[email protected]: Tiffany Veinot: Leveling Up: Developing Upstream Health Informatics Interventions



I introduced University of Michigan in the high school and also in School of Public Health and Tiffany is also the director of the health informatics my master's in health informatics program so if you have any interest in that she's a great person to talk to Tiffany just does really interesting fascinating research that's at the intersection of a variety of different fields and so I mean she really is just this wonderful embodiment of transdisciplinary research and also I as a personal note she and I have met as she organized this workshop CCC workshop that I was involved in and it was just it was really really important for me personally actually it really helped me to just realize just how important it is to really be thinking about health equity and issues of that sort and so I'm just really excited here so I'll shut up so you guys can hear the real main event of Tiffany thank you thank you hey mrs. Hahn great so thank you so much for the fine introduction Derek and hello everybody today I'm going to talk to you about upstream health informatics interventions and I'm hoping that by the end of this talk I will have inspired some of you to start to think about some of these approaches to different kinds of technology interventions in your work so I have three key points that I want to really have you come away with today one of them is that health disparities have upstream origins and by upstream I'm talking about things that affect our health beyond the level of the individual the individual effort behavior and choice that we may have so I'm suggesting to you that the kinds of inequities that we see in health related outcomes and the incidence of disease are related to various social factors so I will talk about that a little bit more I'm also going to be arguing that's the design of technology interventions in the health space should be moving upstream to try to address these disparities and I'm going to introduce you to three different projects of mine briefly that each try to take on this problem of upstream interventions from different angles so first I'll tell you a little bit about me so I am originally from Canada and I initially did an undergraduate degree in women's studies in history so I had strong training in critical theory and social justice and I was a an activist when I was at school and used to arrange all to arrange all kinds of events like demonstrations and different kinds of rallies and sit-ins and things like that so that was kind of my background and I what I was working through that I started to really understand how important information was to people's ability to change the conditions of their lives so I got really interested in that and I started to study Library and Information science so I did a masters of library information science in the early 90s and then I went to work in the nonprofit sector so I was working in two organizations for about 10 years and then I went back to do my PhD and then became a professor I'm going to talk to you a little bit about my time at one of these nonprofit organizations and how that helped to shape the work I'm doing today so I was working at an organization called KT which is the Canadian AIDS treatment information exchange and that is a nonprofit that's national in Canada and that organization tries to deal with giving health related to treatment information to people living with HIV and the organized that organization really came out of health disparities that are characterised the HIV epidemic so one of the biggest things with HIV you might be aware of already is that it's a condition that's disproportionately experienced by men who have sex with men and in Canada as you this is a diagram of the number of HIV cases in Canada and where they were distributed in the population and for many many decades and up until today the condition was disproportionately experienced in that population and it was when this disease became a big part of people's lives there wasn't a lot of treatment available so people were really struggling to stay alive and we saw situations where like whole networks of friends would die and it was a really tragic and horrible time for the community and in order to try to help people live longer and to try to protect and save the community people got really involved in treatment activism so there was an organisation called AIDS Action now we had a similar group in the United States called act up and the organization lobbied for things like faster access to treatments and it started a treatment information exchange for people living with HIV where people would be trying to share their tips and tricks and things they had learned learn to try to boost their immune systems to manage symptoms of medications etc so this organization AIDS Action now actually spun off and had a nonprofit that resulted from that work which is K T so Canadian AIDS treatment information exchange and the organization received money federally in Canada and they started up a kind of national information exchange related to HIV treatment information and they had a number of services that they started as a result of their work they had things like publications for people living with HIV they had websites they had a treatment hotline so there were a number of services that were available to people and for many many years this was a very successful model and Katie was doing quite well and making a difference in people's lives but something started to happen around the time that I started working at Katy and that was that the demographics of new infection started to shift and what we can see here is that we really saw a huge uptick in HIV infections in Aboriginal communities and also there was a growing number of infections in immigrant communities specifically from out of Africa and the Caribbean so that started to create a very different set of people who had the disease and what started to happen is that the model that Katie was using to provide its services the library the toll-free hotline etc were not working for these newer populations and basically the usage of services was dropping over time and there was a huge amount of frustration and agitation against Katie because it was perceived that they were not responsive and not doing a good job of serving these newer populations of people with HIV so when I was working at Katie I got really involved in a number of projects to try to shift service models to try to better serve newer populations got involved in some community-based participatory research and that's really drove me to wanting to go back to school to try to specifically address these issues so I became really interested in this problem of how can we try to design services that might be better suited to diverse populations and to really help us understand what is generalizable about the kind of problem that Katie faced as an organization and what kinds of generalizable solutions might we be able to have for that so I went back to do my PhD in Canada and then became a faculty member at the University of Michigan and really what I the kinds of things I confronted when I was working at Catie have really shaped my career since then and I really came to understand some of as I said the generalizable aspects the problems at Katie so one of them is I started to understand that you know the concept of health despairities a lot more and realized that while men who have sex with men were a disparate population with HIV there were other groups that were also new disparate populations and that what worked for one group might not work for another so it helped me understand the complexity of health disparities and how they can be different and for different groups and I started to understand the variety of different communities that experienced disparities as in the United States and Canada and really to understand that there's different mechanisms of marginalization that people experience based on a number of different factors like place of residence like race like ethnicity like gender and I really started to understand that we might need to do different things for different populations but maybe there would be something common that would be important for all of them I also came to understand this concept of intervention generated inequality and this concept is one that came out of the public health field and it's really about the idea that we we are often very good intentioned designers and implementers of technologies and other kinds of interventions may create something that appears to work in one group but it's disproportionately affecting and benefiting the advantage groups and not working for others and just to share this illustrate this concept in the area of smoking this is an area where there's tremendous intervention generated inequalities since about the 1950s so is what you see in this diagram here is that smoking has fallen in the population as a whole since 1940 and in 1940 there was no association between education level and smoking but by now there's quite a strong association what we see is that people with higher levels of education of and quitting smoking faster and such that by 2015 you can see that for people with a GED or high school equivalency 43% of that group smoked whereas with a graduate degree is just 5.4 percent and this particular spread is something that is attributed to the kinds of models and approaches that have been used in a lot of anti-smoking campaigns like mass media education toll-free quit lines website based interventions those kinds of things and so what we've seen here is that the intervention strategies that have been used have worked much better for the more advantaged groups who have more education and we have seen this spread emerge so this particular concept is one that I think is really important to think about whenever you're designing technology is is there a possibility of reinforcing or worsening inequality and to try to really think about how one intervenes in the context of health disparities I started to try to look into more of the theory of how health disparities emerge and I found a really useful model from the World Health Organization that's connected so health disparities to wider social contexts and what I'll point out is that this model is one that starts on the left or my left is the individual level and as you move further in this direction you move to higher levels of social organization or generalizability and so we see a lot of interventions in the health informatics or technology space that really focus on differential consequences so that's what somebody is already sick so maybe we're trying to develop something for patients who have diabetes and those interventions if they are specifically targeted at you know people with poor glycemic control or african-americans or some other group those are going to be interventions that try to have assess or address those consequences but if we see this as something that is at an individual level there's a very extensive literature in the area of Epidemiology and interventions that show that essentially these are individual level factors that drive those disparities so these are things like behavior and biology and then further to your my left is psychological factors and most of our interventions focus in these areas but there's quite there's a tremendous work on the social determinants of health on things like living and working conditions and social networks and the ways that those condition our behavior and the exposures that we have etc which suggests that we can also try to focus on things like differences in vulnerability and populations as well as differences in exposures to bad things like bullying or air pollution so and then there's the Health System and these three things are what we would describe as the meso level or the middle level of society and there's growing amounts of literature and research that focus interventions that don't involve technology that focus on these areas so this is all kind of the things we can see and touch and know about very clearly but if we kind of go over to this side we start to talk about more macro level forces and the as you can kind of see here there's a recursive relationship but a lot of the macro level forces affect things like our living and working conditions and our social networks so what this shows here is that we have a socio-economic and political context things like culture values the economy policy things like that that produced this social hierarchy and there are multiple processes of marginalization and so class power segregation freedom stigma and discrimination and those in turn produce social positions that are just that lead to disparities right so we have things like race and gender and LGBT identities rural residents disability all of those positions that are marginalized are produced by these conditions so this particular model is one that I've been thinking a lot about because it helps us to really focus our attention on what is driving disparities in health and I've been really interested in initially trying to focus on interventions that address these areas rather than necessarily just the individual and also interventions that might be able to address processes of marginalization so I'm going to talk about ways in which we can move upstream so I'm calling this individual level downstream and at the farther and farther you get into macro level forces it's more and more upstream so I'm going to talk to you about sort of how we've approached this particular model with regards to technology and there's three different projects I'll mention briefly so one project is focused on living and working conditions and this one is related to neighborhood effects so there's a significant amount of work showing that where we live has a significant effect on our health over and above the characteristics of the people that live there so this particular project is trying to identify things like socio-economic and political context on a local level within communities and we're trying to target decision-makers so this would be people like urban planners public health officials policymakers in cities like City Council's nonprofit social activists people like that and so and we worked on this project in partnership with a couple of organizations that are nonprofits in the greater Detroit area including the Detroit fodmap initiative so the background here is that this is focusing on diabetes hypertension and kidney disease and the major takeaway from this particular diagram is that at a county level there's significant differences in the prevalence of diabetes as well as other conditions and the red counties have higher prevalences and the lighter colors have less this is one where they had no data so there's a there's a strong spatial orientation as well to disparities and that's partly because of characteristics of where people live so this is basically just summarizing massive amounts of research in public health that have shown that there are differentials in various health outcomes and health behaviors based on where people live and it's for all the outcomes listed here and all the behaviors listed here and there's studies that have tried to understand why so what is driving these differentials with regards to health outcomes and there it ranges from everything from housing conditions that depend on the housing stock in an area to the amount of green space to the amount of air pollution to the amount of neighborhood crime so there are a number of things that can affect health so this project is about trying to use information and data to be able to better understand and characterize the relationships between community and health and the first thing that we did was we tried to talk to some of these upstream decision-makers so again we're talking about urban planners we're talking about public health officials and other folks like that so we did focus groups and this is who was in the focus groups you can see that there were a lot of healthcare organization folks as well and we were working with community partners and basically from their perspectives we learned about the fact that they were experiencing significant gaps in data and information to facilitate their decision-making they were looking for more local data so this is a quote that deals with how hard it is to find that's at the granular level where people were working this is related to in space people also talked about different kinds of data that was really hard for them but gaped about things like culture and this is somebody talking about the kinds of assumptions that people make about food culture and a marginalized community because they lack information about it so they kind of fill in what they think it is and they also talked about difficulty in understanding what resources were available and how difficult it was to navigate them when we were talking with folks in these three cities where the study took place and people really thought that it was difficult to try to diagnose problems that were happening locally to try to understand disparities and they wanted to help and be able to prioritize what to work on so with that study in mind we really started to connect that to broader issues in the US public health information infrastructure and we understood that there's a real gap with regards to accepted community level measures and being able to integrate those data so in the next phase we try to understand the strengths and weaknesses of data available via social media to characterize the food environment so the background to this is that limited access to nutritious food there's a higher incidence of chronic disease chronic disease in communities and that the environment that people live in affects the choices that they make with regards to food and quality and price are important areas as well and those upstream decision makers that we were talking to were interested in this area and there are a lot of limitations to existing sources so for example we had to do a FOIA request in order to actually get a list of all the grocery stores in this particular area so gives you a sense of how not thoroughly available that information and a lot of what happens with regards to people's eating behavior it has to do with the price and availability at local stores of healthy food and right now the sort of most common ways that people try to assess that is through in-person grocery store audits so that there's a lot of gaps with regards to measuring and understanding this so we sought to try to understand the quality of Yelp as a source of information to identify grocery stores and also to try to characterize the food that's available and its quality so we looked at full line grocery stores this is a definition used by the state of Michigan and we were working in the seven County area around metropolitan Detroit and we selected a subset to do in-person grocery store audits at them so we had two sources of ground truth with regards to grocery stores so one of them was a list of all of the grocery stores that was fired and that were subsequently verified using Google Street View and phoning and we came up with a list of 426 grocery stores then we had a range of ground truth sources related to those visits that people were making to the grocery stores where they were doing the these in-person audits they were using this measure which is the nutrition environment measure survey in stores NEMS s and it looks at availability and price differences in food related to quality and then we also scraped Yelp data using the API for the same stores and we also looked at of this very expensive source that nonprofits want but can't afford that helps them to identify businesses in the area called reference USA or Russa so and we use this num scoring system and we worked with creating measures related to Yelp where we were relying on the dollar signs and stars and then also the written reviews that people were producing and we did sentiment analysis from that so we then went through a process of trying to map or match these stores to one another in the two data sources as well as what we had found in that ground truth source we used a cosine similarity type algorithm and then we were assessing whether or how accurate this was I'm not sure if you've ever come across these kinds of tables but basically this looks at whether a store was there or not in the ground truth source and then whether the data source told it was told us it was there or not and so we were interested in so true positive that those are stores that really are there and the data source told us it was there this this false positive is the data source told it was there but it's not the false negative is the system told us it wasn't there and it was and then true negative is probably obvious and of interest is this idea of positive predictive value which is basically the person it's the proportion of positive test results in cases where stores are present so we thought that with regards to the grocery stores because community organizations use this for planning purposes it would be worse for them to think of stores there and it's not there than the reverse so we felt that precision was the most important kind of measure here and we also did we created some correlations between the Yelp source and the NamUs measured MSS source so you can see the results here basically we found that we knew for sure there were 426 stores in this area reference USA told us there were a lot more of them 1631 and Yelp told us there were 813 and we found looking at the true positive rate that as you can see reference USA had a higher true positive rate but it was at the core the expense of a very high false positive rate and really yelped was much more precise than the reference USA source that costs thousands of dollars then we looked at the kinds of sentiment that was in these Yelp reviews and you can see some positive and negative words that people were using to talk about food related to food related prices and you can see some related to food related quality here and what we found was that there was a significant correlation between the number of reviews and how expensive the food was so kind of suggests there was more how people with higher income were probably posting more but we saw a pretty good association between the dollar signs measure so this tells you that looking at the dollar signs on Yelp will tell you something pretty strong about the quality and the price of healthy food in this area and we also found that there was more negative sentiment related to price in these these reviews if the names names asked measure said it was going to be more expensive so there were some limitations I can talk more about those but basically we found that both reference USA and Yelp overestimated the number of grocery stores in the area but and Yelp was somewhat less sensitive than reference USA but Yelp was more precise so we thought that Yelp was a pretty good source for identifying stores and we found that Yelp was a pretty good source as well for identifying the cost of healthy food but there really wasn't a signal related for availability and quality of that food so basically what we're working through with this is trying to use social media and other sources to be able to characterize the living and working conditions that drive health and where we're going with this is we're trying to develop and validate Aesir of measures that can fill in some of those information gaps that upstream decision-makers face and trying to work on developing some kinds of decision support tools that might help people who are doing this upstream kind of work to better understand things that they currently are guessing about so that's one approach using informatics and technology to try to address living and working conditions another context so here I'm looking at the problem of HIV being a disproportionately experience by men who have sex with men and this is a HIV as a condition where knowing that you're positive will actually it's very common that like health behavior changes so it actually reduces transmission and this is a really important aspect of HIV prevention initiative to the United States is to try to have people understand their HIV status sooner so this is a project where we're working with a non-profit focused on HIV and also a county health department so men who have sex with men in this but in the US as well as Canada are disproportionately affected by HIV and there are national goals related to trying to increase HIV testing in the men who have sex with men population and there's different work showing that there are correlates of HIV testing at individual and network level but the relative role of each of them is pretty unclear so we worked on trying to understand those Network and individual level factors to try to help inform an intervention to promote HIV testing uptake so we are working with this theoretical model where we were looking at HIV testing behavior we were looking at Network characteristics so this is people's social networks their social lives and then looking at what kinds how their networks work so what resources they got from their networks social support and social influence that they might experience through their networks as well as a number of India visual level factors as well so things like psychosocial factors risk behavior etc so we were looking at these factors simultaneously as predictors of HIV testing and we did a web survey with men who have sex with men who were between the ages of 18 and 24 in the Metro Detroit area and we also did individual interviews and these were our dependent variables we were looking at whether someone ever had an HIV test whether they'd had one in the last 12 months which is what is recommended according to the health recommendations and we this is who is in our survey so you can see that it's fairly young people around age 21 on average and we had a sample that was about two-thirds african-american which is very important because that's a population that's disproportionately affected by HIV within the men have sex with men group okay so this basically this model shows you the predictors of ever having tested for HIV and this model is one where I'm just showing you the factors that were significant but you can see that people getting information from their social networks was significantly associated with them ever having an HIV test so other people talking to them about HIV testing and then people who were older were more likely to have tested people who had substance-abuse were more likely to have tested probably because of testing being available at a lot of different addiction treatment facilities and then people who were had more confidence or perceived behavioral control so they believed that they could easily find a testing site in their area they were more likely to have ever had a test the thing that's kind of important here is that I'm going to draw your attention to the fact though 13% of the variants of 45 45 percent of the variance is explained by this model but 13 of percent of that is from the social network factors and then we look at recent testing so this is somebody having tested in the last 12 months as I mentioned that is what is recommended by the CDC for this population and you can see here that Network factors were pretty important for this so basically people having more social influence in their networks so having people in their networks who are more like them led to more more odds higher odds that they would have tested recently and if they perceived stigma in their environment that would have reduced the odds that they would have tested recently and then you can see here that age and attitude towards testing we're also significant the thing that's important here is that you know this model was one in which 26% of the variance was actually predicted by network level factors and that was actually more important than individual level factors for whether somebody had tested recently so this study is the sort of takeaway from this is that our social networks who are really important for whether people had ever made this decision to have an HIV test and this provides a rationale for trying to intervene at the network level to try to promote HIV testing in this group and in particular trying to reduce stigma in social networks is an important potential strategy as well as trying to leverage social similarity in social networks so based on this we took away that Network level intervention seem to have good support in this audience and we asked the question about wealth okay so this is association is there but how is this how does this work so we also did some focus groups to try to understand stigma and social networks with regards to HIV testing and we did a series of focus groups with this population as well as older people older people in the community and we found that so this you can just see who's here and had HIV positive and of people and we we've really found that there was significant evidence in the in the interviews that we were doing through focus groups that for this concept of structural stigma so that's stigma that's kind of functioning at a social level and that involves social networks in a way that's kind of produces Sigma ties positions so this is a model from Lincoln Phelan who are really interesting theorists about stigma but they talk about how you know discrimination does emerges from labeling processes so we label human differences and then we stereotype on the basis of those human differences and then we separate out people based on those differences so this is the US and them kind of idea and the people who are separated experience status loss and then there's this idea of discrimination and so this is young this is people with HIV and not with HIV in the community talking about these processes and how they see them each kind of feeding into stigma that they experience so they talk about kind of the HIV issues related like HIV testing should only be for gay people so this is a major barrier for people around risk assessment because they don't think they're at risk if they're not member of the community they also experience stigma if they go get tested because people assume that because they're getting tested they're HIV positive then there's stereotyping of people who get tested that they're promiscuous or that HIV positive people are predatory and deliberately spreading the disease and then they talk about cleavages and their social networks related to HIV stigma so people don't necessarily interact or talk with talk to one another so there are quotes that are talking about that and then people who are talking about being HIV positive the kinds of treatment that they get from people around ideas that they're unclean and that kind of thing and then we have this idea of sexual or rejection that people experience in their communities through discrimination a lot of which happens online so one of the kind of big things that we also heard about was how so much of the stigma that people are experiencing is actually things are experiencing in online environments so things like online dating platforms Facebook various kinds of social media are situations where there's a lot of people like talking about things or not talking about things but often it doesn't go very well if they're talking about HIV so we really started to think about how we can try to use technology in the context of stigma and social networks to try to redesign some of the opportunities for stigma and you know if you think about it there's a lot of things about technology that make it but in some ways might support stigmatization like requirements to self label when you're creating profiles and things like that so we were looking at how we might try to use social media and and more and also face to face social networks to try to create an intervention to promote HIV testing that would focus on social networks and stigma this is something that's very much in development phases but this kind of shows you though thinking about social networks as a place for intervention and then the last study I will talk about is one that focuses on health care so I have talked about living and working conditions which are one of the drivers of disparities I've talked about social networks and now I'm talking about the healthcare system which is another meso-level factor and here this is a project that is focused on trying to address disparities with regards to complications of humid dialysis so in this particular project the disparity group is and we're trying to address a kind of complication that women experience more often and we're working with dialysis providers and health care providers and I will let a patient tell you a little bit about what we're addressing the problem is introduced hypotension or low blood pressure during dialysis every once in a while and it doesn't feel good you feel a little sick to your stomach when it's coming on you may even feel like you're going to black out and in fact if you don't let someone know soon enough you will black out generally if you feel nauseas and start to feel a little bit blurry I'd get someone's attention right away but nurses will come and check your blood pressure real quick and they will either put your feet up in the air and or give you additional fluid to try to counteract the situation so that person's talking about introduc hypotension and this is actually a problem that happens in one in five emo dialysis sessions in the United States so it's very common and this is something it's so common in fact that for many years health care providers didn't think much of it they just get people more fluids and be on their merry ways but there's been a growing amount of research showing that this particular process of hypotension is causing cardiovascular harm and may be linked to a number of other problems like those listed here so there's various kinds of cardiovascular events that people might experience like angina or chest pain chest pain this kind of symptoms this patient was talking about also people with lower quality of life and more hospital stations and there's more evidence showing that this leads to something called myocardial stunning which is damaging the heart as well as actually the brain and guts and other micro circulatory beds and more more and more we have shown that this is linked to the development of cardiovascular disease and dialysis patients as well as blink to mortality and there's a number of factors that are linked to idh and some of them are modifiable some of them are not but basically it's driven by patient level behaviors as well as provider level behaviors so things decisions that both of them make so you can see here some of the things that affect it but basically if you've ever seen or if you know about dialysis basically all of your blood is taken out of your body it's run through a filter and it's put back in so it's it's a fairly demanding kind of process and when that blood is being taken out fluid is being removed from people's body and when a lot of that happened is taken out in one session or it happens really fast that's when it's more likely that somebody will have intraday low tech hypotension so really the way to try to prevent it we need to have less fluid in somebody's body to begin with so we have an intervention where we are comparing something that is focused on healthcare providers to something that's focused on patients so really what we are looking at is this intro diabetic hypotension and quality of life and hospitalization so we're measuring our in our outcomes at the individual level but we're actually comparing upstream and downstream interventions so we're looking at something that full relies on patient behavior change their change doing something different to try to reduce the incidence of idh or we're trying to change what the health care providers do with a checklist of other kinds of interventions so what I kind of wanted to show you here though is that this is a problem that's very much disparately experienced by women so women are more likely to have internal and a Kaipo tension and then all these factors surrounding it they're more likely to be hospitalized with dialysis they have lower quality of life they've report more symptoms they have a harder time adhering to the dietary requirements some of these reasons might be driven by things like dialysis being designed for larger bodies of men so because there are more men on dialysis than women so some of it might be kind of the model that's being used but then there are also social factors that might be driving it like social roles where it might be harder for women to be able to adhere to a very strict diet because they're feeding their families or things like that so we have developed an intervention for both both patients and healthcare providers and this just kind of gives you a sense of a design process just for the checklist and various stakeholders that were involved so the top row is investigators we had a steering committee which is multidisciplinary involving patients nephrologists nurses and others we had an advisory committee of twenty people from across the country from different stakeholder groups including seven patients and we did clinician interviews and we also did demonstrations of a new tool at the facilities so basically this is a process just to develop a checklist that would help health care providers to be able to identify people who would be at risk of idh in order to be able to try to prevent it from happening so we did the series of interviews and we did quite a bit of analysis of workflow this is activity theory that you're looking at here I can talk more about it if you're interested but basically this is a method for analyzing the interrelationship between tools and workflows and different people who are involved in engaging in technology and in some kind of a process that's related to work and this is used quite a bit in the HCI field in order to try to Lync design interventions to meaningful human activity so this is our analysis of the workflow related to introdu lytic hypotension and the things that are in bold are things that we tried to influence using our intervention and the result of it was that we developed this diagnostic checklist that's tablet based our process involved quite a lot of analysis of both the science to try to understand what the criteria would be for diagnosis as well as trying to understand workflow integration problems and things like that so basically what we had in this was that we developed a checklist where folks would be answering a series of questions about every single patient within the first hour of treatment and what we tried to do was have their the clinicians pay closer attention to three factors that are more likely to occur in women that increase the odds of having hypotension and basically this suggests to them alternative interventions if it looks like this person has an increased risk and it also tries to force people to take responsibility for the choices they're making through acknowledged meds and things like that and so that's basically our approach to the provider intervention as well we are doing team training with the healthcare providers and we've also have this patient intervention which is in partnership with the National Kidney Foundation and through that we are equipping patients with a tablet during that they'll use during their dialysis sessions and in that they will be both looking at interactive educational materials doing things like goal-setting etc based on social cognitive theory and they're also going to be talking to a peer mentor so a peer mentor is a patient who has been trained in providing support and education to other patients and that will also be happening via the tablet and the reason we chose that approach is that peer mentoring is has been actually shown to be effective in the average but it's even more effective in women inin African Americans and Latinos actually so and this kind of gives you a sense of some of the content of the patient activation intervention and we're running a trial in 28 hemodialysis facilities across the country and it's a factorial trial where the interventions are being allocated at the true the facility level and it's going to be in four regions of the United States and basically the trial is going to have at least two thousand patients so we're going to be getting a heck of a lot of data because we're going to have data from dialysis sessions three times a week for a year for this number of patients but basically we're trying to follow them over this year-long period we'll have a baseline period we'll have an intervention period which is 48 weeks and then we're we have a washout period or a follow-up so in this peak in this period of time we'll be looking at the impact on these various health outcomes so basically the trial is starting in fall 2019 and we will be doing heterogeneity of treatment effect analyses and that's basically going to allow us to see whether the intervention works better or just as well with women or in other marginalized groups like people with low literacy so basically I started out by talking to you about how my work at KT this nonprofit really motivated this whole line of research and I wanted to kind of circle back and let you know that KT survived KT is doing very well and the way that they did that was by really changing their service model to something that was more community focused and they focus on capacity building you can see some of the things that they are doing now so there working a lot with training frontline service providers throughout the country on being able to help them provide and deliver treatment formation they're working in partnership with other organizations this is a movie that they made with the canadian aboriginal aids network which focuses on trying to make treatment information a lot more culturally accessible they are they had they restructured their board of directors to allow for representation from across the country they focused on much more accessible materials that showed us the faces of people with HIV and you know this general approach which was capacity building and service coordination is one where they moved upstream and their services and that's really allowed them to be able to better respond to the diversity of needs and so in my work you know there's been a parallel in terms of the kinds of solutions that I found to be worthwhile in terms of exploring and those have focused on similarily upstream issues and I kind of wanted I wanted to circle back and share with you some of the key points I wanted to make so one I hope I've convinced you that health disparities have upstream origins and hopefully you understand what upstream means I also have argued that design of health informatics interventions should move upstream to try to avoid this problem of intervention generated inequality and help us to more systematically address the actual origins of disparities and I've shown you three different approaches to this one is focusing on living and working conditions using social media another is focused on social networks as a form of influence or a barrier to health related decisions and then the other is trying to change disparate healthcare processes so hopefully I've helped you by giving you these examples helped you to think a little bit more about what upstream interventions might look like and I'm hoping that you will be able to go forward and start to try to apply this form of thinking to the work that you're doing in your own lives so thank you everybody I will acknowledge as with everything lots of people to acknowledge funding community partners co-investigators students etc you

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