Meeting Title: Missed SLA Dashboard Follow-up Date: 2025-09-24 Meeting participants: Fireflies.ai Notetaker Katie, Henry Zhao, Brad Messersmith, Katie
WEBVTT
1 00:05:28.020 ⇒ 00:05:30.160 Brad Messersmith: Hey, Henry, can you hear me?
2 00:05:30.160 ⇒ 00:05:31.720 Henry Zhao: Yes, Brad, how are you?
3 00:05:32.090 ⇒ 00:05:34.290 Brad Messersmith: Good, how are you? One second here.
4 00:05:34.290 ⇒ 00:05:36.749 Henry Zhao: Thanks for your time. Thanks. Oh, Katie’s here too.
5 00:05:39.270 ⇒ 00:05:40.210 Katie: Hello!
6 00:05:40.490 ⇒ 00:05:41.320 Henry Zhao: Whoa.
7 00:05:41.890 ⇒ 00:05:43.529 Katie: How are you doing today?
8 00:05:43.530 ⇒ 00:05:44.290 Henry Zhao: Thanks.
9 00:05:44.490 ⇒ 00:05:45.839 Henry Zhao: Thanks for taking this call.
10 00:05:46.210 ⇒ 00:05:53.599 Katie: Yeah, of course, of course. Sorry for being a little bit late. Forgot that it was Zoom, and then I had to do the whole download, and… Oh, yeah, same.
11 00:05:53.600 ⇒ 00:05:54.320 Brad Messersmith: Perfect.
12 00:05:54.320 ⇒ 00:05:55.000 Henry Zhao: Who did that?
13 00:05:55.000 ⇒ 00:05:57.210 Katie: Brad. No, you’re good.
14 00:05:58.000 ⇒ 00:06:08.280 Henry Zhao: So thank you guys for the call yesterday. This won’t take too long, I just wanted to follow up some questions I had from looking into what we talked about yesterday, just to make sure I completely understand, kind of, what we talked about.
15 00:06:08.520 ⇒ 00:06:10.799 Henry Zhao: And that I’m on the right track, okay?
16 00:06:11.130 ⇒ 00:06:12.130 Katie: For sure.
17 00:06:12.130 ⇒ 00:06:22.599 Henry Zhao: So, first of all, I’m basically just copying this order journey dashboard, I think people are still using it, so I’m copying it and calling it the, missed SLA dashboard, I guess.
18 00:06:23.540 ⇒ 00:06:25.160 Henry Zhao: Okay.
19 00:06:25.480 ⇒ 00:06:33.010 Henry Zhao: Wait, what happened? Okay, so first it was removing the other three charts and just making this one bigger and easier to read, right? This one is still useful for you guys?
20 00:06:34.540 ⇒ 00:06:36.950 Katie: I believe so, correct, Brad? This is the main.
21 00:06:36.950 ⇒ 00:06:47.069 Brad Messersmith: Yeah, I think so. We did get some feedback from Danny between the last time we talked and today, that he was concerned a little bit about this chart not including
22 00:06:47.710 ⇒ 00:06:57.820 Brad Messersmith: like, all of the endpoints from all the different pharmacies, but for now, I think we can keep this the way it is, and dig deeper into if something is missing, what that might look like.
23 00:06:58.730 ⇒ 00:07:14.429 Henry Zhao: Okay, so that was my next question, is… one of the things we talked about is, like, the… there’s missing pharmacy data. What is the way that we should be resolving that? So, first we can, like, manually input this data, but I’m just curious, where are you guys getting that data, so I know how we can, kind of.
24 00:07:14.430 ⇒ 00:07:22.310 Henry Zhao: fill in this missing pharmacy data. So you guys are telling me, like, there’s missing pharmacy data, but how do you guys know it’s missing, and where is the, like, source of truth data from?
25 00:07:23.810 ⇒ 00:07:28.980 Katie: Brad, are those the Optio reports and such that we are getting? That he’s talking about?
26 00:07:28.980 ⇒ 00:07:29.590 Henry Zhao: Hmm.
27 00:07:29.740 ⇒ 00:07:42.369 Brad Messersmith: No, I’m not 100%, but I do know what it is you’re talking about, Henry. I’m not sure 100% on where the data connections are currently pointing in terms of, like.
28 00:07:42.650 ⇒ 00:07:47.150 Brad Messersmith: There’s a pharmacy null for a whole bunch of.
29 00:07:47.150 ⇒ 00:07:47.820 Henry Zhao: Right.
30 00:07:47.820 ⇒ 00:07:50.779 Brad Messersmith: the data that’s in a lot of the dashboards I was looking at already.
31 00:07:51.150 ⇒ 00:07:57.829 Brad Messersmith: I think it might make sense for us to, Katie, to show Henry how we’re pulling our SLA report.
32 00:07:57.830 ⇒ 00:07:58.530 Henry Zhao: Yeah.
33 00:07:58.630 ⇒ 00:08:11.539 Henry Zhao: Yeah, if you can show me the… you had mentioned there’s a lot of manual work that Katie does. If you can, Katie, walk me through quickly the, like, the manual work that you do, then that will already answer some of these questions of mine.
34 00:08:11.540 ⇒ 00:08:13.430 Katie: Okay, let me…
35 00:08:13.720 ⇒ 00:08:15.280 Henry Zhao: Cause I’ll see where you’re getting the data.
36 00:08:15.280 ⇒ 00:08:16.960 Brad Messersmith: Yeah, the fields are…
37 00:08:16.960 ⇒ 00:08:17.560 Henry Zhao: Yeah.
38 00:08:18.240 ⇒ 00:08:20.489 Henry Zhao: And I wish there was a way to record this.
39 00:08:20.720 ⇒ 00:08:25.300 Brad Messersmith: And the filters. Yeah, it said it was recording. But who’s it getting recorded to?
40 00:08:27.490 ⇒ 00:08:32.650 Brad Messersmith: Probably not me. I just downloaded the app, so it’s like a guest account.
41 00:08:33.650 ⇒ 00:08:37.890 Katie: Okay, hold on, I have to change my settings so it might kick me out, but I’ll be right back if it does.
42 00:08:37.890 ⇒ 00:08:41.439 Henry Zhao: Okay. Yeah, that’ll be really helpful. That’ll already answer all of… a lot of my other.
43 00:08:41.440 ⇒ 00:08:42.100 Brad Messersmith: Yeah.
44 00:08:42.360 ⇒ 00:08:46.550 Brad Messersmith: That should tell you at least where to make the connections, and then, you know, from there.
45 00:08:46.800 ⇒ 00:08:58.430 Brad Messersmith: I still don’t feel super confident, like, we can watch Katie’s process here and see if, you know, what else she’s excluding in terms of her filters and stuff, but Danny’s concern was basically, like.
46 00:08:58.550 ⇒ 00:09:05.870 Brad Messersmith: A lot of the SLA-type stuff we’ve built in the past has looked really good on paper, because it’s not necessarily including all the data, so…
47 00:09:06.410 ⇒ 00:09:09.309 Brad Messersmith: you know, that’s where his feedback was coming from, I guess.
48 00:09:09.910 ⇒ 00:09:10.510 Henry Zhao: Okay.
49 00:09:11.200 ⇒ 00:09:12.740 Katie: Okay…
50 00:09:17.030 ⇒ 00:09:27.249 Katie: Where is it? So this was the SLA report that I ran this morning, and the way that I grab it is in BASC, we can go into this order page here, and…
51 00:09:27.480 ⇒ 00:09:37.129 Katie: just take a, like, certain amount of dates, and for isolated reports, we go 3 business days back. So I would go 1, 2, 3 business days back, and on the fourth one.
52 00:09:37.130 ⇒ 00:09:48.059 Katie: start going back, and then I usually just go back to August 1st, and then from there, I click this export button up here. I transfer that into this SLA report, and the first thing that.
53 00:09:48.060 ⇒ 00:09:53.240 Brad Messersmith: Hold on one second, though. Why do you go to August 1st? Like, what’s the thought process behind…
54 00:09:53.860 ⇒ 00:09:55.050 Brad Messersmith: That date.
55 00:09:55.730 ⇒ 00:09:56.629 Katie: Well, because they
56 00:09:57.060 ⇒ 00:10:04.629 Katie: They are their stragglers. Typically, it’s 30 days back, but as we talked about earlier, there’s a bunch of stragglers, so I’ve just been going back to August 1st to catch them.
57 00:10:05.210 ⇒ 00:10:08.280 Brad Messersmith: Right, so in my head, I’m wondering if, like.
58 00:10:09.320 ⇒ 00:10:17.830 Brad Messersmith: we want Henry to build everything with 3-month… with, like, a 3-month window or something, so that we can catch everything that’s out of SLA.
59 00:10:18.320 ⇒ 00:10:27.669 Katie: I figured the dashboard… I figured the dashboard would kind of be interchangeable, and I could select a certain week or a certain month if I wanted to, you know?
60 00:10:28.500 ⇒ 00:10:32.030 Brad Messersmith: Okay, yeah, that is a good point. So, being able to filter…
61 00:10:32.160 ⇒ 00:10:39.029 Brad Messersmith: like, a sort of overall filter for just the date for anything that’s on this dashboard, Henry? Does that work?
62 00:10:39.290 ⇒ 00:10:42.780 Henry Zhao: Yeah, I can go back to beginning of time, anything that’s not delivered yet.
63 00:10:43.340 ⇒ 00:10:49.600 Brad Messersmith: Okay, yeah, because then that gives us a little more flexibility to look at, you know, certain windows of time, that would be good.
64 00:10:50.650 ⇒ 00:11:12.839 Katie: So the first thing that I do when I get into this master sheet over here is I go into the payment status, and I select only paid orders, and then there I go to the order status, and there’s two statuses that I need in order status. It’s the sent, and then down… it’s probably not here, but there’s one that’s this shipped estimated arrival with no date. That one is also important.
65 00:11:13.040 ⇒ 00:11:31.349 Katie: But of course, it’s not going to be here. So those are the two statuses that I need for order status. And then over in pharmacy status, all that I do is take out anything that was canceled, completed, delivered, or shipped, and it leaves me with warning sent to the pharmacy and pending, and then a combination of the two.
66 00:11:31.400 ⇒ 00:11:33.680 Katie: So, right here.
67 00:11:33.770 ⇒ 00:11:46.560 Katie: Well, then the next thing that I’ll do is make sure that my date prescribed doesn’t include anything in the past 3 days, because if I had it, all of these, then we would have stuff that was prescribed within SLA, so I just type in…
68 00:11:47.030 ⇒ 00:11:55.769 Katie: 2025, 09, and then the 20s, I clear those out, and those are not out of SLA. Then from here, I can just…
69 00:11:55.770 ⇒ 00:12:01.199 Henry Zhao: Can you clarify to me what exactly is the definition of missing SLA?
70 00:12:01.200 ⇒ 00:12:14.549 Katie: Yes, it’s an order that has been at the pharmacy for 3-plus business days, so it would have to be prescribed and paid and at the pharmacy for 3 days or more for us to reach out.
71 00:12:14.550 ⇒ 00:12:16.990 Henry Zhao: are we looking at? Is it prescribed or payment day?
72 00:12:18.580 ⇒ 00:12:24.860 Katie: I mean… Any of these dates, any of these dates, if they were,
73 00:12:25.610 ⇒ 00:12:31.029 Katie: in the 20s, so right now I’m going 3 business days back, and that takes me back to the 19th.
74 00:12:31.680 ⇒ 00:12:41.199 Katie: So if there was anything from the 20th and forward, then I wouldn’t be able to send that to the pharmacy just yet. I would have to take those out.
75 00:12:42.110 ⇒ 00:12:44.590 Henry Zhao: 23, 22, 21.
76 00:12:45.450 ⇒ 00:12:46.090 Katie: Business.
77 00:12:46.090 ⇒ 00:12:54.230 Brad Messersmith: But eventually, we might want to see visibility into what’s at each of these steps, right? Or am I wrong on that, Katie?
78 00:12:54.230 ⇒ 00:12:58.189 Katie: I mean, you’re not wrong, but I thought I was just showing you guys what I do for SLA reporting.
79 00:12:59.100 ⇒ 00:13:05.690 Brad Messersmith: Well, yeah, I mean, he’s kind of connecting the dots on how he’s gonna build this so that you don’t have to do this every day, right? So…
80 00:13:06.170 ⇒ 00:13:13.779 Brad Messersmith: question is, like, assuming that you’re not making these… most of these filters, I think, Henry can probably replicate.
81 00:13:14.000 ⇒ 00:13:14.470 Henry Zhao: Exactly.
82 00:13:14.470 ⇒ 00:13:20.590 Brad Messersmith: But it’s the question on, like, okay, if we are looking here at… I thought I heard you say…
83 00:13:21.230 ⇒ 00:13:25.260 Brad Messersmith: From… 3 days from within when the pharmacy received that.
84 00:13:26.290 ⇒ 00:13:35.379 Brad Messersmith: how does that relate? This is, I think, part of where Danny’s coming from, which is, like, okay, maybe that tells us a good story for what happens after it gets to the pharmacy.
85 00:13:35.730 ⇒ 00:13:46.049 Brad Messersmith: But then are we missing hang-ups other places, and is the, like, 3-day window for SLA overall, is that what we’re looking at? Do you see where I’m coming from, the questions I’m asking?
86 00:13:47.430 ⇒ 00:14:05.970 Katie: kind of… I don’t understand it fully, because the SLA as a whole is just… once it’s sent to the pharmacy, you’ve got 3 business days for it to have tracking, and if it doesn’t have tracking, then we have to reach out and figure out what’s going on with it, and we have to have a place to figure out how to find those.
87 00:14:05.970 ⇒ 00:14:14.520 Katie: prior to that, these other statuses and the ones that are not within… not out of SLA, but were prescribed, all of those things.
88 00:14:15.050 ⇒ 00:14:30.339 Katie: we don’t have to worry about those within these reports and these dashboards. We just have to worry about what’s out of SLA, have, like, a high-level overview of the orders as a whole, and then errors and pendings are the things that we need from these reports.
89 00:14:32.640 ⇒ 00:14:37.369 Brad Messersmith: So… There aren’t issues, then, in the ups stage.
90 00:14:38.180 ⇒ 00:14:38.960 Brad Messersmith: like…
91 00:14:40.040 ⇒ 00:14:46.660 Brad Messersmith: what… I’m just trying to understand, like, what blind spots we have, then, if that’s how we build this dashboard, you know what I mean?
92 00:14:46.940 ⇒ 00:14:53.849 Brad Messersmith: It seems like that covers our current process, but, like, what are… what happens if there’s breakdowns
93 00:14:54.470 ⇒ 00:14:58.769 Brad Messersmith: before it hits the pharmacy. Like, do we have a process for addressing that?
94 00:14:59.130 ⇒ 00:15:08.980 Katie: MedOps, that’s… MedOps’ whole job is, like, errors and pendings, and she goes into different filters. Instead of sent, she’ll take error…
95 00:15:09.370 ⇒ 00:15:17.050 Katie: And doctor error. One thing you’ll get to know, Henry, about these reports is order status is gonna be the biggest thing that we…
96 00:15:17.310 ⇒ 00:15:34.480 Katie: kind of just have on our report. So, the difference with… what you’re asking, Brad, is if there’s a process before they get sent to the pharmacy, and yes, that’s MedOps. The process after it’s sent to the pharmacy is PharmOps. We need the dashboard to work simultaneously for both.
97 00:15:35.130 ⇒ 00:15:44.680 Brad Messersmith: Well, that’s kind of where I’m coming from, only in the sense that, like, if we’re measuring our SLAs, we don’t want to just measure them for one or the other, you know what I mean?
98 00:15:45.700 ⇒ 00:15:55.200 Brad Messersmith: And that’s where Henry’s trying to kind of build this dashboard for us, but the dashboard, the goal is for it to kind of manage our process long-term. So that’s my question for you, really, is like.
99 00:15:55.680 ⇒ 00:16:00.370 Brad Messersmith: Does it make sense to have his SLA trend chart using this same data?
100 00:16:01.460 ⇒ 00:16:03.720 Katie: Or should he use different dates?
101 00:16:03.810 ⇒ 00:16:05.529 Brad Messersmith: Y-you see where I’m coming from?
102 00:16:07.460 ⇒ 00:16:08.679 Brad Messersmith: Like, if we have…
103 00:16:08.950 ⇒ 00:16:16.649 Brad Messersmith: our… your report, the way you currently pull it, let’s say it showed… what did it show this morning? Like, 700? Out of SLA?
104 00:16:18.960 ⇒ 00:16:24.199 Brad Messersmith: Maybe it’s, like, 900 if you go back to eternity, like, you know, the date… date ranges.
105 00:16:24.800 ⇒ 00:16:26.289 Katie: And it probably is.
106 00:16:26.800 ⇒ 00:16:27.570 Brad Messersmith: So…
107 00:16:27.570 ⇒ 00:16:32.839 Katie: you agree that would be something that’s good to have, and it’s something that I even said
108 00:16:33.180 ⇒ 00:16:46.189 Katie: months ago that we should do with the manual one, so yes, I’m on the same page. We should go back and get all of the orders that are still pending from way back when, but like we discussed earlier, they’ve probably already delivered, because they’re mostly from SmartScripts.
109 00:16:47.280 ⇒ 00:16:58.970 Brad Messersmith: Yeah, okay, maybe that’s true. That, for sure, that’s probably true, but I guess from Henry’s viewpoint, like, how do we want him to create the rules for what the definition is of SLA?
110 00:16:59.190 ⇒ 00:17:05.860 Brad Messersmith: Long term. And is it the same as what you’re doing here, is the question that I’m trying to get an answer to, you know what I mean?
111 00:17:05.869 ⇒ 00:17:19.649 Katie: I don’t think it’s the same as what I’m doing here, because what I want the definition of an SLA to be is exactly what’s in your mind. Anything that’s been at the pharmacy for 3 plus business days, all time back, not just 30 days back.
112 00:17:19.649 ⇒ 00:17:27.549 Katie: I think if we go all the way back, then we can actually have, like, clean records and everything moving forward. But right now, with the limitations we have.
113 00:17:28.089 ⇒ 00:17:37.039 Katie: a few days back, so I agree that what we’re doing now isn’t what, in the long run, we want to happen, but yeah, that’s… this is just what I’ve been doing.
114 00:17:37.390 ⇒ 00:17:45.679 Brad Messersmith: But, okay, I agree, and it sounds like Henry can go back to eternity, or whatever, no problem. So then the question becomes…
115 00:17:46.160 ⇒ 00:17:49.600 Brad Messersmith: the definition of an SLA out of 3 days.
116 00:17:49.730 ⇒ 00:17:55.040 Brad Messersmith: should that be… shouldn’t that be from, like, if I’m a patient, when I click the order button.
117 00:17:55.040 ⇒ 00:17:55.460 Henry Zhao: Yeah.
118 00:17:55.460 ⇒ 00:17:58.559 Brad Messersmith: To… to when I receive my order.
119 00:17:58.740 ⇒ 00:17:59.450 Katie: No.
120 00:17:59.780 ⇒ 00:18:00.480 Katie: They have different…
121 00:18:00.480 ⇒ 00:18:02.670 Brad Messersmith: Okay, so… They what?
122 00:18:02.670 ⇒ 00:18:15.079 Katie: They have different SLAs. It’s supposed to be within 2 days of them clicking the button that they get prescribed, and it’s sent to the pharmacy, and then once it’s sent to the pharmacy, within 3 business days that it gets shipped.
123 00:18:15.220 ⇒ 00:18:19.759 Katie: And then within 2… 2 business days that it gets shipped, it gets delivered.
124 00:18:20.730 ⇒ 00:18:25.899 Brad Messersmith: 3, 4, 5, so what’s our lead time? Like, what’s our overall lead time for our customer?
125 00:18:26.220 ⇒ 00:18:31.229 Katie: The promise is 7 to 10 business days. Internally, I tell my team 3 to 5.
126 00:18:33.900 ⇒ 00:18:40.490 Henry Zhao: So this SLA- you only care about the pharmacy piece. You don’t care about the prescription piece or the shipping piece.
127 00:18:41.410 ⇒ 00:18:46.560 Brad Messersmith: I think we might disagree. That’s what I’m hearing, right? Katie?
128 00:18:46.560 ⇒ 00:18:54.899 Katie: I think we do care about the prescription piece, because the date… okay, so the… the thing about this report I need to share again.
129 00:18:56.910 ⇒ 00:19:15.620 Katie: The thing about this report is this column here, order date, is when the member clicked Submit to Prescriber. Once the member clicks submit to Prescriber, they have to go through their visit, and the prescriber might take some time to answer some questions, or get some clarification.
130 00:19:15.620 ⇒ 00:19:17.410 Katie: Trying to find one that has…
131 00:19:18.950 ⇒ 00:19:35.799 Katie: a vast… yeah, like, this one has a two-day time difference between it, so the prescriber had to go in and ask questions, and then two days later prescribed it. So it wasn’t 9.15 that the order was sent to the pharmacy, it was 9-17 that the order was sent to the pharmacy.
132 00:19:37.030 ⇒ 00:19:46.619 Brad Messersmith: Okay, I am following now. The lead time will change by patient, depending on how reactive they are to the process, the intake kind of process.
133 00:19:47.840 ⇒ 00:19:48.330 Katie: Precise.
134 00:19:48.330 ⇒ 00:19:51.219 Brad Messersmith: And there’s really only so much we can do about that.
135 00:19:51.470 ⇒ 00:20:10.090 Katie: Exactly, and that’s why our only SLAs for before it’s sent to the pharmacy is that Beluga must respond to members within 24 to 48 hours of messages being sent, within an intake being sent to them. They have to do some sort of movement within 24 to 48 hours and keep up that momentum until it’s prescribed.
136 00:20:10.230 ⇒ 00:20:29.019 Katie: And it’s honestly up to the member how long that process takes, so sometimes it takes a couple of hours, sometimes it takes a couple of days. I’ve seen it take a month before. So, that’s why date prescribed is the one that’s very important, because if someone placed an order on 9-12, and then it was prescribed.
137 00:20:29.020 ⇒ 00:20:29.370 Brad Messersmith: Yeah.
138 00:20:29.370 ⇒ 00:20:33.920 Katie: yesterday, and we’re already reaching out to Optio, saying, where the heck is this.
139 00:20:33.920 ⇒ 00:20:35.339 Brad Messersmith: Hey, why is this late? Yeah, okay.
140 00:20:35.340 ⇒ 00:20:37.039 Katie: Now they’re mad at us.
141 00:20:37.170 ⇒ 00:20:44.090 Henry Zhao: But just base it on date prescribed. So, look at 3 business days since date prescribed to look out of SLA.
142 00:20:44.230 ⇒ 00:20:44.930 Katie: Yes.
143 00:20:44.930 ⇒ 00:20:45.959 Henry Zhao: Okay, got it.
144 00:20:46.280 ⇒ 00:20:53.750 Brad Messersmith: That we might need to expand to some of these other kind of categories at some point, but I agree, I think that’s the main focus right now.
145 00:20:55.630 ⇒ 00:20:56.200 Henry Zhao: Fair.
146 00:20:56.900 ⇒ 00:20:57.899 Henry Zhao: Oh, my God.
147 00:20:57.900 ⇒ 00:21:09.960 Katie: These reports are ran based on the order date, which is why I have to take this into account, but I assume with, like, all of your internal stuff, you might be able to just base this off of date prescribed as a whole.
148 00:21:10.380 ⇒ 00:21:11.769 Henry Zhao: Yep, and then order day, I just don’t need to even.
149 00:21:11.770 ⇒ 00:21:16.910 Katie: Mmm, actually, that’s not gonna be good, because date prescribed can go back.
150 00:21:17.560 ⇒ 00:21:33.060 Katie: Oh, Henry, you’ve got a mess, because this… we’ve got a lot of dates on this. Date prescribed can also go back months, because automatic refills… like, yeah, this one was prescribed back in March, and now they’ve just got automatic refills.
151 00:21:33.670 ⇒ 00:21:36.420 Katie: I don’t know how I feel about… ugh…
152 00:21:36.420 ⇒ 00:21:40.150 Henry Zhao: I think the prescribed date is before the order date, I can just take the order date.
153 00:21:40.150 ⇒ 00:21:41.450 Katie: Okay, perfect.
154 00:21:41.770 ⇒ 00:21:43.700 Henry Zhao: Because then they don’t need to be re-prescribed, right?
155 00:21:44.030 ⇒ 00:21:50.189 Katie: Yes, that is perfect, because if the prescribed date is before the order date, then the order date is accurate.
156 00:21:52.200 ⇒ 00:21:52.870 Henry Zhao: Okay.
157 00:21:53.600 ⇒ 00:21:55.299 Henry Zhao: Okay, Steve, that simple.
158 00:21:57.670 ⇒ 00:22:00.060 Henry Zhao: Payment date… what about payment dates?
159 00:22:00.270 ⇒ 00:22:02.360 Henry Zhao: Do they pay when they’re prescribed?
160 00:22:02.890 ⇒ 00:22:08.900 Katie: They… Mmm, the card is ran when the order is sent to the pharmacy, yes.
161 00:22:08.900 ⇒ 00:22:11.480 Henry Zhao: Okay, so I don’t need to worry about payment dates.
162 00:22:11.480 ⇒ 00:22:16.050 Katie: Not necessarily. It’ll prob- it’ll match between these two.
163 00:22:16.230 ⇒ 00:22:16.650 Henry Zhao: Okay.
164 00:22:16.650 ⇒ 00:22:18.170 Katie: It’ll give us what we need to know.
165 00:22:18.170 ⇒ 00:22:18.710 Henry Zhao: Okay.
166 00:22:21.110 ⇒ 00:22:24.180 Henry Zhao: Because you filtered by payment status paid, so yeah, I guess I don’t need to worry about that.
167 00:22:25.300 ⇒ 00:22:25.970 Henry Zhao: Okay.
168 00:22:28.070 ⇒ 00:22:28.809 Henry Zhao: And then what I visited.
169 00:22:28.810 ⇒ 00:22:29.370 Brad Messersmith: So then…
170 00:22:29.370 ⇒ 00:22:30.630 Henry Zhao: I didn’t care about that at all.
171 00:22:31.240 ⇒ 00:22:31.990 Katie: What was that?
172 00:22:32.360 ⇒ 00:22:35.560 Henry Zhao: The visit status, do I need to care about that at all, if they’re denied or anything?
173 00:22:35.880 ⇒ 00:22:46.870 Katie: No, we… there’s gonna be some denieds that are actually approved on this report, we just kind of ignore that. The only thing we care about taking out is if there’s any abandoned here.
174 00:22:48.610 ⇒ 00:22:49.370 Henry Zhao: Okay.
175 00:22:49.470 ⇒ 00:23:01.679 Henry Zhao: And then yesterday, you guys had mentioned a 30-day rolling window. Can you explain to me what that means? I don’t think I fully understand that. I think if we’re just going fully back in time, I can just give you all the out-of-SLAs, and you guys can filter by the dates that you want.
176 00:23:02.970 ⇒ 00:23:10.089 Brad Messersmith: Yeah, I think that’s right. We kinda, in the beginning of this meeting, I think we went… walked back on that 30-day rolling window thing.
177 00:23:10.350 ⇒ 00:23:11.370 Henry Zhao: Yeah, but I think for the chart.
178 00:23:11.370 ⇒ 00:23:19.360 Brad Messersmith: So basically, anything that’s aged greater than 3 days from that date that we just talked about should be in the list, basically.
179 00:23:19.620 ⇒ 00:23:25.880 Henry Zhao: Yeah, but if you want a chart, I can give you a chart of, like, the order dates by week or by month, and then you can look at what percent are out of SLA.
180 00:23:25.990 ⇒ 00:23:33.350 Henry Zhao: So obviously, yesterdays are all going to be in SLA, because it’s only been one day, but then starting 4 days ago, you’re going to start seeing things out of SLA.
181 00:23:36.430 ⇒ 00:23:40.819 Brad Messersmith: Yeah, so then, is that a trend chart, then? Or I’m trying to picture what that would look like.
182 00:23:40.820 ⇒ 00:23:44.360 Henry Zhao: I’ll send you a draft, and you guys can let me know if that is helpful.
183 00:23:44.920 ⇒ 00:23:52.199 Brad Messersmith: Okay, yeah, that works. Yeah, I mean, I’m imagining, like, a simple trend chart where we can see, like, okay, if it was 2 weeks ago.
184 00:23:52.680 ⇒ 00:23:58.060 Brad Messersmith: So, we had 2,000 out of SLA, is it… is it 1,000 now?
185 00:23:58.580 ⇒ 00:24:08.320 Brad Messersmith: you know, a sort of run chart like that, and then a percentage of the total orders. That was the rolling 30-day window, I guess. That’s how we calculated the percentage. That’s the only thing that might be…
186 00:24:08.550 ⇒ 00:24:17.840 Brad Messersmith: Basically, the way we’re doing it right now, Henry, is if we have in this bucket, let’s say, 900 orders today.
187 00:24:18.150 ⇒ 00:24:29.159 Brad Messersmith: that are, you know, older than 3 days since they were sent to the pharmacy. Then they hit this out of SLA, and then that is divided by the total number of orders in the last 30 days.
188 00:24:30.450 ⇒ 00:24:38.720 Brad Messersmith: So it’d be, like, 900 divided by 28,000 or something like that. It’s, like, 3%, or our goal is 2%.
189 00:24:39.400 ⇒ 00:24:42.080 Henry Zhao: Okay, I’ll try to… okay, say it, say it again?
190 00:24:42.290 ⇒ 00:24:43.839 Henry Zhao: How are we calculating this, this.
191 00:24:43.840 ⇒ 00:24:44.580 Brad Messersmith: So…
192 00:24:45.800 ⇒ 00:24:54.900 Brad Messersmith: So, if the number today that we just talked about, like, anything older than 3 days, right? Like, out of SLA orders, we’ll call them.
193 00:24:55.080 ⇒ 00:25:01.530 Brad Messersmith: If that’s 900, it’s 900 divided by the total number of orders for the last 30 days.
194 00:25:01.690 ⇒ 00:25:10.239 Henry Zhao: Right, but I’m taking the same orders from the last 30 days. So I’m just looking at the last 30 days’ orders, seeing how many of those are out of SLA, and just dividing by the total number of orders.
195 00:25:11.860 ⇒ 00:25:12.700 Brad Messersmith: Yes.
196 00:25:12.700 ⇒ 00:25:13.280 Henry Zhao: Yeah.
197 00:25:13.280 ⇒ 00:25:15.889 Brad Messersmith: And that gives us the percentage that we’ve been using.
198 00:25:16.740 ⇒ 00:25:18.950 Henry Zhao: And then I can just also give you a clue. I mean…
199 00:25:19.410 ⇒ 00:25:21.360 Henry Zhao: Yeah, that’s true, okay, that works, okay, that works.
200 00:25:21.480 ⇒ 00:25:21.969 Henry Zhao: That, that.
201 00:25:21.970 ⇒ 00:25:29.319 Brad Messersmith: Does that make sense? It’s a weird way to calculate it, personally, I’m not sure I’m in love with that, but that’s how we’ve been doing it with the leadership team up to this point, so…
202 00:25:29.320 ⇒ 00:25:30.000 Henry Zhao: 3 calories.
203 00:25:30.000 ⇒ 00:25:31.040 Brad Messersmith: Change things.
204 00:25:32.460 ⇒ 00:25:33.330 Brad Messersmith: Sorry?
205 00:25:33.620 ⇒ 00:25:35.720 Henry Zhao: Is it 30 calendar days or 30 business days?
206 00:25:36.960 ⇒ 00:25:46.630 Brad Messersmith: 30 calendar days. I mean, it’s not even 30 days, really, that we’re looking at right now. Technically, it’s 8-1 through today, but…
207 00:25:46.840 ⇒ 00:25:49.770 Brad Messersmith: for some consistency, I mean…
208 00:25:49.770 ⇒ 00:25:50.560 Henry Zhao: I would do…
209 00:25:50.560 ⇒ 00:25:54.419 Brad Messersmith: It honestly could just be 90 days or something. Let me think about this, because…
210 00:25:54.420 ⇒ 00:25:56.349 Henry Zhao: I would… I would do 30… 30 days plus.
211 00:25:56.350 ⇒ 00:25:57.960 Brad Messersmith: If you do 90 days…
212 00:26:00.830 ⇒ 00:26:05.040 Henry Zhao: I would do a month… I would do a month from 3 business days ago.
213 00:26:08.650 ⇒ 00:26:10.250 Henry Zhao: So, like, 3 business days ago…
214 00:26:10.250 ⇒ 00:26:10.900 Brad Messersmith: a…
215 00:26:11.210 ⇒ 00:26:15.870 Henry Zhao: 20th, I would do, like, April 20th, August 20th to September 19th.
216 00:26:17.800 ⇒ 00:26:19.119 Henry Zhao: Total orders divided by automatically.
217 00:26:19.120 ⇒ 00:26:25.699 Brad Messersmith: Yeah, I mean, I’m not sure it so much matters, whether it’s, like, the last 30 days, or the last 30 days starting 3 business days ago.
218 00:26:25.940 ⇒ 00:26:30.820 Brad Messersmith: As long as that window is fixed, I think at least we have the right kind of denominator.
219 00:26:31.050 ⇒ 00:26:32.360 Brad Messersmith: For the percentage.
220 00:26:32.530 ⇒ 00:26:33.110 Henry Zhao: None.
221 00:26:34.530 ⇒ 00:26:45.919 Henry Zhao: My other question is, yesterday you guys had asked, like, we want to figure out the SLA from when the pharmacy actually ships it. How do I know the date of when the pharmacy actually shipped it? Is it…
222 00:26:46.230 ⇒ 00:26:54.859 Henry Zhao: when the… is it the delivery status date when it’s delivered, or is it the ship’s date? Like, can you show me your screen and which one is the actual ship’s date?
223 00:26:54.860 ⇒ 00:26:56.330 Katie: You’re gonna love this.
224 00:26:56.740 ⇒ 00:26:57.620 Henry Zhao: Oh, great.
225 00:26:57.620 ⇒ 00:27:03.969 Katie: It’s actually over here in the order status. So you see this delivered?
226 00:27:03.970 ⇒ 00:27:06.669 Henry Zhao: Oh, it’s just delivered at in there, okay. Yeah.
227 00:27:07.710 ⇒ 00:27:13.000 Katie: There’s some down here where it’s estimated arrival, where they’ve just shipped.
228 00:27:13.620 ⇒ 00:27:20.999 Katie: This one? No idea. But they’ve got delivered and then shipped in the order status with all the different dates.
229 00:27:21.420 ⇒ 00:27:28.110 Henry Zhao: So do we care about the… so yeah, so right now we don’t care about the shipped ones, because you said that’s the two-day lead time where we’re gonna look at that in the future.
230 00:27:28.950 ⇒ 00:27:33.720 Katie: There should also be a completed at date. Where is that?
231 00:27:33.930 ⇒ 00:27:35.360 Katie: Date printed?
232 00:27:41.590 ⇒ 00:27:47.279 Katie: Mmm, the date printed will also have that for… only some delivered. That’s strange.
233 00:27:47.280 ⇒ 00:27:48.980 Henry Zhao: I’ll just use order status for now.
234 00:27:49.200 ⇒ 00:27:49.970 Katie: Okay.
235 00:27:49.970 ⇒ 00:27:52.850 Henry Zhao: We’ll work off further questions. Okay, so that is okay.
236 00:27:55.350 ⇒ 00:27:56.310 Henry Zhao: Okay.
237 00:27:56.810 ⇒ 00:28:02.979 Henry Zhao: So then, yesterday, you also said when an order is in the queue, that just means when it’s prescribed, but not yet sent out, right?
238 00:28:04.520 ⇒ 00:28:07.730 Katie: In the queue… I believe…
239 00:28:08.280 ⇒ 00:28:16.460 Katie: That would refer to… it’s in the prescriber’s queue, so it hasn’t even been prescribed. It would be… they’re waiting on the prescriber to send the prescription.
240 00:28:16.460 ⇒ 00:28:21.879 Henry Zhao: Okay, and we don’t care about that yet, we just care right now about the prescribed to sent to delivered part.
241 00:28:21.880 ⇒ 00:28:22.239 Katie: We do.
242 00:28:22.240 ⇒ 00:28:23.790 Brad Messersmith: Well, I… I think…
243 00:28:23.940 ⇒ 00:28:28.599 Brad Messersmith: Yeah, I think we should take kind of baby steps, though, because there’s kind of… there’s two waves here.
244 00:28:28.700 ⇒ 00:28:32.249 Brad Messersmith: The first wave is like, you know, just give us the…
245 00:28:32.590 ⇒ 00:28:37.680 Brad Messersmith: Basic information that we’re pulling every day, on a day-to-day basis now.
246 00:28:37.990 ⇒ 00:28:45.959 Brad Messersmith: Okay. You know, we can start with that, and a few basic charts, and, you know, the right information, I think we can…
247 00:28:46.500 ⇒ 00:28:55.420 Brad Messersmith: save a lot of time on a day-to-day basis. But then the next wave is the sort of forward-looking, kind of, how many orders are flowing where.
248 00:28:55.830 ⇒ 00:28:58.319 Brad Messersmith: That’s, like, priority 1B, I guess.
249 00:28:58.550 ⇒ 00:29:06.270 Brad Messersmith: Which I think could be, like, a different dashboard at this point. I think we get Dashboard 1, which is the missed SLA stuff.
250 00:29:06.430 ⇒ 00:29:09.809 Brad Messersmith: That’s, like, our current day-to-day structure.
251 00:29:10.400 ⇒ 00:29:18.759 Henry Zhao: And then, you know, wave 2, we can focus on the, like, what’s in the queue, and where are orders flowing, and we don’t have a lot of visibility into, like.
252 00:29:19.080 ⇒ 00:29:27.859 Brad Messersmith: the intakes, what volumes are going from which states to which pharmacies, where we have redundancy. There’s, like, 2 or 3 different kind of questions I think we want to answer.
253 00:29:28.120 ⇒ 00:29:38.459 Henry Zhao: So I’ll work on that next week. So next week… so next week, I’m working on some forecasting for finance anyway, so I’ll just fold this into the same project, and try to have some forecasting of, like, incoming
254 00:29:38.560 ⇒ 00:29:48.869 Henry Zhao: intakes to which pharmacies, which pharmacies are already kind of overloaded or low on SLA, and start to build some dashboards in maybe 2 weeks on the forecasting piece.
255 00:29:50.020 ⇒ 00:29:55.520 Brad Messersmith: Okay, yeah, I mean, we may want to talk even before you get too far on the forecasting stuff with finance, because I think…
256 00:29:57.140 ⇒ 00:30:02.070 Brad Messersmith: Has a file with Cutter that we’re working on getting updated.
257 00:30:02.200 ⇒ 00:30:08.069 Brad Messersmith: Okay. And so, it might kind of all overlap, to be honest with you. What we want… I think what we want to do is use…
258 00:30:08.260 ⇒ 00:30:10.740 Brad Messersmith: The spend? The marketing spend?
259 00:30:11.060 ⇒ 00:30:14.520 Brad Messersmith: To forecast into, you know, some basic
260 00:30:15.010 ⇒ 00:30:20.989 Brad Messersmith: Sort of metrics for what our monthly volumes will look like by pharmacy and by product.
261 00:30:21.610 ⇒ 00:30:26.489 Henry Zhao: Okay, are you guys free at 11.30 a.m. Eastern next month… next Wednesday?
262 00:30:26.800 ⇒ 00:30:32.420 Henry Zhao: Because I have a meeting with the marketing leadership, I might just include you guys in that to discuss the forecasting.
263 00:30:33.950 ⇒ 00:30:35.280 Brad Messersmith: Sorry, which day was it?
264 00:30:35.280 ⇒ 00:30:38.820 Henry Zhao: The October 1st at, 11.30 Eastern.
265 00:30:38.820 ⇒ 00:30:39.920 Katie: Yes, we’re… Yeah.
266 00:30:40.170 ⇒ 00:30:41.380 Brad Messersmith: Yeah, most definitely.
267 00:30:41.380 ⇒ 00:30:42.600 Henry Zhao: I’ll add you guys to that.
268 00:30:42.770 ⇒ 00:30:52.120 Brad Messersmith: Because then we can… I think they’re gonna be asking for a lot of the same stuff we are, so we can just either connect our process to theirs, or figure out exactly how this is gonna flow.
269 00:30:55.030 ⇒ 00:30:59.230 Henry Zhao: Okay, so I’ll have a draft on the forecasting for next week, and then we can discuss it and see.
270 00:31:00.830 ⇒ 00:31:07.519 Henry Zhao: And then I’ll work on just the Miss SLA piece, and then next week we can also work on the, in-queue piece.
271 00:31:09.470 ⇒ 00:31:14.260 Brad Messersmith: Okay. Yeah, that sounds good. And if anything comes up in the meantime, just hit us up, I mean…
272 00:31:14.470 ⇒ 00:31:23.110 Brad Messersmith: You know, if you have questions, or you’re, like, not sure about what filters or whatever, just, you know, let us know, and we’ll keep things moving as best we can on our end, too.
273 00:31:23.110 ⇒ 00:31:26.509 Henry Zhao: Okay. Yeah, I’m lo- I’m… this should be done by tomorrow, so…
274 00:31:28.270 ⇒ 00:31:28.820 Katie: Thank you.
275 00:31:28.820 ⇒ 00:31:29.290 Brad Messersmith: Sounds great.
276 00:31:29.290 ⇒ 00:31:29.880 Katie: Watch.
277 00:31:30.690 ⇒ 00:31:32.050 Katie: Alright, thanks guys!
278 00:31:32.050 ⇒ 00:31:34.179 Brad Messersmith: Alright, appreciate the time. Thanks, Henry. Bye.