Meeting Title: Robert Tseng and Audre Wirtanen Date: 2025-05-13 Meeting participants: Robert Tseng, Audre
WEBVTT
1 00:01:27.260 ⇒ 00:01:29.040 Audre: Hello. Sorry.
2 00:01:29.040 ⇒ 00:01:32.299 Robert Tseng: Hey? No worries! How how are you doing, Audrey?
3 00:01:32.800 ⇒ 00:01:40.359 Audre: Oh, I’m okay. Yesterday was a whirlwind, and I’m kind of behind on things, but I’m happy to chat with you for a moment.
4 00:01:40.950 ⇒ 00:01:42.790 Robert Tseng: Yeah, no, it’s been. It’s been a while.
5 00:01:43.120 ⇒ 00:01:45.769 Audre: I know. How are you? What’s been going on.
6 00:01:46.070 ⇒ 00:01:56.180 Robert Tseng: I’m good. Let me think. Last week that was like a couple of months ago I had just moved into. I meant I’m in Columbus Circle now. So all settled in. And
7 00:01:57.024 ⇒ 00:02:02.420 Robert Tseng: yeah, no, it’s been. It’s been good. I travel for a bit. Again. At the end of month
8 00:02:02.790 ⇒ 00:02:04.797 Robert Tseng: I was in Austin and
9 00:02:05.600 ⇒ 00:02:06.080 Audre: Yes.
10 00:02:06.080 ⇒ 00:02:08.619 Robert Tseng: Conferences, and my team’s out in la, so
11 00:02:08.780 ⇒ 00:02:11.120 Robert Tseng: it’s good good in person time, and
12 00:02:11.490 ⇒ 00:02:14.290 Robert Tseng: going to Europe at the end of the month. So.
13 00:02:14.610 ⇒ 00:02:16.060 Audre: Fun? Where in Europe are you going.
14 00:02:16.260 ⇒ 00:02:23.490 Robert Tseng: I’ll be doing Amsterdam. That’s mostly for business. And then actually, I’m also going to Nairobi. That’s just for fun.
15 00:02:23.490 ⇒ 00:02:27.910 Audre: Oh, that’s cool. I think I remember you talking about. Didn’t you go to Nairobi before.
16 00:02:28.637 ⇒ 00:02:32.030 Robert Tseng: I have. Yeah. But I’m gonna go back. Yeah.
17 00:02:32.030 ⇒ 00:02:33.960 Audre: Nice nice.
18 00:02:34.160 ⇒ 00:02:34.760 Robert Tseng: Yeah.
19 00:02:35.050 ⇒ 00:02:35.390 Audre: That’s like.
20 00:02:35.390 ⇒ 00:02:40.210 Robert Tseng: I would say with you. How’s the lunch? Where kind of preparations for the launch of the clinic and all that.
21 00:02:40.350 ⇒ 00:02:41.260 Audre: God!
22 00:02:41.650 ⇒ 00:02:43.749 Audre: The landlord’s gonna kill me!
23 00:02:44.840 ⇒ 00:02:52.989 Audre: You know everything is going except the landlord is so incompetent I can’t even explain. He’s
24 00:02:53.280 ⇒ 00:03:07.300 Audre: actually have a meeting with him after right after this, where he’s basically we signed the lease a month ago, after months of just like these drawn out negotiations that were totally unnecessary, and he’s single handedly pushed us
25 00:03:08.200 ⇒ 00:03:13.950 Audre: 6 months, and now 9 months delayed, which is a huge problem, or like.
26 00:03:13.950 ⇒ 00:03:14.670 Robert Tseng: Yeah.
27 00:03:14.670 ⇒ 00:03:17.395 Audre: A huge headache, financially and otherwise.
28 00:03:17.850 ⇒ 00:03:18.390 Robert Tseng: Yeah.
29 00:03:18.960 ⇒ 00:03:26.999 Audre: And he never sent me the countersigned lease, and everyone was giving a bunch of excuses.
30 00:03:27.200 ⇒ 00:03:34.659 Audre: Whatever. He’s like a liar. He owns a bunch of buildings. It’s just kind of the regular like New York real estate situation, where, like
31 00:03:36.070 ⇒ 00:03:42.719 Audre: his dad handed him a half a billion dollar company. His Dad’s deceased. I don’t think he knows what he’s doing at all.
32 00:03:42.720 ⇒ 00:03:43.200 Robert Tseng: Hmm.
33 00:03:43.200 ⇒ 00:03:43.905 Audre: Oh,
34 00:03:44.960 ⇒ 00:03:50.639 Audre: And he’s trying to renegotiate terms after the lease is signed. So I
35 00:03:52.100 ⇒ 00:04:00.030 Audre: I don’t. He’s beyond anything I could have expected so hopefully. Now we’re looking at.
36 00:04:01.050 ⇒ 00:04:11.620 Audre: If we’re lucky and we’ll see how today goes. Oh, my God, please like a May er
37 00:04:11.850 ⇒ 00:04:17.500 Audre: we redid the timeline. Yesterday, I think, in April or early May, opening.
38 00:04:17.760 ⇒ 00:04:18.370 Robert Tseng: Okay.
39 00:04:18.370 ⇒ 00:04:23.860 Audre: Year, which is significantly longer than we initially tried to do. November of this year. That was it.
40 00:04:25.205 ⇒ 00:04:25.670 Audre: Yeah.
41 00:04:26.300 ⇒ 00:04:31.119 Audre: So yeah, I I don’t know. I mean, there’s
42 00:04:31.400 ⇒ 00:04:37.100 Audre: I’ve run all the numbers and pulling out of the deal is really not what we should do. But
43 00:04:37.290 ⇒ 00:04:42.819 Audre: I’m also kind of like is this guy gonna be able to do anything? I just have. No, he’s shown me
44 00:04:43.420 ⇒ 00:04:47.960 Audre: nothing but incompetence. And now he has new investors that he didn’t tell us about, and
45 00:04:49.090 ⇒ 00:04:58.039 Audre: I don’t know whatever it’s a it’s a mess but everything else is going, so we’ll see.
46 00:04:59.250 ⇒ 00:05:04.520 Robert Tseng: Okay? Well, yeah, I’m sorry. Things are kind of out of your hands. I will be.
47 00:05:04.780 ⇒ 00:05:08.910 Robert Tseng: I’ll also be kind of praying, wishing you well, the conversation will be things.
48 00:05:08.910 ⇒ 00:05:12.180 Audre: No, please pray for me. Oh, I really appreciate it.
49 00:05:12.180 ⇒ 00:05:12.610 Robert Tseng: Yeah.
50 00:05:13.040 ⇒ 00:05:14.720 Audre: Cause. It’s yeah, I
51 00:05:16.350 ⇒ 00:05:21.985 Audre: It’s just like a never ending saga with this guy. I truly I like I could write a book about him at this point.
52 00:05:22.576 ⇒ 00:05:26.500 Robert Tseng: Yeah, I mean, that’d probably be a good book. Like.
53 00:05:26.650 ⇒ 00:05:31.610 Robert Tseng: you know how to how to work with people in in New York and opening opening stuff and
54 00:05:31.900 ⇒ 00:05:33.669 Robert Tseng: horror stories of that.
55 00:05:33.930 ⇒ 00:05:41.960 Audre: Yeah, I mean, it’s just he’s he’s a total. I don’t know me and my broker joke about a lot of things, but
56 00:05:42.930 ⇒ 00:05:44.569 Audre: it mostly about him.
57 00:05:45.930 ⇒ 00:05:46.680 Robert Tseng: Okay.
58 00:05:47.080 ⇒ 00:05:54.300 Audre: Anyway. But we’re connecting today because we’re talking about data and things right.
59 00:05:54.300 ⇒ 00:06:03.339 Robert Tseng: Yeah. Yeah. So I took some time to think about it. I think if I were just kind of help you, maybe you already have an agenda in mind. But if I could just let you know what my understanding is like.
60 00:06:03.560 ⇒ 00:06:23.319 Robert Tseng: you know, we last time we talked yeah, I thought it was interesting, like, especially the medical coding kind of like problem. You said that actually wasn’t really like the main problem, because it’s like, okay. You already understand the mapping, you know, being able to like, translate that into a system where you’re able to better kind of like match
61 00:06:24.900 ⇒ 00:06:25.600 Robert Tseng: kind of like
62 00:06:26.280 ⇒ 00:06:46.770 Robert Tseng: patient engagements, or whatever you want to call them with, like the right coding. It’s kind of helps with, you know, guiding billing priorities. But like you’re pretty aware of, like, kind of how all that is currently being correct collected. And you know how that works. And so you were more interested in talking about? Yeah, just collecting other like, objective data.
63 00:06:47.375 ⇒ 00:06:54.134 Robert Tseng: Yeah. And just like, kind of figuring out like, what systems you will need in order to capture that.
64 00:06:55.240 ⇒ 00:06:58.990 Robert Tseng: yeah, that’s kind of my understanding of kind of where we left off last time.
65 00:07:00.100 ⇒ 00:07:05.219 Audre: Yeah, so trying to figure out a way to standardize
66 00:07:05.610 ⇒ 00:07:16.970 Audre: mostly self reported measures for data collection. I mean of course, we can pull things like vitals and testing results and things like that. But ultimately, we want to employ, like
67 00:07:17.230 ⇒ 00:07:26.591 Audre: the Sf, 36 which is like a levels of disability and quality of life, like very general screening
68 00:07:27.320 ⇒ 00:07:28.140 Audre: like
69 00:07:28.390 ⇒ 00:07:37.663 Audre: kind of anxiety and depression symptom scales. So less about the actual like do they meet the diagnosis and more like, How’s it going, you know?
70 00:07:38.360 ⇒ 00:07:51.530 Audre: And then the fatigue symptom inventory? We used to use the Mcgill pain scale. But I don’t. I need to like. Look back in and kind of see if people are still using it. People were using it like 8 years ago, and who knows? That’s changed.
71 00:07:52.919 ⇒ 00:07:59.551 Audre: And there’s probably some other like self reported assessments around
72 00:08:00.530 ⇒ 00:08:14.989 Audre: different symptom sets that we can also use. But those were kind of the initial ones that we’re used to using. And we want our care managers who are essentially peers, who are kind of like the connective tissue of the system we’re building.
73 00:08:15.430 ⇒ 00:08:17.219 Audre: They’re gonna be the ones who are like
74 00:08:17.810 ⇒ 00:08:24.509 Audre: communicating with all the staff, making sure that the patient feels like they were listened to, you know, like doing the work of like
75 00:08:24.610 ⇒ 00:08:25.955 Audre: figuring out
76 00:08:27.920 ⇒ 00:08:34.816 Audre: you know, which other providers might be within network for them that they can go to. You know those kinds of things,
77 00:08:35.260 ⇒ 00:08:39.485 Audre: and many more. But like, let’s just say that for SIM to simplify
78 00:08:40.030 ⇒ 00:09:08.988 Audre: and then we’re thinking either them, or maybe like the Med techs depending on. If the person’s coming in or not will be doing these self reported screenings, maybe it’s you know, at each point of care or after each point of care. Maybe it’s also when there’s you know, like an increase in symptoms, or there’s just like communication that something’s going on, even if they don’t come in quite yet.
79 00:09:09.480 ⇒ 00:09:31.209 Audre: so I haven’t exactly figured out like what are the most optimal time points for care flow, and I think that’ll also potentially be somewhat of a trial and error depending on cause. We just don’t have, like the staff quite yet to run everything to see like when when it really makes sense. But then that way, we’ll have way more standardized
80 00:09:31.854 ⇒ 00:09:40.879 Audre: data to pull from in relation to like the care, plan, and treatment options. So if they just if they just finished a course of let’s say.
81 00:09:41.300 ⇒ 00:09:44.820 Audre: you know, 10 saline infusions over
82 00:09:45.220 ⇒ 00:09:57.348 Audre: 3 months, or something like that. And the cardiologist wanted to kind of see if that would like stabilize them to see, you know, do we? Are we? Good now? Kind of thing I mean. Obviously it also requires patient consent.
83 00:09:57.960 ⇒ 00:10:25.695 Audre: But if we have standardized practices like that, then we can just publish a lot easier and be like, look, you know, and so it’ll really be initially just like creating the database and figuring out like, what is that standard methodology? And of course some of it is probably gonna fluctuate. Sometimes patients might not wanna do the assessments. Maybe they, you know, forgot this time, or the care manager didn’t have time, or you know what I mean. Like, I think we are gonna sometimes miss data points.
84 00:10:26.872 ⇒ 00:10:37.067 Audre: But we do want to create some kind of database. And we’re working with like an it specialist like, kind of like a fractional CIO, essentially
85 00:10:37.580 ⇒ 00:10:38.700 Audre: to.
86 00:10:38.920 ⇒ 00:10:52.969 Audre: I think we’re mostly gonna just do everything in Microsoft 3, 6, 5, and have the whole system be hipaa compliant? Just because so many of our even, remote employees are gonna be working with Phi,
87 00:10:54.590 ⇒ 00:10:55.690 Audre: so
88 00:10:56.060 ⇒ 00:11:17.237 Audre: and like when I was doing clinical research. You know, we were using like, I wanna say, it was called like Red Cap, or something to input data like it was a really clunky system of like, okay, here’s what the patient chart notes. Then we have to pull it out manually into this other database that like, does it really show us what we want? Yet? I don’t know.
89 00:11:18.140 ⇒ 00:11:19.489 Audre: And I know that.
90 00:11:19.950 ⇒ 00:11:23.489 Audre: So I’m trying to. Yeah, I’m curious about like.
91 00:11:24.000 ⇒ 00:11:29.460 Audre: are there somewhat automated systems? Does it make sense to have
92 00:11:29.860 ⇒ 00:11:42.394 Audre: these templates in the chart that then the care manager or the Med Tech is doing? But then, what if the patient is doing it later on and not in the chart? You know what I mean. I’m kind of like what is. And we’re using healthy as the.
93 00:11:43.030 ⇒ 00:11:43.420 Robert Tseng: Yeah.
94 00:11:43.697 ⇒ 00:11:50.080 Audre: And I haven’t really looked into what data systems they have back ended into it. I’m not really sure if they do so.
95 00:11:50.080 ⇒ 00:11:50.720 Robert Tseng: Sure.
96 00:11:50.940 ⇒ 00:11:54.060 Audre: Anyway, that’s where I’m my beginning is. Yeah.
97 00:11:54.060 ⇒ 00:12:05.399 Robert Tseng: Okay, yeah. So if I were to kind of just like summarize it in what I heard so definitely like tracking patient outcomes and over the course of like the care cycle is like the most important kind of part of this
98 00:12:05.963 ⇒ 00:12:14.386 Robert Tseng: and then, you know, you’re kind of collecting the self reported metrics. I’m assuming like healthy has. I mean I I know that they have.
99 00:12:15.000 ⇒ 00:12:21.729 Robert Tseng: You know you. You would probably do all the delivery of like those, you know, checkpoints, assessments whatever through healthy. I’m assuming.
100 00:12:22.349 ⇒ 00:12:26.690 Robert Tseng: I don’t know how configurable they are. I I’m assuming that if it’s just like
101 00:12:26.890 ⇒ 00:12:31.650 Robert Tseng: questionnaire or kind of like some chart mapping basic stuff like that, like you should be able to handle everything.
102 00:12:31.820 ⇒ 00:12:45.380 Audre: We can basically like build a template like we, we couldn’t necessarily upload and automatically populate. But we could take, you know, the approved questionnaire and essentially build a template. Someone could just pull into the chart when they do it.
103 00:12:45.680 ⇒ 00:12:46.060 Robert Tseng: Yep.
104 00:12:46.060 ⇒ 00:12:49.750 Audre: Little bit more manual labor. But it’s not too much, you know, once you do it, it’s fine.
105 00:12:50.440 ⇒ 00:12:55.039 Robert Tseng: Yeah, cool. So I mean, that setup makes sense. And then once you have those templates in
106 00:12:55.403 ⇒ 00:13:17.479 Robert Tseng: as far as like how the data could be structured as it like comes in. Well, I mean, I think I think healthy 1st foremost has, like an Api, so you can like pull whatever you want from it. But yeah, I I do think that the data you don’t have to predetermine too much of it like upfront. I think there’s like a few entities that would probably make sense like. Obviously, you have, like a patient entity where you kind of like, have
107 00:13:17.660 ⇒ 00:13:21.590 Robert Tseng: the, you know, patient like different milestones that you want to be tracking
108 00:13:22.260 ⇒ 00:13:25.259 Robert Tseng: and so like taking some time to kind of design that I’m like.
109 00:13:25.260 ⇒ 00:13:25.840 Audre: Okay.
110 00:13:26.210 ⇒ 00:13:44.319 Robert Tseng: What? What is that ideal like patient profile supposed to look like? So if someone were to pull the system, and what would you want to see in there. So I think there’s probably some like work to do there. But then, as far as like the actual questionnaires, with all these different checkpoints. You could kind of just like keep them in single format, like I think.
111 00:13:45.229 ⇒ 00:13:52.740 Robert Tseng: for, like other lifecycle systems, like, I typically see, this is like an event stream. So
112 00:13:53.371 ⇒ 00:14:14.870 Robert Tseng: you know, it’s a pretty simple data model you just need, like the date timestamp, or whatever event name. Maybe like like an id of like the form itself, something to distinguish it. And then, yeah, that gives you more flexibility to, you know you can. You can keep adjusting like, how much data you want to collect from it? And it won’t break the model. So
113 00:14:15.040 ⇒ 00:14:15.690 Robert Tseng: okay.
114 00:14:15.810 ⇒ 00:14:36.220 Robert Tseng: yeah, the risk is that it could fan out like it could just keep getting bigger and bigger, like I mean, wider and wider, because you could just keep adding more and more distinct, unique kind of like things you want to track, but you can always like shrink a deck down later on, and I don’t imagine you’ll be changing that like, you know that often.
115 00:14:37.156 ⇒ 00:14:38.229 Robert Tseng: Yeah. So
116 00:14:38.690 ⇒ 00:14:45.690 Robert Tseng: yeah, like, I think, yeah, that’s kind of what I would say, like, that’s yeah. I think that would probably be
117 00:14:45.810 ⇒ 00:14:49.400 Robert Tseng: like the simplest way to get that get that started.
118 00:14:50.360 ⇒ 00:14:51.135 Robert Tseng: Yeah,
119 00:14:52.310 ⇒ 00:15:02.610 Audre: Or I guess I’m wondering cause we’re probably not gonna work with the Api in our 1st year. We’re gonna kinda see how enterprise goes. See what a lot of the sticking points are in like.
120 00:15:02.610 ⇒ 00:15:03.020 Robert Tseng: Okay.
121 00:15:03.020 ⇒ 00:15:07.406 Audre: Ideas for what some of the sticking points might be unhealthy. But
122 00:15:07.980 ⇒ 00:15:10.039 Audre: we kind of need to pilot it in.
123 00:15:10.520 ⇒ 00:15:16.450 Robert Tseng: Before we know you know what what is working and not so I guess I’m wondering like.
124 00:15:17.060 ⇒ 00:15:19.849 Audre: I mean, and I need to figure out this flow of like
125 00:15:20.170 ⇒ 00:15:22.799 Audre: if those questionnaires are in the chart.
126 00:15:23.220 ⇒ 00:15:33.030 Audre: how we like pull them out really easily. Or if there’s a way to generate a report that like automatically can populate into like an excel spreadsheet or something, or have some way of you know.
127 00:15:33.410 ⇒ 00:15:41.360 Robert Tseng: Yeah. So if you want to circumvent the Api, you can just build your own web hooks. Which is, I mean, that would pretty much, you know. You
128 00:15:41.410 ⇒ 00:16:05.940 Robert Tseng: add something that’s just like a listener on the page, and then you can kind of like just pull things up directly it’s a little bit less reliable than Api, because it works off of the client side, which is just like the interface. So sometimes you know the error rate, I think on is, you know, maybe, like under 5% or something. So you may lose some data along the way. But like, that’s that’s the fastest way to like pull data off of any ui
129 00:16:06.310 ⇒ 00:16:11.889 Robert Tseng: that you want to. Just like test. So yeah, like, I think that’s pretty common practice.
130 00:16:12.030 ⇒ 00:16:13.510 Audre: Webbooks. You said.
131 00:16:13.510 ⇒ 00:16:14.470 Robert Tseng: Web hooks.
132 00:16:14.470 ⇒ 00:16:15.520 Audre: Web hooks. Thank you.
133 00:16:15.520 ⇒ 00:16:16.829 Robert Tseng: Yeah, yeah.
134 00:16:17.320 ⇒ 00:16:21.509 Audre: And those, and you’re circumventing the Api. By how in that way.
135 00:16:21.510 ⇒ 00:16:42.999 Robert Tseng: Yeah. So the Api would send it through the server. So like the the app, like healthy, is probably collecting all this data on their back end, and then they’re giving you access to it, whereas, like the web hook is, you’re adding, it’s like to think of it like a it’s not a cookie, but it’s like a they call it a listener. It’s like A, you know, whatever inputs that are happening. So if it’s like a form input
136 00:16:43.240 ⇒ 00:16:50.129 Robert Tseng: and like it, it exists in the client, or like in in the, in the data layer, which is just like
137 00:16:50.982 ⇒ 00:16:59.159 Robert Tseng: Anytime you open, you’re using a web application like it. You you are. You do have access to like kind of what people are clicking on and what they’re saying.
138 00:16:59.824 ⇒ 00:17:13.300 Robert Tseng: So you’re you’re just like kind of creating a custom listener that will like grab the elements you want, and then just passes it to your your system. So like you don’t. You don’t have to get from the server. You can always get it from the client as well.
139 00:17:14.190 ⇒ 00:17:16.730 Audre: And so from like a
140 00:17:16.859 ⇒ 00:17:33.999 Audre: is that like something that somebody builds into like, how how does that work? What do you mean? It’s coming from the client as well like. It would be like a form that they’re filling out, that. Then we have automatically populating into something that has timestamps and those kinds of things, or.
141 00:17:34.000 ⇒ 00:17:34.590 Robert Tseng: Yeah.
142 00:17:34.770 ⇒ 00:17:35.170 Audre: Okay.
143 00:17:35.170 ⇒ 00:18:03.140 Robert Tseng: Yeah, exactly. So if it’s just like, just think of it like a simple form somebody had like a 3 question form like, yeah, you would set the listener that basically, like, you know, question, question, name, or title. And then, like whatever they put in the form you could, you could extract that without ever going through the obviously, there’s like a you have to do the consent thing and everything. And you know, I think you I mean, you can easily strip out if you want to just get the form data. But yeah.
144 00:18:03.730 ⇒ 00:18:05.230 Audre: Okay? And
145 00:18:07.050 ⇒ 00:18:11.560 Audre: When like, how long does it usually take to set a system up
146 00:18:11.820 ⇒ 00:18:14.930 Audre: like that? I know it sounds pretty simple, but.
147 00:18:14.930 ⇒ 00:18:26.470 Robert Tseng: Yeah, yeah, no, it’s it’s not. It’s not hard. I mean, we’re I’m actually doing it for, like in one of my e-com help clients right now, like they have to do patient intakes on like we basically do it for them.
148 00:18:27.020 ⇒ 00:18:29.740 Robert Tseng: I mean, I think it’s
149 00:18:31.090 ⇒ 00:18:50.020 Robert Tseng: I mean, you could do it within a week. Honestly. So for for like, for a form, yeah. Yeah. So if you wanted just something quick to spin up but they’re testing a bunch of different intake forums connect to different landing pages. And so you do kind of to make some adjustments for every one of those. But just to get a like a proof of concept like for one single workflow. It’s pretty fast.
150 00:18:51.710 ⇒ 00:18:54.900 Audre: For one workflow. So if we had like.
151 00:18:58.140 ⇒ 00:19:03.950 Audre: well and I, I have to make decisions here at some point. But so let’s say, we have
152 00:19:06.500 ⇒ 00:19:10.300 Audre: 1, 2, 3, 4, 5 different forms.
153 00:19:10.880 ⇒ 00:19:17.009 Audre: And then we’d have to have some kind of like decision tree for like when they’re employed, basically.
154 00:19:17.220 ⇒ 00:19:21.970 Audre: And then, once that’s decided on, we can
155 00:19:22.080 ⇒ 00:19:34.499 Audre: test the flow to make sure it makes sense kind of. Once we have the enterprise version of Healthy, and the staff is on. You know, we’ve we’ve gotten some staff, and we’re kind of like running through stuff. And then
156 00:19:35.220 ⇒ 00:19:42.850 Audre: so maybe lead time is more like, I’m gonna say, 8 weeks.
157 00:19:43.650 ⇒ 00:19:44.250 Robert Tseng: Got it.
158 00:19:44.250 ⇒ 00:19:50.610 Audre: Like me, knowing me drafting or having the draft of like the decision tree.
159 00:19:51.470 ⇒ 00:19:54.059 Audre: implementing and trialing it before patients.
160 00:19:54.390 ⇒ 00:19:56.649 Robert Tseng: Yeah. Totally good to do that.
161 00:19:58.010 ⇒ 00:20:07.820 Audre: And so when you work with clients like, what exactly is your process and kind of for for something like this like, how, how would you approach it?
162 00:20:08.340 ⇒ 00:20:24.200 Robert Tseng: Yeah. So if you you know, as you’re kind of building out that decision tree, we kind of just we just call it like a again, data design or intake design, or whatever. So we, I can work with you as early as that point, so that as you’re putting that together
163 00:20:24.930 ⇒ 00:20:42.060 Robert Tseng: like, I’ll be able to tell you like, hey, those like 3 out of these 5 workflows, you can actually consolidate it. It’s the same, the same flow of data like that’s that’s simple. And then, like, maybe these other 2, there’s like a Yeah, you have to go. And maybe there’s like a you have to
164 00:20:42.920 ⇒ 00:21:00.299 Robert Tseng: you could. Maybe you’re collecting payment whatever upfront and like. It’s a whole different like gateway that you need to go through. There. That’s a separate workflow or another one that’s like employer or like employer insurance matching, or something like, I think there’s I’ll be able to help kind of like shape like what those the distinct workflows actually look like.
165 00:21:00.906 ⇒ 00:21:07.640 Robert Tseng: yeah. And then, as far as like implementing the the tagging, tracking, building, the web hook like my team could do that
166 00:21:08.450 ⇒ 00:21:17.720 Robert Tseng: and then, obviously on the reporting side, I think that’s where we would try to get it into a place where it you’ll be able to actually consume the data in the way that you want to.
167 00:21:18.780 ⇒ 00:21:23.290 Robert Tseng: Yeah. So it’s like the whole end to end, like, I think that’s and then.
168 00:21:24.220 ⇒ 00:21:24.610 Audre: And
169 00:21:25.720 ⇒ 00:21:34.310 Audre: one of. And maybe this is a discussion earlier, too. So we are using this autonomic testing unit that I trialed
170 00:21:35.180 ⇒ 00:21:41.390 Audre: 2 weeks ago, maybe. And a lot of these.
171 00:21:41.970 ⇒ 00:21:45.330 Audre: I mean, it’s essentially like a 7 lead
172 00:21:45.580 ⇒ 00:21:57.170 Audre: like channel box. You know what I mean like. It’s nothing. Fancy. You got a 3 lead. Ekg, 3. Blood pressure cuffs, 3 pulse oximeters, and the
173 00:21:57.900 ⇒ 00:22:01.840 Audre: data that comes with the or the software that comes with the system.
174 00:22:02.030 ⇒ 00:22:06.720 Audre: The company will not communicate to me how they’re transforming data.
175 00:22:07.750 ⇒ 00:22:10.820 Audre: And like what they’re doing, which I know is supposed to be a part of their like.
176 00:22:11.950 ⇒ 00:22:14.679 Audre: IP, whatever I think it’s absurd. But yeah, I know,
177 00:22:16.460 ⇒ 00:22:33.740 Audre: and our clients and patients tend to have really weird results, like the system will be like, Oh, this is totally normal. And this is totally not normal, and the person will be like, I’ve never seen these 2 come up together like, normally, they’re separate.
178 00:22:35.604 ⇒ 00:22:38.330 Audre: But I’m having a hard time
179 00:22:38.390 ⇒ 00:22:52.399 Audre: like getting the raw data like I don’t know will even give me the raw data, cause I wanna be able to have a way that we can work backwards to say we got these results. But what is the actual raw data? What is it pulling. How is it doing it like.
180 00:22:52.400 ⇒ 00:23:08.560 Audre: how is it transforming it? And then, apparently demographics impacted, too, which to me, I’m like, I need to know exactly what you’re doing like, are you normalizing, are you not? What are you normalizing to like if we don’t select race? What happens? You know what I mean, like, there’s just like, why is race even a factor here?
181 00:23:09.460 ⇒ 00:23:14.710 Audre: Yeah, etc. So there’s a part of me that’s like.
182 00:23:16.710 ⇒ 00:23:20.990 Audre: well, maybe I try this machine. The software is a total
183 00:23:21.040 ⇒ 00:23:46.899 Audre: bust. I think we’re gonna have to have the tech literally running the software and simultaneously, like handwriting our observations separately, and then can’t actually pull the data immediately into the Emr. It has to be manually downloaded and then uploaded because it’s this old software. But I’m like 7 channel. Essentially like, you know, physiology box like is, can we build? Something
184 00:23:47.351 ⇒ 00:23:51.859 Audre: is a lot easier. But the only thing is, I’m not totally sure what
185 00:23:52.120 ⇒ 00:24:01.369 Audre: the transformation of the data is to get the kind of results other than like heart rate variability like beat to beat. You know what I mean. Things like that. So
186 00:24:02.000 ⇒ 00:24:13.999 Audre: I don’t know like how. I guess the question I have to for you to think about is like, if one were to build something like that with some kind of software that’s just doing some basic transformation with raw data.
187 00:24:14.370 ⇒ 00:24:14.900 Robert Tseng: Yeah.
188 00:24:14.900 ⇒ 00:24:26.679 Audre: Like, how much would that cost? Would it actually be cheaper for me to like hire someone to build a system where we have full oversight of rather than like buying this $42,000 box that like, I don’t really know what’s gonna happen. You know.
189 00:24:26.920 ⇒ 00:24:27.700 Robert Tseng: Totally.
190 00:24:28.020 ⇒ 00:24:36.339 Robert Tseng: Yeah. I mean, I feel like the whole bill versus buy question comes up a lot, especially in this space. So I mean.
191 00:24:36.500 ⇒ 00:24:41.409 Robert Tseng: I would say, if you need help, like getting raw data from these providers.
192 00:24:42.260 ⇒ 00:24:48.350 Robert Tseng: I feel like we ask. We’re like knocking on vendor doors. I’m always asking them for data. So if you need, like another
193 00:24:48.480 ⇒ 00:24:53.249 Robert Tseng: voice to just kind of like, try to speak their language and and try to get them
194 00:24:53.770 ⇒ 00:25:05.040 Robert Tseng: get get more data from them. I think I could probably help with that. And then if you really run into walls, then, yeah, maybe there’s a case to actually build it out. And I’ll be able to help assess like, how complicated it is to build it.
195 00:25:06.570 ⇒ 00:25:11.299 Robert Tseng: Yeah. So I’ll give like one example. We have a telehealth client they work with.
196 00:25:12.150 ⇒ 00:25:16.330 Robert Tseng: Just yeah, they they have like a back end platform that they use.
197 00:25:16.660 ⇒ 00:25:37.220 Robert Tseng: Yeah, they really just that provider just doesn’t give data. And so yeah, like, I kind of help them scope out like, what a kind of build your own would look like. And they’ve they’re doing it, taking them 3 months to do it possibly longer. But I think they think it’s going to be worth it in the long run, so like being able to like go through that like
198 00:25:37.390 ⇒ 00:25:43.770 Robert Tseng: process like we’ve. I’ve done that before to probably help you think through that as well.
199 00:25:44.600 ⇒ 00:25:48.610 Audre: Okay, would you be able to send me like
200 00:25:49.540 ⇒ 00:25:57.630 Audre: a sample proposal or something that I can just kind of have on hand kind of pull when we are ready to jump.
201 00:25:59.380 ⇒ 00:26:00.100 Audre: Okay?
202 00:26:01.990 ⇒ 00:26:03.610 Audre: I mean, what kind of like
203 00:26:05.500 ⇒ 00:26:14.949 Audre: like, do you do? Kind of like a monthly retainer, are you materials and time based, or how? How do you kind of Bill? And what? What do you normally like? What? Yeah, what’s your scope? There.
204 00:26:15.100 ⇒ 00:26:19.124 Robert Tseng: Yeah. So for, like the initial one of just like the
205 00:26:19.770 ⇒ 00:26:32.369 Robert Tseng: what we were talking about with, like the initial tracking and getting that- that initial scope of work that’s pretty straightforward, like. I think we understand that pretty well. So we just probably do a fixed one time fixed price there
206 00:26:32.973 ⇒ 00:26:38.129 Robert Tseng: and then, like, I think we can, we can either do hourly or or do a retainer
207 00:26:38.870 ⇒ 00:26:40.650 Robert Tseng: for, like ongoing
208 00:26:42.150 ⇒ 00:26:52.210 Robert Tseng: kind of just, it’s more like procurement strategy kind of like this, like kind of deeper dive. So like, help you build out the system. So yeah, so we’re flexible.
209 00:26:52.750 ⇒ 00:26:58.799 Audre: Okay. And what is that one time cost like normally? Or can you add that in your
210 00:26:58.900 ⇒ 00:27:01.250 Audre: kind of proposal that you sent me just so.
211 00:27:01.250 ⇒ 00:27:12.254 Robert Tseng: Yeah, I’ll add it. I’ll add the proposal. But I mean, I think it’s it’s usually less than 5 KI think it’s kind of the way we do it. Yeah, like, it’s pretty straightforward. Yeah.
212 00:27:12.900 ⇒ 00:27:15.060 Robert Tseng: cool, amazing.
213 00:27:15.645 ⇒ 00:27:19.489 Audre: That means I can kind of pull you whenever which is good. So.
214 00:27:20.105 ⇒ 00:27:20.720 Robert Tseng: Yeah.
215 00:27:21.310 ⇒ 00:27:29.390 Audre: Okay. Great. Well, yeah, that would be really helpful. I mean, I definitely, if I could clone myself, maybe I could figure this out. But I think I should not clone myself and
216 00:27:29.950 ⇒ 00:27:32.868 Audre: people who can do it much faster than I can.
217 00:27:33.580 ⇒ 00:27:38.519 Audre: So and we’re gonna be. So what I need to do is
218 00:27:39.760 ⇒ 00:27:46.040 Audre: I mean, first, st the build has to get going so that I know what the timeline actually is gonna look like, and then
219 00:27:46.320 ⇒ 00:27:56.819 Audre: which hopefully. It starts in by the beginning of June. So I’ll probably let you know kind of in June what our timeline is gonna look like, and I need to finish
220 00:27:57.080 ⇒ 00:28:08.170 Audre: are kind of base sops, so that we have something to build the intakes around.
221 00:28:08.350 ⇒ 00:28:08.930 Robert Tseng: Yeah.
222 00:28:08.930 ⇒ 00:28:15.899 Audre: And hopefully, that’ll be done by mid like end of summer, depending.
223 00:28:16.080 ⇒ 00:28:16.510 Robert Tseng: Yeah.
224 00:28:16.510 ⇒ 00:28:19.979 Audre: And maybe we bring you in
225 00:28:20.350 ⇒ 00:28:23.899 Audre: kind of end of summer, maybe beginning of fall
226 00:28:24.070 ⇒ 00:28:33.739 Audre: and start going through this stuff because we’re gonna have a little bit of a time buffer period in like October and November, and it would be nice to like front load.
227 00:28:34.380 ⇒ 00:28:39.855 Robert Tseng: Before then, so we can address stuff that comes up kind of in real time. Then
228 00:28:41.920 ⇒ 00:28:48.360 Audre: So, yeah, that’s kind of what I’m thinking now, yeah, how does that sound.
229 00:28:48.360 ⇒ 00:28:54.119 Robert Tseng: Yeah, that all sounds good. And if you want to like, tap the earlier for like, if you’re talking to vendors and
230 00:28:54.310 ⇒ 00:29:09.839 Robert Tseng: like, yeah, I what I mentioned about, if you need me to just like kind of pressure, test any like kind of Bs that people are giving you like, I’m happy to just be your like data person on my call to just like, kind of just be there and ask questions. Try to push things as well.
231 00:29:10.290 ⇒ 00:29:13.409 Audre: Okay, then, once I address.
232 00:29:14.200 ⇒ 00:29:20.320 Audre: you know what I’m also gonna do. Is it? Okay? If I send you the results of this autonomic test.
233 00:29:20.610 ⇒ 00:29:22.110 Robert Tseng: I’d love to see it. Yeah.
234 00:29:22.110 ⇒ 00:29:27.469 Audre: Got it. Okay, send autonomic report, and I have to jump. I’m so sorry.
235 00:29:27.470 ⇒ 00:29:28.110 Robert Tseng: No worries Sam.
236 00:29:29.210 ⇒ 00:29:34.019 Audre: So I’ll send you the auto autonomic report you’re gonna send me like
237 00:29:34.570 ⇒ 00:29:40.219 Audre: some kind of agreement that we can maybe use when the time comes, or just so I can see what you normally have.
238 00:29:40.440 ⇒ 00:29:41.335 Audre: Yeah,
239 00:29:42.850 ⇒ 00:29:49.930 Audre: I’ll be in touch hopefully within the next few weeks about timeline, and when might be good to pull you into certain things.
240 00:29:50.280 ⇒ 00:29:51.640 Robert Tseng: Okay, perfect.
241 00:29:51.880 ⇒ 00:29:53.800 Audre: Awesome. Thanks so much, Robert. I appreciate it.
242 00:29:53.800 ⇒ 00:29:58.660 Robert Tseng: Yeah, thanks, Audrey, good to reconnect and excited conversation going.
243 00:29:58.930 ⇒ 00:30:01.430 Audre: Sure same thanks so much, and I’ll talk to you soon.