So, one of the things we did last time is we discussed how to use some of these reports to identify issues in the data. We can review some of that, repeat some of that, maybe some new discussions on that. But what I recall is the focus of that was finding issues in the data, but not cleaning the issues. For example, a very simple one is that your laboratory number one appears under two codes. It appears as laboratory zero one and laboratory zero zero one. So who not thinks that's two different labs, but it's actually just one lab. So I think we can start by reviewing some of the, so basically there are two aspects to data cleaning. One is finding what potential issues are and secondly is what do you do about it. I do want to distinguish between cleaning the data, which we can do, versus finding bad results like vancomycin resistant staph aureus. This is a different, well, and this is also cleaning. If you find a vancomycin resistant staph aureus, there's nothing wrong with data file, but there is a mistake either in the laboratory test or in the data entry. So what who not can do is help you to find those. And then we can discuss about excluding them. Like if you find that in Ethiopia 99% of the staph aureus or vancomycin sensitive, I would just change that to 100% if you have not confirmed the resistance. Resistance is so rare that whenever you see it, it's almost always a mistake. So I guess they'll separate the data quality aspects. I'm not talking about if the one is the test, what's the test correct? They do proper testing with proper antibiotics. So the solution for that is prospectively going forward, try to avoid these issues and confirm the unusual results versus the data cleaning, where you're just getting the numbers to be valid data. So are you able to share your screen? Not yet. I think you can, you supposed to share, I mean, you should invite me to share my screen. Yes. I cannot. I am not the host. I'm a presenter. One second. Can you try now? Yeah. Now I can share. Perfect. Thank you. Oh, and I do see Rodney is now here. Welcome. So Martin, Rodney is from Uganda, and I think IDI, is that infectious disease institute Rodney, but he's been a Huna trainer in Uganda elsewhere. So we're trying to build a cadre of potential trainers for training activities in Africa. So we've invited him to participate in the series as well. Okay. Wonderful idea. Thank you. Thank you. Okay. Great. So we see your screen. Let's see if I can maximize. What I see at the top is all of the participants, the screen itself is relatively small. I don't know if that's the same for all of you. There's probably some way to go to full screen. Yes. You have a little square. There's a square with an arrow in the middle, and that can help you detach that screen and make it full screen, or you can minimize this, move the top bar to the very top, and then your cameras will disappear. And there is a chat message. Thank you for the introduction, Martin. Thanks, Fern. Well, detach cameras and maximize. Yes, I have done that. It was not the icon you described, but I detached the camera, so I separated the two, and then I had the two separate windows, and then I was able to maximize the screen. I'm saying that not because I need it, but it was interesting for me to know, but for other people on the screen, if you would like to have full screen, please detach the camera and then maximize the new window that has the screen. Okay, great. Okay, good. Can you just to remind myself, can you do file open laboratory? I don't know if you can hear me. So click on file, open laboratory, or maybe stop being responsive in which case file. Yeah, that's it. Open laboratory. Open laboratory. Perfect. So here we see that you have the 01 and the 01N. No, no, this one is actually, I'm going to remove it. Just I did it for plan. Perfect, perfect. Yeah. And then you have another one, then you have another one called 001 on a different computer. Before I can, before I dive into this, I was, I was discussing with one of the laboratories by email the other yesterday actually regarding annual update breakpoints. You know, who that updates the breakpoints who notice modify lab antibiotics update breakpoints. Let's just go ahead and review that. It's a little detour, but click on modify lab. So we're taking a little detour now click on modify laboratory modify. Perfect. And here you see that maximum that has three letter codes. We're going to change that relatively soon to default 10 letter codes. You know, there are a lot of places like San Francisco General Hospital. They would like a four letter code EPHI. It would be nice to have a four letter code. So, so we're going to change this to a 10 letter code as an option. You don't have to use all 10 letters. I think most people will stay with three letters. Click on antibiotics. Okay. And click on breakpoints. Breakpoints. Yes. Yeah. Don't click on anything, but it's the bottom of the screen. You see it says update breakpoints. So this allows you on an annual base to update your who net and to update the breakpoints, which is great. That way you can keep your who net breakpoints up to date over time. Click on okay. We're not going to do it. Well, update the breakpoints. It doesn't matter. Say yes. Yes. What? So it will go into update automatically. Well, what it does, click on okay. Click on okay. Okay. Click on okay. Yes. And click on save. All right. Good. Click on file open. Open laboratory. Yeah. Open laboratory. And that brings us back to the same screen. So now we have the new break points, but I want to comment on the process. What you do is to update the break points. You do two things. Number one is you download and download your who net and update your who net. Once a year. When you do that, it will bring the new break points down onto your computer. And who net does not immediately start using them. Who net does not utilize the new break points until you click on update break points like you just did. There's not a bug. We did that on purpose because we want you to take control of when the break point update happens so that you know when the break points have changed. Because if you're in the middle of doing some big publication and you will report and you're in the middle of the publication. And maybe you want to update who net because of a bug fix or a new feature. I don't want to give you the new break points if you're in the middle of a bunch of big important work, because then you might have to start from the beginning. So when you, so the process for updating break points is number one, download the new software from the website and then the new break points will come down onto your computer. And then step two is you go to that screen update break points and then it starts utilizing them. So does that make sense? It's a two step process. Number one, download the new who net number two, update the break points. So both are the same or I don't know. It's the same break points, but it's it's two different steps in the process process. The step number one is to download the new software and that will download the new break points, but it will not immediately use the new break points. So step number one, download the new who net with the new break points. Step number two, click on update break points. Yeah. My question for you is a, if I already download the new version of who net, I think that who net supposed to have the new break points. Yes. So why, so why we need to update the new version of who net because it has it already. No, no, I'm not, I'm not talking about you. I'm talking about like next February, after we next February, after you download the new break points for 2021, then do these steps next February. I just did it today to show the process. So that's why I didn't tell you to update the break points. It didn't matter. So, so in fact, it's the same break points now as it was 10 minutes ago, but next February, download the new software with the new break points and then click on update break points. Next year, we're going to, in fact, probably within a few weeks, we're going to make it even better to, because right now people don't realize they have to click on update break points. So we're going to give the user a notice and warning to say, oh, you haven't updated your break points. You want to update your break points just to be more proactive because some people think that they're updating who net manually, but they don't know that they're also supposed to click on update break points. So we're going to give them an automatic notice reminder. That was a little detour about updating break points. Yes, another question. But John, yeah, I am. Now, practically, we're going to write our report, maybe in the coming July or August. So still we are using Hoonet 2020. That would be correct. So do you think this step helps us or is there anything that this can help us? No, no, it's not needed. You're already using the new Hoonet with the new break points. This is only relevant when the year changes. So the steps I talked about now for updating break points will not be relevant until next February. Okay. Okay, great. That was a little detour. The reason I mentioned that now is I was telling you that we're going to increase the length of the laboratory code to something like 10 characters because that will allow us to do something like hospital 01 2018 break points. Hospital 01 2019 break points. Hospital 01 2020 break points because sometimes people want to, they don't usually ever do this, but some people eventually, some people say, can I go back and can I use my old break points? So if we make the laboratory code longer, it will make it easier for people to keep track of the multiple different break point sets. Okay. So it's a little detour because we were talking about your two letter laboratory code 01 and the three letter laboratory code 001. I took that detour to say that we're going to make it 10 characters. One of the reasons that we want to do characters is so that EPHI can put EPHI for characters. Another reason is so that you can put the year of the break points if you would like to do that. Okay. Great. Okay. Good. So now we're back on track for doing data cleaning. So click on open laboratory. We did discuss last time the idea of a national configuration. So right now we're going to focus on one hospital. So for one or one laboratory with one laboratory, one configuration is fine. Once we start doing like 10 laboratories, it makes more sense to do a national configuration. So we will also see how to do that. Click on data analysis. Click on quick analysis. Okay. Good. So we were, let's clean you click here on captura. The captura click on. Oh, it's supposed to. Okay. Click on number three, the weekly report. This is actually the one we have last time. Right. I was going to click on delete, but for some reason they're not deleting small bug. I'm just going to take advantage to go to my email. And paste. And then I'll just discuss it with the programmer afterwards. A lot of times during these presentations, I find these little small bugs. Oops. Okay. Okay. Okay. Okay. Good. All right. Okay. I was just going to click on delete because, you know, they were just simply done for demonstration purposes. The problem is if you don't clean up your demo stuff, people three years later, don't remember what was going on. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Okay. Okay. I'm going to click delete three years later. Don't remember why it's there. Okay. Total number three. I'm sorry. The fourth one. The standard report. Is it alerts? Go down further. There are three of these. There are three of these reports. Go down further. I want to see all of them. Go to the bottom of this list. This is the last one. Yeah. The last one is the most interesting one to start with. So let's start with number three. In fact, I might read a number of them later is one, two, three. order. Okay, go to the bottom, weekly report. No, no, no, no, no, that's not what I mean. The last three, I'm calling my standard reports. Okay, thank you. So there's the first, second and third. So in this context, among the final three, it's the third one. Anyway, so the third one, I'm going to start with that click on edit. Good. And you can see that this is a predefined marker that we are giving to you. And it's just going to do some of the very simple basic things to help you describe your data. On the right side of the screen, you see the specific macros that I created for you. The first one is laboratory by month. You see that on the top of the right.  Okay. And age group by laboratory. And location by lab location type by lab, specimen type by lab. So these are just some of the key basic things that people often like to start with. They want to know how much data do I have, how many urine, how many blood, you know, how many male, how many female. So this is this, as you can see the name of this report at the top is patient and sample statistics. So click on exit. The nice thing is this is editable. You can add and delete this. We're just giving you a nice template, but you can change that, you know, to what you would like. And I think there were some examples of that. Like I think that your location, can you click on edit again? Okay. So you see one of the macros here is called location by laboratory on the right side of the screen. These one. Yes. But what I remember is that I don't think EPHI enters the location field. Instead, they enter the department field. So this is a case where we're I'm simply giving you some macros, but for EPHI, location doesn't make sense because the column is empty. Department makes sense because that is filled in. So we're giving you these automatically, but some of them you may want to optimize and customize. Click on exit and click on data files and choose one of your laboratories. Just choose one file. This was actually the one. Either one is one. You did the encrypted to send it to me in Boston. But either for your purposes, either one would be fine. My computer is slow actually. Yes. Well, I can explain part of that. And we're making this part better. So who not for 20 years has used a very old fashioned data structure called debase. And debase is getting less and less compatible. And Microsoft is not supported very well. So another thing we can discuss is this SQL light. SQL light is super fast. So the slowness that you just saw was for two reasons. One, it was copying that file over to a temporary folder. And it was treating it as a debase file. So if we switch everything to SQL light and so either one is Oh, yeah. Okay, you see how fast the SQL light was. So because it's still relatively new to who net, it might be good to think of strategy to start moving people over to SQL light for two reasons. One reason is SQL light is much faster, much more modern, has more security features. It is the best solution for the future. That's one reason. The second reason is you're just having more and more compatibility issues with debase who net will say this is not a valid file when it is a valid file. So there is no urgency to migrate. As long as the debase is working, you can continue using debase. So we don't have to switch people over. And even the new software accepts both. It accepts the note, the new version and the old version. Also, if you see the size of this file, it's 364. The other one was 700. The new files are also smaller. So there are a lot of good reasons to switch to SQL light. But there is no urgency. The only urgency is if the debase is not working. Okay. So we all this question. Yes, question. Yeah, how can you convert? Debase data to this SQL light? Great, great. We we did that last time, which is why this file exists. But we will review that. So I will I will come. So we'll come to that relatively soon. So we're sort of reviewing things that we did on a previous call. But the last call was about three weeks ago, you know, because of the internet issues. So I will tell I will review these aspects of SQL light, especially for people just starting at the beginning, that may as well just start with SQL light, you know, just to avoid these debase slowness and compatibility issues. Great. So these are data from one laboratory. We can do this from multiple labs. And we will do that. But right now, let's focus on the one lab. Click on okay. Okay, and we're going to do this to the screen. So click on begin analysis. It's going to do those. I don't know, seven, six or seven macros. I'm more slowly than I would have expected. But it is what as you said, you have a slow computer. So who net is doing that first macro? If I remember correctly, it is laboratory by date. And there it is. And here you see you have the quote unquote two different labs, labs 001 and lab 01. As you can see, most of the data are in 01 1073 patients. But 416 patients were in a laboratory 001. So these are identifying issues. And later, we will show you how to fix the issues. So as you can see, Hospital 001 was used in February, March and April. And then again, in August and September. So if somebody must have switched which computer they were using. So that we want to merge those two together. So this is an issue. This is identifying cleaning issues. And then a separate state step is later is fixing the issue so that all the 001s become 01. Okay. And so now we see the graph for 001 with a lot in March and September. Click on the graph for 01. And you can see the exact opposite. We have a lot of data for all of the months, except for March and September, because it has a different code. Okay, great. So this is one of the, and we did two labs here. Well, it's really one lab. But if you had 10 labs or 20 labs, each lab would be on a different row. So that if you did this as a national analysis, this would allow you to see which hospitals have data for which month. You can see when they started, you can see if they're behind, you can see if you're missing a month. So this is very valuable just to understand how much data do I have, which laboratories, which months is anything missing? Okay. Any questions? That was macro number one. If not, click on continue to go to macro number two. Of course, everything you see here you can copy and paste like what you could do is copy and paste that to Excel or to Word as a reminder that there's some cleaning issue that you need to go back to later. So here we have the distribution of male and female. I see there are 812 female isolates, 985 male isolates. And I can see the distribution between hospital 001 and hospital 01. That is an artifactual difference. But of course, if you had 10 different labs, you'd have one column for each of the laboratories. So one of the things here is I can see there is all of your data has male or female, except for one isolate. So you have excellent completeness for gender. And also remember that one isolate, either somebody forgot to add the male or female, or they didn't know it wasn't on the sheet, or it might be a quality control strain, or it might be a water sample, it might be, you know, a dialysis bag. So not all of these data come from people. So you need to keep that in mind that one isolate that the male female is missing incorrectly, or there might be missing correctly because it wasn't from a person. What's wonderful is how extremely complete male and female are. I'm also surprised to see more men than women. Typically, because women have a lot of urine infections, you have a lot more data from women from the urine infections. And your EPHI is different. So if you're going to a routine hospital lab, they're usually doing a lot of urines. Also, in the United States, we have a lot more women isolates than male isolates, for three reasons. Women tend to live longer in high resource countries. Men smoke accidents, all sorts of things, blood pressure, obesity, generally men have worse health statistics than women in high resource countries. That's not always true in low resource countries. But so that's one reason is the length of care. Women are often better about seeking health care. You know, because of obstetrical and gynecological issues, they're just more used to going to the doctors. They're more attentive if they have a medical issue with their child for pediatrics. So a lot of times women will seek health care more often than men. And as I said earlier, the urine infections. So this is switched. It's just interesting, the thing else about it. It's always nice to see that these make sense. It's similar to different from what I see elsewhere. You know, click on continue. And now we are seeing age groups. So can you at the bottom right of the screen, click on zero one. No, the second one where most of the data are go to zero one zero one has most of the data. No, the lower right go down one. Perfect. And you can see it's very similar. So you can see that most of the data. Well, there's one formatting thing, you see how the babies less than one are at the right side of the graph. We can format the less than one should be at the left side of the graph. It's just a display issue. So you see you have a lot of babies. That's the biggest category, age less than one. After the babies, your biggest category is ages 25 to 34. So basically, you know, young adults, your mid adults. And what's also very nice, unknown, very few are unknown. Almost everybody has age. This is wonderful for this laboratory. But if you did this for 10 laboratories, my guess is that some labs are very good about entering age and gender, and other laboratories are not. So this is not exactly data cleaning. But it is going back and giving them feedback, asking them, Can you please do this? Why are you not doing this? So it's been of encouragement for them to say that you're not as complete, we're aiming for 90% completeness, but you're only 50% complete. And then over time, you can see if they can start to improve their completeness. It's not exactly data cleaning, but it is improvement of compliance with the recommended national protocol. Okay, this is this is, as you can see, it's age by laboratory. Now click on and these are the WTO age categories, I find the WTO age category strange, you know, 15 to 24, 25 to 34. In future, we will allow you to make your own user defined age groupings. But these age groupings come to us from the virtual. Click on continue. So this was age group by laboratory. Hello, click on continue. Yeah. Yeah, sometimes, you know, yes, maybe I have question for you. Oh, yes, please. And please remind me, you know, sometimes in the request, yeah, sometimes in the request form, we are looking. Can you hear me? Yes, I hear you. Okay, sometimes in the request form, we know that the babies are admitted in neonatal intensive care unit, they are in neonates. So how can we, the age is actually missing. So how can we treat them? Can we put any age between zero up to 28? Yes, yes, who that allows you to put three zero days, five days, three months, five weeks, we usually use weeks for animals, not people's like it's a five week old chicken.  Or if you know, my question is actually, yeah, who needs allows us to do the to put it in number of days in number of months, even in number of weeks. But my question is, if we don't know the age of infant, maybe is zero years old. If you know, it's a baby, and you don't know the age or the months of the weeks, you're just like the baby zero, because the baby is indeed zero until they reach one. Okay, just type the number zero. Okay, make it less than one year. John, sometimes can we make it less than one year? No, who in that does not recognize that who net uses less than one year as a group, less than one as you can see in the graph is a group. But the age is zero, the person's age is indeed zero. So if you do not know the age of the baby, put zero because that's how old the baby is in years. Yeah, that's cool. Okay, other comments or questions. And again, congratulations, at least at EPHI, I believe these are EPHI data, you're doing excellent with both gender and age. Click on continue. And this one is something else. This one is location by laboratory, but you are not entering location, you are entering department. So we can so this is not a meaningful graph, except we do want to standardize it so that the different labs around the country are doing things in a similar way. We can talk about that. So, so right now we have two issues that I've seen so far. One is we want to merge the 001 and the 01. And secondly, can we achieve standardization in the location in department fields to facilitate, you know, comparisons between facilities for some facilities, I want to location other facilities might want to department, which is going to be easier if everybody does it in a similar way. And that we can also fix using we can fix that later, you know, for example, using access or something else, he clicked on continue. So this was location by lab, but the location field is empty. And this one is location type, which you are doing an x well, you're doing an okay job at that. You see how 1393 isolates, the location type is missing. So in the lower right hand corner of the screen, instead of looking at the table, it's easier to look at the graph. In the lower right hand corner of the screen, click on 01. Because 01 has most of the data. So you can see most of your data, the location type is not indicated. The options are inpatient outpatient ICU, which is a special kind of inpatient emergency. So at a PHI, we're not getting a lot of the location types. And this is something we would like to improve in the future. If you happen to know it's an outpatient, I suggest you type the word out. Okay. So this is something for future improvement, because it's nice to know inpatient outpatient, but if we if it's not recorded, we cannot do that separation. I am surprised not surprised, but I do see that when we do know the location, that ICU is bigger than inpatient and bigger than outpatient. I do suspect a lot of your blanks are outpatient. But when it's known, you do have a lot of ICU, reflecting the reference nature of a lot of EPHI work, or the high tendency to take cultures from the ICU patients. Any comments or questions? If I look at the ICU patients, you see that there are 249 isolates. Can you click on copy table? Click on copy table. And now can you open up Excel? Excel, you want to Excel? Yes. If you go if you search for Excel, you should be able to find it. Yeah, but lower right, lower left hand corner, type here for search, just search for Excel. Yeah, my team will be working actually, maybe. Okay, all right, well, go to one of the other window. Yeah, okay, double click on any Excel file. Yeah, that's okay. Now do a new Excel and paste. What I'm doing now is a little bit of quality control, a little bit of double checking the reality to these numbers make sense. I'm going to show you a shortcut that many people do know, but other people do not know. You see that in between the column A and column B, there's a vertical line. In between the letters A and B, there's a vertical line. Double click on the vertical line. No, double click on the vertical, no, double click on the vertical line in between the letters A and B. Okay, we have to highlight everything first. In the upper left hand corner, click on that little triangle. To the left of the letter A, to the left of the letter A, there's a box. Good, that highlights everything. Perfect. In between the letters A and B is a vertical line. To the left of that, left, there's a vertical line. Or in between B and C, you have to highlight everything again. Okay, put the mouse in between B and C. It doesn't matter which letters. Stop right there. Now double click. Yeah. What that does is it resizes the column so that the columns are now wider or narrower. It's a very useful quick shortcut to readjust the column widths. Yeah, that's Other people do it manually. There are different ways to do it, but this is a nice convenient quick way. And the way that you do it is you highlight whatever you want, and then you double click on that vertical line when the icon, when the mouse looks exactly like it does now. Great. It's got nothing to do with Hoonet.  Okay, great. Okay, so you see, you see one column says number of isolates and one column is number of patients. Can you go to column H? Go to column H. Go to the first row of column H. I want to type something. Go to cell H1. H1, perfect. Type equals, I'm just type the following isolates per patient, type the word isolates, sorry, type the word isolates per base isolates per space patient. Okay. Enter. Okay, type equals, type C2, C2, type the letter C, divided by E2. Okay, perfect. Hit Enter. Now drag that down to the rest of the table. Can you copy that to the cells below it? Good. And can you right click on that, right click on that blue area, right click on the blue area? I want to reformat it. Right click on the blue area. Perfect, perfect. Format cells, format cells. I do find a lot of what I do is I teach people Excel and other things. Change this to one decimal point. Decimal points one. So decimal points change it to the number one. Oh, my battery's running. Where's my battery? Oh, is that your battery's running low? Where's that my battery? Oh, it's your battery. Okay, looking okay. No, one decimal point. I think you put three. It doesn't matter. It's good enough. Okay. Good. So here you can see let's forget the first row because it's just unknown. But look at the intensive care patients. So intensive care on average, you have 1.2 isolates for every person. Because sometimes there are repeats. You can see there are 249 isolates. And there's 249 isolates come from I can't see it because of the battery warning. So what I'm showing you here is basically this more scientific and it's, you know, it's also just double checking the quality of the data. Does it make sense? So Roddy, are you looking for your adapter? And I do not hear you Rodney. Oh, that's not Roddy. Sorry, Zalala. We might have lost him. Let me check. He's muted. Zalala, can you hear us? Okay, I believe I do have this file as well. So I if he's dropped off and he doesn't come back soon, we could just switch the demonstration to my computer. Yep. Let me show you. Okay, while we're doing that. So one of the reasons we're doing this is just double checking the quality. Does it make sense? There's certain organisms like pseudomonas that have a lot of repeat isolates, or as a needed factor has a lot of repeat isolates. Other organisms like homophilus and somonella and gonorrhea do not have a lot of repeat isolates. So it's not as if we're going to do a lot with this information. But it is interesting to know. But we also do it for quality checking, especially if you're exporting data from Polytech or these other systems. Sometimes you end up with like 10 isolates per person. And that is not correct. And so sometimes we're using backlink. I use it to find mistakes in the backlink configuration. So the reason we're having this little detour is just to say that makes it makes sense scientifically. ICU patients have more repeat isolates than our patients. So it's just scientifically it makes sense. But sometimes the numbers don't make sense. If you find that there are 10 isolates per person, something is wrong. Sometimes that happens because people have a special patient called test. The patient's name is test and they use it for, you know, they just use it internally. So you may end up that the test person has like 50 isolates. But it's not the same. It's not the same person. It's just a test thing. So what I'm showing you is mostly to discuss the science does, do these numbers make sense to you? But if the numbers don't make sense, then you want to think is there some quality issue that you want to double check. So can you see my computer screen now? Yep, we see it pretty well. And Zalala, we cannot hear you. So if you get back, just let us know. So I think I still have this here, EPHI National Laboratory Open Data Analysis, quick analysis. I'm going to click on patient and sample statistics. I'm going to data files. I'm going to try to move the go to meeting out of the way. So it's not so distracting. You can't see it, but I'm I can see it. So it's a little distracting there. And let me try to close this. Okay, good. So I hope you can see my computer screen and I may have moved this file. Where is it? I've got a lot of junk in here. So I'm going to go to, I believe I keep that data. I go to my countries, countries 2020. So if I go to country 2020, so here on the left, you see all the countries I have helped so far this year, which is something like I don't know, three quarters of the world. Okay, so let's see if I go down to Ethiopia. And if I go to data, here's that file, the encrypted version of the file. And my go to meeting is in the way. So I can't really see it. Okay, there it is. There it is. So I'm going to say okay here. Okay, great. Go to media. Let me move the okay, I think I moved them to a place where they will not be in my vision. Okay, good. Okay, so now I'm just repeating exactly what we did earlier. You can save to screen or Excel. There's a new feature for word. It's not formatted well. But over time, you're going to see this expand and improve in formatting eventually PDF is an option. So those are for the future. Right now we're focusing on the content. So I'm going to screen and click on begin analysis. Even on my computer, it's slower. It's fast enough but it's slower than I was expecting. Okay, this is laboratory by month. This is gender by lab. This is age group by lab. Location by lab, but the location column is empty. Location type by lab. And this is where we ended. And I was just demonstrating that there were 249 isolates from 206 people. On average, I forget what the number was 1.2. So on average, there's 1.2 isolates for each person. In contrast, in the outpatient, there were 57 isolates from 53 people. So there are repeats in the outpatient, but there are not many repeats. So there were only four. It looks like four people had a repeat isolate. Like maybe a woman had a urinary tract infection in January, and then she came back in February with the same urinary tract infection. Or maybe a patient had a wound, they had staff or is in the left arm, they also had staff or is on the right leg. So there are different reasons why people have repeats that happens very often in the inpatient setting, especially the ICU, it doesn't happen so often on the outpatient setting. There is another quality issue to mention here is this only works reliably. If you have a reliable patient identifier. So a lot of laboratories do not have patient identifiers. They just use the name. So and it does happen. Sometimes you'll have two different people with the same name. Doesn't happen often but it does happen. But it doesn't happen often that they have the same name and the same organism in the same year. They might have the same or the same name but one will have the staff or is mobile health equal. I it certainly can happen. They'll have the same organism and the same year, but it's not very often and it's not going to impact the statistics. So I don't really care that much about it. So name is not great, but name is also pretty good. You know that if you have if you the best thing is a is a reliable patient identifier that's used every time the patient comes back. If the patient comes back three years later, perfect. Some countries use the national identification number for this purpose, which is great. Other people use a hospital number that's also fun. That's also very good. Some people get the patient gets a different number with every hospitalization. So then have five isolates in January and five isolates in November. The five isolates in January will have the same number, but the five isolates in December or November will have a different number. So it will look like a different person. So what I'm showing you here only works accurately if it's if there's a reliable patient ID. If there is no patient ID, who net will use the name and the name is pretty good. It's not perfect, but it's pretty good. What is incorrect is some people who do not have a patient ID put a sample ID. That's a bad idea. Because if they put a sample ID, then every one looks like a different person when it isn't. So if there is no patient ID, the best thing is to leave the field empty and then we know we'll just simply use the patient's name. Any questions on this? If not, I will click on continue and I'll click with the next one. So what is this one here? This one is specimen type. So I'm always just going to start down in the graph. And at the far right, you see that there are a lot of urines. And at the far left, you see there are a lot of bloods. So this graph and in the middle, you see a lot of PS. That's PUS. I can see that in the graph. I can also see it in the table. Here at the top is a column heading called number of isolates. I'm going to click on that column once. I'm going to click again. So you can see blood. There were 583 isolates of blood from 540 different people. Okay, cerebrospinal fluid. There were 65 isolates from 64 people. So it's very common that people will have repeat urines. It is not very common that they have repeat cerebrospinal fluid. You know, they're not going to have many, hopefully the patient doesn't have meningitis twice. So again, this is just reassuring myself. Yes, the numbers make sense to me. Same with stool 64 stools from 64 people, you know, patients can have salmonella more than once during the year. But there are certain things like urine or PUS that it's common to have multiple isolates. You know, it's a ribospinal fluid. It's not common. It's not a common sample for a patient. And it's even rarer that you would take two samples from the same person, especially over time. Okay, continue. One thing that we're going to change here eventually, I hope in August, is what you see is the exact specimen type. The problem with the exact specimen type is it gets very specific. Prostate percardial joint, you can see there was only one one one, which is not that interesting. So we're going to introduce a new option called specimen category, respiratory genital soft tissue. So this will be a new future to simplify the summary.  That's just too detailed. So that will be a new future in the relatively near future, I think within a month or two months. If no questions, I will click on continue again. So basically, I'm just we're basically reviewing what we did three weeks ago. And I think that's important, because it's so valuable. And we're also leading into the so I went through that. And for this particular facility, I just noticed a few things. The 01001, the fact that the location column is empty, you know, department would have been more interesting. The I don't think there was anything else really. On location type, location type was usually empty. So as a feedback to the lab, I would recommend, I can tell you're doing an excellent job at age and gender. But for location type, it's not very useful, because most of the time the column is empty. If I had two labs here, I could I could just choose, I could choose 10 labs 20 labs. And then you would see exactly the same analyses that you just saw. But then you'd have each of the laboratories listed in separate columns. So you could see that hospital one and hospital two are doing great with age and gender. But hospital four, it's incomplete. So this is really helping them to come up with metrics to give them feedback about how they can do a better job in the future. Okay, I'm going to continue with this other one here called organism and antibiotic statistics or antibiotic results. Before I do that, any questions or comments? And if not, I'll just continue. Okay, I'm going to click on edit so you can see what who net is going to do here. And here on the right, we have pre prepared for you a number of macros, which you can enhance and increase and change and delete. For example, here's one told organism by location. That's not interesting for you because that column is empty. It's not interesting for the hospital zero one. It might be interesting for hospital zero five. So I don't think just getting rid of it. But for hospital or laboratory zero one, it's not going to be a useful analysis. So this is a great base. But you always want to optimize it for your needs. So this is going to do organism. So that first set of macros that I showed you did not talk at all about organism. All of that's here. Organism by lab organism by date organism by sex age group location type location specimen type. After that, it's going to do the detail percent resistance for step forest and E coli followed by the summary for gram positive anti biogram followed by the summary for gram negative anti biogram. So these are the macros that I chose for you. I'm going to click on exit. I'm going to simply say begin analysis. I'll begin analysis. Why is that not doing anything? What's it doing? Let me just leave that go back in here. Data files. Okay. Okay. And begin. That's very strange. Begin statistics, begin analysis. Oh, it's completely about age group. I don't know what's happening here. And it's always the issue. We're we're making these changes. Let me go to ice alerts, see if that one's working. That's very odd. Okay, let me exit who net. Maybe it was just some temporary thing. Sometimes errors happen always. Those are the easiest ones to debug.  Okay, so let me just cancel who net. So there are errors that happen all the time. Those are the easiest to diagnose and defects. There are other ones that happen frequently, but are hard to reproduce. Those are very annoying. And then there are things that just happened once. I don't even see who net here. What was Oh, there it is. It says not responding. So end task. Okay, and then I'll just restart who net and hopefully once I restart who net, then everything will start working again. So I'm going to go to EPH I open data analysis quick analysis, choose that second option data files and let's see and I choose no natural that's a different country.  Okay, good. And I click on begin analysis. Okay, everything's fine. Now I don't know what it was. So sometimes it's restarting the computer, restarting the software fixes it. There's just doesn't mean that there's not a problem. But sometimes it's a problem just for that computer. Okay, so now all of the analysis we see the organism is going to be in the row. You didn't see organism yet. Now everything is about the organism. You see organism for acinetobacter or let me go to the data vector species. There were six in hospital 001 or laboratory 01 and 41 and 01. Of course, that's the same lab. But if you had data from 10 different labs, you'd see you'd see the national total here. And then you'd see the laboratory specific total separate in each of the columns. I'm going to click, for example, on E coli. And you see that most of the E coli are from hospital 01 but some are from 001 again because of that data entry issue. Or if I click on let me click on hospital laboratory 01.  I can also do that in the graph. So here you see no growth. 800 isolates from 700 people. No pathogens 210 from 184 people. E coli 126 isolates from 123 people, etc. So I can see the most common organisms. I can also look at the rare organisms. You know, are there difficult organisms here? Like you have one sphingomonas posimobulus, one rickettsia conary. Sometimes it's a typing mistake, one rizobium radiobacter. That's one that's one that's either excellent or bad. Rizobium is a fungus. So either somebody did an excellent job identifying this difficult fungus, or alternatively somebody made a typing mistake in the honet organism. So if some of these things don't look right to you, there might be a typing mistake. So I'm always thinking first about the quality. And then I'm thinking about the epidemiology, like Charcot-Fighting. Again, it's a fungus. It might be a typing mistake. So you just want to say, does this make sense or not? Okay, we had the long discussion last time about the negatives. There is value to the negative. There is tremendous value to the positives and the antibody results. Of course, obviously. There is value to the negatives, but it's not much value. How many bloods did we do? What percent of the bloods were positive used to enter the negatives? We need to think about long term sustainability. What is going to work? The best thing is if they have a lab information system like Polytech, the Polytech would have the positives and the negatives. So if you are exporting data from a lab information system, usually getting the negatives is very easy because they're in the system already. But if you are doing manual data entry, if you are doing manual data entry, it is a lot of work to do the negatives. And it's just a discussion for you to have with your colleagues, what is their data volume? If they only have like 10 samples a week, it's not a lot of data entry. And sure, please enter the negatives because it's a very small amount of data. But if they do 100 a week and 70 of them are negatives, it's a lot of extra work. So you'll just need to make that decision among yourselves. WHO's recommendation is put the negatives, but they're also not getting easy and how much work it is. And one thing you can do is just start timing yourself. How much time does it enter a positive? How much time does it enter a negative? Entering the positives takes a bit more time because you have the antibiotic results, but they're a lot more negatives. So you can start doing calculations of estimates of how long it takes to do this work. I am repeating exactly what I said on the last call, but I think it's a useful repeat because maybe not everybody was there and it's just re-emphasizing these important points. If I'm spending too much time repeating myself, I don't have to and I can move on to newer things. I'm going to click on continue. So that's organism by lab. This is organism by date, which is valuable for outbreak detection. So here Acinidobacter balmani. I can't see the months on the left. So we see no Acinidobacter balmani from January to September, except for one in April. But then we have a bunch in October, November, December. Maybe that's a little outbreak. Or I don't think it is an outbreak. I think that the lab, so most of the year, they call this Acinidobacter species. But then if I look at Acinidobacter balmani, in November it's mostly Acinidobacter, they just called it something different. So I don't think there's an outbreak. I think most of the year they call this Acinidobacter species. And then for some reason that I don't know, in November they decided to speciate it to Acinidobacter balmani. So that's not an outbreak issue. It's just the degree of speciation. Let me go to something else. This is very interesting. I'm looking at right now. This is Brickledarius sepacea. And there are almost none in the first half of the year, January to June. But then you have a lot, July, August, September, October, maybe there was an outbreak. Were they in the hospital or were they in the community? And that's where the location is very important. Or maybe the lab did not have the ability to speciate Brickledaria early in the year. I do see in the table here, I see they had 36 Brickledaria sepacea, Brickledaria sepacea patients. There were two Brickledaria sepacea complex. So in total it's 38. So I don't, there's something strained, there's something important going on here. Either the laboratory did not have the ability to find Brickledaria in the first half of the year. Or there's a real outbreak going on here that you would want to investigate further. Okay. No, it's an organism often associated with burns and respiratory issues. Let me go down to E. Coli. It's often good to start with E. Coli because the amount of E. Coli shouldn't be changing very much.  It does vary a bit. There were a lot in November, not a lot in October. This is more variable than I usually see. But that might be because EPHI does not have a systematic, they don't have regular collection through the year. Maybe they're busier months where they're helping with the labs. So I don't really know. You would know much better than me why these numbers vary so much. I'm going to go to Staph aureus. Again, also a common organism. So you see Staph aureus is relatively consistent in the first half of the year, but a lot less than the second half of the year. Why? I don't know. That's up to you. Why did the volume go up? Why did the volume go down? Or is this purely random? Okay. So this is very valuable for finding the strange species.  So I'm just using my arrow key right now on the right side of the screen. I'm just using my arrow going up, up, up, up. And if you keep on doing that, you'll find possible outbreaks. Yes, there's a question. As part of the snigger, can you explain on that? It's mentioned there. Or I'll do a similar explanation for coagulase negatives. Okay. Okay. So here you see the graph for Staph aureus coagulase negatives. And here you see the graph for Staph aureus epidermidis. But of course, the majority of coagulase negative staff indeed are epidermidis. Sometimes your lab staff are calling it Staph epidermidis, but sometimes they're calling it coagulase negative staff. So you could see in January and February, you had Staph epidermidis. And then it was very rare in the rest of the year. But for coagulase negative staff, there were none in January. So basically in January, they were calling it Staph epidermidis. But most of the rest of the year, they call it coagulase negative staff. So somehow in the laboratory, they changed their practice. There was a different lab tech. Like in my own laboratory, I see this sometimes. There are certain lab techs that speciate everything. And there are other lab techs that don't like to speciate. So these Staph epidermidis and these coagulase negative staff are basically the same graph. It's the same organism. And those two graphs should be combined together. I don't see any Staph. I do see Staph hemolyticus. Staph hominuses are relatively rare organisms. I do not see any Staph, Staph saprophyticus, which is also another common coagulase negative staff, especially in urine. So basically what I'm saying in this particular example, this graph for Staph epidermidis and this graph for coagulase negative staff is basically a continuation of the same thing. It was the laboratory that changed their practice or policy for identification. Sometimes this happens because they run out of reagents, catalase oxidase, like this month, we're going to call it Staph, we're going to call it Staph epidermidis because we have the needed things. I don't personally don't know. I'm not a microbiologist, optica and Callista, all those other things. So this month we are able to speciate like this month we will call Ecclepcelonemonia because we have the API strips. But this month we're going to call Ecclepcelon species because we ran out of strips and we just did it manually. So that comment is about Staph coagulase negative and Staph epidermidis. My comment is exactly the same for the Acinetobacter. Here we have Acinetobacter bromani and if I look only at this graph, it looks like a possible outbreak of Acinetobacter bromani at the end of the year. But here's the graph for Acinetobacter species and there are a lot of these throughout the year, especially in April and May. So basically what I'm saying is this graph for Acinetobacter bromani and this graph for Acinetobacter luwati and this graph for Acinetobacter species should basically be added together. Does that make sense? They have different HUNET codes but they're basically more or less the same thing. Okay, good. So this one is organism by date, very valuable for finding missing months, you know, if they stopped entering data and that's where that's where E. coli is good. If you find some month there's no E. coli, no staff, then probably just the month is missing and it allows you to see the common organisms, the rare organisms, possible outbreaks. So there's organism by date. I'm not going to click on continue and this one is organism by gender, by male-female. So I'm going to click on female, I'm going to click on the table, column heading F for female. I click on that once, I click on it again. So in women, let me ignore the first two, no growth, no pathogens found. In women, the three most common pathogens are E. coli, then klepsiola, then staph aureus. In men, I click once, I click again. In men, it's the same three pathogens but in a different sequence. So women have a lot, I'm just going to, I'm going to exaggerate a little bit, but women have a lot of urinary tract infections, but men have a lot of skin infections and wounds. That's approximately correct. But so the top three organisms in men and women are the same, but not in the same sequence. I can see, I can see E. coli, women had 74 E. coli and only 32 staff, men it's opposite, they had 58 staff but only four young E. coli. Okay. So it's just showing these little epidemiological differences. It's scientifically, it makes sense. For example, if I look at, really there's not a big difference between men and women for most species, let me look at for, you know, for most species, you know, men and women are at the same rate, yeast. Women, yeast are much common in women than in men. Of course, that's not what I'm seeing here. I see Canada. Well, there's a number of ice, it's so small. Let me look for yeast. I'm going to put this in alphabetical order. Well, in this example, women, there were eight women with yeast, seven men. It's not what I was expecting, but you know, it's what the data are. Okay. So I'm going to click on continue. So now that was organism by gender. There's no organism by age group. So let me look at the age group distribution for E. coli. So E. coli, it's a lot of, a lot of, you know, men and women, this is mixed together. So E. coli, it's a lot of people between age 15 and 74. Very few babies, like less than one. If I go to Staph aureus, Staph aureus is different. You know, E. coli is pretty consistent across many age categories. Staph aureus is more common in the age group of 25 to 34, which is kind of consistent with the working age group of like construction workers. I'm exaggerating a bit, but I want you to think of potential explanations. You people who are injuring themselves with soft tissue infections. Let me look at Shigella. Well, there's a few. Let me look at some. Well, let me just start at the top. I'm just using my down arrow key. And Acinetobacter is more common in the younger population. Burkle Daria. Look at this. Burkle Daria, it's in the babies. We saw a lot of Burkle Daria at the end of the year. So I think indirectly I have accidentally found an outbreak of Burkle Daria in the babies, maybe the ICU or something. I'm just looking for anything else like that. Enterobacter cloakie, more common. You have a lot less than one and then it jumps up to age category 25. Enterococcus, most common in the babies. E. coli, more common in the adults. Lomophilus is usually more in the babies, the pediatrics. I'm going to any questions on the age distribution. You can see I continue to mix my review of data quality with my mix of epidemiology, but I always think first about the quality issues. Do the data make sense? And in this case, yes, the data makes sense epidemiologically, which makes me feel better about the data quality. Click on continue. In other words, an understanding of epidemiology helps you to establish that there are quality issues. This is now organism by location type. And as you can see, unfortunately, unfortunately, if I click on the column, unknown, you know, most of the things, the location type is empty. So I do not know what's happening there. So if I go to E. coli, where's E. coli? E. coli. The majority isolates, there's no location type. So I'm assuming they're outpatient, but I don't know that with certainty. Followed by ICU, followed by outpatient. Let me look at the Birkhold area to see what I can see there. Look at this. The Birkhold area, there are a lot in the ICU. So this very much looks like an outbreak in your nursery. Did you know about an outbreak in the nursery at Birkhold area at the end of last year?  No comments all continue, but that's what the data is suggesting to me. So this is organism by location type, which is interesting. This is organism by location, but it's not interesting because you're entering the department field, not the location field. So we can, we can change this macro so that it does department instead. This is organism by specimen, specimen. Of course, I wanted to do this by category. But let me look at E. Coli. So E. Coli, E. Coli obviously is mostly urine. But let me look at the Birkhold area. The Birkhold area is mostly blood. So it looks like a blood outbreak in the nursery in the NICU last year. Of course, this will be more interesting. You know, like for example, here is the distribution of organisms in blood, XXX. So here you see XXX is the most common thing in blood. Not a surprise. Followed by Klebsiella pneumonia, Birkhold areisapacea, and the far left, that's the PCE, the SCN, that's coagulates negative stuff on the right. So that the blood distribution is interesting, but here I have the distribution of joint fluid. But that's not so interesting. There was only one. So this would be more interesting with respiratory genital soft tissue. So a specimen category would help to make this more interesting. I'm going to click on continue. So that's the end of the organism in antibiotic results. Finally, I'm going to click on Isodilerts and begin analysis. That's strange, and it's not allowing me to repeat. Okay. I'm going to... Okay. I don't know what that is. It seems to be my computer. I'm going to leave Hoonet and let me go to Hoonet and not responding and task. If the problem is just my computer, then I'm not so worried about it. If other people start to report this thing, then of course I'm more worried about it. Okay. Ethiopia, data analysis, quick analysis, Isodilerts, data analysis, and NRL. I think these are your data. The NRL, I didn't recognize. And I think I chose the wrong macro. I think I just put the default. No, I guess I did do the right one. So it is working. So I don't know what the problem is. It seems as if rebooting, or not rebooting, but restarting the application seems to fix it. So there are problems that... Anyway, so eventually I'll try to figure what's going on there. Hopefully it's just my computer. So, and let me just cancel out of this. Okay. So we have discussed patient and sample statistics, organism and antibiotic statistics, and now we're doing Isodilerts. If I click on edit, this macro only has three reports, three macros, called important species and important resistance. The next one is called quality control alerts. And the final one is invalid data. And I'm going to click on exit. And I click on begin analysis. Do you want to count? No. Okay. So this is number one. What you see here are... I forget how many isolates you have. You have a few thousand, but 209 of them, if you look at the top of the screen, it says 209. These are isolates that have an alert, an important species or an important resistance alert. And those appear in red. The reason, like this one is Maripena, here you see red, Maripena resistant. So as you can see on the right, I'm going further to the right, the reason for the alert is indicated here. There are high priority alerts like CRE, medium priority alert, amicase and non-susceptible, medium priority alert, possible ESBL producer. This column called priority, I'm going to click on it twice to alphabetize it. I clicked on it once. And these are my high priority alerts. And there are a lot... Let me make this column wider. So there are a lot of CRE, Carbopenam resistant, Enterobacteriaceae. These are all the same. So, and here we have, and we saw this on the last call, we have three of these that are vancomycin intermediate or resistant Staphylococcus. Polyamistate. You see, in fact, one of them was R, two of them were I. Vancomycin resistant staphors almost does not exist in the world. In the United States, in the last 20 years, there have been fewer than 20 people who have had this confirmed. So VRSA is almost definitely a mistake. It's either mistaken the antibiotic test or maybe it's not Staphylococcus, maybe it's Enterococcus, maybe it's a mistake in the organism. And then two of them are intermediate. So these here are, could easily be mistakes. That's not who next job, that's your job. Who next is showing you that in your very large database, we had one VRSA, it might be, it is important resistance, but also probably a mistake. So the idea of these alerts at the national level is to help you to find the medium priority alerts are really not so important. I want to know the statistics, I want to know the percent MRSA, but I don't need a list of all the MRSA. At the national level, usually I'm just interested in the high priority alerts at that high, high level. I'm going to click on continue. And it's now a summary of these organism and resistance alerts. So for example, I see at the bottom, there were three kinds of high priority alerts, carbon-pennium resistance, vancomycin intermediate and vancomycin non-susceptible. You have a lot of medium priority alerts. So I want to know the statistics on the medium priority alerts, so the statistics are interesting. And here I see the statistics separate for each of the hospitals, which is also interesting. And then here at the bottom, I also want to see the list of these high priority alerts to verify that it was real, like Vibrio cholera, if I saw any Vibrio cholera, or from a public health perspective, I want to act on that. Any questions on these resistance or organism alerts? Whom to chose you the list, followed by the summary of the list. And the summary is separated by laboratory. If not, I'll just click on continue. These alerts were about organism and antibiotics. I'm sorry, high priority, important organism, important resistance, continue. These are exactly the same analysis, except we are not looking for important results. We're looking for possible mistakes. Let me find an example on the left. And usually, let me look for if I can find any club cellos. Here, for example, you see Burkholderia sepacea. This organism used to be called Cynomonas sepacea, which is why it starts with a P. This is ampicillin sensitive. It's usually resistant. Or where's my... So, yeah, I was looking for C. There aren't any of those. How many in the enterobacter? No, so there just simply are not any. So as you can see, the enterobacter are resistant for ampicillin, which is normal. If any were susceptible, that would be a potential mistake. So if I go to the right of the screen, it'll explain why this is a possible mistake. Let me make this column a bit wider. So here you see enterobacter is usually resistant to these things. But this one is susceptible. Well, actually, that doesn't seem right. I'll have to double check the rule. Then you see this one here called discordant results. What does discordant mean? Discordant means something doesn't match. So for example, here you see that these bacteria are gentamicin sensitive, but they're amicase and intermediate. And that's unusual. According to CLSI and according to common experience, if a strain is gentamicin susceptible, I'm sorry, if a strain is sensitive to gentamicin, it is usually sensitive to amicacin as well. It's not always true. It can be, you know, you can have amicacin resistant. Gentamicin is susceptible. That doesn't exist in the world. It's not common. So if you do see gentamicin, if you do see amicacin resistant or intermediate, gentamicin sensitive, it might be true, but CLSI recommends that you retest it because it might be a mistake. Similarly, if you see a bacteria, look at, you see these two, what is organism is this? E. coli. Look at this one here. I'm clicking here, but I lose the color red. Just above my mouse, you see that this strain is ampicillin sensitive. Amp is ampicillin. AMC is augmentin or amoxicillin clavulinic acid. It's sensitive to ampicillin, but it's resistant to augmentin. That doesn't make sense. AMC is amoxicillin plus clavulinic acid. If it's sensitive to ampicillin, it should also be sensitive to amoxicillin plus the clavulinic acid. So this just doesn't make scientific sense. So if I go over to the right side of the screen, it says penicillin beta-lactamase inhibitor is discordant. It should not be sensitive to the older, cheap drug, but resistant to the newer, more sophisticated drug. And as you can see, these are all markers quality control. So your infection control and epidemiology and infectious disease colleagues will be very interested in the first alerts I showed you about important organism and important resistance. These quality control alerts are going to be more relevant for laboratory internal testing. A lot of times this isolate's been thrown away. If you still have the isolate, please retest it. If the isolate has not been thrown away, I'm sorry, if the isolate was thrown away, just educate your laboratory staff. The next time you see this, please recheck it because it does not make scientific sense. And what you'll sometimes find is like, you know, I'll take a very simple example. Clepsilin ammonia sensitive to ampicillin. Clepsilin ammonia is almost always resistant to ampicillin. Not 100%, but it's usually 95, 98, 99% ampicillin resistant. What you might find is hospital one, 97% resistant. Hospital two, 95% resistant. Hospital three, 60% resistant. If you see that hospital three has Clepsilin ammonia, 60% ampicillin resistant, they have a problem. 60% ampicillin resistant for Clepsilin ammonia is not possible. Either the ampicillin result is wrong, or it is not Clepsilin ammonia. So on the next screen, you can see these errors, not errors, possible mistakes. You see these possible mistakes separated by lab. So if you see discordant results primarily for hospital number 10, then you want to educate hospital number 10 as to what they might be doing wrong. And that's what it's useful to see here, data from multiple facilities. Trying to click on continue. And now, okay, there was a third macro here. And we did important species, important resistance. We did quality control. Your data had no invalid data, who does not do a comprehensive check, but it's looking for invalid, male, female. It is looking for invalid dates. It's looking for small things. It didn't find any. Therefore, it did not show me any results because there weren't any. Okay, any questions about that? We're basically reviewing what we did last time, but I hope to re-review, re-emphasize these points. There are two things I can do. The two things that I think would be very relevant to do, what is the time? No, I thought we still have half an hour. Oh, no, that is time. That's an hour and a half. Yes. Yes, we still have half an hour, but we lost a lot of them. I just wanted to let you know. He didn't ever come back. No. Okay, okay. Well, as the lead, a lot of what I was saying, he would be hopefully very familiar with, but if not, he could watch the video if he wanted to. And of course, he can discuss any questions that his colleagues have. Of course. So the two other things that I thought could be useful to do, well, there are a bunch of things, but one is I have not shown how to make a national configuration. And that would be valuable because right now I'm analyzing data from laboratory one with the configuration of laboratory one. But what happens if I want to analyze the data from hospital 10? So I can discuss that and make a national configuration. The other thing I can discuss, I think that's better to wait until Zalalaam is on the call, is about how to fix the issues. Basically, how do we merge 01 and 001? What I usually do for that is well, I mean, I can do part of that right now. There's an easy way. Well, depends on how extensive the problems are. Okay, that's what I'm going to do now is you saw the issue of the 01 versus the 001. And I want to standardize it so they all say 01. So I'm going to go to data entry, open data file, and I find the data and I click on open. Okay. So far so good. I'm certainly just doing I'm quote unquote, doing normal data entry for this data file. I'm going to click on view database and here you can see all the data. And here you see some of them are 01 and some of them are 001. In fact, if I click here once, they're now sorted. So now I have all the 01s at the top of the screen. And then if I go way down to the bottom of the list, I have the 001s at the bottom of the screen. How can I fix that? And the answer to the question depends on how much do I want to fix? I'm going to show you three different ways to fix it. One is I click on the first isolate. I click on edit isolate. Oh, I forgot that's a hidden, it's not even on the screen. So I'm not going to, I'm not going to show you that way because the laboratory is not shown here on the screen. It's an invisible hidden column. So okay, I'll show that again. Edit isolate. This screen would allow me to fix a bad vancomycin result or a bad birthday or a bad organism. So using edit isolate, I can edit almost everything. So this is one way to fix them. I fix them one at a time. And I will do that if there's a vancomycin resistance drain. I can manually delete it or change it to sensitive. So you would use edit isolate to fix things one at a time. I can't do that with laboratory because the laboratory field does not appear here on the screen. It doesn't need to be on the main screen because it's all hospital 001. So that's one way to do it is click on edit isolate. A second way to do it is to click on edit table.  First I have to delete it. Edit table. Oh, that is not working. Okay, well, this is, there's a bug here. It's probably because of the SQL light. I'll discuss it without it. Oh, this is working. Why? That's working. Oh, what's interesting is some things I can change. Let me go to female mail. So certain things I can change. I don't know why I can't change this. I'll have to discuss with Adam. It's probably because it's a hidden field. Same thing. Yeah. Okay. That's a bug. That's unintentional. And we will have it fixed hopefully by the end of today. Let me go back to my email for Adam. Cannot edit laboratory code in view database. Okay. So just ignore that bug because I think it's going to be fixed tomorrow. Okay. I maybe not tomorrow, but I think by the end of the week because we have a busy day and other things to do. But this is where it's always good back to if you have a problem, let me know. And a lot of times the problem's been fixed already. So sometimes you just go back to the website. Right now we're making so many changes and new features that we are not yet making a change log so that you know what we're changing. We used to do that regularly, but it's just too many things going on too quickly. Once when it stabilizes, we'll start making a log about the things that we are changing. So coming back to this example about how do I edit it, normally what you would do is you would just come to the screen and you just say edit to isolate and you just change the zero ones to zero. You just change the zero zero ones. You click on edit table. You change the zero zero ones to zero one. And it just depends on how many they are. And you can either type zero one, zero zero one, or you can do paste, paste, paste. Yeah, let me, I'm going, I cannot do it with laboratory, but I can do it with gender. So here I can type the letter F. Well, let me choose a better one. Here I'm just doing yeast. I'm doing, I can type yeast. I can type yeast. I can type yeast. I can type it manually at time or I can copy yeast, paste, paste, paste, paste, paste, paste, paste. So that's what I would do here is I would edit the laboratory. I would copy zero one and then do paste, paste, paste, paste, paste, paste until you got to the bottom of the list. So I've now shown you two ways to fix this issue. Way number one is you click on edit isolate. Way number two, you just do edit the table and then you just do paste, paste, paste, paste. But if it's 3000, I don't want to fix it 3000 times. If it's fixing it 50 times, it's not a big deal. I'll just go zero one, paste, paste, paste, 50 times. So I've shown you two ways to fix this. One is fix them one at a time using Hoonet. One of them is using paste, paste, paste, edit table and I fix all of them here. So those are two ways to fix the 001 issue. There's a third way. I can either use Microsoft Access for the debase files or I can use SQLite for the SQLite files to edit it. So if I want to change like 50 or 100, I'll just do it here quick manually because I'm fast about paste, paste, paste, paste.  But if it's more sophisticated, then I take advantage, I import the data to access or to SQLite and then I make the changes there. I won't show you that now because, you know, that's something that Rodney may want to use. Plus the two methods I showed you are good enough for doing the majority of the cleaning. Like usually if some of the, like let me click on dates, let me go to the column called dates. Here you see the column called specimen date. I'm going to, let me go back to the top of the list. I'm going to the top of the list. I'm going to click here on specimen date. The very first date is January 1st, 2019. Perfect. I'm going to click on specimen date again. The very last date is December 30th, 2019. Very often people make typing mistakes. It's they put the year 2015 or the year 2030 some date in the future. So a lot of times it's just a small number. So in that case, what I will do is I will just delete, delete, delete, you know, it's resorting it, that's why it's disappearing. It'll just delete, delete, delete it. Good. So I've just unsorted it. So you can, you can just start to delete, delete, delete. And then, right, okay, that's fine. So if it's a small number of deletes, the two methods I've shown you are very good ways to fix them. If you have a lot of editing to do, then I can show you at that point how to use SQLite or access to do it. Okay. Other questions on how to do these kinds of editing? Use edit isolate, if you just want to edit one or two. Edit table, if you want to edit a lot, like 50 or 100, but if it's a huge number, there are ways what's called an update statement, and then you can say delete or replace or things like that. In fact, in Hunet, okay, I also want to mention that, you see here it says replace. I forgot about that. There are some things here that Hunet will be able to do in the future that's just functionality we haven't gotten to. One of them is replace feature. So if we had that feature, you could do replace 001 with 01. So this will be a feature for doing an automatic replace like that. That will be very valuable, but it's not a feature yet. Let me go to Adam. It's already on our list of things to do, but it's just not a high priority one at the present time. Replace 001 by 01. That's one change we want to make this, to make it more like Excel. The other thing we want to do is we want to put filters here. So here you see if I wanted to filter just on blood isolates, it's very easy to do that in Excel, but it's not easy to do that in Hunet. So you see no filters here. Hunet has the ability to do filters on other screens. Like if I go to modify lab, and I go to antibiotics, and I go to breakpoints, and I go to species specific. So in Hunet, we do allow filters on some of the tables. We just need to put these filters into data entry as well. Filters for data entry will be extremely valuable, as well as a replace feature. So these will be available to you. Right now we're focusing on bugs, bugs and other important things like we have different projects for glass, and captura, and Fleming. So those take first priority, but these other things are also on our early priority fix list. Okay, so I showed you two ways how to do some data fixing. Let me leave this, leave that, let me leave that. Now I'm going to show you how to, and that would fix. You could use the approach I just showed you wrong. There's something else I wrote to Adam, just a couple of days ago. At the present time, this would be a nice idea. It would be nice if Hunet has a little checkbox someplace, exclude isolate. Like sometimes you don't want to delete it, but you don't want it part of the analysis. So we're going to put a checkbox someplace, maybe at the top, just to say exclude isolate. That way you don't have to delete the mistakes, because it might not be a mistake. You might not want to delete it, but you just might want to flag it as exclude. So excluding is going to be a feature that I just put on our short list. I said Adam, August could be a good timeframe for that. Okay. So that's a bit about some of the data cleaning issues. Now I want to talk about a national configuration. So let me do File, Open Lab. So here you see data, you see some WHO ones, the WHO Test Hospital, Glass, Augie Star for Food and Animal Demonstrations. These are, I was doing some work with Sweden. This one is from Papua New Guinea. This is one is yours.  I'm going to go to Browse and go to a different folder. I have a folder here where I keep a lot of other laboratory configurations. You can see I have that folder clicked. And you see here, I have a ton of things from all over the world. A lot of them are just junk. A lot of these are just teaching. A lot of these are just demonstrations. Let's see. So this one is for the National Reference Lab. So this lab, in fact, does not have any of their own data. Well, let me have research data. But this is basically their national configuration. This configuration can be used for any of the laboratories. It has all of the antibiotics, all of the data fields. Let me see if I have another one. Usually we use the word all. So here you see one, South Africa Project, all data sources. Laboratory one, laboratory two, laboratory three. Here you see another one, Myanmar. So Myanmar, you see hospital one is Navitah General Hospital with a thousand beds, the National Health Lab, test demonstration. So they have data from different facilities, but they also have a configuration called Myanmar, all hospitals. And that's what I would like to create for Ethiopia. Ethiopia, all hospitals. Why do we do that? So I'll explain why. Let me go. Okay, well, I'll do it with Myanmar or Burma, you know, so if I go to Myanmar. Let's, so if I want a data specifically from hospital NGH, I can open laboratory NGH. If I want data from NHL, I can open, oops, I didn't mean to double click on it. I just meant to single click on it. So that's the four at the top. If I want to analyze the data from NGH, I can open NGH. If I want to analyze data from NHL, I open NHL. If I want to open data from tests, I can open tests. But what happens if I want to analyze data from all three at the same time? Because at the national level, you very often want to do that. One way that works relatively well, like I can open up NGH and when I'm opening up NGH, I open up NGH and then I go to data analysis and I go to data files and I open, I say all files. And what I can do is I can open the data from NGH. Oh, I'll come back. Okay. I'll come back to that. I'm going to choose it. I'm just going to choose some different data files. But I'm going to choose data files from different hospitals. So these are data from different hospitals. So I am able to analyze the data from any hospital in the world using the configuration from hospital number one. But there's a problem with that is it's only going to analyze things they have in common. So let's say, for example, laboratory one, test imipenem, but laboratory two, is testing imipenem. If I try to analyze the data from hospital two with the configuration from hospital one, it's only going to analyze things that they have in common. So it's not going to analyze the imipenem because hospital one doesn't test imipenem. So I'm going to do this analysis right now. Let me just put something simple like E. coli. E. coli. And let me do this by, you know, let me do this by summary and by laboratory begin analysis. So I'm choosing data here from like five different laboratory, well, five different files. I don't know how many labs that is. So it's analyzing data from all of the hospitals, from all of those different files, which is very convenient, but it is only analyzing the data fields and antibiotics that they share in common. I should have chosen a smaller example. Okay. It's almost finished. 10,000. Next time I'll choose a smaller example. I'm saying it's slow. It's not really, you see it's a lot of data. So in the scope of things, it is still pretty fast. I don't want to complain too much about it. It's 74,000 isolates. And here you can see, well, in fact, some of those files I showed you were from actually also admitted multiple files. So here you see the data from BMH. So these are different networks. So here's the data from a bunch of different facilities. For example, if I look at ampicillin, I don't know which is a better example. Okay. Here's a better example. So here you can see the data for the different antibiotics, but it's only analyzing things that have in common. So if laboratory one, there's this diffusion and laboratory test to MIC, they don't have anything in common or if laboratory test is 12 drugs, including imipenem, but hospital two test 12 drugs, including meropenem, it's going to find most of the drugs, but it's not going to find the meropenem.  So if I, I just am repeating myself, but I hope that's helpful to reinforce the point. If I want to analyze data from any Hoonet lab in the world, I can use the configuration for NGH, but we'll only analyze things that they have in common. So that's why we created this lab called all laboratories, all hospitals, because this configuration will have all data fields, all antibiotics, all locations, all everything. So then I can comfortably use this configuration for my national data analyses. So I hope that you understand the general intent of a national configuration. It has all the data files, or at least all the interesting data files and all the antibiotics or all the antibiotics you care about. So when I'm doing the Nash, if I'm analyzing data from hospital one, I can use the configuration for hospital one, but I can also use the national config. And I can use the national config to analyze the data from any of the hospitals. Okay, so that's the value of having a national configuration. A national configuration is extremely important if the laboratories are doing very different things. If some do disc and some do MIC and some do ME-PEN-EM and some do MAR-PEN-EM and some collect the diagnosis and some don't collect the diagnosis, a national configuration can solve all of those issues. On the other hand, if your hospitals are mostly the same, basically if you had hospital one and then copied it to hospitals two, three, four, five, and six, your laboratory configurations are all pretty similar anyway. So in Ethiopia, you do have like those 10 whatever different laboratories if those 10 different laboratories are identical, you do not need a national configuration. You just need any one of them because the configurations are all the same. They have the same data fields and they have the same antibiotics. So a national configuration is extremely important if the configurations are different from each other. If the configurations are basically the same, then you don't need a national configuration because they are the same. What happens very often in a network is in the first year, they'll just do a configuration that they will copy for everybody. So at the beginning, the labs often are identical. So in that first year, you don't need a national configuration because everybody's doing the exact same thing. But then over time, they start to deviate. Some labs start testing meropenem. Some start labs start testing deptomycin. Some labs start doing this and then they start to deviate from each other. And under that case, like some will put a diagnosis and some won't. So under those conditions, then a national configuration can be useful just so that you can use one centralized configuration to do everything. I'm now going to show you how to make a national configuration. But did what I described make sense? Why we want a national configuration? Yes, it makes sense. Yeah, it makes sense. Yes. Basically, there's an example of that. It was one of these labs. So if I choose the pastor Institute in Algeria, everything that you see in data analysis and data entry is customized for Algeria. So if you do things in common with Algeria, same antibiotics and same data fields, it doesn't matter. But it's focusing on Algeria. So same thing within Ethiopia. If I choose Ethiopia Hospital 01 and choose data from Hospital 10, if Hospital 10 is basically the same as Hospital 01, it doesn't really matter because they're so similar. But if Hospital 10 is different from Hospital 1, the national configuration allows us to reliably capture everything despite the differences in the configurations. So that's why we want a national configuration. How do we make a national configuration? Well, number one, we don't do it on the screen. It's a nice option, but we don't do it on the screen. So the first thing I'm going to do is cancel. The feature I'm going to show you is not on the main menu or it's not on that main screen. So here under file, I'm going to show you how to make a national configuration. One way that I do not recommend is called New Lab. Well, in fact, there's another way. Let me go back to this. I just realized something here. You hear me say it's Copy Lab? I just realized it's not on the other menu. Let me just make a small note for Adam. Copy Lab is not on the main menu. Okay, so when you made your Ethiopia configs, I don't know if you did them one at a time. I don't usually recommend that for a network getting started from the beginning. What I usually recommend is make a nice configuration. Let me choose this one here. This one's from Algeria. So what I can do is I can do Copy Lab. I make a nice one for hospital zero one and then I could say hospital two and then I say zero two. And then I can make Copy Lab and I say hospital three and then I say zero zero three. And then I can do for my 20 labs and then they are identical and they're identical so you do not need a national configuration. However, after I make these 10 identical configurations, then I will often go to Modify Laboratory and I'll customize it. The antibodies, I'll just make these small tweaks. So it's just easier to use Copy Lab to make multiple copies that are identical. Once you have 10 identical copies, then you can start to customize and optimize them separately for each of the facilities. So that's one way to make a national configuration is just copy, copy, copy and then the first one is your national configuration. And then you can tweak it slowly over time. So one way to make a national configuration is simply to choose new lab and do it from scratch or find a pretty good one and then do Modify Lab from there. For example, when I go to a country that's been using Hoonet for a long time, sometimes I'll have to start with the University Hospital which is usually the biggest, most comprehensive one and then I'll use that as the national configuration until I have a chance to make a more complete national configuration. So way one is new lab to start from the beginning. Way two is just to copy lab just so that they're all the beginning the same. But I'm going to show you the better way, the best way. File, there's an option here called create a lab from a data file. So what we're going to do here is create a national configuration. So let me click on that. So the country is going to be Ethiopia. The laboratory is going to be called Ethiopia all laboratories. We're going to give it a code of all.  They didn't call it all. They called it ATB which was the name of the National Reference Laboratory. Antimicrobianos that's why they called it ATB or antibacterianos or whatever it is. So I'm calling Ethiopia all laboratories or I can also say national configuration give it name what you want. I'll call it all or you can call it 000 you know just to say that it's the definitive one. Call it whatever you want. And then I'm going to go to data files. And this is the this is the good part, the trick. I'm going to choose some data from hospital one. I'm going to choose some data from hospital two. I'm just going to choose some data from hospital three. In fact, you only need one file. You only need one file per laboratory. You don't need many files per laboratory. So what you can see here is a variety of data from different hospitals. In fact, in my example, they're from different parts of the world. And I'm going to say okay. And now what Hoonit is going to do is it's going to scan those files. Oh, okay. This is this is not a bug. It's an unfinished feature. Combine SQL like files, etc. Create a lab from the data file. So as you can see, it's a work in progress which is why we always have to prioritize, you know, what we get to next. I'm going to do this again. I'll just be more careful about create a lab from a data file. Data file. Let me just choose some of the older files. Let's see. I think I got the archive. I'll put it back. Okay. And let me put here all files. All files. Okay. So I'm just going to avoid some of the bigger ones. So dummy data. I'm just going to get this on about the database file. Let me go to the literature test database. All these should be reasonable. In fact, a lot of these are empty. You can see that the size of the files, 161, 12, I did not pass them over to the right. Okay. Let me choose all files. And let me go back to the archival folder.  Let me select those files. Okay. So what Hoonat is going to do is let me change this back to Ethiopia. All Ethiopia. All laboratories. All. And I say okay. No error message. Okay. I know what that is. That's because of the debase issue. Okay. I want to just show you that issue as well. Okay. Basically what Hoonat would have done is it would have analyzed all of that. It would have created a new configuration called Ethiopia All Laboratories. Let me see if I can do this with a more limited set. There's a different point I want to show you that's interfering with what I'm trying to do. Ethiopia. All hospitals. Ethiopia. All laboratories. All. Let me just choose a few SQLite files from the data folder. And let me just choose this one and this one and this one. Hopefully that will be good and all. No. It's still complaining because of the SQLite that's not ready. Okay.  The thing that's blocking me. I'm going to go to the simple Deborah Cho test database. No. I'll do Ethiopia one. I need to go back to browse. And I need to go back to just the main Hoonat folder. I'm clicking on okay. And I'm going to go to Ethiopia open laboratories. I'm going to go to data analysis. Analyze data. Analyze data percent resistance. Okay. Organism E. coli. Okay. Let me just type that. And good. And I got rid of both of them. E. coli. Okay. Data files. So this. Okay. This is a normal Hoonat file. Oh, and it worked this time. Let me see if I can get another one. Let me choose. Let me go back to data entry. What I'm trying to show you here is the debase compatibility issue. Well, I can just show you on the screen that I was trying to show you. So I'm going to show you what's blocking me here. I'll just put all all data files. And let me go to the archival data there. And let me put all files. And let me just open up one of those files. So this time it's working. Okay. Well, okay. That part worked, but this part's not working. You see this message where it says unexpected. This is because of the debase compatibility issue that I've been emphasizing. There's not exactly an error, but Hoonat is unsuccessful at reading this file. So that's why we're encouraging people to go to SQL Lite. So this is not a problem. So it's a problem in my own laptop. There is a way for me to fix this one is to go to SQL Lite. I can also manually install the Microsoft access engine that fixes it. On my computer, I know how to fix this, but it only fixes it for me like a day or two days or a week or two weeks. So what you are seeing is because of the debase compatibility issue. Fortunately, most people in this world in the world are not having this debase compatibility issue. And I'm glad about that. But they're going to start having this issue, which is why I encourage people to start exploring the SQL Lite because otherwise you're going to have more and more of this exact issue over time. Okay, it's about time. I almost finished what I wanted to show you with creating a national configuration. If we did not have this error message, then what Hoonat would have done is it would have said Ethiopia all laboratories here at the top of the list. It would have had all the data fields, all the antibiotics, and we can utilize that for the national data analyses. And I know exactly why it's blocked and the reason it's blocked is because my computer is short-term not working with debase. I can rescue the debase. Let me go back to the Hoonat website. Let's see, I'm going to go here. Let me go back to the Hoonat website, Hoonat.org. So recently, not recently, just before the end of June, you know, before the programmer went on vacation, he's back now. We did read the website. And here on the main page, we talk about the new SQL lighted as advantages. We also talk about the problems with debase. So I'm going to click on problems with debase. So, and this is what I was saying earlier. Hoonat might tell you not a valid file, could not decrypt, AOL, exception, that error message that we just saw is not on this list. But we're going to add it to the list. So these are debase compatibility issues. There is a short-term fix that usually works. The problem is that this file has been influenced by some Windows update. Hoonat was working fine. Somebody does a Windows update or install something else.  So usually, if you manually install it using the steps that we describe here, usually that fixes it. And it does fix it on my computer. So if I just did that, then it would start working again, but then it would stop working a week later or a day later or even that afternoon. It's unpredictable how long this solution works. Some people, it fixes it and it fixes it forever, or at least for a month or two. I've told people about this and they haven't had the problem since. It has to do with automatic Windows updates or you install something else that interferes with this crucial file. The second solution is to use SQL light which completely avoids these debase issues. So that's why we're having trouble with debase and then what are the advantages of SQL light? One of them is the compatibility issue. So our other advantages, let's look at some of these speed issues. With the old Hoonat 5.6, I'm just going to focus on the left. It's a new feature for Deborah Treblast. It now takes 45 seconds. And if you look at backlink, well backlink, the old old backlink, this data file was too big. And then it was three minutes and a half. Now it's three minutes. It's a modest improvement on that one. And then, so then it goes, why we encourage people to switch to SQL light? It's like the carrot in the stick. There are very good reasons to switch to SQL light. Better performance, smaller file sizes, better. And then there's the stick. The stick, if debase is not working, you can try the short-term fix, but at some point you should switch over. So even though it's unfortunate that they didn't finish the national data file, I'm glad to re-emphasize why we're encouraging people to switch over, especially if they are early in their surveillance network. Thank you so much, John. We're already a little over time and some people have to drop off already. But do you want to wrap it up? Sure. I think I just left Hoonat already. It's stuck. My computer does that a lot. And that's my computer issue. Yeah, to do SQL light is very simple. You just go to data entry and SQL light is now the default. So working with creating debase, creating SQL light, there is no difference in how you create the files, how you save the files. But we can talk more about that on the next call because we might want to change the names of the files just so that you don't have... We don't even have to discuss that now. We can just have that discussion on the next call.