Category: eHealth

The Promise of Data for Transforming Global Health

by Jean Armas, posted in APHA IH Section, Data Analysis & Informatics, eHealth, Policy and Advocacy

I recently came back from a field visit and as my organization’s designated data person (among the many other hats I wear), I think constantly about the role of data in our work and more broadly, its role in global health.

We’ve always had a problem with data in our field, more specifically the dire lack thereof. Recent efforts to spotlight the lack of high quality data in global health has led to somewhat of a data renaissance. And you know it’s a big deal when Bill Gates throws his weight behind it. It seems like every global health innovation talk I go to nowadays portrays data (in all its forms, from big data, predictive analytics, and machine learning) as the ultimate game changer in global health. Data is so much easier to collect now with the various technologies and innovations available. Its potential is pretty obvious and I don’t disagree that data can and will create more positive changes in global health. But every time I attend one of these talks or I get an email alert about another new data innovation challenge, part of me gets really excited and the other part remains skeptical.

Anyone who has tried to implement a data collection initiative in the field, whether for research, monitoring and evaluation, or donor reporting, knows the many challenges faced when working in already resource-limited clinics and hospitals: the questionnaires are long and time consuming, we don’t have the resources to hire people to do just data collection (which is especially true in smaller facilities), data collection activities take away from clinical activities, data quality is poor, the staff spends a whole week every month doing reporting, every donor wants a report on different indicators, no one at the clinic knows how or has the time to analyze the data, the data is not in a format that is easy to use, etc. And the list goes on.

One huge barrier to accurate data collection involves the inordinate amount of burden placed on health care providers and/or clinic staff to collect and report data. Data collection is often a task that already busy doctors and nurses have to undertake in addition to their clinic duties. Hiring an extra data collection person is one solution, but may not always be sustainable outside of a research study setting. Reporting data to donors is not any less painful. It is too often a rote and uncoordinated endeavor. Donors ask for the same data, but sliced and diced in a slightly different way. Those asking for data haven’t exactly done a good job making data collection easy to do. Shorter questionnaires, standardizing indicators, simplifying and coordinating reporting are different approaches for addressing these issues. Getting providers and clinic staff to collect high quality data though is another beast. Some argue that doing regular data audits will fix the data quality problem. Others argue that mobile data collection has reduced data entry errors. Mobile data collection has certainly made it easier to collect data and scale-up data collection activities.

And while a lot of work is being undertaken by major development agencies and smaller NGOs alike to improve their data collection efforts in order to deliver on the promise that data has to offer, I’m not entirely convinced we’re there yet. A huge part of my skepticism in why data hasn’t yet reached its transformative power in global health is because even though I think we’ve spent lots of resources in building capacity to collect data, we haven’t spent equal amounts of efforts building capacity for local team members to use the data in a meaningful way.

If those who collect the data don’t understand why or how the indicators they collect impact patient care, then why do it? Although national level data is helpful in understanding what the different health needs are and how to allocate resources to address them, the interventions needed to dramatically move the needle when it comes to decreasing morbidity and mortality happen at the individual facility level, outside of the research setting. The frontline healthcare workers that help in the collection and reporting of data very rarely get the data back in a way that can help them understand how to improve care delivery and health outcomes for their patients.

I believe in the potential of data to transform global health but there are many obstacles to overcome before this happens. First things first, instead of thinking about data collection as an activity that providers and clinic staff have to do, it should be an activity they want to do. By having data available to providers that is easy to understand, timely, and meaningful, only then can the promise that data holds for transforming global health be fulfilled.

The next big thing in global health innovation? A little less innovation, a little more implementation

by Jean Armas, posted in APHA IH Section, eHealth, Health Systems, Research, Technology

A post like this should come with the qualification that I am no luddite when it comes to technology and innovation in global health. Quite the opposite actually. I have dedicated my entire career to championing ideas. Whether that was working in academic research evaluating new ways of helping people with chronic diseases live well or researching the technology and innovation pipeline to help healthcare organizations make decisions on what technologies and innovations to invest in; I have been and will continue to be a health technology and innovation advocate (and when I talk about innovation, I’m not just talking about clinical and biological technology or information and communication technology but more broadly about new programs, interventions, etc).

Five years ago I embarked on a new career path in global health which transformed the way I now think of innovation. One of my first projects was to help a local partner organization implement a logistics management information system to manage their post-rape care medication inventory. Since then, I have helped our partners through the process of implementing other technologies and in that short time, I learned the many pain points of implementing innovations.

When you have spent a good part of your career as I did working in controlled research environments where the protocol is often laid out months ahead of time with little room for deviance and with study participants who are often given incentives to participate, working on the last mile problem required a skill set refresh and a change in the way I viewed the innovation pathway. Whether it is learning how to integrate an innovation into a user’s workflow; getting users to trust you enough to tell you when something is just not working for them; finding out how to get innovations to stick; making mistakes and reiterating; using real-time data to enable feedback loops; understanding (and dealing with) organizational politics and leadership; mapping out relationships, etc. – graduate training in public health does very little to prepare you for the trial by error approach required for these undertakings. Researching and evaluating is very different from implementing. So many of us in this field spend much of our time working on research studies and programs based on the models and theories we’ve learned in school that we very rarely think closely about whether or not the studies or programs we work on are scalable, sustainable, or even ethical.

I recently attended a panel at Stanford consisting primarily of philanthropic organizations discussing how those of us working in the social sector and those of us supporting the work need to rethink innovation in terms of scale. One of the things that struck me during the discussion was that when it came to what metrics we use to define success we’re often talking about success on a small scale.  And too often they’re developed with the mindset of pleasing the donor or funder. When we think success metrics, we usually talk about some quantitative statistic that goes something like this: X% reduced morbidity or mortality in our sample size of N. At the end of the study or funding period, we leave the site, taking with us our intervention. We then go on to write a paper about it, submit it crossing our fingers it gets accepted in a high impact journal, we publish it, we present our ideas at conferences. We then call it a success and move onto our next grant.

While this is often the gold standard of success for academics and should still remain an important part of the innovation pathway, there are parts of this road to innovation success that are concerning, especially in the low-resource settings we work in. Firstly, is it ethical to put in an innovation into a site and then remove the intervention once the study period is over if we know it has helped them? Would the site be even able to afford the innovation once it passes the research phase? Secondly, is it enough to define the success of an innovation by saying the intervention did what we wanted it to do? After all, I’m pretty sure a company like Facebook didn’t call themselves a success after running a small study of 250 users that found that everyone liked the product and it changed their lives. They are successful because they have 1.94 billion daily active users worldwide (scale) and have been around for 14 years (sustainability) and they have changed the way we connect with others.

Dear global health colleagues, we have an enormous task at hand. One that requires us to roll up our sleeves and stop thinking small and start thinking big. Let’s end this epidemic of health technology pilotitis and start innovating in the implementation space. Let’s start thinking about ways of innovating outside of the academic space and in real-world settings with real-world obstacles. Implementing innovations demands collaboration so let’s also make sure that we influence those around us. We need to change the conversation on impact and start asking our colleagues and the organizations that support our work to start thinking about the long game. From there we need to make it easier to decide which technologies and innovations to adopt. Let’s also not forget about training our next generation of public health professionals to focus on creating true impact by teaching effective implementation in schools.

Implementation work is incredibly unsexy and a risky investment but needs to be the next big thing in global health as its value proposition is substantial. It is of notable importance when the future of funding for global health is becoming more uncertain. We need now more than ever to deliver long-lasting solutions, not just short-term fixes.

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A study looking at the proportion of children’s health grants funded by the US National Institutes of Health and the Bill and Melinda Gates Foundation found that 97% of grants were for developing new technologies and only 3% for improving delivery and use of existing technologies. Additionally, they found that new technologies would only reduce child mortality by 22% compared to 63% if existing technologies were fully utilized.

Although this study looked only at children’s health grants, the implementation gap can be found universally throughout global health. Learn more about how to bridge the “3/97” gap: