1 00:00:00,000 --> 00:00:05,860 Welcome to the Clinician Researcher podcast, where academic clinicians learn the skills 2 00:00:05,860 --> 00:00:11,260 to build their own research program, whether or not they have a mentor. 3 00:00:11,260 --> 00:00:17,340 As clinicians, we spend a decade or more as trainees learning to take care of patients. 4 00:00:17,340 --> 00:00:22,380 When we finally start our careers, we want to build research programs, but then we find 5 00:00:22,380 --> 00:00:27,780 that our years of clinical training did not adequately prepare us to lead our research 6 00:00:27,780 --> 00:00:29,200 program. 7 00:00:29,200 --> 00:00:35,480 Through no fault of our own, we struggle to find mentors, and when we can't, we quit. 8 00:00:35,480 --> 00:00:40,580 However, clinicians hold the keys to the greatest research breakthroughs. 9 00:00:40,580 --> 00:00:46,200 For this reason, the Clinician Researcher podcast exists to give academic clinicians 10 00:00:46,200 --> 00:00:51,800 the tools to build their own research program, whether or not they have a mentor. 11 00:00:51,800 --> 00:01:01,100 Now introducing your host, Toyosi Onwuemene. 12 00:01:01,100 --> 00:01:03,560 Welcome to the Clinician Researcher podcast. 13 00:01:03,560 --> 00:01:07,440 I'm your host Toyosi Onwuemene, and it is such a privilege to be here. 14 00:01:07,440 --> 00:01:12,760 I am super excited about today's episode because I have a really extra special guest. 15 00:01:12,760 --> 00:01:14,920 It's Dr. Eman Metwaly. 16 00:01:14,920 --> 00:01:16,780 And Iman, I want to thank you. 17 00:01:16,780 --> 00:01:17,780 Welcome to the show. 18 00:01:17,780 --> 00:01:18,780 Hi, Teoci. 19 00:01:18,780 --> 00:01:21,540 Actually, I want to thank you too. 20 00:01:21,540 --> 00:01:25,440 Thank you so much for the invitation, and I'm really excited. 21 00:01:25,440 --> 00:01:27,040 Thank you for being here. 22 00:01:27,040 --> 00:01:30,180 So Iman, the audience is excited to get to know you. 23 00:01:30,180 --> 00:01:34,680 I want you to introduce yourself to the audience, especially from the perspective of yourself 24 00:01:34,680 --> 00:01:37,160 as a clinician and a researcher. 25 00:01:37,160 --> 00:01:38,160 Yeah. 26 00:01:38,160 --> 00:01:43,160 So currently, I am a second year post-doctoral center in the epidemiology department of the 27 00:01:43,160 --> 00:01:48,360 School of Public Health at the University of North Carolina, Chappell Hand. 28 00:01:48,360 --> 00:01:51,280 I used actually to be a pulmonary and critical care physician. 29 00:01:51,280 --> 00:01:56,680 I practiced for nine years in Egypt as a pulmonary and critical care. 30 00:01:56,680 --> 00:02:03,260 So during my residency and then my PhD program after I finished my residency, and then maybe 31 00:02:03,260 --> 00:02:07,280 one year after, I practiced pulmonary medicine. 32 00:02:07,280 --> 00:02:12,600 Then when I immigrated here to the U.S., I studied biomedical health informatics, and 33 00:02:12,600 --> 00:02:16,840 then I joined UNC as a research fellow. 34 00:02:16,840 --> 00:02:19,240 And this is my current position nowadays. 35 00:02:19,240 --> 00:02:20,240 Wow. 36 00:02:20,240 --> 00:02:22,120 Iman, thank you for sharing. 37 00:02:22,120 --> 00:02:28,000 So you have a unique story where you actually finished your clinical training, and you practiced 38 00:02:28,000 --> 00:02:31,720 as a clinician, and then you made the transition to research. 39 00:02:31,720 --> 00:02:37,800 So I would love for you to share with our audience, what were the big ahas for you, 40 00:02:37,800 --> 00:02:43,500 where you had already been the expert clinically, and now you are kind of starting training 41 00:02:43,500 --> 00:02:45,120 as a researcher? 42 00:02:45,120 --> 00:02:47,320 What were the big surprises for you? 43 00:02:47,320 --> 00:02:48,680 Well, thank you. 44 00:02:48,680 --> 00:02:51,440 This is a great question, and I really love this question. 45 00:02:51,440 --> 00:02:52,440 When did it start? 46 00:02:52,440 --> 00:02:54,080 When did the spark start? 47 00:02:54,080 --> 00:02:57,200 So training started at the bedside. 48 00:02:57,200 --> 00:03:02,040 So I was a resident, maybe towards the end of my third year as a pulmonary and critical 49 00:03:02,040 --> 00:03:03,040 care resident. 50 00:03:03,040 --> 00:03:07,340 And the system there in Egypt is a little bit different with me back up. 51 00:03:07,340 --> 00:03:13,900 So in Egypt, we do six years for medical school, and then one year as an internship, and then 52 00:03:13,900 --> 00:03:16,360 we apply for our residency. 53 00:03:16,360 --> 00:03:21,320 And the residency does not start with internal medicine, it just starts with the specialty. 54 00:03:21,320 --> 00:03:27,440 So I went straight from the internship to pulmonary and critical care specialty. 55 00:03:27,440 --> 00:03:33,480 And as I said, I was interacting with patients with lung cancer and chronic obstructive pulmonary 56 00:03:33,480 --> 00:03:35,040 disease. 57 00:03:35,040 --> 00:03:41,800 And treaties got my attention into how the patients, for example, with lung cancer, would 58 00:03:41,800 --> 00:03:49,560 be staged based on radiology, CT, and other investigations into stage one. 59 00:03:49,560 --> 00:03:54,840 And maybe after following up this patient, you would find different trajectory for them. 60 00:03:54,840 --> 00:03:59,400 Like some patients with stage one would do very well, and some patients with stage one 61 00:03:59,400 --> 00:04:01,720 would do very unwell. 62 00:04:01,720 --> 00:04:03,560 And some would have the same for survival. 63 00:04:03,560 --> 00:04:07,340 It's not only like their quality of life, but also for survival. 64 00:04:07,340 --> 00:04:13,720 So this heterogeneity in the patient's outcome, despite they have the same classification 65 00:04:13,720 --> 00:04:19,640 that we classified them initially on clinical side, made me think about the heterogeneity 66 00:04:19,640 --> 00:04:28,040 of disease and the link between how we classify and diagnose patient and put them into boxes 67 00:04:28,040 --> 00:04:33,880 based on our classification, like lung cancer based on the stage and the histology, COPD 68 00:04:33,880 --> 00:04:37,600 based on the pulmonary function severity. 69 00:04:37,600 --> 00:04:43,440 And how this box that we put the patient in, this are in their outcome when we follow up 70 00:04:43,440 --> 00:04:48,960 them during their clinical course and during our clinical care for them. 71 00:04:48,960 --> 00:04:54,220 So I started in, during residency in Egypt, we have to prepare like a master degree. 72 00:04:54,220 --> 00:05:02,080 So I prepared my proposal was about like description of the clinical profile, histology, radiology 73 00:05:02,080 --> 00:05:03,520 for patients with lung cancer. 74 00:05:03,520 --> 00:05:06,320 That is attending our university hospital. 75 00:05:06,320 --> 00:05:14,240 So the hospital that I worked in was a tertiary level hospital that served a big area in Egypt. 76 00:05:14,240 --> 00:05:20,120 It's not only in Alexandria city, but also in the area, like the small towns around Alexandria 77 00:05:20,120 --> 00:05:22,320 city. 78 00:05:22,320 --> 00:05:31,400 So I had maybe around more than 300 patients for my study, 112 of them had lung cancer 79 00:05:31,400 --> 00:05:35,920 and the rest were controls, patients who did not have lung cancer. 80 00:05:35,920 --> 00:05:40,960 And I started to look up into these patients at all levels from how they are diagnosed 81 00:05:40,960 --> 00:05:45,400 initially and how their diagnosis was confirmed. 82 00:05:45,400 --> 00:05:50,560 And later on their trajectory towards treatment, who got surgical treatment and who got like 83 00:05:50,560 --> 00:05:52,920 chemotherapy or radiotherapy. 84 00:05:52,920 --> 00:05:59,080 And I got fascinated by, as I said, how much different their course would end up despite 85 00:05:59,080 --> 00:06:04,800 they had been classified initially into the same category, based on clinical side. 86 00:06:04,800 --> 00:06:12,120 Then after finishing my master degree, I, in the same year, it was around 2011 maybe, 87 00:06:12,120 --> 00:06:14,000 I attached it to the BHD program. 88 00:06:14,000 --> 00:06:19,320 And my initial proposal was actually to continue into understanding more about heterogeneity 89 00:06:19,320 --> 00:06:20,880 of lung cancer. 90 00:06:20,880 --> 00:06:26,520 I was looking into doing some, you know, molecular signature type of study to understand how 91 00:06:26,520 --> 00:06:32,200 the genotyping of these patients might explain the heterogeneity in their clinical outcome 92 00:06:32,200 --> 00:06:33,200 later on. 93 00:06:33,200 --> 00:06:40,840 But the techniques that is necessary for doing the genotyping and the molecular data analysis 94 00:06:40,840 --> 00:06:44,760 was not feasible to me at the institution I worked in. 95 00:06:44,760 --> 00:06:47,360 So I had to switch gears. 96 00:06:47,360 --> 00:06:53,680 So I'm glad because I said, okay, so maybe lung cancer was very complex and, you know, 97 00:06:53,680 --> 00:06:57,820 the neoplasm stuff and the progression of the disease is very aggressive. 98 00:06:57,820 --> 00:07:01,980 So maybe that's why there was heterogeneous outcome in the patient. 99 00:07:01,980 --> 00:07:08,420 Let's switch to another maybe chronic, more benign disease, and that's what COVD. 100 00:07:08,420 --> 00:07:13,320 And then after, just not to make it like a long story, after I started the clinical profile 101 00:07:13,320 --> 00:07:18,740 again, radiology, and yeah, at that time during my PhD, I had to do something more complicated. 102 00:07:18,740 --> 00:07:24,360 So I also did endoscopic visualization of their airways and found that the heterogeneity 103 00:07:24,360 --> 00:07:25,960 is so severe. 104 00:07:25,960 --> 00:07:30,860 The more you go into detail, the more you examine the patient, not only clinically and 105 00:07:30,860 --> 00:07:36,120 maybe physiologically and based on the lab, but the more you go inside the patient by 106 00:07:36,120 --> 00:07:41,240 the endoscope, for example, in my case, and looking into how their endobronchial erythema 107 00:07:41,240 --> 00:07:47,520 is visualized and process their samples from the bronchial wall, the more you discover 108 00:07:47,520 --> 00:07:53,100 the shortage of how we classify them based on the clinical side. 109 00:07:53,100 --> 00:07:59,760 So I got fascinated by that we should improve classification of our diseases. 110 00:07:59,760 --> 00:08:02,160 And these are common diseases, COVD and lung cancer. 111 00:08:02,160 --> 00:08:08,560 They are very common everywhere in the world, not only in developed or developing countries. 112 00:08:08,560 --> 00:08:14,140 So that was like the thing that's pushing me that we have some, I might have something 113 00:08:14,140 --> 00:08:20,520 that I can contribute to improve how the patient can be classified. 114 00:08:20,520 --> 00:08:25,320 Because our classification determine our management of the disease. 115 00:08:25,320 --> 00:08:30,560 So if we classify them more precisely, we would be able to treat them more precisely 116 00:08:30,560 --> 00:08:33,840 and accordingly we will improve their outcome at the end. 117 00:08:33,840 --> 00:08:36,040 So yeah, that's a lot. 118 00:08:36,040 --> 00:08:37,040 Wow. 119 00:08:37,040 --> 00:08:38,040 No, thank you for sharing. 120 00:08:38,040 --> 00:08:41,560 One of the things I have to say is, first of all, I love the passion with which you 121 00:08:41,560 --> 00:08:42,800 speak about the work. 122 00:08:42,800 --> 00:08:44,800 I mean, it's just so awesome. 123 00:08:44,800 --> 00:08:49,240 And another thing I see is that, so you're talking about lung cancer classification or 124 00:08:49,240 --> 00:08:52,440 COPD classification or classification of lung diseases. 125 00:08:52,440 --> 00:08:54,640 And these are diseases that have been classified. 126 00:08:54,640 --> 00:08:57,980 But as a clinician, you could see that there's a gap. 127 00:08:57,980 --> 00:09:02,600 And so the classification doesn't always help you tell how they're going to do. 128 00:09:02,600 --> 00:09:07,240 And so you're seeing opportunity for a new or a more refined classification. 129 00:09:07,240 --> 00:09:13,560 And so we already have solutions, but you're looking for finer, more specific solutions 130 00:09:13,560 --> 00:09:17,440 so that we can classify patients according to how they actually do because it's going 131 00:09:17,440 --> 00:09:18,440 to affect outcomes. 132 00:09:18,440 --> 00:09:23,400 I love the excitement that you talk about with which you talk about it. 133 00:09:23,400 --> 00:09:24,600 It's so awesome. 134 00:09:24,600 --> 00:09:30,840 I want you to speak to what are the advantages you've had as a clinician who's gone into 135 00:09:30,840 --> 00:09:34,040 kind of like a very solitary research focus. 136 00:09:34,040 --> 00:09:38,320 And what are the disadvantages of being a clinician in this space? 137 00:09:38,320 --> 00:09:41,720 Yeah, thank you for asking this question. 138 00:09:41,720 --> 00:09:48,920 So I feel that one of the biggest advantage I had as a clinician is that I was at the 139 00:09:48,920 --> 00:09:51,320 bedside of the patient. 140 00:09:51,320 --> 00:09:57,280 I felt the patient, how the patient is confused at the beginning, their suffering to know 141 00:09:57,280 --> 00:09:59,680 their diagnosis. 142 00:09:59,680 --> 00:10:01,920 Are they like, and their prognosis. 143 00:10:01,920 --> 00:10:03,100 How much are they going? 144 00:10:03,100 --> 00:10:08,000 How much time I have doctors, especially when it is a new blast, getting in touch with their 145 00:10:08,000 --> 00:10:14,680 caregivers, knowing about the burden of caring for a patient with lung cancer or whatever. 146 00:10:14,680 --> 00:10:20,920 And at that time during my residency, I was around 2008 and there was in the institution 147 00:10:20,920 --> 00:10:26,920 I worked at and there was no much, if I would say like targeted treatment or maybe advanced 148 00:10:26,920 --> 00:10:29,320 treatment for lung cancer. 149 00:10:29,320 --> 00:10:35,040 So even if the patient was diagnosed at an early stage, it was like kind of maybe an 150 00:10:35,040 --> 00:10:38,680 early notice of death for their caregivers. 151 00:10:38,680 --> 00:10:42,460 And some would like even hide the diagnosis from their patient. 152 00:10:42,460 --> 00:10:51,380 So feeling all of that, I think was understand like the force that is moving me and pushing 153 00:10:51,380 --> 00:10:57,240 me forward to like what I am doing, the research I'm doing now will make a difference, not 154 00:10:57,240 --> 00:11:01,560 only in the life of the patient, but also in the life of the people who are surrounding 155 00:11:01,560 --> 00:11:04,240 the patient, who are taking care of the patient. 156 00:11:04,240 --> 00:11:12,360 So this was one of the biggest advantage I feel that I had and that is living with me 157 00:11:12,360 --> 00:11:17,120 now even after I left the clinical practice side. 158 00:11:17,120 --> 00:11:23,340 Another point is that the mentorship, I really was like, it was my pleasure actually to work 159 00:11:23,340 --> 00:11:30,360 under the supervision of great mentors who despite many of the like maybe like there 160 00:11:30,360 --> 00:11:37,520 was in, I was working in a university hospital, tertiary level hospital, but not all the techniques 161 00:11:37,520 --> 00:11:41,360 that we need to conduct the research was there. 162 00:11:41,360 --> 00:11:46,800 And actually one of my like mentorship team like connected me with some of the mentors 163 00:11:46,800 --> 00:11:50,800 or some of his colleagues and friends in Europe. 164 00:11:50,800 --> 00:11:55,800 And we started to talk about maybe some of the stuff that we can do together, but again, 165 00:11:55,800 --> 00:12:00,720 some of the regulations, especially regarding like transferring some of the samples to be 166 00:12:00,720 --> 00:12:09,360 processed overseas was like kind of not allowed maybe from this point. 167 00:12:09,360 --> 00:12:16,880 And it stopped the research from going farther, but at least just having access to other like 168 00:12:16,880 --> 00:12:22,120 the experts in this field through my mentors was a great advantage. 169 00:12:22,120 --> 00:12:23,840 And it was like, there is no limit. 170 00:12:23,840 --> 00:12:29,220 You can reach there or maybe after you finish your PhD here, maybe you can travel overseas 171 00:12:29,220 --> 00:12:32,620 to continue your line of research. 172 00:12:32,620 --> 00:12:39,240 So I was fortunate to have these mentors as role models and as like someone who always 173 00:12:39,240 --> 00:12:45,880 encourages someone who listen despite all, you know, I was doing my PhD in palm tree 174 00:12:45,880 --> 00:12:52,680 medicine at the same time I was practicing and just having someone who supervise you 175 00:12:52,680 --> 00:12:56,760 who is flexible, who is understanding was a big thing to me. 176 00:12:56,760 --> 00:13:03,780 And then some of this advantage is that not everything was bright and good. 177 00:13:03,780 --> 00:13:10,400 Like as I had some good mentors, I also had some other voices who would tell me because 178 00:13:10,400 --> 00:13:17,640 who would tell me like, why you are aiming too big in your research? 179 00:13:17,640 --> 00:13:23,400 Why you are like, like part of my PhD actually research was done in collaboration with Harvard 180 00:13:23,400 --> 00:13:24,400 University. 181 00:13:24,400 --> 00:13:30,400 So I was in Egypt in doing my research and I was sending them some of the image radiological 182 00:13:30,400 --> 00:13:37,720 images over the email and they were developing a new software for quantitative image analysis. 183 00:13:37,720 --> 00:13:43,600 And their software was not yet on, you know, ready to be used, but they really helped with 184 00:13:43,600 --> 00:13:46,680 me and we were like experimenting together. 185 00:13:46,680 --> 00:13:55,160 How can we use my relatively primitive CT images to get into how the COPD can be classified 186 00:13:55,160 --> 00:13:59,100 in a quantitative manner? 187 00:13:59,100 --> 00:14:05,520 So while I was doing that, I heard other voices actually from around me in the institution 188 00:14:05,520 --> 00:14:08,240 I work in and why I'm going too far? 189 00:14:08,240 --> 00:14:09,720 Why are you doing this? 190 00:14:09,720 --> 00:14:13,640 Maybe this will be your last maybe research you are doing in this area. 191 00:14:13,640 --> 00:14:19,160 So just finish, you know, finish and do a good job, but you don't have to be very sophisticated 192 00:14:19,160 --> 00:14:20,160 to this extent. 193 00:14:20,160 --> 00:14:22,280 But I didn't listen to that. 194 00:14:22,280 --> 00:14:26,520 Like I know that if I wanted to do something, I want to, like, I don't know what will come 195 00:14:26,520 --> 00:14:27,520 next. 196 00:14:27,520 --> 00:14:30,360 I didn't know that I'm going to immigrate to the US. 197 00:14:30,360 --> 00:14:32,200 I didn't know anything about that. 198 00:14:32,200 --> 00:14:39,960 But I was fascinated when I go, when I finish my like here for my patient and at when I 199 00:14:39,960 --> 00:14:45,600 at the end of the day at night, I would go into my laptop and I greet other people research 200 00:14:45,600 --> 00:14:48,880 in this area and I would say there are a lot to do. 201 00:14:48,880 --> 00:14:49,880 There are lots and rules. 202 00:14:49,880 --> 00:14:53,160 There are a lot that I can collaborate on. 203 00:14:53,160 --> 00:14:56,720 I can bring to the, you know, to the table. 204 00:14:56,720 --> 00:15:02,200 And that's really what fascinated me about research is that no matter your background 205 00:15:02,200 --> 00:15:09,640 is, no matter your country of origin is, no matter your, how you look is, it doesn't matter 206 00:15:09,640 --> 00:15:14,080 as long as you are going to have a good idea and you will have the good people to collaborate 207 00:15:14,080 --> 00:15:16,380 with it will work. 208 00:15:16,380 --> 00:15:24,160 So yeah, so that's how I tried to use my advantage to overcome my disadvantage. 209 00:15:24,160 --> 00:15:27,880 And I think that I did something good. 210 00:15:27,880 --> 00:15:30,840 Yeah, thank you so much. 211 00:15:30,840 --> 00:15:33,200 That was, that was really, really insightful. 212 00:15:33,200 --> 00:15:39,280 I hear you talk about just the relationships with mentors that really helped you move forward 213 00:15:39,280 --> 00:15:43,860 and allowed you to find the expertise you needed to move your research forward. 214 00:15:43,860 --> 00:15:48,400 But then also people who were naysayers who say, well, you're moving too fast. 215 00:15:48,400 --> 00:15:49,600 You're trying to do too much. 216 00:15:49,600 --> 00:15:51,320 You're being too sophisticated. 217 00:15:51,320 --> 00:15:53,260 Slow down or pull back. 218 00:15:53,260 --> 00:15:56,320 And really you had a sense that this was so important. 219 00:15:56,320 --> 00:16:00,020 And that's why even though people told you don't do it, you still moved forward, which 220 00:16:00,020 --> 00:16:01,540 is so awesome. 221 00:16:01,540 --> 00:16:04,600 And I just want to encourage our listeners because this is something that comes up all 222 00:16:04,600 --> 00:16:09,080 the time where people will hold you back or people will say you're trying too hard or, 223 00:16:09,080 --> 00:16:11,880 you know, slow down or try a different perspective. 224 00:16:11,880 --> 00:16:16,840 I think what I'm hearing, and you didn't say it explicitly, Iman, was just that your heart 225 00:16:16,840 --> 00:16:22,720 and your gut, like your sense of the importance of your work really does matter. 226 00:16:22,720 --> 00:16:26,880 And even when you don't find support, it's important to continue to push forward. 227 00:16:26,880 --> 00:16:27,880 Yes, exactly. 228 00:16:27,880 --> 00:16:33,400 Thank you for liking it, for using it in maybe a much better way than mine. 229 00:16:33,400 --> 00:16:34,400 It is this. 230 00:16:34,400 --> 00:16:35,400 Yeah, it is this. 231 00:16:35,400 --> 00:16:39,400 The message is that when you believe in the cause, even when you believe that the cause 232 00:16:39,400 --> 00:16:43,280 is bigger than me, it is not me that I wanted to do it. 233 00:16:43,280 --> 00:16:49,720 It's just that I wanted to be part in order to push this, you know, push this cause to 234 00:16:49,720 --> 00:16:51,560 a better place. 235 00:16:51,560 --> 00:16:54,480 Push our patient care to a better place. 236 00:16:54,480 --> 00:16:55,480 And I see some light. 237 00:16:55,480 --> 00:16:59,840 There are some light here and not a lot of people are paying attention to this area. 238 00:16:59,840 --> 00:17:01,720 So let's highlight it. 239 00:17:01,720 --> 00:17:05,600 I don't know how I'm going to highlight it, but I will keep pushing and see how it goes 240 00:17:05,600 --> 00:17:06,600 from there. 241 00:17:06,600 --> 00:17:07,880 So absolutely. 242 00:17:07,880 --> 00:17:10,360 And that's where great breakthroughs come. 243 00:17:10,360 --> 00:17:14,840 So one thing you talked about that I really want us to talk about is collaboration. 244 00:17:14,840 --> 00:17:20,320 So now you're a full-time researcher, sometimes looking to collaborate with clinicians. 245 00:17:20,320 --> 00:17:26,400 Tell me about how clinician scientists should think about their collaborations and especially 246 00:17:26,400 --> 00:17:28,960 with regard to the clinician perspective. 247 00:17:28,960 --> 00:17:30,440 Yeah. 248 00:17:30,440 --> 00:17:38,080 So let me talk about when I started as a clinician who collaborated during my residency and my 249 00:17:38,080 --> 00:17:39,080 PhD study. 250 00:17:39,080 --> 00:17:43,160 Like I did research in lung cancer and in COPD. 251 00:17:43,160 --> 00:17:47,840 And at that time I was collaborating with other people who maybe are not practicing 252 00:17:47,840 --> 00:17:54,000 with patients like pathology professors and those from the community medicine or public 253 00:17:54,000 --> 00:17:56,820 health school in Egypt. 254 00:17:56,820 --> 00:18:03,160 So really as a clinician, I had the idea that I wanted to look into this question, but I 255 00:18:03,160 --> 00:18:11,800 didn't know that I should go maybe might, I should might have gone earlier in my research 256 00:18:11,800 --> 00:18:20,440 question phase to maybe an epidemiology expert just to see how I should design them. 257 00:18:20,440 --> 00:18:25,420 I study how many patients are required, sample size calculation, all of these details. 258 00:18:25,420 --> 00:18:31,680 So being a clinician who wanted to do something like at population level, I should have gone 259 00:18:31,680 --> 00:18:38,480 earlier to someone who's expert just to give me guidelines about how many should I enroll, 260 00:18:38,480 --> 00:18:40,420 what are the settings of enrollment. 261 00:18:40,420 --> 00:18:41,420 These are important. 262 00:18:41,420 --> 00:18:46,080 So collaborating and engaging and talking about your research question while you are 263 00:18:46,080 --> 00:18:52,480 a clinician with a large, larger group of people, people who are outside your departments, 264 00:18:52,480 --> 00:18:54,840 people who are not practicing medicine. 265 00:18:54,840 --> 00:19:00,200 Even what I learned later on is that just talk it with the day people, people in your 266 00:19:00,200 --> 00:19:06,760 family, see how they see that research question and its impact on whatever their friends or 267 00:19:06,760 --> 00:19:09,540 whoever had the disease and they know of. 268 00:19:09,540 --> 00:19:14,920 So listening to many perspectives would add a lot of depth to your research question. 269 00:19:14,920 --> 00:19:21,200 So this is a thing when I came here to, and be patient. 270 00:19:21,200 --> 00:19:28,160 So as a clinician, we have also like narrow time maybe to spend in research and we had 271 00:19:28,160 --> 00:19:31,480 in our mind, like some things are common sense. 272 00:19:31,480 --> 00:19:36,560 Like the collaborator in front of me should have, you know, understand it from my first 273 00:19:36,560 --> 00:19:38,600 time I have said it. 274 00:19:38,600 --> 00:19:41,600 But the actual or the reality is not that. 275 00:19:41,600 --> 00:19:46,480 Like what is common sense to you from your perspective is not necessarily the same from 276 00:19:46,480 --> 00:19:48,200 the other person perspective. 277 00:19:48,200 --> 00:19:54,840 So maybe talking about your research in different ways, visualize your research question, visualize 278 00:19:54,840 --> 00:19:58,040 why it is needed in different ways and multiple ways. 279 00:19:58,040 --> 00:20:02,160 And now it is very easy because we have a lot of visualization tools and we have a lot 280 00:20:02,160 --> 00:20:07,520 of evidence that is available, you know, online from previous research. 281 00:20:07,520 --> 00:20:11,760 All of this like advertise for your research question. 282 00:20:11,760 --> 00:20:17,280 And if you don't know how to talk about it, eloquently talk with the people from your, 283 00:20:17,280 --> 00:20:21,880 you know, field who can talk about it and, you know, steal their words. 284 00:20:21,880 --> 00:20:26,120 If this is like after under their permission. 285 00:20:26,120 --> 00:20:31,880 The whole idea is try to highlight the need for your research question as much possible 286 00:20:31,880 --> 00:20:33,440 as you can. 287 00:20:33,440 --> 00:20:36,960 Use all the resources around you and be patient. 288 00:20:36,960 --> 00:20:40,160 So this is I think when I was a clinician. 289 00:20:40,160 --> 00:20:45,080 And now when I moved to the U.N. and I am included, like I'm enrolled only in research 290 00:20:45,080 --> 00:20:52,440 sites, what I see, like what I would love for my clinical collaborators to do is that 291 00:20:52,440 --> 00:20:59,480 maybe attend more often the research group meetings, like listen to how they receive 292 00:20:59,480 --> 00:21:00,720 your research question. 293 00:21:00,720 --> 00:21:08,160 How they there are many decisions that are needed to be taken regarding the study design, 294 00:21:08,160 --> 00:21:14,400 regarding the number of patients needed, regarding the study settings that will not be solved 295 00:21:14,400 --> 00:21:20,040 unless they had this clinical understanding of how the magnitude of the problem at the 296 00:21:20,040 --> 00:21:21,820 clinical sites. 297 00:21:21,820 --> 00:21:28,040 So really talking with the people back and forth and being patient sometimes like the 298 00:21:28,040 --> 00:21:33,160 person who abstracted data from electronic cancer record or, you know, manipulate the 299 00:21:33,160 --> 00:21:37,000 data to create the variables we need. 300 00:21:37,000 --> 00:21:42,520 They need a lot of back and forth just to create the correct variable to make sure that 301 00:21:42,520 --> 00:21:47,200 after the data analysis is done, that you are, that the data analysis is answering your 302 00:21:47,200 --> 00:21:50,360 question, not answering another question. 303 00:21:50,360 --> 00:21:54,240 And be patient because sometimes after all of that work, there might be a small error 304 00:21:54,240 --> 00:22:01,280 in the coding that you have to maybe repeat the data analyst has to repeat the data analysis 305 00:22:01,280 --> 00:22:04,240 again in order to answer your research question. 306 00:22:04,240 --> 00:22:08,400 So this is another point that I learned really during doing my research and I am learning 307 00:22:08,400 --> 00:22:16,120 now at UNC is that always question your results. 308 00:22:16,120 --> 00:22:22,320 So just don't be very happy about breakthrough or very large association you found. 309 00:22:22,320 --> 00:22:28,120 Always question the validity of your results and try to do as you know, sensitivity analysis 310 00:22:28,120 --> 00:22:34,000 as much as you can in order to make sure that what I am exporting to the scientific world 311 00:22:34,000 --> 00:22:35,000 is correct. 312 00:22:35,000 --> 00:22:37,400 Do the best of my not. 313 00:22:37,400 --> 00:22:42,760 So this kind of checking, check, checking my results and, and making sure that what 314 00:22:42,760 --> 00:22:51,080 I am saying is credible, I think is very beneficial for myself first as, as a researcher, you 315 00:22:51,080 --> 00:22:57,480 know, early stage researcher trying to build a name for myself in the research world and 316 00:22:57,480 --> 00:23:02,280 also for the research, scientific research environment in general. 317 00:23:02,280 --> 00:23:04,120 We wanted to have like good data. 318 00:23:04,120 --> 00:23:08,000 We want to have to build the trust in the research community. 319 00:23:08,000 --> 00:23:15,680 So again, it needs patience, a lot of involvement, talking a lot to each other at the collaboration 320 00:23:15,680 --> 00:23:16,680 table. 321 00:23:16,680 --> 00:23:18,280 So yeah, that's, that's really awesome. 322 00:23:18,280 --> 00:23:19,280 Thank you, Iman. 323 00:23:19,280 --> 00:23:23,880 What I hear you saying is that, so clinicians have a lot of knowledge that they don't remember 324 00:23:23,880 --> 00:23:25,880 that nobody else has. 325 00:23:25,880 --> 00:23:26,880 Yeah. 326 00:23:26,880 --> 00:23:27,880 Yes. 327 00:23:27,880 --> 00:23:34,200 And the importance of not being, not being afraid to over communicate, like continue 328 00:23:34,200 --> 00:23:38,680 to just because you shared something once doesn't mean you can't share it again or doesn't 329 00:23:38,680 --> 00:23:40,720 mean that they got it the first time. 330 00:23:40,720 --> 00:23:46,640 And so taking responsibility for communicating and also checking that the communication was 331 00:23:46,640 --> 00:23:48,280 delivered or understood. 332 00:23:48,280 --> 00:23:49,280 Correctly. 333 00:23:49,280 --> 00:23:50,280 Yeah. 334 00:23:50,280 --> 00:23:51,280 Yes. 335 00:23:51,280 --> 00:23:56,340 Because they might seem that they got it, but they do not get your point, your find 336 00:23:56,340 --> 00:23:57,340 point. 337 00:23:57,340 --> 00:23:58,340 Sure. 338 00:23:58,340 --> 00:23:59,340 Sure. 339 00:23:59,340 --> 00:24:00,340 Yeah. 340 00:24:00,340 --> 00:24:04,760 And then I'm also hearing that it's important for the clinician to stay involved and to 341 00:24:04,760 --> 00:24:10,880 help question the data when it comes out, where it's like, okay, why does this say this? 342 00:24:10,880 --> 00:24:11,880 Does this make sense? 343 00:24:11,880 --> 00:24:17,280 Even if it's an exciting finding to actually be willing to question the data so that you 344 00:24:17,280 --> 00:24:22,760 can make sure that what you have is high quality before you release it to the scientific community. 345 00:24:22,760 --> 00:24:23,760 Yes, exactly. 346 00:24:23,760 --> 00:24:24,760 Yeah. 347 00:24:24,760 --> 00:24:25,760 Yes. 348 00:24:25,760 --> 00:24:26,760 I love that. 349 00:24:26,760 --> 00:24:31,260 I love your comment about staying involved because clinicians can be very busy. 350 00:24:31,260 --> 00:24:35,400 And so I want you to just, actually you gave great recommendations. 351 00:24:35,400 --> 00:24:37,120 You said attend the meetings. 352 00:24:37,120 --> 00:24:38,120 Yeah. 353 00:24:38,120 --> 00:24:42,560 So that they can help answer questions at that point, which is awesome. 354 00:24:42,560 --> 00:24:47,520 I'm wondering if there are any other practical steps that clinicians can use to stay involved 355 00:24:47,520 --> 00:24:49,140 in the research? 356 00:24:49,140 --> 00:24:58,400 I think also having basic information about how the data is generated. 357 00:24:58,400 --> 00:25:01,200 For example, I'm not sure. 358 00:25:01,200 --> 00:25:03,380 For me myself, I'll talk about my journey. 359 00:25:03,380 --> 00:25:10,120 When I first started just looking at the research, I started here as a fellow, I started just 360 00:25:10,120 --> 00:25:12,880 looking at the data output. 361 00:25:12,880 --> 00:25:18,360 This is how, for example, lung cancer is coded based on histology. 362 00:25:18,360 --> 00:25:22,160 This is how the patient's socio-demographic is created. 363 00:25:22,160 --> 00:25:25,720 But I did not know what happened behind the scenes. 364 00:25:25,720 --> 00:25:26,720 Why socio-dermography? 365 00:25:26,720 --> 00:25:30,720 For example, why the race is classified this way? 366 00:25:30,720 --> 00:25:34,520 Why the socioeconomic status was classified this way? 367 00:25:34,520 --> 00:25:38,560 What was behind the score that I used in my classification? 368 00:25:38,560 --> 00:25:46,800 And what are the implications of describing the socioeconomic status using this score 369 00:25:46,800 --> 00:25:48,160 versus another score? 370 00:25:48,160 --> 00:25:51,160 I was not getting into details of that. 371 00:25:51,160 --> 00:25:55,880 And I was just trying to understand the table, the Excel sheet in front of me, and maybe 372 00:25:55,880 --> 00:26:02,120 talk with the analyst to why not to look into the situation between that and that until 373 00:26:02,120 --> 00:26:08,960 I started to learn about how the data was generated, first of all. 374 00:26:08,960 --> 00:26:14,800 And the difference between using this score versus that score and how this affects the 375 00:26:14,800 --> 00:26:17,880 outcome of the analysis at the end. 376 00:26:17,880 --> 00:26:23,840 So just maybe not all the clinicians will have the chance to do a master's program in 377 00:26:23,840 --> 00:26:24,840 clinical research. 378 00:26:24,840 --> 00:26:31,480 But just having a basic or maybe listening how the data was created, was generated, is 379 00:26:31,480 --> 00:26:37,640 very helpful for them because it will affect, again, how their research question can be 380 00:26:37,640 --> 00:26:39,280 answered. 381 00:26:39,280 --> 00:26:47,840 The second thing is also to have maybe basic also knowledge about the available data analysis 382 00:26:47,840 --> 00:26:49,920 tool that are there. 383 00:26:49,920 --> 00:26:56,240 Like for example, I want to make sure that this research question can be asked using 384 00:26:56,240 --> 00:27:02,480 electronic health records because it is really different than using maybe another data set 385 00:27:02,480 --> 00:27:04,560 like claims data. 386 00:27:04,560 --> 00:27:13,400 So taking some time to understand which data source would be best for my research question 387 00:27:13,400 --> 00:27:19,760 is very important because I learned that the hard way. 388 00:27:19,760 --> 00:27:25,760 I formulated research questions for a grant proposal and I submitted my grant. 389 00:27:25,760 --> 00:27:31,360 I proposed to use actually both data, electronic health record and claims data. 390 00:27:31,360 --> 00:27:37,800 And at the end, when I received the feedback, of course, it wasn't accepted, but the feedback 391 00:27:37,800 --> 00:27:39,040 was one of them. 392 00:27:39,040 --> 00:27:44,560 This is a little bit theoretical because this kind, maybe the first question cannot be answered 393 00:27:44,560 --> 00:27:48,880 using this type of data set. 394 00:27:48,880 --> 00:27:53,600 And again, this kind of information will not be known until you talk with the people. 395 00:27:53,600 --> 00:27:58,560 It will not be known using the ABAP Med and searching for similar study that try to answer 396 00:27:58,560 --> 00:28:01,080 your research question because that's what I did. 397 00:28:01,080 --> 00:28:06,320 But just talking with the people who used both different types of data sets will help 398 00:28:06,320 --> 00:28:15,920 a lot to choose and to save you time and effort and emotions after the grant got accepted 399 00:28:15,920 --> 00:28:17,920 or whatever or rejected. 400 00:28:17,920 --> 00:28:21,120 So yeah, it's again talking with the people. 401 00:28:21,120 --> 00:28:23,960 I appreciate your comments and thank you for that. 402 00:28:23,960 --> 00:28:29,840 So what I'm hearing is that don't try to do it by yourself. 403 00:28:29,840 --> 00:28:35,480 Connect with the people who know and ask questions and don't just accept things at face value. 404 00:28:35,480 --> 00:28:37,480 I appreciate you saying that. 405 00:28:37,480 --> 00:28:41,000 And I can see that it's born out of your experience. 406 00:28:41,000 --> 00:28:44,200 You've done that before and then you've learned after the fact. 407 00:28:44,200 --> 00:28:47,240 And so what you're doing is giving people shortcuts, which is awesome. 408 00:28:47,240 --> 00:28:49,360 Yeah, yeah, yeah, exactly. 409 00:28:49,360 --> 00:28:51,400 Exactly, this is the case. 410 00:28:51,400 --> 00:28:56,040 And also my first point was that try to learn how the data was generated. 411 00:28:56,040 --> 00:29:04,400 Yes, this is very important because before I learned that, I was just like, you know, 412 00:29:04,400 --> 00:29:08,480 there was something missing in my understanding what happened. 413 00:29:08,480 --> 00:29:14,480 And sometimes I miss parts of the conversation because people who are not clinician are very 414 00:29:14,480 --> 00:29:24,440 knowledgeable about the scores and the measurements and how they can operationalize, like how 415 00:29:24,440 --> 00:29:30,400 to get a concept and make it consumable during data analysis. 416 00:29:30,400 --> 00:29:35,440 This is called operationalization of a term or of a concept. 417 00:29:35,440 --> 00:29:42,880 So this operationalization kind of thing, I think the clinician need to understand it 418 00:29:42,880 --> 00:29:49,920 because this is the transition between the idea in their heads and how it can be analyzed 419 00:29:49,920 --> 00:29:51,000 on the ground. 420 00:29:51,000 --> 00:29:52,000 I love that. 421 00:29:52,000 --> 00:29:53,520 You know what it makes me think about? 422 00:29:53,520 --> 00:29:57,600 It's like as clinicians, when we're getting data from patients, we're first of all getting 423 00:29:57,600 --> 00:30:01,000 stories and we're converting stories into data. 424 00:30:01,000 --> 00:30:04,320 When epidemiologists and biostatisticians are looking at the data, all they have is 425 00:30:04,320 --> 00:30:05,960 data without story. 426 00:30:05,960 --> 00:30:10,200 And so it's a different way of looking at it and they don't have the element of story 427 00:30:10,200 --> 00:30:11,800 to interpret things. 428 00:30:11,800 --> 00:30:17,000 And so as you say, when they're trying to operationalize race or social demographic 429 00:30:17,000 --> 00:30:23,400 information, or even diagnosis, they have to look at all these codes to come up with 430 00:30:23,400 --> 00:30:25,360 the story. 431 00:30:25,360 --> 00:30:30,320 And so being able to see how that story is being created from the data helps you, the 432 00:30:30,320 --> 00:30:32,880 clinician, because you can say, oh no, that story is not plausible. 433 00:30:32,880 --> 00:30:34,480 This is what makes more sense. 434 00:30:34,480 --> 00:30:35,800 And so I like that idea. 435 00:30:35,800 --> 00:30:39,480 Just make sure you understand how that data is being generated. 436 00:30:39,480 --> 00:30:41,040 Don't just take it at face value. 437 00:30:41,040 --> 00:30:42,040 Yes, exactly. 438 00:30:42,040 --> 00:30:43,040 Thank you so much. 439 00:30:43,040 --> 00:30:47,360 I love the description of a story and you have to listen to for different sides of the 440 00:30:47,360 --> 00:30:48,360 story. 441 00:30:48,360 --> 00:30:49,360 Yeah, I love it. 442 00:30:49,360 --> 00:30:50,360 Yeah. 443 00:30:50,360 --> 00:30:51,360 Thank you. 444 00:30:51,360 --> 00:30:52,360 That's awesome. 445 00:30:52,360 --> 00:30:53,360 Well, thank you. 446 00:30:53,360 --> 00:30:57,280 So, okay, we're coming up on the end of our podcast episode and I want you to just share 447 00:30:57,280 --> 00:31:00,720 any insights with people who are thinking, this is too hard. 448 00:31:00,720 --> 00:31:01,720 I can't do it. 449 00:31:01,720 --> 00:31:02,720 I'm just a clinician. 450 00:31:02,720 --> 00:31:04,680 I can't do research. 451 00:31:04,680 --> 00:31:08,640 What advice do you have for them and what things should they consider as they're moving 452 00:31:08,640 --> 00:31:10,640 forward in their decision? 453 00:31:10,640 --> 00:31:12,120 You are needed. 454 00:31:12,120 --> 00:31:13,800 I have to say to that to the clinician. 455 00:31:13,800 --> 00:31:15,560 You are really needed. 456 00:31:15,560 --> 00:31:20,440 No research can advance without the input from the people at the front line. 457 00:31:20,440 --> 00:31:29,520 And being a front line like heroes as clinician, really you have the responsibility, besides 458 00:31:29,520 --> 00:31:32,240 your responsibility, taking care of the patients. 459 00:31:32,240 --> 00:31:38,840 If you really have the passion for the research, because doing any work, doing any job can 460 00:31:38,840 --> 00:31:41,760 be tedious if you don't have the passion for it. 461 00:31:41,760 --> 00:31:46,680 So if you really have this question that you couldn't answer during your conversation 462 00:31:46,680 --> 00:31:50,760 with the patient and you know that the answer for this question will come from researching 463 00:31:50,760 --> 00:32:00,020 it more, you have the passion, but maybe you did not dig deeper into it. 464 00:32:00,020 --> 00:32:01,840 So just talk. 465 00:32:01,840 --> 00:32:08,640 And the need for clinician in the research, you might not need to devote big time. 466 00:32:08,640 --> 00:32:12,880 Just conversation with someone with the researcher. 467 00:32:12,880 --> 00:32:18,360 You don't know how much, like for example, me myself, I practiced for nine years and 468 00:32:18,360 --> 00:32:20,560 then I shifted into research. 469 00:32:20,560 --> 00:32:26,760 But sometimes I feel that because I practice in different setting than the US, just talking 470 00:32:26,760 --> 00:32:34,380 with clinical research or the clinician, really you can say that, illuminate me into 471 00:32:34,380 --> 00:32:41,880 how I can better tweak my or refine my question in order to fit and benefit the patient at 472 00:32:41,880 --> 00:32:43,120 the bedside. 473 00:32:43,120 --> 00:32:50,520 So clinician can really contribute to the research that is going on at different level, 474 00:32:50,520 --> 00:32:53,240 at different time, you know, requirements. 475 00:32:53,240 --> 00:32:59,640 And like I would say just to start with a small time devoted and then how it goes, maybe 476 00:32:59,640 --> 00:33:03,240 you will get into it and you'd love to continue and contribute more. 477 00:33:03,240 --> 00:33:10,320 But don't make like the time and business like a big barrier for contributing to the 478 00:33:10,320 --> 00:33:16,960 research because I am sure that every clinician at some point, whatever experience they had, 479 00:33:16,960 --> 00:33:21,560 they had unanswered questions with their patients and they are responsible. 480 00:33:21,560 --> 00:33:22,560 That's how I see it. 481 00:33:22,560 --> 00:33:25,760 We are responsible to answer this question somehow. 482 00:33:25,760 --> 00:33:34,880 And how this how can vary from few minutes talking with a researcher to maybe a few hours 483 00:33:34,880 --> 00:33:37,280 every week and so on. 484 00:33:37,280 --> 00:33:38,920 So yeah. 485 00:33:38,920 --> 00:33:39,920 That's really awesome. 486 00:33:39,920 --> 00:33:41,000 Iman, thank you so much. 487 00:33:41,000 --> 00:33:42,920 I love what you said. 488 00:33:42,920 --> 00:33:46,200 Just having passion is important and having unanswered questions. 489 00:33:46,200 --> 00:33:51,960 So for any clinician who has unanswered questions, you're already able to contribute because 490 00:33:51,960 --> 00:33:55,840 your unanswered question is the source of much research that could lead to an answer 491 00:33:55,840 --> 00:33:58,400 to the question, which is so awesome. 492 00:33:58,400 --> 00:34:01,620 And you started, you said clinicians are needed. 493 00:34:01,620 --> 00:34:03,800 We absolutely are. 494 00:34:03,800 --> 00:34:08,200 And I just want to invite as many people who are listening to just recognize that if you 495 00:34:08,200 --> 00:34:11,420 don't ask the question, nobody else may answer it. 496 00:34:11,420 --> 00:34:16,200 But you're asking the question, can precipitate others answering that you're asking the question 497 00:34:16,200 --> 00:34:18,520 may precipitate others answering that question. 498 00:34:18,520 --> 00:34:21,520 So definitely recognize that you are needed. 499 00:34:21,520 --> 00:34:24,760 Iman, you have been just such a wonderful, wonderful guest. 500 00:34:24,760 --> 00:34:29,240 I appreciate your time, your insights, and it's just been a pleasure having you on the 501 00:34:29,240 --> 00:34:30,240 show. 502 00:34:30,240 --> 00:34:31,240 Thank you for being here. 503 00:34:31,240 --> 00:34:32,240 Thank you so much, Toyosia. 504 00:34:32,240 --> 00:34:35,680 The same here really, I felt so happy and so glad. 505 00:34:35,680 --> 00:34:36,680 And thank you. 506 00:34:36,680 --> 00:34:38,080 Thank you for your exciting questions. 507 00:34:38,080 --> 00:34:39,080 It was an honor. 508 00:34:39,080 --> 00:34:40,080 Thank you. 509 00:34:40,080 --> 00:34:41,080 Thank you, Iman. 510 00:34:41,080 --> 00:34:42,080 All right, everybody. 511 00:34:42,080 --> 00:34:44,600 You've heard Dr. Metwally. 512 00:34:44,600 --> 00:34:48,620 If you're a clinician, you're absolutely needed in this research enterprise and definitely 513 00:34:48,620 --> 00:34:53,600 connect with your collaborators and really be connected as the research questions are 514 00:34:53,600 --> 00:34:54,600 being answered. 515 00:34:54,600 --> 00:34:59,200 All right, I will, I am excited to see you on the next episode. 516 00:34:59,200 --> 00:35:00,200 Thanks for joining us today. 517 00:35:00,200 --> 00:35:09,400 And I'll talk to you again the next time. 518 00:35:09,400 --> 00:35:14,760 Thanks for listening to this episode of the Clinician Researcher podcast, where academic 519 00:35:14,760 --> 00:35:20,480 clinicians learn the skills to build their own research program, whether or not they 520 00:35:20,480 --> 00:35:21,560 have a mentor. 521 00:35:21,560 --> 00:35:27,520 If you found the information in this episode to be helpful, don't keep it all to yourself. 522 00:35:27,520 --> 00:35:29,400 Someone else needs to hear it. 523 00:35:29,400 --> 00:35:33,440 So take a minute right now and share it. 524 00:35:33,440 --> 00:35:38,920 As you share this episode, you become part of our mission to help launch a new generation 525 00:35:38,920 --> 00:35:44,880 of clinician researchers who make transformative discoveries that change the way we do healthcare.