:42,504
behind Frank and Lillian's collaboration.
291
00:17:43,074 --> 00:17:48,045
People would see this enormous family
coming at them and ask Frank, well,
292
00:17:48,045 --> 00:17:51,115
what's it like to have all these kids?
293
00:17:51,115 --> 00:17:52,884
And, oh, they're cheaper by the dozen.
294
00:17:53,625 --> 00:17:54,004
So
295
00:17:54,365 --> 00:17:58,824
We hope you'll join us next time for a
historic deep dive on Problem Solved.
00:00:00,389 --> 00:00:03,480
Today on Problem Solved, we're
speaking with the recipient of
2
00:00:03,519 --> 00:00:07,579
IISE's highest honor, the Frank
and Lillian Gilbreth Award.
3
00:00:07,840 --> 00:00:12,109
Over the course of his career, Dr. Carter
has helped pioneer healthcare systems
4
00:00:12,149 --> 00:00:16,209
engineering in Canada and mentored
hundreds of students along the way.
5
00:00:16,670 --> 00:00:20,149
In this conversation, we talk about
how he found his way into industrial
6
00:00:20,149 --> 00:00:24,229
engineering, why healthcare became
his life's work, the future of AI in
7
00:00:24,229 --> 00:00:28,670
healthcare systems, and what receiving the
Gilbreth Award means to him personally.
8
00:00:29,139 --> 00:00:31,659
Here's our conversation
with Dr. Michael Carter.
9
00:00:35,229 --> 00:00:40,479
Today we are joined by Dr. Michael
Carter, a professor in mechanical and
10
00:00:40,479 --> 00:00:45,359
industrial engineering at the University
of Toronto, where he has been since 1981.
11
00:00:45,700 --> 00:00:49,609
Over the course of his career, he has
become a leading voice in healthcare
12
00:00:49,609 --> 00:00:54,710
systems engineering, with decades of work
focused on healthcare resource modeling
13
00:00:54,710 --> 00:00:56,730
and improving how care is delivered.
14
00:00:56,810 --> 00:01:00,919
He is the founding director of the
Center for Healthcare Engineering and
15
00:01:00,919 --> 00:01:04,550
has mentored hundreds of students,
many of whom are now working
16
00:01:04,550 --> 00:01:06,520
directly in healthcare systems today.
17
00:01:07,079 --> 00:01:12,320
His impact spans research, education, and
real-world implementation, contributing
18
00:01:12,320 --> 00:01:14,520
to healthcare in Canada and beyond.
19
00:01:15,149 --> 00:01:20,329
Dr. Carter is a fellow of the Canadian
Academy of Engineering, the Canadian
20
00:01:20,329 --> 00:01:27,280
Academy of Health Sciences, INFORMS, and
IISE, and most recently, he has been named
21
00:01:27,280 --> 00:01:33,360
the recipient of the Frank and Lillian
Gilbreth Award, IISE's highest honor.
22
00:01:33,780 --> 00:01:37,660
So Dr. Carter, thank you so
much for joining us today.
23
00:01:38,299 --> 00:01:39,320
Well, thank you for having me.
24
00:01:39,849 --> 00:01:43,669
Well, let's start at the beginning
of your journey, if you don't
25
00:01:43,669 --> 00:01:45,869
mind taking us back a little bit.
26
00:01:46,610 --> 00:01:51,229
can you tell us a little bit about
how did you first find your way into
27
00:01:51,269 --> 00:01:56,010
industrial engineering and ultimately
into a career as a professor?
28
00:01:56,760 --> 00:01:57,380
That's right.
29
00:01:57,380 --> 00:02:04,389
Though it's, it's interesting that, when
I started out, now in 1960s, I decided
30
00:02:04,389 --> 00:02:06,139
I wanted to be a computer programmer.
31
00:02:06,720 --> 00:02:11,050
I wanted to learn about programming,
and so I enrolled in computer science
32
00:02:11,050 --> 00:02:12,719
at the University of Waterloo.
33
00:02:13,100 --> 00:02:18,209
Waterloo at that time was the only
university in Canada where undergraduates
34
00:02:18,209 --> 00:02:19,809
were allowed access to computers.
35
00:02:20,649 --> 00:02:26,600
And, so I went there, and, around
in my third year, I took a course
36
00:02:26,600 --> 00:02:30,779
in operations research, and it sort
of changed the direction of my life.
37
00:02:31,410 --> 00:02:32,360
I really enjoyed it.
38
00:02:32,360 --> 00:02:35,200
I really liked it, and I wound
up, I decided I wanted to
39
00:02:35,680 --> 00:02:37,869
specialize in operations research.
40
00:02:38,349 --> 00:02:43,919
I switched, specialties from
computer science to combinatorics
41
00:02:43,919 --> 00:02:47,860
and optimization, sort of very
theoretical operations research.
42
00:02:48,240 --> 00:02:52,869
And, I wound up, when I graduated, I
actually got a job at the University
43
00:02:52,869 --> 00:02:55,169
of Waterloo as a systems analyst.
44
00:02:55,169 --> 00:02:59,509
I worked there for seven years,
and I decided I wanted to do
45
00:02:59,509 --> 00:03:03,119
graduate work to, just to find out
more about operations research.
46
00:03:03,810 --> 00:03:08,329
So I did a master's and part-time,
and then I started a PhD
47
00:03:08,329 --> 00:03:09,799
just to pick up more courses.
48
00:03:10,170 --> 00:03:14,460
I really had no idea about,
being a professor or going on.
49
00:03:15,160 --> 00:03:20,660
And, my joke was, during my PhD,
they wanted me to do theory.
50
00:03:20,820 --> 00:03:24,329
They wanted me to solve
theoretical, theoretical problems.
51
00:03:24,410 --> 00:03:25,969
I wanted to solve real problems.
52
00:03:26,719 --> 00:03:30,560
And I should've been in industrial
engineering all the time, but, frankly,
53
00:03:30,560 --> 00:03:33,729
because of the name, I didn't know
this, that they were really the
54
00:03:33,729 --> 00:03:35,999
application of operations research.
55
00:03:37,579 --> 00:03:42,149
In 1981, I got hired as assistant
professor in industrial engineering
56
00:03:42,149 --> 00:03:45,850
at the University of Toronto, and I
ended up where I should have been.
57
00:03:46,590 --> 00:03:49,539
So it kinda took you a while
to, to find your way there, huh?
58
00:03:49,679 --> 00:03:50,649
Exactly.
59
00:03:51,170 --> 00:03:51,460
Yeah.
60
00:03:51,840 --> 00:03:52,619
You know, I, I know.
61
00:03:52,619 --> 00:03:56,600
I meet, undergraduate students today
who have decided, you know, "I'm
62
00:03:56,600 --> 00:03:58,799
gonna do a PhD and be a professor."
63
00:03:58,799 --> 00:03:59,549
That was not me.
64
00:04:00,210 --> 00:04:00,429
Hmm.
65
00:04:00,689 --> 00:04:00,960
Yeah.
66
00:04:01,660 --> 00:04:04,009
So at some point along the
way, though, your work began
67
00:04:04,009 --> 00:04:05,439
to kind of focus on healthcare.
68
00:04:05,439 --> 00:04:05,499
Yeah.
69
00:04:05,499 --> 00:04:08,589
So when... Can you kinda tell us
when that transition happened?
70
00:04:08,589 --> 00:04:11,850
Was there something, like, in particular
that made you realize it could re- you
71
00:04:11,850 --> 00:04:13,699
could really make an impact in that field?
72
00:04:14,014 --> 00:04:14,635
Exactly.
73
00:04:14,635 --> 00:04:19,955
Like it's-- And, and, I spent, well,
sort of the first ten years of my, of
74
00:04:19,955 --> 00:04:26,204
my, academic career working on scheduling
problems with, with companies, you
75
00:04:26,204 --> 00:04:30,925
know, looking at, process optimization,
operations research applications.
76
00:04:31,475 --> 00:04:36,964
And then, in around nineteen
eighty-five, I, I got, hired to
77
00:04:36,964 --> 00:04:41,255
do a small consulting project with
one of the hospitals in Toronto.
78
00:04:41,884 --> 00:04:47,565
And the problem they had was that nurses
in Ontario at that time, the standard
79
00:04:47,565 --> 00:04:49,704
workday was eight and a half hours.
80
00:04:50,475 --> 00:04:52,894
And the reason it was eight and a
half hours is there was a half an
81
00:04:52,894 --> 00:04:56,114
hour for, for turnover, for handoff.
82
00:04:56,765 --> 00:04:59,954
You know, the, the nurse from the
previous shift could talk to the
83
00:04:59,954 --> 00:05:03,434
nurse coming on, and they spent a
half an hour talking about patients.
84
00:05:03,914 --> 00:05:08,675
And in nineteen eighty-five, the
nursing union in Ontario, they
85
00:05:08,675 --> 00:05:12,524
changed the-- they, they, got an
agreement to do eight-hour work.
86
00:05:13,315 --> 00:05:16,754
And now it's-- it has created
handoff problems that continue
87
00:05:16,754 --> 00:05:18,225
today, but that's another story.
88
00:05:18,994 --> 00:05:23,824
Anyway, one of the hospitals came to
me, and they had, they had, operating
89
00:05:23,824 --> 00:05:29,604
room nurses, fifty-three operating room
nurses, and, they said, "Well, we're going
90
00:05:29,604 --> 00:05:31,525
from eight and a half to eight hours."
91
00:05:32,125 --> 00:05:36,644
The nurses were basically working
from Monday to Friday from,
92
00:05:36,765 --> 00:05:38,155
seven thirty to four o'clock.
93
00:05:39,025 --> 00:05:42,135
And, they're going eight-- they're
losing a half an hour per nurse.
94
00:05:42,705 --> 00:05:47,869
So they figured they probably need to hire
three more nurses to fill the gap So they
95
00:05:47,869 --> 00:05:52,420
asked me to look at the problem, and, they
gave me two months' worth of schedules.
96
00:05:52,990 --> 00:05:56,809
And what I quickly realized was
that the nurses were showing up at
97
00:05:56,809 --> 00:06:00,240
seven thirty in the morning, spend
a half an hour prepping the room.
98
00:06:00,669 --> 00:06:02,339
The room was ready to go at eight o'clock.
99
00:06:02,990 --> 00:06:06,670
The doctors were showing up at
nine and ten, and then the doctors
100
00:06:06,670 --> 00:06:10,580
were sometimes working overtime and
going till six or seven at night.
101
00:06:10,939 --> 00:06:12,369
The nurses were working overtime.
102
00:06:13,160 --> 00:06:17,639
And not only that, but on Friday
afternoon, nothing was happening.
103
00:06:18,020 --> 00:06:22,169
They had virtually, virtually no
operations on Friday afternoons.
104
00:06:22,849 --> 00:06:24,910
So the nurses were sitting
around with nothing to do.
105
00:06:25,590 --> 00:06:27,919
So I looked at it and I said, "Well,
you know, really what you need
106
00:06:27,919 --> 00:06:29,779
to do is you really need to do...
107
00:06:29,779 --> 00:06:31,700
You know, this, this
doesn't make any sense.
108
00:06:32,209 --> 00:06:35,849
So what we should have is we should
have staggered start times for the
109
00:06:35,849 --> 00:06:41,070
nurses and probably longer days Monday
to Thursday, shorter days on Friday."
110
00:06:41,799 --> 00:06:45,770
And, and so I made this report and
I said, "You don't need new nurses.
111
00:06:45,770 --> 00:06:47,989
You can just redistribute
the work you've got.
112
00:06:48,340 --> 00:06:53,499
Makes perfect sense." And, and I gave it
to them, and they were furious because
113
00:06:53,499 --> 00:06:57,770
what they wanted was they wanted Professor
Carter to write a report to say they
114
00:06:57,770 --> 00:07:01,689
needed to hire three nurses to go to
the board, and that's not what I said.
115
00:07:01,689 --> 00:07:04,869
Mm. And I, and I realized,
like, the inefficiency of it
116
00:07:04,869 --> 00:07:07,170
all, it was, it was crazy.
117
00:07:07,170 --> 00:07:11,740
Now, the doctors in Ontario typically
do not work for the hospital.
118
00:07:12,299 --> 00:07:13,539
The doctors are private.
119
00:07:13,539 --> 00:07:17,759
The doctors are-- work on their own,
and they are paid fee for service,
120
00:07:17,759 --> 00:07:19,460
so they get paid for what they do.
121
00:07:19,990 --> 00:07:21,200
So they really didn't care.
122
00:07:22,340 --> 00:07:26,609
anyway, so... And then, then four
years later, I got involved in a
123
00:07:26,609 --> 00:07:30,749
large project, major project, looking
at operating room scheduling- Mm-hmm
124
00:07:31,649 --> 00:07:36,109
and, a simulation project with
five large hospitals in Toronto.
125
00:07:36,880 --> 00:07:42,079
And I realized that the hospitals were
incredibly inefficient, not just with the
126
00:07:42,079 --> 00:07:44,299
doctors' time, but all over the place.
127
00:07:44,329 --> 00:07:45,560
There were no standards.
128
00:07:45,960 --> 00:07:47,469
There was no data collection.
129
00:07:47,990 --> 00:07:50,609
There were no performance
metrics nor error tracking.
130
00:07:51,100 --> 00:07:54,899
Each physician was running their
own show at their convenience.
131
00:07:55,379 --> 00:07:57,340
No one was allowed to question the doctor.
132
00:07:57,340 --> 00:07:57,399
Mm.
133
00:07:57,849 --> 00:07:58,509
There,
134
00:07:58,709 --> 00:08:00,800
there were no appointments
on Friday afternoon.
135
00:08:01,300 --> 00:08:04,110
Surgery started late,
finished early, finished late.
136
00:08:04,170 --> 00:08:04,869
They didn't care.
137
00:08:05,340 --> 00:08:05,369
Mm-hmm.
138
00:08:05,720 --> 00:08:09,519
There was no concept of, of
patient flow or process flow.
139
00:08:10,050 --> 00:08:12,289
Every part of healthcare
was working in silos.
140
00:08:12,940 --> 00:08:16,550
I mean, if, if you ran a company like
this, it would fold in, in weeks.
141
00:08:17,009 --> 00:08:18,800
And I just-- And I looked at
it and I just... You know,
142
00:08:18,800 --> 00:08:22,489
from an industrial engineering
perspective, it was a disaster.
143
00:08:22,899 --> 00:08:24,869
I mean, things are quite a bit better now.
144
00:08:24,869 --> 00:08:28,209
There-- We've still got a lot of
issues, but, things have changed.
145
00:08:28,739 --> 00:08:35,879
And nobody was doing anything, and in,
in Canada, very little in the US, and,
146
00:08:37,019 --> 00:08:38,759
and I felt something had to be done
147
00:08:39,529 --> 00:08:39,809
And
148
00:08:39,899 --> 00:08:43,959
I decided the only way that, Mike
Carter, I could influence, assistant
149
00:08:43,959 --> 00:08:48,150
professor, that I could influence
the industry, I had to create a small
150
00:08:48,150 --> 00:08:50,909
army of IEs working in healthcare.
151
00:08:51,869 --> 00:08:56,510
And so I've spent the past 38 years
trying to convince engineers to work in
152
00:08:56,510 --> 00:09:00,669
healthcare, and I also had to convince
healthcare they needed engineers.
153
00:09:01,290 --> 00:09:03,139
So this became my passion.
154
00:09:03,659 --> 00:09:05,139
This is-- I wanted to make a difference.
155
00:09:05,139 --> 00:09:07,870
And th-this isn't a, a research project.
156
00:09:07,870 --> 00:09:10,210
This has been a, a full-time passion.
157
00:09:10,450 --> 00:09:14,829
I'm, I'm, I'm, I'm pushing 80 years of
age, and I'm still working full time.
158
00:09:15,419 --> 00:09:17,140
It's, it still needs me.
159
00:09:17,690 --> 00:09:21,120
You know, but-- And one of the things
that I realized was that there were
160
00:09:21,120 --> 00:09:27,039
many people within the industry who
knew that things were bad . And they
161
00:09:27,039 --> 00:09:30,949
knew that change was necessary, and
but they didn't know how to do it.
162
00:09:30,949 --> 00:09:35,840
So my approach was to sort of dig
out these individuals and then match
163
00:09:35,840 --> 00:09:39,940
them up with student projects to
demonstrate the, you know, the obvious,
164
00:09:41,390 --> 00:09:46,779
advantages of an engineering approach
to a wider audience and demonstrate the
165
00:09:46,779 --> 00:09:48,810
value in process improvement methods.
166
00:09:49,480 --> 00:09:53,599
And so over the years, my net- my
network, I just been networking.
167
00:09:54,980 --> 00:10:00,160
networking, that's been my, basic
method, has been trying to network
168
00:10:00,160 --> 00:10:01,389
with people within healthcare.
169
00:10:02,389 --> 00:10:07,429
Well, when you look back over the,
the past of your career and the way
170
00:10:07,429 --> 00:10:11,160
that the field has evolved, and then
when you look at the future, like, we
171
00:10:11,160 --> 00:10:14,740
can't talk about the future without
at least talking about AI a little bit
172
00:10:14,740 --> 00:10:16,249
and things like advanced technology.
173
00:10:16,249 --> 00:10:16,310
Yeah.
174
00:10:16,679 --> 00:10:20,740
So how do you see the way-- how do
you see AI and advanced technology?
175
00:10:20,740 --> 00:10:24,980
How do you see that changing the way
that healthcare modeling is going to
176
00:10:24,980 --> 00:10:27,399
be taught and practiced in the future?
177
00:10:28,249 --> 00:10:33,840
Well, it's, it's, it's funny, you
know, that, I-I'm a dinosaur . My--
178
00:10:33,840 --> 00:10:35,740
All of my students all wanna do AI.
179
00:10:36,300 --> 00:10:36,849
Mm-hmm.
180
00:10:37,550 --> 00:10:41,539
and, and I actually took a two-week
boot camp intro to AI a couple
181
00:10:41,539 --> 00:10:44,770
of years ago, so at least I would
know what they were talking about.
182
00:10:45,170 --> 00:10:45,539
Yeah, that's
183
00:10:45,539 --> 00:10:45,840
pretty
184
00:10:45,840 --> 00:10:46,170
good.
185
00:10:46,209 --> 00:10:46,630
You know?
186
00:10:46,660 --> 00:10:47,689
It's not bad, right?
187
00:10:48,209 --> 00:10:52,289
And, everybody in healthcare,
they all wanna do AI applications.
188
00:10:52,970 --> 00:10:59,920
And one of the, one of the major problems
with that is that, you know, AI typically
189
00:10:59,920 --> 00:11:03,700
requires large, comprehensive data sets.
190
00:11:04,439 --> 00:11:07,019
You know, like, it-- I wanna, I
wanna be able to predict what's gonna
191
00:11:07,019 --> 00:11:10,780
happen to these patients, and so I
need a data set with a few million
192
00:11:10,780 --> 00:11:12,390
patients and all their information.
193
00:11:12,959 --> 00:11:13,170
Mm-hmm.
194
00:11:13,170 --> 00:11:16,670
And, and typically in
healthcare, we don't do it.
195
00:11:16,670 --> 00:11:18,610
The-- People don't share data.
196
00:11:19,189 --> 00:11:23,159
So, you know, primary care docs
have electronic data, but they
197
00:11:23,159 --> 00:11:24,439
don't share with the hospitals.
198
00:11:24,439 --> 00:11:28,769
The hospitals don't share long-term
care, rehab, walk-in clinics.
199
00:11:29,179 --> 00:11:33,409
I think we're moving to a point
where, you know, researchers are,
200
00:11:33,409 --> 00:11:39,510
are beginning to get access to large
Comprehensive, de-identified dataset.
201
00:11:39,879 --> 00:11:41,380
It's a, it's a work in progress.
202
00:11:41,960 --> 00:11:45,650
And, I think it's a huge,
you know, huge opportunity.
203
00:11:45,679 --> 00:11:50,919
I have students doing work on natural
language processing to extract
204
00:11:50,919 --> 00:11:53,659
labeled data from doctors' notes.
205
00:11:53,710 --> 00:11:57,909
You know, doctors are recording their
notes now in machine-readable form,
206
00:11:58,270 --> 00:11:59,620
so we pull information out of it.
207
00:12:00,099 --> 00:12:05,470
I have students developing models to
predict adverse events before they happen.
208
00:12:05,499 --> 00:12:11,139
For, for example, for patients who are
on chemotherapy, they often wind up in
209
00:12:11,139 --> 00:12:15,159
the emergency department, and if we could
predict who might be doing that, we could
210
00:12:15,159 --> 00:12:17,220
maybe change their protocol a little bit.
211
00:12:18,060 --> 00:12:22,369
I tell, high school students about
industrial engineering and, in
212
00:12:22,369 --> 00:12:24,779
particular, information systems.
213
00:12:25,369 --> 00:12:29,709
And, I tell them that, you know,
like, computer science develops tools
214
00:12:29,709 --> 00:12:35,589
and computer engineers develop the
machines, and, industrial engineers help
215
00:12:35,619 --> 00:12:40,260
users apply these tools to make their
work easier, faster, more effective.
216
00:12:40,859 --> 00:12:43,539
You'd never send a computer
scientist to help a user.
217
00:12:43,539 --> 00:12:46,380
They would come up with
something inappropriate.
218
00:12:46,960 --> 00:12:51,329
And I think the same is true for AI,
that Indies really need to know about the
219
00:12:51,329 --> 00:12:56,899
tools and how to design systems to make
it easier for users to do their jobs.
220
00:12:57,469 --> 00:13:04,020
And I, and I think that's our, that's our
role, and, it's that, interface with the
221
00:13:04,729 --> 00:13:04,850
user.
222
00:13:04,850 --> 00:13:09,450
What's your big hope for the
field of industrial systems
223
00:13:09,450 --> 00:13:11,600
engineering in the future?
224
00:13:12,339 --> 00:13:15,710
Well, I th-- yeah, I think it's, it's--
In, in a sense, it hasn't changed, right?
225
00:13:15,710 --> 00:13:16,780
So you know- Yeah.
226
00:13:16,780 --> 00:13:21,450
-back to, back to, you know, the
Gilbreths, what they were trying to do
227
00:13:21,450 --> 00:13:27,180
was, you know, make it easier for people,
to do things and make it, you know, make
228
00:13:27,180 --> 00:13:28,930
people more efficient, more effective.
229
00:13:29,839 --> 00:13:34,880
industrial engineering, you know, in,
in my own area, in healthcare, we really
230
00:13:34,880 --> 00:13:39,259
didn't have industrial engineers, you
know, thirty-five years ago looking
231
00:13:39,259 --> 00:13:42,409
at, processes within healthcare.
232
00:13:42,890 --> 00:13:45,130
And, today they really are.
233
00:13:45,130 --> 00:13:50,059
You know, today we now have, in
every hospital that I know about,
234
00:13:50,570 --> 00:13:56,199
has, performance measurement and
pro-- improvement teams, reporting.
235
00:13:56,799 --> 00:14:02,409
they are tracking and, and,
measuring safety and quality metrics,
236
00:14:03,150 --> 00:14:06,540
optimization of, operational processes.
237
00:14:07,119 --> 00:14:12,550
So at this stage in your career,
what does receiving this award,
238
00:14:12,550 --> 00:14:15,510
the Frank and Lillian Gilbreth
Award, what does it mean to you?
239
00:14:16,210 --> 00:14:17,380
Oh, it's, it's huge.
240
00:14:17,380 --> 00:14:20,100
It's, like it, it, it is truly special.
241
00:14:20,100 --> 00:14:21,010
It was unexpected.
242
00:14:21,010 --> 00:14:21,119
Mm-hmm.
243
00:14:21,920 --> 00:14:26,210
You know, I think the, you know, the
Gilbreths were pioneers in the application
244
00:14:26,210 --> 00:14:30,349
of industrial engineering concepts,
and it's, it's a privilege and an honor
245
00:14:30,349 --> 00:14:32,310
to be counted among people like them.
246
00:14:33,070 --> 00:14:37,210
You know, I, I, I don't think of
myself as a pioneer like them, but it
247
00:14:37,210 --> 00:14:39,589
was sure up the old battle for years.
248
00:14:40,010 --> 00:14:45,760
I, I've had several awards from Canadian
engineering, and, and healthcare
249
00:14:46,130 --> 00:14:53,170
organizations, but, the- getting this
from, IISE, the, it- it's international.
250
00:14:53,920 --> 00:14:59,030
And, I, you know, I, I look around
at, colleagues in other areas and
251
00:14:59,030 --> 00:15:04,350
going like, "Why me?" And, like I say,
I was really surprised and honored.
252
00:15:04,959 --> 00:15:08,470
Well, you've, you've influenced
many people throughout your career,
253
00:15:08,679 --> 00:15:13,790
many students, and so your impact,
it's directly on your students,
254
00:15:13,790 --> 00:15:17,639
but then it's also through what
your students have gone on to do.
255
00:15:17,639 --> 00:15:22,620
So w- if you s- take a moment to stop
and reflect on that kind of ripple
256
00:15:22,620 --> 00:15:26,989
effect that you've had, I mean, how
does that make you feel when you stop
257
00:15:26,989 --> 00:15:28,260
to think about that for a minute?
258
00:15:28,669 --> 00:15:32,220
I mean, that, that's... You know, you
know, a lot of the th- the things that
259
00:15:32,809 --> 00:15:35,179
I have accomplished, I didn't really do.
260
00:15:35,319 --> 00:15:42,190
I mean, what I did was I, I, I encouraged
students to get out into healthcare.
261
00:15:43,120 --> 00:15:46,050
and you know, I've been doing this
for so long now, many of them have
262
00:15:46,410 --> 00:15:50,450
worked their way up the food chain,
and they are making a difference.
263
00:15:51,020 --> 00:15:54,540
And they are, they are changing things.
264
00:15:55,010 --> 00:15:59,480
And so I sort of... I've, I've
created a path for them to go
265
00:15:59,480 --> 00:16:00,830
out and do wonderful things.
266
00:16:01,249 --> 00:16:06,190
So I, to me, this is, this is
just, th- this is, this is amazing.
267
00:16:06,220 --> 00:16:10,059
I just sit back and watch them
and go, and go, "Wow," you know.
268
00:16:11,499 --> 00:16:15,750
Dr. Carter, congratulations on your very
well-deserved honor receiving the Frank
269
00:16:15,750 --> 00:16:19,890
and Lillian Gilbreth Award, and thank you
very, very much for talking with us today.
270
00:16:19,920 --> 00:16:20,650
I appreciate it.
271
00:16:21,175 --> 00:16:22,614
Thank you very much, Elizabeth.
272
00:16:23,044 --> 00:16:24,125
It's a pleasure.
273
00:16:26,105 --> 00:16:30,745
Dr. Michael Carter, recipient of the
2026 Frank and Lillian Gilbreth Award.
274
00:16:31,145 --> 00:16:34,445
And hearing Dr. Carter reflect
on the Gilbreths themselves
275
00:16:34,884 --> 00:16:36,365
raised another question for us.
276
00:16:36,844 --> 00:16:40,545
Who exactly were Frank and Lillian
Gilbreth beyond the names attached
277
00:16:40,545 --> 00:16:43,494
to the awards, scholarships,
and engineering history?
278
00:16:43,904 --> 00:16:47,764
Next time on Problem Solved, we take
a deeper look at the real people
279
00:16:47,764 --> 00:16:52,144
behind the legacy with Mike Farrelly
of the Montclair History Center.
280
00:16:54,015 --> 00:16:58,354
He gave Frank a job as a helper,
and he started paying attention to
281
00:16:58,354 --> 00:17:02,405
how each individual bricklayer did
their job and noticed that they
282
00:17:02,405 --> 00:17:04,015
didn't all do it the same way.
283
00:17:05,175 --> 00:17:11,545
And he began to start
thinking about the best way.
284
00:17:11,545 --> 00:17:13,655
Nothing deterred Lillian.
285
00:17:14,685 --> 00:17:17,925
She was interested in the
psychology of how things worked.
286
00:17:19,435 --> 00:17:22,115
That, by the way, is one of the
biggest differences between them
287
00:17:22,144 --> 00:17:27,034
and Frederick Taylor, is they
were most, Frank and Lillian were
288
00:17:27,034 --> 00:17:29,055
very interested in the individual.
289
00:17:30,424 --> 00:17:37,375
And in many ways, even though she was
the quiet partner, she was the dynamo
290
00:17:38,905 --> 00:17:42,504
behind Frank and Lillian's collaboration.
291
00:17:43,074 --> 00:17:48,045
People would see this enormous family
coming at them and ask Frank, well,
292
00:17:48,045 --> 00:17:51,115
what's it like to have all these kids?
293
00:17:51,115 --> 00:17:52,884
And, oh, they're cheaper by the dozen.
294
00:17:53,625 --> 00:17:54,004
So
295
00:17:54,365 --> 00:17:58,824
We hope you'll join us next time for a
historic deep dive on Problem Solved.
