 Okay, well, as I said, this is not going to be a technical human training because most of you, many of you already know who led very well. So as I mentioned previously, I'm very interested to see how you're receiving the files, the names of the files, where you have the files organized, how much data you have. So that's what we need to learn about what you've accomplished so far. And then I would like to move over to your questions and your priorities. More on configuration, more on data cleaning, more on analysis, more directly to backlink. So who would like to take the lead from your site? And actually instead of me sharing the screens, it would be helpful if one of you could share your screens so that you can show me what you're doing. And if you want, you can also go directly to some of your current questions. Whenever you have these kinds of sessions with advanced users, I really would like you to take the lead because I don't know where you are with regard to data management and your questions. So who on your side would like to take over? Nobody's speaking. If you are speaking, then please unmute yourself. Zalal, Zalal, you're able to share your screen and share some of the files to show you've done the data that you're collecting or give a brief overview of the process now. I have to, I have to select who's going to be presenting. So who's going to, who wants to present? Maybe Zalal. I don't see that name on the list. How did he sign in? Is he using, or is he using? Is he using to pick 12 of those? I have a user that doesn't have a name. Or Seraphit. Seraphit is the one? No, that's probably Zalal. Zalal. Okay. He's maybe a user. His name, he's a user. User, yeah, user. Okay, okay. User, yeah. So Zalal, are you there? Can you speak? Yes, yes. Okay. Great. If you can bring it, Zalal to share his screen. Yes, already there. Great. So Zalal, we will have to say, I forget, share screen so that we can see your screen. Okay. That's perfect. Thank you. Yeah. Until now, we have nine configuration specific to the site. So I'm going to share you each of, maybe by selecting one of the configuration. So that you can see the contents of our data. I have a question. You have four configurations. So you have nine data from nine hospitals, is that right? Yes, yes, nine hospitals. Okay. And you have four configurations. Do you have more configurations or just the four? No, no. We do have actually eight configurations for eight hospitals. Yes. This is great. That's excellent. Thank you. I have a recommendation. Maybe Zalal or Zalal, maybe somebody can take some notes with some recommendations that we will come to later. I see you have eight configurations here. I would recommend having one called easy. In other words, we can have one configuration specific and customized for each of the hospitals that can be configured very closely to the need of that hospital. But we can also have a national configuration. It's a national configuration. We can analyze data from any hospital. So we'll talk about that later. How to create a national configuration. Please continue. Sorry. You said for the national. Yes. Later, I will show you how to make a national configuration. Okay. Okay. Thanks. So let us open one of the configuration. Yeah. Maybe I can show you this one for Jimma. Jimma is one of the hospital in Ethiopia. Yeah. Maybe I can modify this laboratory to show you the contents of the. Yeah. Overall. Configuration. So Ethiopia Jimma, the code is a five. We can use also letters. But so far we have been using just numbers. Antibiotics. We have list of antibiotics, which is a specific to that hospital. They don't have actually by tech. They are using only disk method. Okay. The location. The location. We already got information from HMI department. Yeah. HMI. Can you please go back to the antibiotics. Antibiotics. Okay. Cancel. Cancel. Okay. Antibiotics. Okay. Cancel. Cancel. Okay. Antibiotics. Yeah. So I think most of you have seen this before, but it's not on the left. We see all of the possibilities for this. And we see. We test about 300 antibiotics and CLSI 2020. On the right is a list of antibiotics for your hospital for your casting for your local needs. I do see some problems here. For example, a box of selling. There are a lot of drugs here that are not valid drugs. It's not a valid drug for a CLSI or your casting by this diffusion. So not a valid antibiotic. It's not so common. It's a new drug. I don't know if you have those disks. It's a very old drug. Can you go down? So you have 37 drugs on the right. Can you go down more on the list? Go down on the list. Yeah. Yeah. A little more. A little more. Yeah. So everything has to tell identity. You have that. Cross the ceiling is not valid. Go down further. Go down further. Go down to the bottom. You can see the bottom. No one just put the ceiling. People just put the ceiling. But not put the ceiling by itself. The end of the robotic list. Where? Where? The bottom of the list. The end of the list. So basically the bottom goes to and ends. And the bottom. This is your step. Okay. So those are perfectly fine. That you are testing. If you aren't accepting them. It is wrong. Some of these are not valid. See the sons. on the purchase. Before we continue are there any questions on this screen? Yeah maybe I can ask you one question on this antibiotic list. Yes. Okay some of the antibiotics that belongs to TLSI guidelines are not available in the list so we are trying to create user-defined antibiotics. Okay that is not good. Every aficion, low cast and TLSI and FDA antibiotic is on this list. However sometimes people have trouble finding it. For example there is a drug called Cotramaxazole. Cotramaxazole is not the official name. The official name, I see it on your list, we show that to themselves. There is another drug called Cotramaxole, but the generic name is Amosicillin clavioenic acid. There is a drug similar to that, these are brand names. The chemical name of the generic means 20% alpha person. So you're telling that there are certain drugs that are missing on the left, but there shouldn't be. Can you give me an example of an antibiotic that is missing? Normally I cannot remember right now, but I would send you some of the antibiotics later on. I cannot hear anything. Maybe there is some confusion. Okay proceed for today. Yeah, also Zelalem is joining or? Yes, yes, yes. Okay. So if you were thinking about it, Michael, do you also designate someone to take notes? Yeah, exactly. We're recording the call. We're recording the meeting too. I'm trying to take some notes as well. Thank you. So I have the point that some of these are not valid antibiotics. You had the point that some drugs they want are missing from the list on the left. That's not correct. Are the list on the left is all of the fissure antibiotics. There are some research drugs, nutrients that are not on the list, but that's a different question for research. Okay, here's some questions. Yeah, maybe azitromycin. I cannot find it from the list. So, is this your my son? Azitromycin. Can you please go to the search box? No, no, no, no, no. I stopped moving the mouse. And there's a box there that says search. Please go to the search. No? Okay. So, azitromycin, but it believes it's a letter E. Azitromycin does not come with a letter A. You're in the A area. Can you please go down to the area beginning with a letter E? That means? Yeah. Are you looking for azitromycin or anisromycin? But both of them are there. Okay. So, that is azitromycin. Now, one side is the search box type E-R-Y. E-R-Y, no. Can you please go down to the A and the Z? No. In the search box it says A. Well, I want to understand. Are you looking for azitromycin? I think this one, this one. Oh, okay. Well, it is there. Yeah. There are also some other antibiotics, but I cannot find them from the list. But usually what I can do, I will create my own by using the option user defined. But the problem is, you know, there are breakpoints, right? Yes. Hold on. I understand. So, you said there's a problem with the user defined antibiotics. That is correct. With the user defined antibiotics, you don't have to put the breakpoints yourself. The problem is, you don't need the user defined antibiotics. For everyone who see our site and request antibiotics, it is on the list. If you can't find it, we'll find it. It is there. Okay. So, please tell me the list of the antibiotics that you cannot find. Okay. In addition to that, in addition to that, you know, the MEC option, the concentration or the vitic, we have a PHI. So, we cannot actually enter the amount of concentration in HUNE. The reason is, most of the time, they provide greater than something, greater than something. I don't know what to do on that. I understand. That is not a problem. But let's talk about that later when you go to data entry. HUNE is a great problem with MEC data. Okay. So, when we get to data entry, I will show you how to manually enter the MEC or the E-test results. Of course, if it's from the Vitec, we also want to do backlink. Because if we do backlink, you don't have to do double data entry. So, in a couple of issues, the manual entry of MEC values and also backlink. But we'll talk about that later when you get to data entry and we get to backlink. Okay. And we have a question on this screen. Okay. Good. I have two points. On the right side of the screen, can you go to the top of the list? I want to see the beginning of the list of five. Perfect. Perfect. Perfect. So, here I see that you started in alphabetical order. Now, in a case in a mafiasso or an ampersand, that's remission. Okay. So, right now it's in alphabetical order. Can you go down to the end of the list? Go to the very end of the list. Yeah. Good. So, here, it was in alphabetical order, but then you added more drugs. You added amiccation, self-tax, self-taxing, deprimicent. That's fine. We added the three drugs and they went to the end of the list. I want to show you that you can change the list. Please click on amiccation. Well, let's click on the three. Select amiccation, self-taxing, and deprimicent. Right now, click on the amiccation. Where is it? Down to the bottom of the list. Go to the bottom of the list. The bottom. Okay. The bottom is this one. Yeah. Yes. Bottom. Yes. Click on amiccation. You want this one? Yes. Click on move up. Click on this one. Yes. Good. So, I want to make sure you know how to do that. So, what do you do? You click on move down. Move down. Yeah. Yes, please. And we'll just leave it there. We'll just leave it there. So, people ask me what is the best order for the antibiotics? Should I put alphabetical or should I put it? And that doesn't really matter. Put the order that you want it to be in. But I do have a recommendation. If they have a law of us, if they have the lab at your notebook and it's a lab at your notebook, so it's going to say in the same sequence. The data entry for who that will be easier is they enter the data in the same sequence. So, if you have a sequence on paper, kind of sell an office phone with a mison, then put the same sequence of the who and that. It just makes it easier to avoid transcription error. You know, if you see antibodies 1, 2, 3, 4, 5 on the screen, actually antibodies 1, 2, 3, 4, 5. Okay. So, if you have a manual data entry, put the sequence that makes the data entry easier. Okay. If you're using backlink, it doesn't matter because if you're using backlink, you do not get a manual data entry. But for people doing manual data entry, you can use move up and move down to make the sequence easier for the data entry person. Okay. That's one point ahead. Now, click on panels. Click on panels. At the bottom of the screen, there's a button that says panels. Down to the left. No, no. On the left side of the screen, on the left side of the screen, you see the word breakpoints. What is it? It's on the screen on the left, on the bottom. No, no, I don't see it anymore. No, no, stop. Stop right there. It's good. Okay. Do you see what is at the bottom of this screen? Do you see the word breakpoints? Down a little more. Down a little more. After all the antibiotics, after the serious box. And do you see the word breakpoints? Yeah, this one? Yes, that's it. To the right, panels. Please click on the word. No, not that we don't want the breakpoints. We want the panels. Now click on panels. Yeah. Good. I am very glad to see that you completed this. So here's the screen. It's to make the rise of the data easier. For example, for Staphylococcus, we do not test all of the antibiotics. For Staphylococcus, it's from Icine's GERD, Sepoxicine's GERD, clinical. So I am happy. I think you did a good job on this. I think Staphylococcus pneumonia. Staphylococcus pneumonia usually is different drugs, but in gram-negative, I see amyquicine, augmented ampicillin. So at first glance, this is relative and good. Okay? But first you can optimize this. If there is an example, Sepoxicine. Oh, this is a simple example here. Here on the left side of the screen, you see carbenicillin. On the left side of the screen, you see carbenicillin. Carbenicillin. It's about halfway down the list. No, no, no. Do not use the screen if I don't say. Click on panels again. Click on panels. Okay, now we wait. There's a little bit of a delay. So in the list of antibiotics here, do you see the antibiotic carbenicillin? You see the first antibiotic on the list is amyquicine. Yes? This one, antibiotic sequence, you mean? No, no, no. Do not move to the right. On the left side, there's a column. The first column says amyquicine. You see that? Yes, of course. You know that it says amyquicillin. You know that it says carbenicillin. You see carbenicillin. I'm just I'm just reading the list. Yeah, this one. Well, the next one, carbenicillin. Okay, this one? Okay. Yes, that's it. So I'm just looking at that and there are no check boxes. So you see the message is checked for carbenicillin. In other words, this drug is on the list, but you're not testing it. Yes, of course. Therefore, you can probably remove the antibiotic because it's not an antibiotic that you are testing. Okay. So in short, I'm pretty happy with this. I don't want to say it's perfect, but this is pretty good. You have the correct idea. This allows the data entry person to enter certain drugs for certain organisms. So I am happy that you focus in. So please click on okay. So I see what I'm going to say. Now let's click on okay. Yeah. Click on okay. Okay, perfect. Click on okay again. This one. Okay. Maybe this option, this option. John, can you see this option? Yes, I was expecting all the antibiotics that belongs to CLSI 2013. But here, we are also looking at other antibiotics, for example, from new... I can explain. Yeah. It's something that maybe will change. So our people who test official CLSI drugs with official CLSI breadcrumbs, like normal. So our people who do the CLSI method, on other antibiotics. So, for example, as you commented, there's a drug called viamycin. There are no official CLSI breadcrumbs, but people can test it. If they test it, there are no official breadcrumbs, but they can still do the tests. So a lot of time, researchers will do that. So I really see we can make it a little bit less confusing. If the person says CLSI, we should be able to give them two options. Option one is shown in the complete list. That's what we know it does now. But we can also give them a short list of official antibiotics. So in the future, to make the life easier for people, if they choose CLSI, we need to give them two choices. CLSI with the official CLSI antibiotics or CLSI with all of the antibiotics, that would make it less, that would make it more convenient, that would make it easier for people. I agree with you that if somebody says CLSI, it would be easier to say only the CLSI. Let's see, I also want to make a general comment. I personally went through that in DOS and in Windows for about 20 years. I've had an excellent new programmer, Adam, for about the last 10 years, and we're modernizing everything. And that's the big difference between UNED 5.6 and UNED 2018-2020. So we took a lot of time and effort to modernize. During the modernization efforts, we did not change the screens very much. So the reason that the screen is like this is because this is the way that I did this many years ago. I want to make this better by doing what I just said, CLSI selection, CLSI antibiotics. I've wanted to do that for a long time, but it was more important for us to modernize the software. We are now finishing, next week, the final step in modernizing the software. So the first thing is, UNED functionality has not changed very much because in the last several years, UNED functionality has not changed very much because we've been modernizing the software. Now that we have finally finished the modernization, I can finally start doing new things. One of the new things is this was the antibiotics. So in the next several months, we start to see a lot of new features. So this is just a general comment. New suggestion is a good suggestion. I've had that idea for many years, but I was not able to work on that idea because the modernization was a bigger priority. Now that the modernization is finished, we can go back and we can start adding new features. Yeah. Okay. Thank you. Sure. Okay. I am finished with this screen. If you want, we can continue with locations. Okay. This one. Yes. Perfect. Yes. So one comment to begin with is that the word location just so brings location. If you are a hospital, the location usually means the name of the ward or the name of the clinic. You know, emergency room in the care unit, diabetes clinic. So if you are a hospital, the location is usually the name of the ward or the name of the clinic. But if you are a national wrestling laboratory, the location might be the name of the city or the name of the hospital. Or if you are an animal laboratory, the location might be the name of the farm or the name of the market. Or it might be the name of the food or it might be the name of the river. If you are taking water samples. So the word location means different things to different people. If you are a hospital, location usually means water clinic. If you are at the national level, it's usually about the city or the hospital that sends you. So that's one general comment. Now, I see what you're doing. I see stroke, I see to the unit, I see medicine, female medicine, male. Yes, this is correct. This looks like a hospital configuration. I see that you have the name, you have a short code. It's all of the same hospital. That's perfect. The two columns on the right are not necessary, but they are very useful. The two columns on the left, you put whatever you want. The two columns on the right are to introduce local and national and international standardization. For example, on the left, the ones on the left are very obvious. For example, on the left, I see ophthalmology. At the national level, they don't really care about ophthalmology. You know, usually they'll just call it surgery. So the two columns on the right are to help to standardize things just to make it easier for the national person to look at intuition versus outpatient. At the national level, they do not care about five south and five north and medicine one, medicine two, medicine three. At the national level, they don't care about intuition versus outpatient. Maybe they care about medicine versus surgery. Okay. For example, I do see an example. Can you just go to all maxillotation? You see all maxillotation? On the left side of the screen, one of the rows is a location called all maxillotation. Do you see that? Not yet. Maybe, I don't know. No, it wasn't. Okay, the last one, we mean the... No, no, no. Go down again. Go down, go down, go down. Okay, good. Next question. Do you care if you're not off as a mouse? Do not lose a mouse. Good, good. Stop right there. Do not lose a mouse. Okay. Row number three. Row number three says all maxillotation. Yeah, this one, yeah. Yes, okay. I'm happy with column one and column two and column three. I'm happy with column five. Is this inpatient? Yes. I'm not so happy with column four. The department says OMSS. I agree that OMSS as the department is useful for the hospital, but you already have OMSC in column two. So my recommendation in general is that in column four, don't get OMSS because OMSS is not going to be useful to MPHI. At the next level, it would be better to say just surgery, S-U-R. Okay. Do you see what I mean? Because the three columns on the left, it's good to customize those for local purposes. For local needs, just make all of this, make all of this. So two columns on the right, it's easier for the national person if it is standardized. Maybe John, you know the difficult thing to standardize all the sites in Ethiopia is the department or maybe the world. When you move to one site to another site, it is totally different. So we are trying to customize for them specifically to them. But finally, while we are trying to combine the whole data, we will be using to the maximum level all the data is what we have without avoiding any variables. That is our orientation. Yes. So usually when you let us go into one facility for their own local purposes, I recommend optimizing everything for that facility. And that's what you're doing, optimizing for that facility which is great. However, at the national level, it's right to have some standards. So I would like, usually in the arch, columns one, two, and three, I recommend column three, do whatever you want. Columns five, I suggest introducing some general national standards, especially column five. At the national level, at the national level, in-person versus out-person is very important. At the national level, medicine, surgery, pediatrics is less important because of the problems of standardization. So I do, I am glad and I am very happy that column five, you have completely standardized. Column five is exactly what I would have done. In-out, in-out, it makes it nice and easy at the standardized level. Column four, as you pointed out, is a little bit of a concept here. We're trying to optimize it for the laboratory. We're trying to have national standards. What you have done is we've optimized it for the laboratory, which is great for the lab. It also means it's going to be the less useful at the national level. For example, at the national level, they again have one code for surgery. They have one code for surgery, ophthalmology, natural facial. So I think that's reasonable. At the national level, many countries do in-patient versus out-patient. At the national level, most people don't do medicine, surgery, obstetrics. If they want to, they want it standardized. What you have here is not standardized. So this is making it more difficult to standardize the department at the national level. And that's okay. Most countries do not do a lot of analyses using the department. They do analyses with in-patient, out-patient. It's reasonable. You don't have to change this. It does mean that this is well optimized for the laboratory use, but it does make it more difficult at the national level to analyze this column. But this column is usually not analyzed at the national level very much. Okay, good. Do you have any more questions on the screen? Any more questions? If you have no more questions. John, I have a logistical question. This is Vern. I'm typing some of your recommendations and comments in the chat screen. I don't know if people are seeing that. Do you know what is the problem? We can say this later. I haven't seen you go open. Okay. Yes, I can see the chat now. Okay, yes, good. Okay, so, so there's also, this is the kind of discussion that's good to have at the beginning. Because if you have a lot of data, you don't want to change the data. If you don't already have two years of data, and you change them in the middle, then you have a different problem. So you have to compare your old data and your new data. Sometimes it's important to do that. Sometimes people make mistakes on these problem scenes. So in that case, I try to fix the old data. So if you change in the middle of a project, then you have to decide you have one or two situations. If you change in the middle of the project, then either the old data and the new data will be different. And if they're using a lot of different design, we can work with that. Or sometimes what you do is you fix the old data. So there are many choices where I do help the people to fix the old data. Well, sometimes people have manual typing. They'll have, for example, medicine one, medicine two, medicine three. But some people would put M1. Some people would put med one. Some people put medicine one because they didn't need codes. So sometimes people are just doing free tax. And in those cases, sometimes we take the time in the effort to clean up the old data. So you just need to make the decision. I think you can continue exactly with what you were doing. Because you have the old data, it does mean that for in-patient outpatient, there's no problem. But it did standardize very well. I'm glad you did that. For department with what you have it now, it's going to be more difficult at the national level. And that's okay. If it is not a variable, time to analyze. If you want to standardize department at the national level, we're going to have to fix the old data. And I can show you on a different call. I use access. We cannot fix it in a minute. I get the data in access. And then I just fix it and just do a replace to standardize it. If I need a different comment, from Korea I need it because I'm sure a lot of you need it. There's a comment. I'm happy with the screen. So let's continue. Before you speak, there are two more features here. One called data fields, one called alerts. They're both important features. But most people don't change them. Like the alerts, it's a very valuable and medical feature. A minute is 190 alerts. But you don't have to change the alerts. If you're walking, you can change the alerts, but you don't have to. So most people do not change the alerts. If you want to change the alerts, we'll talk about that later. For data fields, most people do not change the data fields because they're happy when you're defining the laboratory and when it gives you a nice list of patient, location, specimen, organism, microbiology questions. Most people are happy with that list. That's the list that we see here. So most people don't change this list. But some people do. And there are good reasons to change the list. For example, who doesn't ask the Gramsthen? But the Gramsthen is merely for analysis. But if you're using it for clinical planning, a lot of people do want the Gramsthen. So the purpose of this feature called data fields is to add additional fields that you want. The name of the doctor, the name of the disease, the name of the counting, the province. So these are the reasons to add more questions. There are also reasons to remove questions. For example, if you work in an animal laboratory, you do not need the animal specimen, you do not need the animal's date of birth. If you are a food laboratory, you don't need the food's date of birth. I don't want to know how old my hamburger is. And the food's gender, I don't want to know if my hamburger is male or female. So there are good reasons to add questions if you want to add those. Those are good reasons to remove questions. So I'm sure it may be within change the list. Those are good reasons to change the list. So that's the purpose of this feature called data fields. I'll stop right there. Now you know questions about this feature. They don't have questions about the feature because they don't change it. So that's up to you. Do you have any questions about the data field feature? Just in color. Two has arrived. I don't know where that is, but. Okay, I'm not. Can you go down to the bottom? No, no, no, don't touch on anything yet. You see the list there? Country, laboratory origin. I want to see the bottom of the west. Go to the end of the west. The bottom. Yeah, this one. Yes. Good. And I see that you made no changes. This is the normal standard we might risk to give to everybody. So for this laboratory, we didn't change it. You just use the normal list that has. Do you have any questions on this list? Yeah, maybe, uh, uh, John. Yes. Uh, this is a little, uh, maybe I went to ask you if I have, uh, additional variables out of these, uh, maybe I can, I can include by using the model, I mean, modify list. Uh, then there is user defined here. Yes, that is correct. That is correct. So what if I list. So on the rest of this list, you see many predefined categories and data fields. For example, on the left, I see the database. I see the list. The patient sitting, the patient state. I see a long list of predefined option or a predefined list. And the other left, can you please click on clinical information? The other left, click on clinical information. That's yes, that's it. So now you can see there's the diagnosis, the operation, the name of the doctor, the name of the surgeon. So these are optional predefined data fields. We can choose for these. Or, as you said, you can do user defined. For example, if you want to keep track of the, if you want to keep track of the patient, the name of the patient's father. Hey, that's not a unit, but you can do user defined to add it as a locally relevant useful field. So do you have an exact, what is an example of something you might want to add? Maybe within, within even the list, I have one question. For example, prior antibiotic therapy is, it is already in the list, but it is actually a dichotomous variable. That means a kind of yes or no, right? No, in fact, it's empty. So for this one, okay, let's see. Can you double click on that? Double click on prior antibiotic therapy. Yes. It's now on the list, it's on the list. Click on okay. Okay, yeah. And now it appears at the bottom of the screen on the left. Yeah, it is a quad leaf. You see? Click on it. Yeah. Okay. Also, you see, when you've added the field, we have not added valid values. So it's simply free text. It's a 10 character field. You see length is 10. You can add text short, one or two. Or you can make it 30 or 50. If you want to write out something from text. Okay. Maybe, maybe to add some questions on this one. Let's say the patient has been given more than two antibiotics. So how can I inter these, the whole antibiotics? Let's change the length, let's change the length to 30. Let's change the length to 30. Yeah, you can make this one actually 30, but during data analysis, I don't know how can we separate this antibiotic because who knows, does not allow you to write separately in a different field. So how can we separate them, you know, finally? Okay. Yes. There is a way. I'm going to give a short answer now, but we can do a longer answer later when we eventually get to data analysis on this call in a different call. Okay. So here are definitely the use of wildcards. For example, if somebody writes here, amp comma sip comma SXT, when you get to data analysis, then you can say, I want to see therapy, asterisk sip asterisk. So we will look for the sip somewhere in the middle of the text. So there are ways. That's one general comment. So if you can define for me exactly what your needs are, I can give comments on the best way to do that. And a few additional things. A lot of people do use HUNET to analyze the HUNET data, obviously. Because HUNET data is HUNET to analyze the data. But HUNET files are also compatible with Excel, Access, SAS, SPSS. So there are people who use HUNET to do some of their analysis, but they use other spreadsheet or database or statistical software for other analysis. So for example, HUNET does not do logistic regression. HUNET does not do chi-square. But give them HUNET files and put it in a statistical software to do those things. So there will be examples where there might be something that you want to do, and the data are in HUNET, but HUNET doesn't give you the analysis option. I have a little secret. Microsoft gets paid more than I do. Microsoft has a big company with a lot of software, with a lot of capabilities. So of course, they're going to have a lot of things that HUNET is not able to do. For the very important ones, we try to put them into HUNET. So it makes you do your question. There are certain things that you want to do that you can do with HUNET, and I will show you how to do that. But there's certain things that are in HUNET. You can do HUNET and type them into HUNET, but HUNET does not do the analysis that you want. In that case, you can use a different software that you know, SQL Server, access, Excel, and the info to analyze it. So then I can show you later how HUNET can be used by these other softwares. Okay. Maybe one final question regarding this window. I just realized I should not be eating peanuts. I do not have COVID. I just have peanuts in my throat. Maybe? I have another important comment. Sometimes when people are starting their projects, they are overly optimistic. Sometimes they want to say, I want to know the therapy. I want to know the name of the doctor. I want to know the outcome to the patient die. I want to know the disease. These are all great research questions. There are a lot of questions. Does the patient have a ventilator? Does the patient have a catheter? If you are doing a research study with a research protocol, yes, of course you can collect everything that people agree to collect. For routine data, however, a lot of times the information is not available. The laboratory does not know the diagnosis, or the request forms if it says fever. Fever is not a diagnosis. So regarding the plant antibiotic therapy in a research context, there can be great value in that. But then you should think about a nicely designed protocol and survey and answers to standardize those responses and get agreement from the data collectors to collect it and to enter it in the standardized way. What I've seen, however, many times is there's a protocol diagnosis. People on the list, like you are doing, people put diagnosis. And then when I try to analyze the data and I look at the diagnosis, it's empty. They didn't enter it in. They entered it in 20% of the time. Somebody wrote the word fever. Somebody wrote the word S-E-V. Somebody put F-U-O and fever of unidentified origin. So sometimes if you want these additional data fields, you do need to think about a group. Is it practical? Is it realistic? Is the laboratory going to have these things? And if the answer is yes, then how are we going to standardize the coding for it? So the idea from a research perspective of prior antibiotic therapy, I like it from a research perspective, but a lot of people are going to know the answer. Are they going to enter it? So this is a general comment. When people add additional fields, sometimes they add additional fields for very good reasons. However, sometimes people add additional fields because they are overly optimistic. They say, oh, we're going to get everything. And when we get to the data, nobody has it. So that's just a general comment. Okay. That's great, by the way. Maybe for the attention. Before we leave this one, you see on the left, on the left, you have click on prior antibiotic therapy. On the right, you see it says human animal food. You see this here, checkbox is human animal food. Yes. We leave the checkbox for food. Okay. So prior antibiotic therapy is a relevant question if you're dealing with humans. It is a relevant question if you're dealing with living animals. It's not a relevant question if you're dealing with food. The foods aren't dead. So let me give a better example. On the left side of the screen, go to the top of the list. Go to the top of the list. Yeah. Click on first name. Click on first name. So you can see the lower right hand corner, first name is a human question. We do not need the first name of the animals. We do not need the first name of the food. Exactly. On sex, on the left side of the screen, click on sex. That question is for human animals. It's not a question for food. So I just, it's a small feature, but if you're still working with food and animals, it's nice to know what the purpose of this feature is. Okay. That's all I wanted to say on that. What was your next question? Yes, I am here. Okay. If you add additional variables in addition to the one what we have in the unit, the variables will be placed at the end of the list. Even if, if you use move up and move down, these options will not work on the additional variables. I don't know. Maybe you have some tricks on that. It does work, but it doesn't work. Yeah, it doesn't work. For example, yeah. This one, actually this is from the list of units, but if I add one variable, I cannot actually use these two functions on that. It works, but when you come to the data, you will see them at the last, at the end of the data. I don't know why the reason. I know why. Let's see. I don't want to change things. Before we do anything, can you click on okay? Okay. I want to change the laboratory code. I don't want to change anything you have. Okay. You see the laboratory code is 05? Yeah. Let's just change it to something else. 0, 0, test, just change it. Perfect. Perfect. Click on save. Save? Yes. Save. Yes. We're changing the code. We're changing it from 05 to 11. Say yes. Now it's changed. Good. Good. Now we can make changes. I didn't want to make changes in the real one. Click on data fields. Okay. Click on modify list. Yeah. Then let us make one new variable here. We click on modify list. I just want to do a few more. Click on modify list. Yeah. Okay. And double click on patient city. And the lower left, down, down. No, no, no, no. There are two boxes on the left. We're in the upper box for data categories. There's a box for data fields. So patient, what are these? On the screen of the lower left area, the lower left, there's a big box that says data fields. Okay. Left, left, left, left, here, here. That's it. Yes. Do you see patient city? Patient study is? Patient city, patient city. This one or this one? The third one, city, C-I-T-Y. Okay. City. Yes. Double click on that. Yeah. It's coming. Good. Okay. Now go, now in the upper left, click on microbiology. Upper left, click on microbiology. Yes. We'll click on gram stain. Gram stain. Okay. Yes. Okay. Good. Now let's do user defined. Double click on user defined. Yeah. Okay. And call this test, just call it test, T-E-S-T. T-S. Test. Yeah, that's fine. That's fine. On the description, type the word test. Capital T, small E-S-T. Tests. No, A, the type of test. Yeah, anything is fine. Type of test. Perfect. Click on OK. Now it's coming. Yeah. That's fine. Click on OK. Okay. Good. Click OK. Yeah. And the left side of the screen, go to the bottom. On the left side of the bottom. So you walk right there, all at the bottom of the screen. Okay. I'm going to show you in detail that you probably did not notice. Okay. You click on gram stain. Gram stain. Okay. Yes. So you see on the right, it says section is microbiology. Yeah. You see section is microbiology. You see that? Yes. Yes. Yes. Okay. On the left side of the screen, click on patient city. Yeah. It says origin. Great. Let's change that. Let's change that to location. No, no, no. No, patient city. Patient city. Yeah. It says origin. Yeah. Change the word origin. So here you see the means of the different boxes inside of the data entry screen. So click on location. Yeah, I see. Now on the lower left click on types of tests. No, it says auto. But another is the very last section in the data entry program. And that's what you think it's saying with the other side of the end because it's saying inside of the other box. Can you now change the, change other to microbiology? Other to microbiology, did? Yeah. Now click okay. Not yet, not yet. I made a mistake. Types of tests. Click on type of test. I'm not well, it's already clicked. Click on move up. I told you to do something. Click back and do the fields. I want to go back. But move up, move up, move up, move up, move up. Yeah. What if before data life to miss? Sorry? Move up. Wait. Press it. Just a little bit there. No, that's too far. That's fine. That's fine. Okay. You see now, now it is located between the specimen type and isolate the number. You see? Type of test is located between specimen type and isolate the number. Yes. Click on the isolate number. Isolate number. Yes. Yeah. So isolate number. You see on the right is a hidden question. Yes. We can move. Let's use it to microbiology. Okay. Now click on specimen type. That's it. So that's in the specimen box. So when you do move down the two things, which box you wanted to be in, and where do you want it to be inside of the box? So just click on okay, and then click on save, and then go to data entry. Okay. And then click on save. I'm just going to create a new file. So now maybe look at type of test. Look in the microbiology box. Yeah. Yeah. That's great. Great. Great. So the problem is the move up, move up, the move up, move down worked. However, you have to change the box. So because you did not change the box, did you go down to the bottom of the left? I want to see the end of the data entry form. Yeah. I understand. But if you have people on the call, I want to show them what it looks like. I want to see the other section. So go to the bottom of this form. Yes. Go to the end. I want to see the other, I want to see the comment. I want to see one or what's, I can't see it because it's below the bottom of my screen. But as to the left in the body, you can see the other section. So the other section is going to have prior to body therapy, the Grams thing, or not the other thing. So basically you have to change two things. You do the move up and move down. But you also have to change the box with a question mark here. Okay. Okay. So if we did the entry, go back. Well, okay. Well, you don't have to. Do you have any other questions? Well, no, I have an answer. Okay. Let's go back to data fields. Click on exit. Okay. And then file, modify laboratory. Yeah. You want this one, right? And then yes. And then data fields. Okay. Go down to the bottom of the left. I want to see the end of the list of data fields. Go to the end of the bottom of the left. Okay. And for example, click on patient sitting and click on code list. Yeah. So right now there is no code list, meaning it is a free text field. The person will simply type it into the city. They simply type whatever they want. Okay. But we can also give them a list. Can you click on the second option, use code from the table? And for city number one type artist out of them. And then for the code, click A or artist, you know, you call whatever you want. It's just a little short and easy. I can put in a second city, put in another city. Okay. Okay. Okay. I've locked the loop in. J, J, M, J, I. I don't know if it's a standard list. Okay. Okay. That's perfect. Okay. So you are entering this list of locations inside of the table of this configuration for hospital number 11. Okay. And that is for hospital number 11. But it's not useful for hospital number one, two or three, because they have their own configuration. So if you want to make a list of codes for this hospital, this is the correct way to put it. You put it in the table for this. There's another option. At the bottom, can you click use code from the file? At the bottom, click on use code from the file. Uh, this one, you mean? Yes, that's right. That's right. We're not going to kill him, sis. I want to talk about it, but we're not going to do it. Um, as I just said, the table in the middle with artist Kim, is very valuable for hospital number 11. But it's not useful for the other hospitals because they're not in the other configurations. They're only in this configuration. But if we put the codes into a file, then we can share it around with all of the hospitals. Um, to do this in Argentina. In Argentina, they correct the patient disease, the patient risk factor, and so the national level makes a file that they update every year with the diagnosis and the names in the settings. So then that is at the national level. And then they update that file to give to everybody so that all of the hospitals are using the same drop-down list. So I'm sure on the screen, you have three options. Option number one is no covalidation, but sort of free tax. You know, like if you want to put the Grams thing, you decide to Grams in free tax. That's option number one. Option number two is to create a nice standard list for this laboratory. And the third option from a data file, from a file is to give everybody the same list. And then at the national level, they can update the list. Is there a difference between those two? Does that make sense? Yes, yes, yes. Okay. So most countries do not use a lot of user defined fields, but if they do, it is nice to have it standardized. Can you click on the second option here, use code from the table below? No, no, no. Go back to code list. Click on code list. Correct, yes. Click on code list. I want to do the... Oh, I didn't even remember. That's a buggy in that. I'm disappointed. It forgot the names of the fields. Click on number two, use code from the list. Click on where? Number two, use code from the table below. Click on this. Use code from the table below. Okay, this one. Okay. Yes, yeah. There was a bug. I mean, I did not remember them. I didn't remember it because we didn't select the feature, but I mean, I still should have remembered. Okay, so just like those three cities again, you do that and I'm going to make a note. I'm going to make a note for Adam the programmer. Okay, and the table from the list below disappears if you change the option. Okay, I'll tell you about that later. Okay, generally, good. I'm going down. Click on okay. Can you click on code list again? I just wanted to show you the numbers. Click on code list. I just wanted to make sure there's not a bug in this. Okay, that's fine. This one. Okay, click on okay. And then click on okay. And click on save. Good. And let's go to data entry. Choose the last one. Press save or press yes. Okay. So now you can see those three options are there. So there's a few comments about these user defined fields, or not all of the user defined fields, but additional fields. There are the one at predefined optional fields and the user defined fields. So if a hospital wants to make their own list of fields, that's fine. Then you don't need those at the national level because it's very specific. Sometimes hospitals want a lot of strange things. They want to phone number. So in that case, at the national level it's not important and they can do what they want. On the other hand, sometimes at the national level, there are things that you would like, now maybe the district or maybe the state, that you want everybody to do in the same way. In that case, you and I can discuss what those additional fields are. We can make it as a predefined option. We can give them predefined lists. So you just need to decide, we make them as they're customizable. So the hospital can hopefully have exactly what they want. Some of what they want is nice to fill it for them and it's not important at the national level. There are other things that are important at the national level. Anything that's important at the national level, you want to standardize as much as possible. So we discussed this earlier about the locations. I think it's very important to standardize in-patient and out-patient. Regarding the part of medicine surgery, it depends. Some countries care about that. Some countries don't. So you need to think about the data fields, the fields that you want to be standardized, and try to introduce clear lists and drive-down boxes so that everybody is doing things the same way. Because if they don't do these things the same way, it's going to be difficult for you to analyze the data later at the national level. I think I talked about everything I wanted to talk about for laboratory configuration. Are there any other questions about laboratory configuration? From anybody on the call? Maybe Dawid and Gabri, Rafael. I don't know if Gabri is still in the call. And if you do have a question, make sure that you unmute yourself. Because right now we don't ask you any questions. So it's more so than one hour, 20 minutes, so we still have plenty of time. And we're now inside of data entry. I'll leave it up to you. Do you want to talk about data entry to the analysis packet or something else? Maybe in the data entry, Joan. Hello, Joan. Can you hear me? Yes. Okay. While we are entering the data, is there any mechanism to prevent duplication data? No, there isn't. So, as I mentioned, we have been modernizing Hoonat in steps. Our first big effort was to replace Microsoft Visual Basic version 6 with Microsoft Visual Studio now in Visual Studio 2019. So our big modernization effort was modernizing the programming language. We finished that about two years ago, Hoonat and last year for backlink. So we've modernized the programming language. In the last few months, we have modernized our data access routines. So we were using a very old technology called DAO, this is not important for you, but DAO was very, very old from the 1990s. And we started to see a bunch of compatibility issues. So for the last few months, we've been getting rid of DAO. That's another big modernization step. Another big modernization step that we are now completing is replacing Hoonat's internal data structure internally. Hoonat has always used, the Windows has always used Microsoft access. So the Hoonat storage files that we are familiar with, the Hoonat files that you receive, is an old data structure called DBase or DBF. DBase is the ancient, DBase is from the 1980s. If I selected it in the 1990s, it was old at that point. The reason I chose DBase I did not want to choose DBase because even in 1995, it was already old. But we wanted to use Microsoft access. In 1995, Microsoft access was extremely unreliable. This was access too. And with any large database, access would crash. So my client in 1995, when we switched from Microsoft to Windows, I wanted to use access, but we could not use access because it was just too many bugs and it bombed and didn't work with these data. That's why we chose DBase because even though it was old, it was reliable. So the external Hoonat files are DBase. But in particular, Hoonat has always used access, the Windows version has always used access. Because if it has a problem, it's only a problem for the analysis. When Hoonat started over, everything was fine. So in general, we were always using access. But also in the recent years, we have started to have trouble with access for even internal purposes because of compatibility issues. Because we were using access using old technology. So it's two things. We're using old files with the old technology. A lot of these details are really not important for most of you on the call. So if I'm confusing you, please ignore what I'm saying. And I have a point. I'm getting to the point. So one of the things that we'll have done in the last two months is we have replaced Hoonat internal access. We replaced it with a new structure called SQLite. SQLite is similar to SQL Server, but it's smaller and more suitable if you do not need a database manager. So if you are using SQL Server or Oracle or MySQL, you need a DBA, you need a database administrator. And Hoonat users do not have database administrators. So SQLite is a lot easier for that purpose. SQLite is more secure and it's faster and it's more modern. So there are many benefits to SQLite. Now that I mentioned this, we have switched to SQLite internally, and we continue to use the same Hoonat logic, which is called flat files. In Excel, everything is on one row. I have the patient name and date of birth, and organism and specimen types in episode seven is what we call a flat record. All of the information is on one row, just like you do when you go to Excel. When you do a flat file, the issue of duplicates is a problem because each record is different. You have this patient, this patient again, this patient again, this patient again. And because it is flat, Hoonat is not looking for repeats. So this is getting back to your question. When using our current flat approach, we have no simple way to look for a repeat data entry. But now that we have SQLite, we have a new possibility. We can change from a flat file structure to a sort of relational file structure, which is much more modern. Our patient has samples. Samples have isolates. I say it's antibiotics. So we have a series of tables that are linked together. So with a relational database, it is much easier to look for repeats. So I've given you a very long answer. I probably should not have given you that long answer. But in short, we ask you if we can check for repeats. And with the flat data file structure, we don't have an easy way to do that. But now that we have switched to SQLite, flat, it's still a problem. But now that we have SQLite, we will have the ability in the future to switch to a relational database structure. There will be many advantages to a relational database structure. Please show me all of the people with the main spelling. Please show me all of the results for John Stelling. Please show me all of the isolates. So with a relational database structure, we can make whom that work more like a modern laboratory information system. A simple one. A simple one. I'm not going to do a full laboratory information system. But with a relational database structure, we can offer some of these tools about repeats and lookups. So it's a long answer to your simple question. Okay, so let's see. So first of all, we do some happen very often. We enter the data on Monday, you come in on Tuesday, and you just continue entering the data. So it doesn't happen very often. And if it does happen, I don't think it's going to impact your statistics. If the two members of the E. coli is 100, and you type two of the E. coli twice, you'll have 102. It's not going to impact your statistics. Also, when you get to data analysis, who in it is very well-prepared to remove repeat isolates? Repeat isolates is different from, when you say duplicate, what I have in mind is to enter the same record twice. Entering the same record twice is a mistake. Somebody just entered the same record twice. And it doesn't have a standard way to get rid of that to identify that. But what doesn't happen very often, and this is proper microbiology, this addition might be has E. coli on Monday, and on Wednesday, and on Friday. So they have the same organism, but it's a different sample. Yeah, might be a blood, it might be urine. So I'm not going to call this duplicate because we're duplicate. It's a different sample. It's an organism, but it's still a different sample. So even if there's a lot of nice analysis ways to remove these repeat isolates. The situation now is E. coli on Monday and Wednesday and Friday. I want all three E. coli in my database. They're not duplicate. They're different. Maybe they have the same organism, maybe they have the same resistance pattern. It's a different result. It is not a duplicate. But when I analyze the data, so I want all three E. coli in the database, when I analyze the data, I just want to count that patient once for E. coli. So this becomes an analysis issue. So you mentioned about duplicate data entry. When you do duplicate data entry, then it usually be nice that it's going to get rid of those also. So if you enter exactly the same record five times by accident, for analysis, who can remove them during the analysis? So long answer to your very simple question. Maybe, yeah, that's good. During analysis, it will not be a problem. But for example, one patient may provide urine as a two-day, but another time he will come and provide the same specimen urine. From both the specimen, the microbiologists, they detected let's say E. coli. I don't know whether it's possible from urine E. coli. E. coli, they detected. So are you going to consider this data as a duplicate or I don't know? No, and that's one point is that if the patient is E. coli on Monday and Wednesday and Friday, the data entry person should enter all three. The data entry kids, they're not identical. These are different samples. They're different sample numbers, maybe different date, maybe different sample type. They're not identical duplicates. They're simply repeat samples. So I wrote all of the repeat samples in the database. For example, maybe the patient has E. coli on Monday that's very sensitive, but maybe they have E. coli again on Friday that's very resistant. I want those of them so that I can work. Does the patient, is there a mutation, is there a quality control problem? So there are different issues here. Duplicate means they enter the same data twice. But if they enter the same organism many times, that's not a duplicate. It's a repeat sample in isolation. But I want all of the repeats in the database. I want them to screen one black and then log in and come back again. So I want all of the repeat samples so I can see if there's more resistance, if there's some organism. Did the patient move? Maybe the patient had E. coli on Monday as an outpatient. Maybe they had E. coli on Wednesday as an inpatient. So these are not duplicates. And also the data entry person doesn't know. The data entry person is entered and resolved. I don't want the data entry person to look to see if it was entered already. If the patient already had something. So the data entry person, if they see 10 islets for today, they should enter all 10 islets. Later when we get to data analysis, we do it as a feature called first-ice location, most resisted, more sensitive. So if the patient is equal on Monday, Wednesday, Friday, I want all three for data entry. For data analysis, it depends on what I want to do. Sometimes I want one patient, sometimes I want all three. It depends on analysis. So don't worry about the repeats. Just whatever is on the data, whatever in the lab notebook, just take all of those results in. And then we can analyze it in the way that we want. Yeah, that's great. Maybe if you are interested to compute maybe the positivity rate, you know the denominator will be overestimated because of one patient is supplying many specimens during maybe one month or whatever. So I want to go to data analysis. So please leave the data entry program. Leave data entry and go to data analysis. This, okay, you now go to data analysis. Click on analysis type. I want the first option. I see it listening like someone. Click on isolate listening in some way. Click on OK. Organisms and choose all organisms. And just that there is ALL in mid-enter. And you can get the data files. Data file, you mean? Yes, data files. Yes, working. And of course, we're still in hospital number 11. Hospital 11 is the copy of hospital number five. You see, at the upper right, you need to change to all files. You see what's there? It's German University Medical Center. Change their paper files. Files. Find a real data file. Do you have a real data file? Hospital number five. Yeah, this is good enough for the purpose. Double click on that file. Yes, click on that file. Double click. Click on OK. OK. That's fine. Click OK and begin analysis. OK. Begin analysis and then begin analysis. OK, as an example, I don't see them on the screen. I'm looking for repeats. I don't see any repeats on the screen. So just wanted to see if there's a repeat patient I did. So go down further. I know some of them are repeated. So go down further and translate for repeats. We can start. I want to go down to see if there are any repeats. I don't see the same patient I did repeated. Well, it doesn't matter. Let's take a look at this one. OK. See another one. I saw one. I see one. I see one. No. Stop moving. We have to stay on one screen. OK, good. Stop right here. No. Let's just stop. Stop. OK. OK. I see a patient I did. We're ending in 973. Close to the bottom. Do you see 5973? Further down. Further down. Down. Down. Stop. I'm looking at the last four numbers. The last four digits. The last four digits. 5973. Two more. 59. Take on that. Yes. Take on that. So look, you only have four results from the same person. This number appears four times. Yeah, exactly. Right. As you see, it is different. The case of negative neurology and a patient. So in January 25th, the person was in neurology. January 25th, the person was also in outpatient. On January 29th, at the 31st, they came back to the emergency room. So, yeah, they're not again. These are not good tickets. These are all different samples. They're here in Australia. Three of them are here. One of them is around. So I do like to see the rankings. The different dates. The different specimen types. The different rooms. And what are the antibiotic results? What could be the end of so on? No, the first one is six millimeters. That's very resistant. So it looks like this particular person is Andrew Tarkas. What is very resistant and what is very sensitive? So this is showing that in this example, I do want to see them in peace. No, no, no. John, maybe this one, as far as I understand from the data, the specimen type is urine, right? From urine, the organism, it is the same organism actually. Yes. The specimen number is the same, you see? Yeah, like it happened. Because sometimes what you have is, they have again, they'll see some different colonies. They'll cross this colony one, and they'll find it's an arcacus. They'll cross this colony two, and they'll find it's also an arcacus. So sometimes, sometimes when they look at the plate, it looks like two different things. They say, oh, there's colony one. I wonder what that is. And colony two, that looks different when they look at colony two. And when they do that, usually it's two different species. Colony one is maybe an arcacus. Colony two is in coli. I love it. Sometimes colony one is an arcacus. Colony two is also an arcacus. So in which case, they just found the same thing twice. Sometimes they'll have the same resistant pattern. Sometimes they'll have a different resistant pattern. Anyway, I don't want to spend too much time on this particular example, because there are new cases where I want to see the repeats. I want to see the resistance characteristics, the species, the date location. So in many cases, I want to see the repeats. Now, can I continue? So maybe during creating the configuration, are you going to include a repeat variable? Or I don't know. Maybe there is a variable. Okay, let's talk about this screen. For example, you see here on the left side of this screen, it says enter a carcass. Enter a carcass species. Enter a carcass. How many islets? So 81 islets. How many islets? There are 67 patients. So this is an example of analysis where it counts in both ways. For 81 islets, that is a true number. So 81 different islets. But the 81 different islets came from 67 different people. So these are denominators. The question is, which denominator is more interesting to you? For epidemiological cases, I'm more interested in a number of people. So if I'm interested in tracking disease, I'm interested in 57 people. Another thing I want to know, how much work is the laboratory doing? The laboratory did 81 samples, so I want to give them credit for it. So in terms of the laboratory workflow, in terms of the laboratory work, they did 81 enter a carcass. They did 81 came from 67 people. So this is an example of who that is showing the most. Yeah. Can we now click on continuum? We can also see in January, it's counting the number of people. 67 people, 67 people. Now let's click on the feature called one per patient. Click on one per patient. So it says by isolate, meaning we're not just counting all of the islets. It's counting all the isolates, it's counting the patients, it's doing everything. The second option is by patient. And we can see the next and then the second option says first isolate only. Yeah. And there's a number of first isolate with antibiotic results that there's similar. So now let's click on OK. Okay, before that, John, before that, I want to understand the term first isolate only. That means if the patient is providing both urine and blood, I don't know, in that context, how do you explain this one? Sure. It also depends a little bit on which analysis you do. But it's a question of urine on Monday and the blood on Tuesday. We'll get to urine because the urine came first. Okay. So the first one will be the considered. Yes. It's a specimen type. If the patient is urine and blood on the same day, then you never see the specimen number and it will use the one with the smallest specimen number. For example, if the urine is specimen number 10 and the blood is specimen number 11, it'll use. But it doesn't take the first isolate. Use the patient again. We use this. They use it in the country and the name of the laboratory and the number of the patient. So use the patient again. And it takes the specimen date and it goes to the specimen number on the specimen date. So click on OK and click on begin analysis. So there are going to be 67 people within our office. That has not changed. There were 67 people before. There's still 67 people. Yeah. Okay, baby. People for repeats. You're not going to find any repeats. I do take that back a little bit. Can you see what says identification number? Click on the heading. Click on your identification number on the column heading. That sorts it. Good. So click on that. Tony? Good. So good. You see what that means. No, no. Just click it once and stop. Click on identification number again. I want to go back to the beginning of the list. Good. Let's cover that. Do not click on anything. You see the number three has a patient date. Oh, no, that's a bad example. You see the number four? The last four numbers are A013. No, at the top of the list. No, the one after that, that one, the next one, the next one. Yeah, yeah, yeah. I got it. That's the same number twice. There's a same number twice, but my criteria is that it's two different spaces. You see the first one, this is ABN. The second one is PE. So when I say first eyes for a patient, it's the first eyes for a per patient per species. It's the first of the meaners, the first eyes to meet a marker, the first to eat koa. So when I say first eyes for a per patient, I mean first eyes for a per patient per species. Okay. That's a small point. Now if you're going to continue in the summary, we will look at the welfare enter a caucus. So good. You see, so now you see the number of eyes for it is on the 67. So for some of us is who that is by ISEAT, some analysis it does it by patients, some analysis it does it both ways, like the first one dead. And then using one per patient, we can remove the eyes once. So does that make more sense to clear things? Maybe that's really pretty good. But which one is the correct analysis? That means which one is the correct analysis one per patient or one per isolate? I don't know which one is the correct one. Can you go back and continue? What's in my screen, right? Yeah. Click on the options at the right-hand corner options. Yeah. Okay. So let's see. So here you see several of the options give you the choice number of patients or number of isolates. Do you see that on the lower left and on the lower right? Yeah. Number of isolates and number of patients. So let's see which is correct. And the answer is it depends on what you're looking at. For disease tracking, more interesting is the number of patients. Okay. But if you're interested in the laboratory volume and the laboratory testing, the number of isolates would be more interesting for that. So in general, the number of patients is more interesting. And that's why the default is number of patients. Because I think this is where you want the number of isolates and which one do you want both? It is a simple example. Can you choose open Excel? Sorry? Can you choose open Excel? No. I can't. I'll do it. I'll do it. I'm looking at Excel on my computer. And regarding the endocrine example, if you will remember, what would you guys say, 81 isolates from 67 people? Yeah. Okay. So if I divide 81 by 67, I get to number 1.2. We mean that on average, people with endocrines have 1.2. So that itself I find an interesting number. Like, let's take an extreme. If somebody was the real cholera, they usually only have the real cholera once. You send him a diarrhea, you take the sample, then you find the real cholera, and you treat them, and the patient goes home, and they're fine. So it is not very common to have the real cholera more than once from a person. So you might have maybe 70 real cholera isolates from 70 different people. So on average, that's one isolate per person. On the other hand, for hospitalized patients with burns with esonidobacter, sometimes the person will have esonidobacter in their left arm, and the right arm, and in the blood, and in the urine. So you end up with a lot of weakened isolates. So on average, what you might find is that a person with esonidobacter, on average, would be a 3-esonidobacter for every person. So this is an example where I want both the number of isolates and the number of patients. So I can look at the average number of isolates per person. For community pathogens, usually it's just one isolate per person. But for hospital pathogens, sometimes you can have many, many weak peaks, because the patient can be hospitalized for a few weeks, they can have their blood, urine, and urine. So in that case, I want to know the number of isolates and the number of people, so that I can look at my average isolate per person. So the opportunity to question is, you know, it depends. Most of the time, you'll be more interested in the number of people. But there are examples where sometimes the number of isolates is more interesting or equally interesting. Okay, so I give that an example. I'm now going to go back to your computer. I just want to do recalculation. Okay. Okay, screen. Okay, it's about 10 minutes left. I'm not going to be flexible, it doesn't have to be exact, but also I don't want to overload people. I mean early calls, as I said, but I like to do these early calls, is really have this directed by your questions, because this is the most important questions. As soon as you're answering your priority questions, I have additional ideas about feedback reports and outbreak detection and optimization. So eventually, I will have a more specific agenda to propose. But at this early time, I think it's just more important to answer your questions. We have sent a lot of numbers from the data from one hospital, and on the next call, I think it makes sense to start looking at the national level. How can we do a national configuration, national benchmarking? Some hospitals have user defined fields that are not interested in the national level. So we can make that a subject of maybe the next call. So we have about 10 minutes left and it is flexible. Do you know, or does anyone on the call have any questions? Any questions? This is a quick I would like to do, just to start off this idea of feedback. No, no, go back into a minute. Let's click on hospital number five. I see hospital number 11. We can do that later. Open my return, yes. And then go to data analysis. But then go to quick analysis. Go to data analysis and do quick analysis. And then some of you go to the next standard report. I did this about 20 years ago. I like it, but it doesn't look great and I want to improve the content. But it's quick and easy and it's what we're doing now. So I'll show you the quick analysis using the standard report. Click on data files. And I want you to find one of the data files from hospital number five. No, I want to use my data. Do you have one on your computer? Do you have access to any data from hospital number five? Maybe we can use another close detail. That's fine. No, yeah. This is a different hospital then. So this analysis is going to be a quick introduction to our next session. Because we haven't looked at any of your data and that wasn't the objective. But the next, because we did the entry and configuration, the next time we can touch over to data analysis. Stop right there. Click on cancel. Click on cancel. No, no, click on cancel. I want to leave this. Cancel. Sorry? Cancel. I want to stop. Click on cancel. Which one you want? Cancel. Cancel. Exit. You want to exit? Yes, exit. Cancel. Correct. Yes. Click on exit. Click on open laboratory. Click on file. Open laboratory. Yeah. You opened up hospital five. So we have hospital five open. But now you want to show you a different hospital. So I want to open up. Which hospital do you want to show me? Maybe our deletivity case. And that's why I imagine you leave. Because I wanted to choose the current laboratory. So we are not going to put MSC, also the advantage of a national configuration. With a national configuration, you don't need to switch. So now we opened up the configuration for laboratory number one. Click on quick analysis. Well, that's not correct. Click on exit. Okay. Maybe the quick one. Yes, that's right. Click on analysis. Click analysis. Now click on data files. Maybe wait me for until I find. Okay. Now please look for the data file from hospital. Okay. Next time we can look at your file organization and the file news. Yeah. Because, you know, we're using a desktop. So this is my personal computer. Well, that's fine. So can you just find one file from the DHA? Okay. Thank you. The combined data or I don't know. Before you do anything, I just want to make a few comments. I see that you use the words Jan, Seb, March. I see that you use the word for the month. I don't use the word for the month. I have a, no, no, no. Don't click on anything. Stop moving the mouse. I don't actually use Jan, Seb, March, etc. I don't like that because it's not, I'm going to ask you a question. It's a trick question. What is the first month? This one is data belongs to December. The first one. Yes. I have a general sense of the general question. Every year there's 12 months. What is the first month of the year? January. In alphabetical order, yes, we're correct today, January, but not in alphabetical order. In alphabetical order, the first month of the year is April. So what I don't like about here, I think we have this one here, which says, let's see. Yeah. So here you see December is alphabetically before November. November is alphabetically before October. October is alphabetically. So what we have here is you see the month in alphabetical order. So if you look at the year 2019, it starts off with April. So I don't like to use the word January. I'd like to use the number 01, 02, 03. It just makes it easier to know, you know, which, if I give you 11 files, if I give you 11 months and which month is missing, it's easy to look at 1 to 12 to see that number 8 is missing. But when you're in alphabetical order, it's a little bit inconvenient. So I do not recommend putting the word December. I recommend putting the number 12. Okay. That's a small comment. What did you buy? One file is enough. Can you double click on December? One is fun. Just one is fine. Click okay. Click okay. Then begin. Yes, begin. So you're going to see the current content of the standard report. So use this to help with validation, finding problems. Yeah. So for example, what we have here is one month of data and from one number to one, it has 72 isolates. Yeah. And all of the data are from the year 2018 that you need to go from December 3rd until December 31st. Good. I'm happy with that. People may take the mistakes on the dates. Click on where the first thing, A. Now go to section B, the data fields. Yeah. So here you can see that the first thing is 100% complete. Age 100% complete. The location type is usually missing. Yeah. I mean, this location there is 1%. And that's because it's not relevant or it's not configured. You know, it's not a hospital so it's less relevant. So what did you fill in? The following fields of no data, motivation is empty, data lack and this is empty. And you look at male female, 60% are female, 40% are male. Okay. I don't want to go into detail because we don't have time. But click on see organisms. Let's click on, I just want to go quickly through the rest of the scenes. Click on see organisms. And you see all of the data are from December. Yeah. Click on the results. So the most common organism is XXX. We know both. Here are some of the most important organisms like MRSA. Click on microbiology alerts. So it's telling me, it's telling us we have hyperbiology with E. coli resistant to any panel. A carbon panel resistant to coli and capsule oxytocin. A medium-large, possibly, SPL prevents MRSA. This is the highlight for some of the most interesting findings. Click on F identify the finder gene. Now the clear configuration. I'm interested in this gene. Good. So here you can see in the middle, you also have 49 antibiotics. These antibiotics that you see listed have no break points. The reason they have no break points is one of two reasons. Either the drug is invalid, like an office one, not a valid drug. So when there are no break points, that means there's some problem with configuration. An office one, it's not a correct drug. You should not be testing that. I also see easy sephalotoxin. Sephalotoxin has no break points. The second is a valid drug. There's a different problem though. Sephalotoxin is supposed to be a 30 microgram desk. Yeah, exactly. The 5 microgram desk. So there's a configuration issue here. So that's enough for today. Can you go to H? Can you click on H? Can you click on H? Data file configuration. Okay, that's fine. We'll even talk about that later. Could I invalid data? Great, no invalid data. So this is going to be a good basis for our discussions next time about reviewing quality and configuration issues to make sure that everything is being perfect. I do not want to focus on resistance and epidemiology and statistics yet. My interest is data training and configuration and everything. Once the data is training and we understand the data, that's when we want to focus on epidemiology.