Viewing entries tagged
#datafordevelopment

What’s APPening in Malawi? An overview of the National mHealth Landscape Analysis

Comment

What’s APPening in Malawi? An overview of the National mHealth Landscape Analysis

By Andrea Fletcher, Lead Data Strategist

Chances are if you aren’t reading this on your mobile phone, you at least have one within reach. Mobile phones have become ubiquitous and are changing the way we do everything from banking to healthcare. This is true globally and even more pronounced in countries such as Malawi, where the uptake of smart phones skyrocketed in the past few years and now 85% of households have access to a mobile phone. It’s therefore not surprising that the Malawian healthcare industry is seeing a large influx of mobile projects as a means of reaching the population for healthcare delivery, data collection, and supervision.

Over the past few months, Cooper/Smith worked with Malawi’s Ministry of Health and Population to register the numerous mHealth projects deployed in the country by various development partners. The analysis kicked off with a memo from the Secretary for Health requiring all projects to register, which allowed us to identify and gather information on 31 mHealth projects. The 2018 mHealth Landscape Analysis provides aggregate information regarding the scale of projects, health domains, alignment with national strategies, and budgetary information.

mHealth_timeline.png

mHealth projects in Malawi have existed since at least 2007, with an average of 2 new projects coming online each year. The average lifespan of a mHealth project is 5 years. 2016 was the most active year for new projects with 7 new projects coming online. As of March of 2018, there were 22 live mHealth projects.

With so many active projects, the report provides a foundation for decision-makers to better understand the role of mHealth in the National Health Information System and coordinate the efforts of partners. It is already being used to provide recommendations for the development of the new National eHealth Strategy and is a valuable tool for communicating with stakeholders on the successes and challenges with mHealth deployments. Some key questions we’ve been asking based on the results of the landscape analysis include:

  • How do we do a better job of coordinating mHealth in Malawi?
  • How do we minimize duplication of efforts among partners?
  • Where should we invest mHealth resources to see the most impact?
  • What standards are necessary to achieve integrated service delivery at community level with mHealth?

Enjoy reading! And stay tuned for the results of the technical deep dive report from the Kuunika Project, where we go under the hood of several mHealth projects!

Comment

Comment

"America First" & Foreign Aid

At the end of a long, dusty red dirt road bordered by fields of maize sit several huts owned by Luzi Orphan Care. This community organization in rural Malawi receives around $500 a year to care for children orphaned by AIDS. Five hundred dollars go a long way here. We know this because we saw the organization’s meticulously kept records: Binders of crisp white pages, filed neatly in boxes, recording how those $500 were spent and how many orphans received care.

We asked the group if they needed anything. They didn’t request more money. Instead, they asked: “How are we doing? How does our work compare to others? Are there things we could do better?”

Luzi Orphan Care Community Organization with Tyler Smith (Cooper/Smith Co-Founder)

Luzi Orphan Care Community Organization with Tyler Smith (Cooper/Smith Co-Founder)

Their questions were instructive. For too long, foreign aid programs measured their worth based on the size of their budget, not on the results achieved. Fortunately, that is beginning to change.  Instead of using last year’s funding amounts to justify budget requests, organizations as large as the U.S. government and as small as Luzi Orphan Care are looking to accomplish more with existing resources. 

This interest in effectiveness and efficiency is being demonstrated across the foreign aid world. Increasingly, organizations are focusing on using data, evaluations (such as randomized control trials) and advanced analytics to measure results and demonstrate value for money.  And they are deploying the power of technology to pinpoint where development interventions are needed.

This revolution arrives just in time.  With the Trump Administration’s proposed budget portending fewer resources for foreign assistance, providers must accomplish as much as possible with available resources.  In addition, better data about what well-run programs can accomplish, will help persuade the public that foreign assistance is a wise investment.

Donor governments and aid organizations should take three basic steps to generate that data:

First, every aid program should be accompanied by a rigorous framework to assess whether it achieves its goals. Backward-looking evaluations after a project’s conclusion are not enough; at that point, the donor has a powerful vested interest in ensuring that the project is deemed a success. Under those circumstances, without a pre-determined baseline against which to measure the results, a passing grade is virtually guaranteed.  That kind of evaluation tells us little about whether the project achieved its intended goals or delivered good value for money.

There is a better way.  Instead, before the project begins, donors and implementers should agree on the metrics, or “indicators,” they will use to measure the project’s effects.  The next step is to gather baseline data for those indicators and set clear targets for project success.  With baseline data and pre-determined indicators and targets, donors and implementers can rigorously measure what a project achieved.  

Second, development professionals need to collect the right data—and sometimes that means collecting fewer indicators. One of the downsides of the drive to show aid effectiveness is that both donors and implementers have become overwhelmed with indicators.

For example, in a recent study in Malawi, we found that health workers collect more than 3,500 different HIV-related data elements. This means that staff at already-overburdened health facilities spend, on average, several days each month filling out reports. This takes time away from patient care and leaves little time to use the data to improve the clinic’s services.

Data collection at a health facility in Malawi

Data collection at a health facility in Malawi

One way to reduce this burden is for donors to better coordinate the indicators they ask governments and organizations to collect.  An African health system supported by five separate donors should not have to collect five slightly different datasets to respond to each donor’s requirements.

Third, donors must analyze and use these data to better tie resources to results. Some implementing partners provide great value for money; others do not.  For example, in countries receiving support from the President’s Emergency Plan for AIDS Relief (PEPFAR), we found that the unit expenditure to deliver HIV treatment varied substantially.  Fortunately, a relatively new technique called Expenditure Analysis allows PEPFAR to explore why it costs more or less for different organizations to deliver similar results. Sometimes there is a straightforward reason for the higher cost: the work takes place in a remote area, labor costs are higher, and so forth. Sometimes it is a warning sign that something is amiss. This information allows planners to identify best practices that can be replicated elsewhere.

Today, the political winds are blowing against foreign aid.  In this challenging climate, donors, implementers, and advocates should continue to mount a principled defense of foreign assistance. But pushing back against budget cuts is not enough: Like Luzi Orphan Care, we should strive to do better with the resources we already have.

Hannah Cooper and Tyler Smith are co-founders of Cooper/Smith, a DC-based startup that uses data to increase the effectiveness and efficiency of development programs.  They previously served in the State Department’s Office of the Global AIDS Coordinator.

Comment

Why is data important? Because it makes people count.

Comment

Why is data important? Because it makes people count.

While I was at a facility in Lilongwe this week, piloting a study about data use for the Kuunika project, I was inspired to create a little photo essay. I asked some of the facility staff a very simple question: “Why is data important?”

Each person’s eyes lit up when I asked them this question. Having just participated in the pilot for our survey, they were very engaged and really wanted to discuss the importance of data and how to improve data systems.  

So, I wrote down the first thing they said in response to my question and asked them to hold it up for a photo:

Facility staff in Lilongwe holding up why they think data is important. Staff include nurses, clinical associates, data clerks, health surveillance assistants, and our data collection staff.

Facility staff in Lilongwe holding up why they think data is important. Staff include nurses, clinical associates, data clerks, health surveillance assistants, and our data collection staff.

Although I’m typically sitting in a room full of people who all care deeply about data, getting to why they care can be more complex and nuanced than you might think.  I’ve worked in global health for several years now and have been focused on data systems, but I’m guilty of taking this basic question for granted.  So, this also got me thinking about the question. Why do I think data is important?

For me, data is important because it makes people count.

It makes the patients who come to the facility and are entered into the register or electronic medical record system count. It makes the work of the healthcare workers count. They spend hours caring for patients and tallying the numbers of patients they’ve seen. The tally sheets are sent to decision-makers who are challenged with figuring out how to treat large numbers of people in an equitable way despite resource constraints. It makes the decision makers at the district, national, and international levels accountable to the people whose lives they are trying to make better, people who might otherwise not be counted.

Data is important because it represents people who are important. At the end of the day, behind those numbers are people that matter. People who come to the health care clinic for services that they desperately need to stay alive. These people are important, and that is why data is important.

-- Andrea

 

 

Comment