This post is the second of a three-part series, ‘Teach Yourself mHealth.’ In the first post, we focused on what mHealth is and what mHealth projects look like. In this post, we’ll map how mHealth can strengthen information systems to build better health services, step-by-step. In Part 3, we’ll explore some common design challenges in mHealth projects and review the best online resources for bringing your mHealth knowledge up

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to speed.

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We’ll focus particularly on how an mHealth information system works on a detailed and practical level. Of course, SMS can improve other health services and plays an active role in communications for behavior change. My interest lies in systems strengthening, particularly in resource-poor settings, so that is the mHealth example I’ll choose to focus on here.

 

mHealth Data: How It Works

Information for decision-making can be divided into two main categories: quantitative, to do with numbers, and qualitative, to do with language. Quantitative data, in public health and most social sciences, involves getting the results of random clinical trials or demographic surveys,

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or perhaps the routine information systems that Ministries of Health use to assess their population’s disease burden. Qualitative data involves someone telling a story. For example, what does your new community health worker think about her level of supervision by the District Health Team?

By SMS, mHealth coordinators can gather both types of data, quantitative and

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qualitative. For the purposes of national level public health decision-making, numbers are a much easier information source to scale. In the example of outbreak early warning systems, a simple text message can specify the number of deaths, location and suspected diagnosis of a priority disease under surveillance, alerting District Health teams and central level Ministry staff to the need to respond.

Such a system’s mHealth information system could look like this:

SMS Priority Disease Surveillance Reporting © Calhoun

Of course, whenever gathering and analyzing data is involved, things can get complicated. As with any health information system, we need to identify exactly what we want to know, and trim off extraneous steps to simplify data reporting protocols as much as possible. Smart phones running mHealth apps can lead health facility staff through step-by-step reporting in greater detail than basic mobile phones (though they may be more vulnerable to theft and rough treatment).

 

Isolating Variables

During one of my consultancies, I had the opportunity to interview Dr. Theo Lippeveld, President of the Routine Health Information Network, who quite literally wrote the textbook on the Design and Implementation of Health Information Systems. I was a bit star-struck during the interview, but I remember his emphasis on “data for decision-making”.

mHealth projects interface

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directly with health information systems to strengthen the flow of accurate information for improved decision-making. To present information effectively, they must be as simple as possible for the health facility, for the team sending the information, and for the Ministry of Health decision-makers to analyze and take action on.

Breaking down a system to look in detail at the part each reporter and each variable plays helps us identify where systems can be simplified and where the most important information can be prioritized. For example, if I want to use an mHealth SMS platform to gather real-time information about coverage for routine immunization, I will want to identify who will be sending and analyzing what kind of data, when they will

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send it and how it will be received–and ideally, I want to know why:

Potential Variables in an mHealth Routine Immunization System © Calhoun

 

Sending data by SMS is important to keep simple and to the point. At the level of Ministry decision-makers, however, there is a bit more room for visual interpretation.

 

Visualizing Data

When displaying quantitative information and communicating about numbers at scale, design becomes important. In the international development world, many tools use crowd-sourced information and mapping to create real-time displays that shape emergency response (as in the case of Ushahidi in Haiti). In public health, we are used to seeing demographic data on maps and graphs.

The SMS platform (the one that ideally interfaces with

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the Ministry’s electronic information system, complementing rather than duplicating the flow of data) selected for an mHealth project will often interpret that data graphically for easier analysis, enabling decision-makers to manipulate and customize the system to tell them exactly what they need to know. I’m currently reading ‘The Visual Display of Quantitative Information’ by Edward Tufte, who shows a lot of interesting historical examples of how large data sets can be creatively and meaningfully portrayed.

 

mHealth Applications for Developing Country Health Systems

As we see with each new post about an mHealth project in the developing world, using mobile systems to improve information flow can play a large role in systems strengthening. Routine reporting can be distilled into a text message that communicates priority data to government decision-makers. Qualitative information gathering through polls and SMS or radio outreach provides health campaigns with data to develop better communications for behavior change.

In the next post, we’ll explore some common challenges that mHealth projects face and review the best online resources for keeping your new mHealth knowledge up to speed.

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