I just have to correct one thing that Peter said. There is one factor that will drive patients. It's parking that's well-known. Okay. There we go. So what I'm going to do is give an overview of some of the definitions of precision health and learning healthcare system. Actually not spend much time introducing the Geisinger My Code Community Health Initiative since Alana did that this morning, but then talk more about the Geisinger Genomics Screening and Counseling Program and then discuss some of the key processes and how we've actually utilized the tools of implementation science to get this large scale population sequencing program off the ground. So precision health, at least in the way I'm using it, emphasizes prevention while encompassing the interventions that are inherent in precision medicine, which is a more disease-focused as opposed to a health-focused entity. So in precision medicine, the definition that I use is one from Clay Christensen, the provision of care for diseases that can be precisely diagnosed, whose causes are understood, and which consequently can be treated with rules-based therapies that are predictively effective. That's from the innovators' prescription, a disruptive solution for healthcare. We do view our project as a precision population health initiative and as such we've renamed it as the My Code Community Health Initiative to distinguish it from where it started, which was as a biorepository. Now inherent in this are educational efforts that are directed at participants, providers, payers, administrators, and other stakeholders. And as we talked about earlier today, this is endorsed at the highest level of the organization as a strategic initiative. So it's explicitly represented in the strategic plan of the organization, both the clinical organization as well as the research organization. You saw the slide earlier, the Community Health Initiative. We're looking to do exome sequencing at a minimum of 250,000 patients. We're going to go through those exomes to look for medically actionable results and then return those results to patients and providers. And we'll support those patients and providers to the follow-up along with long-term management. And then we'll be operationalizing this as a scalable return of results and infrastructure in this integrated health care delivery system. So again, this is the biorepository was where we started 11 years ago. It was a very traditional biobank at that time, but it was informed by extensive consultation with our patients that informed the design. And at that time they were saying, you know, we would really, if there's information that you think is important to our health, we would like to have that back. So there was interest from our patients already about the possibility that we could return something to them. So the participants signed a broad consent to combine their EHR data, which can be prospective as well as retrospective, can be identified or deidentified as well as biospecimens. And a more recent version of the consent which we're trying to convert people over to includes the ability to re-contact participants for future projects and to communicate medically actionable results. So all the patients that are being consented now are being consented under this broad re-contact and return of results. To date, actually this is not to date, this is August 1st. This number is now 877, and we have, as Alana said, 216,000 consented at the present time. Our initial analysis indicated that we thought about 3.5% of the population would have an actionable genetic condition. I think as we've learned more, particularly about some of the genes that are associated with the inherited rhythm disorders and cardiomyopathy, we're revising that down a bit, maybe closer to 2, 2.5%. This has also changed since I turned in my deck here. We're not the guys in your 61, the Brugata paper came out and said, well, we really don't have any good associations with 20 of these 21 genes, and so we said, well, if that's the case, we shouldn't be returning them. So it illustrates the dynamic nature of implementation and trying to really use best evidence, but we do focus this on conditions that we consider to be actionable, meaning that there is a medical intervention that can be done. We recognize that actionability can go well beyond medical interventions, but we're using a medical model for this. It does build on the ACMG incidental findings list, which was recently updated in 2016 and continues to undergo revision, a lot of focus on cancer predisposition, cardiovascular disease. This again is from August, and I did update the numbers here. We've now returned 881 results to 877 patients, so four of our lucky patients have two disorders that we identified. We've put right up front here the CDC Tier 1 conditions, the ones that the Office of Public Health Genomics thinks that there's adequate evidence to consider public health interventions here, and so you can see that we've had over 250 hereditary breast ovarian cancer, 107 FH, and 85 Lynch syndrome results that we've returned. We're going to take a look under the hood and look at some of the challenges that we occasionally encounter when we lift up the hood. So this is the high-level process. We start with consenting and sample collection. We move to sequence interpretation, confirmation, and reporting. That's reporting back to our clinical return teams. That's reporting from the laboratory to the Genomics screening and counseling program. We then report results to participants and do cascade contact and testing for family members, and then we measure outcomes that are attributable to reporting. So I'm going to go through each of those at a high level. So consent and sample collection. Participants can consent in person. They can consent online through a my-guysing or portal, much as Peter was talking about, or through a smart device, also tethered to the patient portal. We consent between 800 and 1200 patients per week. We found, though, that in-person consenting is, by far, in a way the most efficient way to do it. We do sample collection as part of the routine blood draw. And then because we have a standing order, additional samples can be drawn over time to replenish biospecimens. So as opposed to many biorepositories where you get a specimen, and then when that specimen's gone, you have to recontact the patient, potentially reconsent them. Here I go in from my annual physical. I go to get my lipid study, and they said, oh, you're in my code. Can we draw a couple of extra tubes of blood? Sure, go ahead. And then we have that ability over time to replenish. I want to just make one point here about how we implement the consenting. We put concenters in clinics. What we recognize is after a concenter's been in a clinic for a certain period of time, they achieve saturation. So what we actually did was to design a system by which the scheduling software would communicate with our research software. This was a purpose-built iteration. What it allows us to do is to say for each of the patients that are being seen in this clinic, how many of them are already in my code, so we're not approaching people that are already in there and bothering them. How many people have said, I don't want to be in my code, so we're not really not bothering them? And then which patients have not been previously approached, and those are the ones that the concenters target in real time. And then we can also look to say, okay, what percentage of people that are coming into this particular clinic are eligible? And as we see that drop, then we can move that concenter into a different clinic that is more naive to my code consenting. And by this way, we've been able to kind of keep our numbers up and also achieve satisfaction because we're not constantly bothering people, particularly those that come into the clinic frequently. Sequencing confirmation and reporting. This is in theory. You have our eligible participants that are sent for exome sequencing. That sequencing is done in a research laboratory that is not CLIA certified, so we have to do a bioinformatic analysis against the genes that we're interested in reporting if we identify variants in those genes where there's a potentially reportable result and we send that for confirmation to a CLIA CAP certified laboratory that issues us a report. If there's none, then we just save that exome sequence for a feature bioinformatic analysis as Yogi Berra once said, in theory theory is better than practice and practice it ain't. This is what it actually looks like for just one laboratory in terms of actually trying to operationalize that system. I am not going to walk through this other than to say that if you don't understand the underlying workflow, you will miss opportunities. And even since this slide was collected, we had a process by which we had the sequence being analyzed by an external vendor. We now have an internal sequence analysis pipeline, so this is now excised for this particular process. But for each of our laboratories, including our internal laboratory, which is doing testing for C282Y hemochromatosis, we had to build a workflow of this type and then have monitors in place to make sure that the system stays up and running. For reporting results to participants and their family members, this is our workflow. When the result comes back to the laboratory, it does go to our genetic screening counseling program where we do a second review. A to say, did we call this pathogenic? Did the laboratory call it pathogenic? And is there new information or is there information within patient's chart that would lead us to believe that perhaps this is not correct? So we screen all of the results, but once we have met that threshold, then they are released to the primary care physician. Now, this is somewhat different than our usual process. It's not unusual when I go in from my lipid profile that when I'm on the hall going back to my office that I get my guising or alerts saying your lab results are ready. And they go, they're released simultaneously to the patient and to the provider. But in consultation with our providers and with our patients, they said these are kind of different. These are a little bit more complicated. We think we should build in a lag. So we were able to engineer the process such as a five day waiting period after the result goes to the PCP so that they can take advantage of getting up to speed. The PCP in some cases will just go ahead and contact the patient. Otherwise, we then send an electronic message for those patients that are on the patient portal or a letter to those patients that are not. And then we call the patient to make sure that they did receive the result. If we can't contact the patient or get no information that they've opened the message in the patient portal, after three attempts to contact, we do send a certified letter. And so they at least have the results. So they will have that result in a certified letter. And then sometimes they will contact us. And at that point, then we disclose the result using phone scripts that are specific for the different results. We begin to collect family history information and then we can send the result and support materials. So we send information for as many first-degree relatives as the patient says that they have so that they can distribute that to support cascade testing. The patient has the option they can follow with their primary care physician with the genomic screening counseling program or both. We also then take advantage of existing programs where we have conditions specific specialists for all the conditions that we're returning. We have a respiratory pressure-varying cancer clinic, high-risk colorectal cancer clinic, cardiomyopathy clinic, et cetera that we can utilize. And then we serve a care coordination role in this process to make sure that the patient can navigate the system. Outcomes are critically important to implementation. Many of you have seen this before. But when a result comes back, patients can fall into one of five categories. They may have a prior diagnosis through clinical testing where if it's a BRCA result, we say, hey, we found this variant. Yeah, I know I had clinical testing. Hopefully they found the same variant. However, we found there's still value there because there has been inconsistent communication of what exactly that patient should be doing and what their family members should be doing. So there are missed opportunities that we can fill in a care gap here. In the second group, this is a patient that has a condition like breast cancer but does not in fact know that it's due to a genetic variant. So we now provide a unifying diagnosis and provide opportunities for differential care as well as testing for family members. In this case, this is an individual that is not aware that they have a condition but on the basis of the intervention. Say breast imaging, they find a subclinical breast cancer which can be hopefully dealt with at an earlier stage. And then two groups have no phenotype at the time of the disclosure after the initial evaluation. In one of those groups, the phenotype will emerge over time but we do know that none of these conditions are completely penetrant. So we know that there's going to be a group for which we've returned a result that they will never have a benefit from personally because that for whatever reason their phenotype would never emerge. But of course we don't know how to distinguish those groups. Maybe at some point in the future as we understand the complexities of this we'll be able to understand that better. But again, here there are still our opportunities for cascade screening for family members that might in fact develop the condition. These are all the outcomes we look at. There are process outcomes. Did a person get a test? Intermediate outcomes, what is the actual lipid level? If we do an LDL, we ultimately want to get the health outcomes. Did we prevent a cancer? Did we treat a cancer in an earlier stage? Did we reduce heart attacks? What's the cost? There are behavioral outcomes or patient reported outcomes. We heard about some of the things that Peter's programs collecting. All of these are really important. There are also outcomes from the system perspective, costs incurred or avoided, utilization, visibility and reputation which actually is kind of a big deal. We've been told by our business people that we've had groups that have come to us because we have this program. I wasn't expecting that in central Pennsylvania. And then the patient experience, we actually get paid for that. If our patients have a good experience with us, we get more money. So ultimately what we want to do then is to create this precision health in a learning healthcare system. And again, you saw this PDSA cycle that Alana had presented earlier. So we plan this, we release it. But again, we guarantee it's going to be wrong when we send it out there. But we have to know how do we collect data and how do we fix it? We study it. We disseminate it. We might try it out in one clinic and then from what we learn we move it to others. And so there's a constant analysis but at each of those points you have to collect data to really know what's happening. And so that's what we're really trying to do. And you might ask the question, well, this is pretty complicated. Does it actually work? And I'm happy to present at least one piece of data that suggests that it might work. So this is our results returned to date. So these three are annual returns. So we returned 99 from May 1st of 2015 to May 31st of 2016. The next year we returned 209, 254. But we realized that this was really, we had a lot of people sitting out there that weren't getting results. So we in the process here took a look at all the different aspects of the process. And in three months from June 1st of this year to the end of August, we returned 315 results more than we'd done in any year previously. So over a third of our results were returned in the last three months due to extensive re-engineering of our sequence interpretation pipeline, the confirmatory testing and reporting and then how we can improve the timeline of reporting results to participants and family. So in conclusion, precision health is an emerging technology. We have to be able to demonstrate improved value in the healthcare delivery setting before it will be adopted. Outcomes have to be defined ahead of time and systems built to support measurement to determine which services actually add value. And implementation is complex, who knew. And requires a systematic approach. The learning healthcare, this was recorded wasn't it? I'm in big trouble. The learning healthcare system framework may represent a robust implementation model. And I would like to thank all of our participants, again over 200,000, the great team of the Genomic Screening and Counseling Program and then all of our collaborators at Geisinger and at Regeneron. Thank you. Thanks, Mark. Any quick question? Seems not, thank you. Okay, next to Lincoln Nadald on implementation of cancer genomic medicine.


Gun Smoke, brought to you by L and M Filters. This is it. L and M is best, stands out from all the rest. A round-dodge city and in the territory on West, there's just one way to handle the killers and the spoilers, and that's with a U.S. Marshal and the smell of Gun Smoke. Gun Smoke, starring William Conrad, the transcribed story of the violence that moved what with Young America? And the story of a man who moved with it? I'm that man, Matt Dillon, United States Marshal, the first man they look for and the last they want to meet. It's a chancey job, and it makes a man watchful, and a little lonely. What is it, Justin? Look up the street there. What? Coming out of the long branch. Come on. I don't understand it. I didn't hear no gunshot. Well, that man, they carry him, got hurt somehow, Justin. If he'd only passed out drinking, they'd probably left him under the bar and stood on him to improve their reach. Oh, I've seen that happen, Mr. Dillon. I've really seen it. Now look, there's Doc with him. He's got better ears than I have if he heard any gunfire. Well, there are other ways of killing men, Justin. Oh, yes, sir, that's true, Mr. Dillon. I saw a man killed with a bull whip once. Oh, that was bad. You didn't carry him up to my house. The door's open. I'll be long as soon as I talk to the Marshal. Okay, take him on the way. Matt, you better get into the long branch, or there's going to be more of this. What happened, Doc? That's Bill's aunt they're carrying. I don't know him. Neither do I, but that's what they said it is. Well, it doesn't matter what happened to it. He's been cut bad with a bowie knife. Who did it? That big drunk buffalo man, Noley Meeker. Noley Meeker, huh? Is he still on the long branch? Yes, he is, Matt, and you watch out for him. He's in a mighty dangerous mood. Do you see it? Well, I saw part of it. I was in there having a beer when it happened. Zant had a knife too, but Noley Meeker knocked it out of his hand. Oh, then Noley did this himself. No, he didn't. Everybody says Noley started it. Well, I've got to go and take care of Zant before he dies. Now, you watch out for Noley, Matt. Nobody can get anywhere near him. Come on, Justin. I saw Noley Meeker drunk once, Mr. Dillon. He sure does get me. You mean enough to start killing people? Yes, sir. But when he's sober, he always seems pretty calm. I just don't know what happened to him. Now, that's what happens to other people I don't like. Being drunk's no excuse for murder. No, sir, it sure ain't. Oh, look at him.  How are you going to take him? You stay here. Evenin', Noley? Get away from me, Marshal. Why don't you drop the knife, Noley. You're in enough trouble now. I killed Zant, didn't I?  Zant isn't dead yet, Noley. Then I'm going to have to keep you in jail till we find out if he's going to live. I ain't going to jail. Not if I have to cut me a path all the way out of Dodge. If Zant dies, you murdered him. You're not leaving Dodge. Now, you can't be so drunk you don't understand that. Come on, Marshal, let's fight. But my six gun against your bowie knife, you wouldn't have a chance, Noley.   No, I'm not going to go get myself a knife. He will fight this way. Forget about fighting, Noley. I can cut good with this knife, Marshal. I can throw it, too. Stop it, Noley. Didn't know that, did you? What? Well, you don't throw too good when you're drunk, Noley. This is it. L&M is best. Stands out from all the rest. This is it. L&M filters. Perfect gift for Christmas, too. Light and mild. L&M filters. Holiday cotton. Just for you, just for you. This Christmas, why not give the very best to all the filter tip smokers on your list? This is it. L&M is best. Stands out from all the rest. L&M stands out for flavor. The miracle tip draws easy. You enjoy all the taste. L&M stands out for effective filtration. No filter compares with L&M's miracle tip. King size or regular, L&M is best. Stands out from all the rest. It's got everything. Feel pretty good today, do you, Noley? I got a head like I've been sleeping under a buffalo. Yeah. Well, you don't deserve it, but I brought you some coffee. I'll put it right there. I'd do better on a pi to triple X. You sure learn hard, Noley. Go away. Go away. Let me sleep, Chester. Last time I scalded my thumb bringing in coffee. You're in great. It's our prisoner this morning, Chester. Well, I don't think it's the liquor so much as the way you hit him, Mr. Dillon. Well, he had it coming. He sure did. That knife couldn't have missed you more than an inch. I still don't know why it didn't shoot him. Well, how would it look for me to shoot somebody that didn't have a gun? Besides, I don't think Noley really knew what he was doing. Well, I ain't sure he knows yet. Good morning, Mac. Chester. Hello, Doc. How's that coming along, Doc? Well, that's what I came to tell you, Matt. Zant's going to live. Huh? He's going to be awful thin for a while, but he'll live. Bring Noley in here, would you, Chester? Yes, you. Nothing vital was cut, Mac, but the man bled so much. It took me over an hour to get it stopped. And for a while there, I thought he'd surely die. You know, it's too bad that he's in some way to get blood back into a man when he loses that much. Well, I don't know how you could do that, Doc. Oh, if I could. I'd save twice as many lives as I do. Well, I'm going to go to bed, Matt. It's been a long night. Thanks for coming down, Doc.   I'll take that on any time, Chester. What do you want, Marshall? You sober enough now to tell me why you took your knife to the Zant last night, Noley. First no matter? Well, maybe you better tell me whether it's personal or not. I'm not going to turn you loose to go stab him again. Turn me loose? He didn't kill Zant. He's going to live. Now, what were you fighting about? A woman. Is that all? You got something against women, Marshall? That's not what I meant. I've known you for a long time, Nolan.  When I'm drunk, I get mean, Marshall. I'll fight over anything. Even a woman, huh? Now you're fogging me up again, Marshall. Okay. Why did you know Zant? We've both been working for Ezra Marcy. Collecting buffalo bones? We gather them up off the prairie and bring them back to Dodge and sell them to Marcy. And he ships them back east on the railroad. Beyond me, huh? They can make fertilizer and China and stuff out of them little buffalo bones. Noley, you and Zant work together, is that it? No. I've got my own wagon. He's got his. But I still don't like him. You're going to leave him alone? Marshall, I wouldn't hurt nobody less than I was crazy drunk. You tried to kill me, too, do you know that? I did? Oh, Marshall, I don't believe that. You threw your boy-knife at me.  I shouldn't have done that.  All right, you can go now, Noley. But you came off all close to hanging. You just remember that. My pal always said I'd hang. I won't cause no more trouble, Marshall. Go on, Chester. Goodbye, Noley. Mr. Dillon, do you believe that about them fighting over a woman? No, there might be more to it than that, Chester. Why don't you ask Zant about it? Well, if Noley's lying, Zant'll lie, too. But whatever they were fighting about, I doubt that it's over. He hurts aren't too bad for that. Oh, kitty. Are you waiting for the stage to come in? No. No, I just got tired sitting in my office. That's a funny thing about your job.  Or you're the most violent man in Kansas. Well, that's a good thing. I don't get paid for the job, isn't it?  Oh, hello, Marshall. Hello, John. But still, nobody could ever pay me enough against Noley Meeker and his knife the way you did a couple of weeks ago. Well, I could have shot him, kitty. No, you couldn't. Not you.  Or worse.  Matt, I'll feel better when I've got some breakfast. Oh, haven't you eaten yet? I don't get up as early as some people. I'll go with you, kitty. I can use a cup of coffee myself. Here comes Ezra Marci. Look how he wants you. Morning, Miss Kitty. Marshall. Hello, Marci. Marci. I got a job for you, Marshall. Uh-oh. I wish I'd never made a deal with them men. Neither one of them. What do you mean, sat and Noley Meeker? They have been complaining and fighting more than they've been gathering buffalo bones for me. Oh, why don't you deal with somebody else? Most men would rather hunt for hides and bones, Marshall. Pays better.  Now, he ain't no use at all. Why not? Noley's got a shack down past the opera house in the edge of town, Marshall. Let's go ask him. He got drunk again last night. I thought you were talking about Zandt. It's about Zandt I want to talk to Noley. Oh, why do you need me? Noley killed him, Marshall. What? Zandt was doing with a wagon load of bones this morning, and he was late.  You think Noley did it? Well, Noley tried to kill him before didn't he?  Of course Noley killed him. Does Noley have a sharp sphifty? He used to be a hide-hunter until he got tired of it, and it was a sharps that killed Zandt who anybody can tell that. Yeah, I guess they could. That Noley makers a murderer, Marshall. You go arrest him. Did you leave Zandt at the river, Marcy? I ain't gonna bury him. Come along and show me where he is. All right, I'll send somebody out to bury him, but let's go find Noley. I don't want no murderer to get away. I'll talk to Noley later. No, we'll get him first. We're going to the river, Marcy. Now, I went by the office and picked up Chester, and he and I followed Marcy up the river. Zandt had stopped in a little cottonwood grove. Apparently, the water is oxen, a couple of miles above Dodge, and we found him there. Slumped over the seat of his big Studebaker wagon, shot in the back. The wagon, piled high with sun-bleached buffalo bones, was headed toward the river, but the oxen had stopped when Zandt was hit and were standing patiently, waiting for their next command. We laid Zandt's body onto the sand, and then I had Chester lead the oxen down to the water. A few minutes later, he was back. You're gonna bury Zandt out here, Mr. Dillon? Well, it's as good a place as any. While we're fooling around here, Noley's probably on the run. I'll find him, Marcy, to prove he did it. Well, you got all the proof you need. What if he's got a good alibi? What if he can prove he was someplace else when this happened?  I'm kinda against murder myself, Marshal. You're awful slow showing it, wasting time coming out here and now all this talk about proof and such. I'm telling you, Marshal, if you don't see Noley Meeker hung for this, the lunch talks something I won't stand for, Marshal. I don't make it around me and don't start making it around Dodge. You're threatening me, Marshal. I don't threaten people. I warn them. Now, you know me. You know what I mean. We'll go find Noley and see what he has to say. Chester. Yes, sir? We'll send somebody out for the wagon and those oxen, they can stand here a while longer, they've had their water. Well, you know, that's the funny thing, Mr. Dillon. Them oxen didn't drink no water. They didn't? No, sir. And they hadn't been to the river, neither. There wasn't no tracks between where they were standing in the river. Then why was Zant heading them toward the river? I don't know. But you think Zant would have known if they wasn't thirsty? Of course he would. Well, I guess it don't matter much, Mr. Dillon. I'm not so sure, Chester. I'm not sure at all. Now let's go find Noley. No filter tip smokers, this is it. Ellendem is best. Stands out from all the rest. Yes, Ellendem is best. Stands out from all the rest. Ellendem stands out for flavor. The miracle tip draws easy. Let's you enjoy all the taste. Ellendem stands out for effective filtration. No filter compares with Ellendem's miracle tip for quality or effectiveness. Ellendem stands out for highest quality tobaccos. Low nicotine tobaccos. Ellendem tobaccos. Light and mild. Ellendem's got everything. King size or regular. It's America's best filter tip cigarette. Yes, Ellendem stands out from all the rest. This is it, Marshal, right here. I just hope Noley ain't awake in watching this out of one of them cracks. You can ride back if you like, Marshal. No, I want to be here when you talk to them. Okay, I want to talk to you, Noley. What's Marsy here for?  No, I ain't drunk. I done slept it off.  I didn't cause no trouble last night. Were you drunk? Of course I was. I didn't see you on any of the saloons. I was out here, sitting on the ground. I was sitting right there against the wall, me and a friend of mine down to jug of corn whiskey. Took this most all night. What did you want to know for? You had a friend with you? He was here till about an hour or two ago. Who was it? Well, you don't know him. He's an old Indian, a shy ant. Where is he now? He's on his way home. Where? Absaroka Mountains. Absaroka Mountains in Wyoming? That's what you're doing here.  Well, you won't find him, Marshal. That old shy ant's traveling alone and he lives like a wolf. You'll never find him. Now, I don't expect we could. Well, there goes his alibi. What are you talking about? About Zant's murder. Zant's murder? You'll hang for it now. Wait a minute, Marshal. Zant was shot in the back with a Sharps 50 this morning over a couple of miles up the river. Marshal, you were thinking I did it? How did you try to do it once before?  If I was to kill a man, I wouldn't do it that way.  Arrest him, Marshal. He can do his talking in jail. I didn't do it, I tell you. You know I ain't that kind of a man. I ain't a good man, but I ain't like that. Okay, Lily. I believe you. I couldn't prove it anyway. Now, look here.  And you remember what I told you. I don't want to hear any talk out of you. Come on, Chester, there's something I want to do in town. You after down here to the depot, Mr. Dillman? I don't want to talk to Sam Vestal, Chester. Just hope he's in. Hello, Marshal. Chester, come on in. All right, Sam. Fine, Chester. What can I do for you, Marshal? Sam is agent for the Santa Fe Railroad. I figured that you might know something about Ezra Marseille's bone shipments out of here.  Well, I want to know if there's been any trouble about them. There sure has, but how do you know? Marseille told me not to say anything about it. Tell me about the trouble, Sam. Well, the buyers back east have been calling Marseille a crook. But I know he ain't, Marshal. I weigh them loads myself, and I'll personally guarantee they ain't short on weight when they leave here. But they are short when they arrive back there. Some of them are. Zantz. How do you know that? I guess that... Well, you're right. I checked on it for Marseille. And it's the loads Bill Zantz brings in that come up short back east. Marseille pays Zantz by the load, doesn't he? Sure, sure. But it made Marseille terrible mad being called a crook like that when he ain't one. If you ask me, maybe it's them fellas back east. They're the crooks. All Marseille wants is to be paid for the weight of the bones he ships out of here. They come up short back there? Ain't his fault. Well, thanks, Sam. I'll go talk to Marseille about it. I, uh... I think maybe I can straighten this whole business out now. Hey, Marseille's coming, Mr. Jones. I found him in the Texas trail. You know what he was doing. What, Justin? Stirring up a bunch of men in there about how they gotta go get an oldy meeker.  I'm gonna do something about it. Here you come. It's no use, Marshal. You can't stop me now. I already got ten men in there on my side. Well, that's eleven to one against noly, isn't it? You shouldn't have much trouble taking him. Except for one thing.  I'm stopping you right now. All I gotta do is yell. They'll be out here, Marshal. Marseille, I had a talk with Sam Vestal down at the depot. What? The bones Zant's been hauling in. They've been losing weight on their way back east. All right. So he told you. That's my trouble, Marshal. I'll handle it. You've already handled it. What are you talking about? You're scared to death, aren't you? I don't know what you're saying. Those oxen weren't thirsty at the river. Zant wasn't headed for water because of them. It was to soak those sun-dried buffalo bones. You figured it out, too, didn't you? Waterlogged they weigh in heavy here, but with the time they're weighed again back east, they're dried out. Zant was a crook. Yeah, sure he was. But you shouldn't have murdered him, Marshal. Me? Murdering him? I'm arresting you for it. Oh, no. Oh, you ain't... I'm gonna take your gun, Marshal. No. He shouldn't have tried that, Mr. Dillon. He's about the poorest gunfighter I ever saw. Yeah, he was slow, Chester. But he'd have killed me if I'd let him. Why'd he try it? I don't know how you could have proved he murdered Zant anyway. I couldn't. But Marshal was feeling so guilty and so scared he didn't stop to think. If he had, he might have bluffed the whole thing out and gone free. Now it was his own guilt that did him in, Chester. And now our star, William Conrad. When you are choosing Christmas gifts for Filter Tip Smokers, you can figure a carton of L&M's will please them most of all. Filter Tip Smokers know that L&M is best. Stands out from all the rest. And L&M's come in just about the handsomest Christmas carton you ever laid eyes on. Give King size or regular L&M's. And you're giving America's best filter cigarette. Gun smoke, transcribed under the direction of Norman McDonald, stars William Conrad as Matt Dillon, U.S. Marshal. Tonight's story was specially written for Gun Smoke by John Meston with music composed and conducted by Rex Corey. Sound patterns by Bill James and Ray Kemper. Featured in the cast were John Boehner, Herb Ellis and Frank Cady. Marley Bear is Chester, Howard McNeer is Doc, and Georgia Ellis is Kitty. Join us again next week as Matt Dillon, U.S. Marshal likes to bring law and order out of the wild violence of the West in Gun Smoke. Tuberculosis can attack at any age, and it can invade any home, rich or poor. But Christmas seals fight tuberculosis. Help protect you, your family, and all of us from our country's number one infectious killer. This year, use Christmas seals on your cards and packages. Why not go home for the holidays with Perry Como? That's the title of Perry's new RCA hit record. We'll sing it for you next week when Chester Field brings you all the top tunes on radio. Perry invites you to be his guest every Monday, Wednesday, and Friday and reminds you to get a carton of Chester Field for the weekend. Remember, listen again next week for another story of the Western Frontier when Marshall Matt Dillon, Chester Proudfoot, Doc, and Kitty, together with all the other heart-living citizens of Dodge will be with you once more. It's America growing west in the 1870s. It's drama. It's gun smoke. Brought to you by L&M Filters. This is the CBS Radio Network.


All right, happy release day everybody. This is the Edge Group update for August 22nd. I'm Robert Spiker, I'm a developer on the Edge team. I'll be talking about what we've been working on since the last update. And what we're working on going forward and later Christian will update us on what he's been working on on Git itself. So let's get started. Because the project is so central to just about everything GitLab does, we create a lot of them during our automated tests. Previously, every project that got created came with a real popular Git repository unless you explicitly said you didn't need one. Most of our tests don't need one. For example, if we're testing issues, those need to be associated to a project but they never touch the repository. We've now inverted this, so you only get a repository if you explicitly ask for one. Creating a project without a repository is about two times faster than creating one with it. So this was a good change across the entire board. Similarly, every project we created also created two users. One for the namespace the project belongs to and one for the creator of the project itself. Now instead we piggyback on the namespace user record and just reuse that as the creator. This results into your database records across the entire suite. For example, in this one example, there's 253 in a single spec. While we were making those two changes, we discovered that whenever a project factory needed a repository, we were actually setting it up a second time for no reason. So we decided to stop doing that. Our longest reigning champion for slowest test file, which just yesterday took 45 minutes. While we were building the final RC was reworked and now performs the exact same tests in just over three minutes. We now keep track of flaky specs. So when a test fails, but then passes on the automatic retry, we mark it as flaky. This is a big first step to preventing new ones from being introduced as well as removing the ones we already have, which are the bane of our developer's productivity. Blackstone from the core team, as well as Alexander Ronda have continued to convert spinach tests to our spec features. Our goal is to eventually convert all spinach tests and only have our spec tests, mostly because there's some separation and duplication around the infrastructure for the two suites that we want to eliminate and spinach tests are just more of a pain to work with in general. We now take advantage of cache policies in our own CI configuration. The one job doesn't modify the cache that it downloads at the start of the job. We don't waste time uploading it unchanged at the end of the job. Change log entries are now categorized and grouped by types such as bug fix, new feature, security fix. This is a personal highlight for me because it was the first time someone besides me added something to our change log generator, which means my code wasn't totally insightful and terrible. So thanks, Jacobo, from the community for that. Genshin added a handy linter that detects conflict markers so that when we don't accidentally leave any behind during the CE to e-merge, for example, this is really good for docs which don't get compiled and we might leave those behind without noticing. York added another custom Rubikop rule that helps us prevent us from creating slow migrations plus a new merge request template so we don't forget something when we make database changes and also more database-related documentation which is always appreciated. We merged 36 test-related merge requests and 82 community contributions in 9.5. That's really a lot of community contributions, especially when you consider that everyone goes through the same review process that ours do. Genshin started to organize our e-only files into a top level directory for better code separation, which will continue to help reduce the number of conflicts in the daily CE to e-merge. And now I'll hand it over to Christian and I'll talk about what he's working on in Git itself. Hi, everyone. So I've been working on external or DB support on Git itself since May last year. So what we want to do is nearly the same as Git LFS, but without the need to use another command, you would need only Git itself. So it's based on previous work that Jeff King started in 2012. And it's more generic because you use helpers, which are external commands. And in fact, they only need to implement the init instruction. And they could also implement some gate or put instructions or have instructions, but it's more modular and it can do other things as you will see later. So it's performed thanks to work from last Schneider on the Git filters, which are used by Git LFS by the way. So we take advantage of this work that was done for Git LFS. And also from work from Ben Peart. So Ben Peart is working for Microsoft. They have been working on something called GVFS. I don't know if some of you saw the presentation they gave at the last Git merge conference. So it was about that. So some of their work has been integrated into this work to make it faster. And so, yeah, so there is the possibility to forget, to get or to give, objects that are blobs. In fact, in different formats, either the war blobs or the Git objects, which are blobs that have been compressed and a header has been added. Or the helper can also access by itself the Git loose objects or the Git files. And either put new objects in them or just get objects from them. So there are three different modes to make it more generic. And also it can be used to have restartable clones, which is a problem that some people have complained for a long time about. So, yeah, because as it's very generic, it can do more things. I don't know if we can see the second slide now, Robert. Yeah, thanks. Okay, so I submitted a presentation so I submitted five versions. The last one on August three was the first one that was not an RFC one because I think it's good enough to really build on it. And I think the reviews have been good. So, and if you are interested, you can get the documentation in one of the patch I sent. So there is a link. And hopefully what I hope is that it will be merged before the end of this year. That's it. Okay, thanks Christian. So for what's next, we want to replace factory girl create calls with build stubbed everywhere we can since it doesn't hit the database and it's just way faster. For most objects that belong to a project such as issues and merge requests, we can piggyback on the project owner record instead of creating a new user record for the author, for example. We're still looking into that. And currently we treat the unit test for the notification service class as more of an integration test for the class that builds a list of recipients. I'll spare you all the boring details, but it's by far our slowest test now and we can definitely make it faster, but it's complex. So if you're interested, there's more details in the issue link there. Does anybody have any questions? Let me look at the chat. No questions there? Yeah, 45 minutes to three minutes is a big difference. All right, I guess that's it. Thanks everybody. 20 minutes.


Can you hear me now online? Hello Johan. I can hear you. Can you hear me? Thank you Eric. Yes, we can hear you. Okay, thanks. I'll start again. Welcome to this session on DHS2 in other domains. We have a packed session with three presenters in 45 minutes. So the presenters have been asked to keep it short. So we have some time for questions. In any case, there's the possibility of writing down questions in the associated community of practice page. So the presenters can address your questions, comments, later after the session. So the three presenters. So we have today we start from Uganda. Eric Minam Van Basi will talk about how DHS2 is used across all sectors in the country to monitor development indicators. Then we'll follow with Leila Ismail based here and also Nadia Semtruk online on the use of DHS2 for reporting human rights violations. I believe that case is Ukraine. And then lastly we have Henrik Kalisti presenting from Tanzania how DHS2 supports child protection services there. So I'll just then give the word to Eric Minam Basi from his Uganda. Thank you Johan. Good morning colleagues and members in the audience. It's good to be speaking to you about again once these use cases that we have implemented here in Uganda. In the audience I have my colleague Patrick who I think he's physically attending. So as far as questions are concerned we will be sharing those questions. Also I wanted to note that the use case that we will be discussing is a collaborative effort between his Fugan and his Ethiopia. So yeah we've been working on a use case for e-government services to foster national involvement planning using DHS2 and in here we will be discussing a number of things from the background to to the implementation and to what is really happening currently. So quickly the national development planning process is a government endeavor that by and large is run by the government of Uganda through the national planning authority and supervised by the office of the prime minister. So with funding from the European Union the government attempted to take on this activity to develop we call it NDP3 because it's the third version of the national development plan. So we refer to it as NDP3. We're based M&E system to monitor planning for the government both central and local. NDP3 like I mentioned is a program based approach to planning, budgeting and implementation as well as results reporting which all of this is really consolidated into a single vision for the country that we call vision 2040 that has a number of activities to be achieved by 2040. The system is built on DHIS2 platform with custom applications covering up to about 20 programs. These programs are basically made up of the action plans. We call them a program implementation plans that is PIAPS that specify outcomes, outputs, activities and the resources required to deliver the different program targets or indicator targets. So it's a really comprehensive M&E framework that addresses planning for the entire government. The technical assistance was focused on enabling efficiency and accurate data entry, exploration, reporting and analysis for two arms of government. We have the central and the local government. For now what we've been focusing on has been mainly the central government. The methodology or implementation methodology focused on putting in place an NDP3 M&E web-based system that is based on national documents and some of those documents are what we are referencing here. We have the third national development plan that goes up to 2025. Then the program implementation action plans and also the Uganda vision 2040 which have specific targets like I've already mentioned. These programs really cover government ministries, departments and agencies all over and so we focused on putting in place five applications. These applications are by and large custom made because of the complexity and the way the framework for monitoring this program is developed. So we will see the later on that we did go ahead and develop the NDP landing page which is a summary page that basically gives highlight information on all programs, indicators, actions, outputs, outcomes and so on. Then the target data entry, the performance data entry and results tracking as well as a document library within DHS too. We are used to DHS within health doing a lot of qualitative data but in here we'll probably experience the idea that DHS could also be used as to some extent a document repository partly because when government does something they want evidence, they want reports, they want pictures, they want images and so on. So quite really interesting in terms of what has been done on this use case. In terms of the products we like I've mentioned we have put in place an application based on DHS too that summarizes key results, result areas for the program and then also provides performance figures against targets overall. So what you see on this side is really a landing page which is one of the applications that I mentioned that summarizes data and information. Then this screenshot on the right hand side lower also shows a bit of performance for different program indicators, objectives and key result areas. The application allows filters to make use experience dynamic and users can also export reports for further analysis. I also need to re-emphasize that I will keep talking about the application being custom made because it's not part of the DHS to core but these are apps that were custom built to work within DHS to as a platform and to provide the M&E requirement for this use case. Then in terms of how data capture is done you'll notice that where we normally have organization units here we have what we call government votes and these votes basically list all ministries, departments and agencies and then in terms of data capture for each of the votes we have created the individual data sets for different result framework levels. We're aware we have outputs, outcomes and then intermediate results so we will find that that is the way it's captured and then for each indicator the teams provide a baseline, a target and the actual performance per year and then they are explanatory or attachments like I mentioned earlier if for example the government plan to build a road and maybe the monitoring team goes there to see the progress of the work, they'll take pictures, they'll write reports and those are the things they'll attach in this section and that's the little caption below also is the extension of when you click in this battle you will get a place where you can attach the file and also provide an explanatory note on the file that you're attaching. Yeah so we're able to do indicator-based targeting and reporting and then also provide baselines as well as explanatory notes. Out of the data captured we are able to do dashboards and provide key performance indicators analyzed over time on government interventions as well as indicators. So yeah what has basically come out of this and what we we are now looking at is that due to the scale of the work we've worked on close to 11,300 indicators, 5,500 groups, data sets and so on. It's quite a large system because it covers the entire government operation and it's also another very good use case. We've done a lot of capacity building in terms of training close to 263 personnel have been trained and what you see in the breakdown is the number of participants or people trained per agency. So we have development partners close to 123 government agencies represented, past participants represented and then government ministries 128 the judicial and parliament and then public universities. So this is a government-wide initiative and also covers quite a really large scope in terms of training. Yeah this chart is a summary of the numbers that we see here and by and large you can see that still government MDAs take up a larger part of participation ownership and reporting. Yeah so in terms of what would really consider as key success factors buying and full ownership is one of these. I mentioned earlier that this is really supervised by the office of the prime minister with TA through the national planning authority and also minister of finance. Then we've also shared the successes recorded by the minister of health and the minister of education in implementing similar initiatives and this has also helped. Of course the assurance of local tech and persistence through his groups has also been very very key to this. The flexibility of the platform to generate visualizations has also been seen as one of the other success factor. We've mentioned that this is really a hundred percent fully owned and the capacity building is also going to be a hundred percent through government and then ability to integrate with other government systems. I will also take note that we've done integration with IBP. IBP is integrated bank of projects. One of the other government systems that also does the monitoring of government projects. The IFMIS, the PBS, all these other government projects that will be integrated but for now the integration has been with the first one which is the IBP. There have of course been some challenges, some of which include the functionality of the application has mainly been heavily customized due to the complexity of the framework because the framework is really really heavily complex and heavily disaggregated. There is also one of the other issues that we know as bureaucracy which has been mainly leading to time wastage and you find that decisions take long, requirements take long to be decided on and that sort of delays in terms of delivering on some of the items. There has been also an issue of limited funding because again government is no long really in developing countries the noteness society puts quite adequate funding on some of these things so you find for example doing in-person workshops, trainings cannot easily be conducted due to obsessive funding. Then online training sessions that have been organized before have been not very successful partly because participation is very limited and it's quite also difficult to gauge the participation and the concentration of participants on the other side. Then like I mentioned in number two three requirements for some of the tasks have also not been well defined and clarified especially where the application requires programming and then there are still some considerable quality related issues with indicators and and and assigning MBAs so those are some of the highlighted issues. In terms of lessons learned participation and interest is key for stakeholders. End user perspectives on the development process and quality assurance has been also very very important integrating other elements in this initiative has also been important for example where people have quite related data indicators that they need to be collected but rather than run a parallel systems having this integrated in one has been also key. I mentioned the issue of linkages with other government related systems as well as a beverage on the expertise in country. Yeah so these have been really some of the lessons the initiative is about nine months no one year and nine months and there are some other components that we are still working on but we do believe that it's one of those success instances and use cases that we have seen especially outside of the health domain. So in terms of looking forward and where we are we do plan on improving coordination and engaging or engagement with stakeholders. Government has so many stakeholders and we only do meet and work with them through the supervising entity which is the office of the prime minister and yeah so every time you meet the this one then the other one comes up with a new requirement you meet this one this one comes with a new requirement so that is an area that we are working to to to to solo then strengthen in-house capacity with fully designated with fully designated to coordinate and to coordinate the development. We are trying to train the the technical teams at the office of the prime minister and the national planning authority to fully coordinate the development of the of the system but also the the training specifically for the government ministries and departments because not again not all of them were trained we had representative from 123 ministries and agencies but we are still they are still many more that did not participate and we do think that the best way to to build capacity and to support them is to really have a core team on a national level that is looking at this and able to scale in terms of support and providing technical assistance. We are also reviewing the indicators they are they keep changing the indicators and like you saw they are close to 11 000 so whenever they review a number of those have to change so it's really a massive undertaking and we work with with the departments and ministries to be able to date the changes on indicators and also the the other requested issues. Then we are also working to finalize the development and deployment of other key features especially analytics they had requested the team requested for a dashboard that they call it GAPA. GAPA means government annual performance reporting dashboard that they want to customize out of the data that is collected through the system so we are working on making that one available and then we are continuing to also support and train users and also create more awareness and use. Yeah I think that is the end I will ask maybe I don't know if Patrick who is in the audience wants to add anything and thank you Johan hopefully I'm still in the time over. Thank you. I think in the interest of time I'll defer all of you to post your comments and questions on the community practice page and then I'll give the word to Leila and Nadia. Hello can you hear me? Very close okay I'll try and be as fast as possible the slide is the slide deck is available on the shed if you want to catch up on anything if I zoom through a bit too quickly. Is the other notes going to be on that one? Sorry great so this presentation is on a program that we're running at Frontline AIDS and the Alliance for Public Health in Ukraine it's Rights Evidence Action my name is Leila Isma I'm the Senior Advisor for Monitoring Systems at Frontline AIDS and my colleague Nadia Semchuk is online and ready to give half of the presentation with me from Ukraine in case you don't know who we are we were set up in 1993 to work with community groups in the countries most affected by the AIDS pandemic epidemic. Today we work as a partnership in more than 40 countries taking local national and global action on HIV health and human rights. We're a global partnership that's open to everyone and we're working with partners from grassroots community groups right through two national governments. The React program itself I won't go through this now but you can look at the slide later was built in response to increasing stigma discrimination and criminalization of people living with HIV and other marginalized populations it's a community-based human rights monitoring and response system. It stands for Rights Evidence and Action and it's designed for civil society organizations to document data about human rights violations as well as providing and referring clients to health legal and other public services but also using the data collected to inform quality human rights-based HIV programming as well as policy and advocacy at national regional and global levels. This is the illustration of the project but I'll let you explore that later. This is a DHIS2 conference so of course we built it in DHIS2. It's what we're using for the data documentation side of the project. We've named our instance Wanda so if I keep referring to Wanda that's why we named it after Wanda Fox who was a trans rights activist from Colombia. Having the data in DHIS2 allows us to do relatively easy monitoring and analysis of the human rights data. We use quantitative and qualitative data and it's to inform programming to evidence for advocacy to influence decision makers and used for evidence in bids and proposals to resource human rights programs amongst other things. This is kind of what it looks like it's tracker capture pretty straightforward client information and case information. It's based on a generic template which we then customize for each country who are using it they might want to customize it depending on their programming depending on who they're reaching in terms of populations but from the front line needs perspective we really would like to be able to review all of the data across countries at a global level so we do want to keep it as a template with similarities across all the programs we run. For example at the moment we're looking particularly at the role of police as a perpetrator to do a global human rights report. This means that we want to ensure wherever possible our data elements and our indicators are standardized across all countries. What we've done with it so far since it's been on DHIS2 since 2019 there's some stats here I'll let you read through later but I'm going to go straight down to this bottom one which is our partners Alliance for Public Health APH in Ukraine have successfully migrated to their own instance of DHIS2 and that's what we want to talk about today the sustainability of the model and of the program and how we can go from an organization being in our instance of DHIS2 to then owning their own. It's really encouraging for us to see this kind of thing from a sustainability and a localization point of view. So what are the benefits of organizations hosting their own instance? I think it's pretty self-explanatory they'll have total control over the program design and governance they will have complete ownership and sharing of the data of course they're always going to own their own data we make that clear but complete ownership and sharing there's additional options to use DHIS2 for other programs we use it for other programs ourselves in service delivery programs etc. It can really strengthen internal capacity in an organization and it's good for our agenda of localization and program sustainability overall. A quick slide about the process of it APH piloted React in our instance of wonder for one year and they recorded about 2,000 cases which may not sound like a lot but it was a really good feat and achievement so based on the success they decided to set up their own instance of DHIS2 independently of us. The people that were involved were frontline aides BAO who's in the room and APH for themselves so BAO were setting up the new system based on the configuration of our wonder and they also had to migrate all the existing data from the system into the new instance. It began in late 2020 went live at the start of 2021 so it didn't take that long to do but some key considerations APH were already an exceptional partner who had strong capacity in running monitoring systems they have been using Syrex since before DHIS2 we've tried this process with a couple of other organizations it hasn't always been this easy or successful so we'll point out this is definitely a good practice story but we're going to hope to share some of our lessons learned I'm going to hand over now to Nadia to talk us through the next challenges are you there Nadia you're muted I think oh thank you thank you Leila good afternoon dear participants so Leila just briefly said about the process we had and actually it was time and expertise confusing consuming and we want to share with you some challenges and advice as well that we faced during the process so the process of existing data migration had to be kept really carefully at all the stages for some period of time all of the current users of the system were disabled and restricted to enter new data so this was done for us to finish the the process of data migration and fully test the new instance as for now data is no longer all in one system so for front-line A's who would like to keep some oversight for all react program it is important to keep good communication and sharing if we want to combine data use for global advocacy activities for example another challenge that should be mentioned it is budget so you should consider that the cost of setting up a new instance is definitely would be much higher than sharing such costs within several programs and among other challenges it is the process of creating the analytic objects like program indicators and dashboard so we had to create them from scratch so it also took sometimes for that but anyway we could advise we could provide you with some advice next slide please yeah sorry for this so some advice for other organizations thinking of creating their own instance so first of all really have a plan prepare a plan of steps involved stuff both internal and external and consider budget that needed while setting up the system make sure that you have enough internal technical capacity and it is really about internal as it is more beneficial to have than external however you also should consider some budget for additional system administrator training also evaluate your needs in using the system like setting configuration using languages using the system within different countries and contexts and if possible consider it further expansion and scale at the early stage or at the middle if that possible also it would be a good idea to have piloting on an existing system it could be beneficial before investing in your own instance spend enough time for testing the system and its revision we do spend a lot of time in testing and revision and this process was done and again and again and maybe this is the reason why why we succeed in that so be patient with that also consider the server location in terms of proven security and data protection issues and maintain resources so our servers were under the maintenance of BAO systems and we decided to have them at the same place and we are we defined that it was for it was a good decision also keep fundamental data elements unified between instances so it is for you for further collision or standardizing something or comparison data elements or data you actually receive and also it is a good idea to have some basic standardized data indicators also for you to compare the data you have and with all of this for sure make an ongoing monitoring of the system functionality and response so it is needed to do for several times for us during the whole process of data migration so having said that next slide it's been a year and a half that APH having its own DHS to instance and how it looks today what are the results we have so far so we have more than 10 000 cases registered in the system and the cases were registered in 13 countries so far under APH management more than 170 NGOs implementing React and we have more than 300 users so far but the system is growing and new countries new users are creating having shown such results we constantly do in regular monitoring of external and internal program needs and this facilitates the program the prompt response to program changes and requests also we spent a lot of time to ensure data quality in the system and it is possible through continual trainings for users and providing them guidance in interactive forms like online trainings, video and so on as a result we have a reliable source for advocacy actions in implementing countries so the data produced during within our DHS to instance is fully used and it is a reliable source and also it is important to share the results we have through different platforms like being here and sharing at another platforms next slide we are inviting you to take a look at our React data portal where we actually have statistics success stories of response reports examples of advocacy actions taken using the data from our DHS to instance and much more please use the QR code you can see on the screen to enter the website or you can find the presentation and look through this later the presentation is available on the web page of this session on DHS to conference website that's all thank you for your attention if you have any questions or need any support or an advice in setting up your own DHS to instance we are here with Leila to answer your questions or provide some assistance or you can reach us later after the conference our speaker will be Henry from Tanzania on integrated child protection management using DHS to right right good morning i hope you can hear me i'm going to be very brief so that i can spend at least the the remaining time which is less than five minutes probably just to take you through another area that DHS to can find it very interesting and necessary to explore where i'm going to present a particular use case in this street case monitoring system in Tanzania which look forward to integrated child protection management uh using the DHS to so uh my name is Henry Karisti of course i would like to acknowledge my colleagues Lillian and Dr. Hones Kimaro as well as happiness Nyanda who worked it together in putting out putting this together for sharing um right thank you oh yeah so just a little bit of background uh of course child protection is the response bit of every member in of course any society uh and through different policies the government of Tanzania has also committed to improving and securing well-being of uh child of course through this it has established the various policies among those policies uh the national costed plan of action for most vulnerable children uh two uh we as well as the national plan of action to end the violence against women and children and all these policies mostly aimed to mitigate the negative impact of various aspects including the issue of child neglect child aid and forced marriage and violence against children when we are not but along with those policies it's also implemented uh national integrated case management system framework which aim to harmonize uh the the processes around the implementation of child protection uh aspects so uh despite of the existence of this of course along with this of course the government has established a ministry very specific for community development gender women and special groups which is uh directly responsible for dealing with a social welfare aspect including uh child protection but along with that it has also another ministry which is uh president office it has another ministry which is the president uh pure edge that is the president office regional administration and local government authority which is responsible for implementation of all the policies that has been established by the ministry responsible for that so uh despite of course existence of all these initiatives still child protection issues has been happening and more intervention definitely are required by various authorities so while these efforts are in place of course uh there is a need for information related to all these initiatives that are being implemented to be well organized to ensure data driven planning and decision making so there was a lot of existing initiatives including the government establishing appropriate regulations and tools in place that uh supposed to be used during the provision of these services and documentation uh most of them being paper tools of course a microsoft access database was also uh somehow established but most of this becomes they were not efficient enough and probably not sustainable enough uh especially considering the the geographical area as well as demographic of the country so just to give you a hint on uh Tanzania is uh located in east Africa and it has a total area of nine thousand nine hundred and forty five thousand with the administrative areas which include 26 regions 184 councils and a good number of wards as villages so uh the population was projected to be 57 by 2020 and among this population you can see the young population was um 543 above 43 percent and of course the youth 15 to 24 was uh 59 more than 99 percent basically this is based on the nbs which is the national bureau of statistics and we are based on the last sensor conducted in 2020 and 12 of course we are expecting to have another sensor this year so considering these settings of course most of the issues related to child protection they happen in the villages of course from the community and they can originate from uh from the household being reported by either the child himself or probably anyone in the community it can be institutional within the community but mostly they will engage some village executive officers but at the end the law requires social welfare to be uh uh directly engaged to any child protection matters who has to of course initiate the file and handle the case until the child gets the required uh protection however of course social affairs are mostly available at national level region level as well as districts very few wards has social welfare so far and most of the village do not have at all but there exist of course uh an engagement of the community workers who mostly are connected coordinated by partners working closely in the rural areas of course I mean in the villages or streets uh to provide these uh social fair services just uh some facts and figures that were based on UNICEF Tanzania uh 2017 child protection fact sheets actually these are the one which led to the implementation of this system in one or another you can see more than twice of all number of girls than boys experienced some form of sexual violence before turning 18 in Tanzania and basically orphans are more vulnerable to sexual and emotional abuse but the interesting fact was that uh domestic violence is was somehow widely accepted by even women than men and considering the fact also that almost 60 percent of women aged 25 to 49 years were married before the age of 18 by then so those were just some hints on the facts in the ground to show the concern in place so when there was all these issues being discussed the concern was also on how then the data monitoring could be done in order to inform the uh planned decision making and of course UNICEF while working with the government uh can't get to us just to know what kind of technology could help uh to improve the situation that's where we found the HS2 being somehow useful uh considering the fact that we were leveraging on the existing system by then uh all these services was were offered by the same minister which was the minister of health uh so given that the minister of health has already implemented the HS2 in most of the aspects and the similarities between those two services we found it necessary to build on existing installments uh also the potential of linkage of all other social fair services around within the same platform uh ease of scale up and the extent of course to engage the rest of the other services the potential for integrating it with other system as well as linkage with partners around and of course most importantly the power of influencing data driven decision making which comes with the HS2 all these factors uh uh made us of course believe uh dhs2 could be the right platform to be used to support those initiatives so we went on and come up with the design that was aiming to uh incorporate of course a tracker model that could of course trace all track uh all child protection concerns so for every case uh there was some of course necessary agreed variables and stages that were supposed to be captured because of course there are there are many aspects that they are always filed through the case but of course given the resources around the number of social fairs so we had to choose only the minimum number of uh variables that of course were necessary also for for data use uh aspect so you can see uh on the dcms side those are the stages and potential variables that were considered to be captured for any cases that is charged from the when it is reported in this investigations up to the pointy it is closed so this is the tracker model within dhs2 and then the rest would be benefiting from the analysis that comes with the features in dhs2 so this is an example of just uh dashboard that was uh put in place and mainly it was trying to answer some comparison uh based on children living in a street versus child protection concerns because of course within the same system as you may notice there is also aggregate data for most vulnerable children being captured and uh child protection are part of the most vulnerable uh children so for instance this was basically dashboard trying to compare and see uh the relationship between the children living in a street versus those who uh got the child protection uh concerns um so this is the scored as well that was implemented in order to uh in order to simplify management uh decisions and try to enforce accountability around the system so among the achievements of course is having the just user friendly dhs2 tracker capture uh and we managed I mean this started as a pilot of course in selected district in 2017 but by March 2021 uh it was rolled out to the entire country so uh the rollouts was done in phases and a bit delayed because of resources but of course in average I would say more than 2000 cases are reported through dcms every year and the dhs2 core analysis tools are widely used uh for data use analysis data use purpose so this map is actually showing the reality that all districts are now reporting uh child protection concern through the dhs2 so what is more interesting out of this uh is the fact that when we started our vision was to also make use of dhs2 uh to improve the data collection and and and of course use within the social welfare and we started of course with the child I mean with most vulnerable children concern and and the child protection but our vision was of course to see it including all other services around and lucky enough the existence of this system has actually spear added the vision that the ministry now have to integrate uh all social welfare services within the system of course this includes different aspects in social welfare and as I'm speaking of course the government is uh in its initiative to start of course engaging the callers coming up with the requirements for this comprehensive system and they are looking much forward for the dhs2 to play a very important role in this of course the challenge has been of availability of resources they are really banking on some technical assistance on the implementation of dhs2 but unfortunately not so many partners are interested in everything in social welfare probably a very specific aspect mostly in child protection but for them they want to achieve a comprehensive thing within dhs2 so we believe this is an opportunity uh for for for providing the technical assistance at least stage to establish dhs2 within the social welfare uh sector as an attention to practice page um unless you have some comments now and you're not eager to join the other sessions because we have this room there are no other sessions taking place here if there are from online or on site okay thanks again to all three presenters