1
00:00:00,640 --> 00:00:03,199
Philips is a health tech leader focused on

2
00:00:03,199 --> 00:00:06,000
innovation that improves the health and well-being of

3
00:00:06,000 --> 00:00:09,460
people. Our health care technology and informatics solutions

4
00:00:09,599 --> 00:00:12,559
help care teams diagnose, treat, and manage more

5
00:00:12,559 --> 00:00:16,244
patients with greater precision, speed, and confidence across

6
00:00:16,244 --> 00:00:19,225
the care journey. With Philips, clinicians are empowered

7
00:00:19,285 --> 00:00:21,765
with streamlined insights in the moments that matter

8
00:00:21,765 --> 00:00:24,904
for every patient. Better care for more people.

9
00:00:25,204 --> 00:00:25,704
Philips.

10
00:00:29,010 --> 00:00:31,250
This is Gracelyn Keller with the Rutgers Healthcare

11
00:00:31,250 --> 00:00:33,090
Podcast, and we are live at the 9th

12
00:00:33,090 --> 00:00:36,630
Annual Health IT Digital Health and RCM Conference.

13
00:00:36,929 --> 00:00:39,329
I'm currently joined by Michael Hasselberg, who is

14
00:00:39,329 --> 00:00:42,274
the chief digital health officer at UR Medicine.

15
00:00:42,655 --> 00:00:44,254
Michael, thanks so much for joining me today.

16
00:00:44,254 --> 00:00:46,015
We'd love to have you introduce yourself a

17
00:00:46,015 --> 00:00:48,174
little bit further as we get started. Well,

18
00:00:48,174 --> 00:00:50,114
thanks for having me. Excited to be here.

19
00:00:50,254 --> 00:00:52,114
Hello, everyone. Michael Hasselberg.

20
00:00:52,655 --> 00:00:55,240
First and foremost, I'm a nurse, a psychiatric

21
00:00:55,299 --> 00:00:56,119
nurse practitioner.

22
00:00:56,820 --> 00:00:59,700
I started my career in a very rural

23
00:00:59,700 --> 00:01:01,219
part of New York state where I was

24
00:01:01,219 --> 00:01:01,880
the only

25
00:01:02,260 --> 00:01:04,980
psych provider for about 6 counties. So became

26
00:01:04,980 --> 00:01:07,814
very passionate about how do we get high

27
00:01:07,814 --> 00:01:10,935
quality care out to underserved patient populations. Ended

28
00:01:10,935 --> 00:01:11,834
up going on

29
00:01:12,215 --> 00:01:14,555
to get a doctorate, then a business background

30
00:01:14,935 --> 00:01:17,094
after that, and I I currently serve as

31
00:01:17,094 --> 00:01:19,989
the chief digital health officer for University of

32
00:01:19,989 --> 00:01:20,969
Rochester Medicine.

33
00:01:21,270 --> 00:01:24,090
Those who are not familiar with UR Medicine,

34
00:01:24,150 --> 00:01:26,069
we are a large academic health system,

35
00:01:26,629 --> 00:01:29,189
in upstate New York, the largest health system

36
00:01:29,189 --> 00:01:31,555
outside of the New York City. And in

37
00:01:31,555 --> 00:01:34,275
my role, I oversee the digital transformation and

38
00:01:34,275 --> 00:01:35,415
innovation strategies.

39
00:01:36,275 --> 00:01:38,674
Wonderful. Well, thanks for being here. And we're

40
00:01:38,674 --> 00:01:39,894
gonna start our conversation

41
00:01:40,194 --> 00:01:42,295
with AI adoption. This is

42
00:01:42,594 --> 00:01:45,009
exploding in health care, as you know. So

43
00:01:45,009 --> 00:01:46,849
in your view, what is the most significant

44
00:01:46,849 --> 00:01:49,109
or promising application of this technology,

45
00:01:49,489 --> 00:01:52,229
and how is that informing your organization's innovation

46
00:01:52,450 --> 00:01:52,950
strategy?

47
00:01:53,649 --> 00:01:55,090
Love the question. I don't think we could

48
00:01:55,090 --> 00:01:57,090
ever go with a conversation these days without

49
00:01:57,090 --> 00:01:59,534
talking about AI, so thank you for for

50
00:01:59,534 --> 00:02:01,614
bringing it up. You know, one of the

51
00:02:01,614 --> 00:02:03,715
things that I think is super

52
00:02:04,174 --> 00:02:05,614
exciting in terms of,

53
00:02:06,015 --> 00:02:06,915
AI applications

54
00:02:07,375 --> 00:02:11,215
are the actual generative AI foundation models themselves.

55
00:02:11,215 --> 00:02:12,914
So not any specific tool,

56
00:02:13,290 --> 00:02:14,189
not any specific

57
00:02:14,729 --> 00:02:15,229
application

58
00:02:15,530 --> 00:02:17,689
off of these foundation models, but just the

59
00:02:17,689 --> 00:02:19,710
foundation models. You know? I think

60
00:02:20,010 --> 00:02:22,490
that having access as a health system to

61
00:02:22,490 --> 00:02:25,710
these foundation models is essentially a game changer.

62
00:02:25,770 --> 00:02:28,909
It's it's level setting. It's never been easier

63
00:02:29,424 --> 00:02:32,064
for a health system like mine to develop

64
00:02:32,064 --> 00:02:34,784
our own AI tools using our own data

65
00:02:34,784 --> 00:02:36,405
building off of these pretrained

66
00:02:37,264 --> 00:02:40,324
large models that were already trained by

67
00:02:40,625 --> 00:02:42,004
big industry corporations.

68
00:02:42,305 --> 00:02:45,020
And so, you know, for an innovation team

69
00:02:45,020 --> 00:02:47,680
like mine, you know, we are now developing

70
00:02:47,740 --> 00:02:49,040
our own AI tools

71
00:02:49,500 --> 00:02:50,639
in a development

72
00:02:50,939 --> 00:02:52,699
time that has gone from 6 months to

73
00:02:52,699 --> 00:02:55,120
a year to essentially days at this point.

74
00:02:56,275 --> 00:02:58,455
And switching gears just slightly

75
00:02:58,914 --> 00:02:59,895
over to data,

76
00:03:00,194 --> 00:03:02,835
we're seeing health care leaders managing greater volumes

77
00:03:02,835 --> 00:03:05,314
of data and more devices on a growing

78
00:03:05,314 --> 00:03:08,354
number of care settings and population. So this

79
00:03:08,354 --> 00:03:11,189
complex environment that has kind of popped up,

80
00:03:11,349 --> 00:03:14,469
I'd love to know what your clinical data

81
00:03:14,469 --> 00:03:17,590
integration tools or practices that you're seeing drive

82
00:03:17,590 --> 00:03:20,550
improvements in patient outcomes, and if you could

83
00:03:20,550 --> 00:03:23,050
share an specific example of that as well.

84
00:03:23,349 --> 00:03:25,155
Yeah. You know, I think, you know, one

85
00:03:25,155 --> 00:03:29,074
of the the biggest wins thus far, specifically

86
00:03:29,235 --> 00:03:31,235
let's just stay with AI for a second,

87
00:03:31,235 --> 00:03:35,094
generative AI. It's forced health systems to make

88
00:03:35,314 --> 00:03:38,240
investments in their data infrastructure. And, you know,

89
00:03:38,240 --> 00:03:39,699
not to say that we didn't

90
00:03:40,000 --> 00:03:42,639
have investments in data infrastructure prior, but now

91
00:03:42,639 --> 00:03:45,680
it's even more important that we have a

92
00:03:45,680 --> 00:03:47,620
robust enterprise data warehouse

93
00:03:48,080 --> 00:03:50,340
that, you know, we have cleaned and curated,

94
00:03:50,719 --> 00:03:53,087
and that we have a governance process,

95
00:03:54,014 --> 00:03:56,014
over our data. So in terms of kind

96
00:03:56,014 --> 00:03:56,995
of best practices,

97
00:03:57,614 --> 00:03:59,294
you know, we have spent a lot of

98
00:03:59,294 --> 00:04:01,555
time specifically over this last year,

99
00:04:01,854 --> 00:04:04,114
you know, setting up what our data governance

100
00:04:04,175 --> 00:04:06,974
process is and, you know, essentially then on

101
00:04:06,974 --> 00:04:08,949
top of that, putting an an

102
00:04:09,250 --> 00:04:12,069
AI governance council together starting at the university

103
00:04:12,209 --> 00:04:14,289
level then down to the health system. And

104
00:04:14,289 --> 00:04:17,269
that's allowed us to really unleash our data

105
00:04:17,649 --> 00:04:20,229
and drive insights into our health system.

106
00:04:21,834 --> 00:04:25,595
And as we discuss data and AI and

107
00:04:25,595 --> 00:04:26,735
technological innovation,

108
00:04:27,194 --> 00:04:29,514
I'm curious your thoughts on how health care

109
00:04:29,514 --> 00:04:30,014
organizations

110
00:04:30,475 --> 00:04:33,115
can better support both IT and clinical teams

111
00:04:33,115 --> 00:04:35,939
as we're carrying out innovation efforts. And what

112
00:04:35,939 --> 00:04:38,019
are some common pitfalls that you've seen in

113
00:04:38,019 --> 00:04:38,519
this?

114
00:04:39,699 --> 00:04:41,399
You know, I'll I'll start with the pitfalls.

115
00:04:41,539 --> 00:04:43,319
You know, one of the things that,

116
00:04:44,339 --> 00:04:47,220
is, I think, pretty common across health care

117
00:04:47,220 --> 00:04:48,404
is silos.

118
00:04:48,705 --> 00:04:50,404
You know, we have a lot of,

119
00:04:51,345 --> 00:04:51,845
disciplines,

120
00:04:52,305 --> 00:04:53,845
you know, that work in silos,

121
00:04:54,464 --> 00:04:58,064
and what silos create is essentially groupthink. And

122
00:04:58,064 --> 00:04:59,045
groupthink is

123
00:04:59,584 --> 00:05:01,524
the downfall of all innovation,

124
00:05:02,064 --> 00:05:04,189
in my opinion. You know, one of

125
00:05:04,569 --> 00:05:07,449
the things that is unique about University of

126
00:05:07,449 --> 00:05:09,689
Rochester Medicine is we're one of only a

127
00:05:09,689 --> 00:05:12,169
handful of academic medical centers left in the

128
00:05:12,169 --> 00:05:14,649
country that is still fully integrated into our

129
00:05:14,649 --> 00:05:16,810
parent university. And so when we think of

130
00:05:16,810 --> 00:05:17,310
innovation,

131
00:05:17,904 --> 00:05:19,524
we have a very diverse

132
00:05:20,064 --> 00:05:22,704
group of thinkers around the table where we've

133
00:05:22,704 --> 00:05:25,665
essentially broken down those silos. And I have,

134
00:05:25,665 --> 00:05:28,064
you know, faculty from my engineering school and

135
00:05:28,064 --> 00:05:30,544
computer science department and data sciences to even

136
00:05:30,544 --> 00:05:32,485
faculty from my music school

137
00:05:32,785 --> 00:05:33,285
sitting

138
00:05:34,360 --> 00:05:37,240
elbow to elbow with faculty from my medical

139
00:05:37,240 --> 00:05:39,720
school, dental school, and nursing school. And it's

140
00:05:39,720 --> 00:05:42,120
when you have that diverse thought process that

141
00:05:42,120 --> 00:05:43,879
we can really come up with some out

142
00:05:43,879 --> 00:05:44,620
of the box

143
00:05:45,399 --> 00:05:47,879
innovations to solve some of the most pressing

144
00:05:47,879 --> 00:05:49,420
problems within our health system.

145
00:05:50,194 --> 00:05:52,754
And as we wrap our conversation today, I'd

146
00:05:52,754 --> 00:05:54,355
love to know your top piece of advice

147
00:05:54,355 --> 00:05:56,675
for health care leaders as we prepare for

148
00:05:56,675 --> 00:05:59,555
further advancements in technology and greater demands for

149
00:05:59,555 --> 00:06:00,055
care.

150
00:06:00,595 --> 00:06:02,935
You know, be okay with failure.

151
00:06:03,395 --> 00:06:06,310
Just be able to fail fast. You know,

152
00:06:06,310 --> 00:06:07,689
this technology is

153
00:06:08,230 --> 00:06:11,110
is moving at such rapid pace, and there's

154
00:06:11,110 --> 00:06:12,949
so much that we don't know about it.

155
00:06:12,949 --> 00:06:14,949
And when we go in and test these

156
00:06:14,949 --> 00:06:17,210
tools and applications within the health system,

157
00:06:17,514 --> 00:06:19,915
we're bound to have failures. But to be

158
00:06:19,915 --> 00:06:22,314
okay with that, because it's from learning from

159
00:06:22,314 --> 00:06:25,194
those failures that we can really develop the

160
00:06:25,194 --> 00:06:27,535
solutions that are are are gonna drive improvements

161
00:06:27,595 --> 00:06:29,535
to our patient outcomes

162
00:06:29,995 --> 00:06:33,214
and outcomes for our clinicians delivering that care.

163
00:06:33,970 --> 00:06:36,129
Absolutely. Well, thanks so much for joining me

164
00:06:36,129 --> 00:06:38,849
today on the Becker's healthcare podcast. Again, we

165
00:06:38,849 --> 00:06:41,169
are live at the 9th annual health IT

166
00:06:41,169 --> 00:06:43,349
digital health and RCM conference.

167
00:06:43,649 --> 00:06:45,250
Thank you for having me. It's a lot

168
00:06:45,250 --> 00:06:45,909
of fun.