Measuring OpenMRS Impact

We’d like to start working on how to measure the impact that OpenMRS is making on healthcare and patient lives. Currently, the only way we have ever been able to understand impact “in the field” is through self reporting from implementers, which has been widely varied in accuracy and consistency and types of data reported. The summary of the discussion so far is that we need to start with simple metrics and work on obtaining those accurately and consistently before adding more complex ways of measuring impact.

Since we agreed to start simple, here are a few proposed ways of looking at impact:

Implementation metrics suggested by @janflowers:

  • How many facilities have OpenMRS implemented? Stratified by service (HIV, TB, MCH, etc)?
    • Do we care which OpenMRS is installed?
    • Community versions or distribution distinction?
  • What countries have OpenMRS as a national system (need to define: what is a national system)
    • What services
    • how many sites
  • How many active patients are being tracked in OpenMRS (need to define: active, maybe we create the query for people to use to give us this info so it is consistent?)
    • Do we care about numbers of patients over the entire implementation? That is impact, but maybe not as relevant as the currently active patients being tracked?

Clinical impact suggested by @jteich:

Does the scope of this discussion extend to clinical/health impact measures? The measures that are proposed are mostly about OpenMRS operations: downloads, implementations, patient records – all very important to track. I would suggest that equally important, especially to public health agencies and funders – would be things like number of patients with recorded Tb treatment, HIV treatment; number with recorded immunizations and how many of those are up-to-date; implementation and use in health crises such as Ebola; and so on.

These are somewhat harder to measure in our environment, which has not forced medication and diagnosis standard codes; however, measurement is still quite feasible. The importance of these findings could be great for OpenMRS’ sustainability as well as to genuinely track impact on health.

Other interesting ways to look at impact:

  • How many jobs are created by OpenMRS being implemented
    • of IT folks employed to support sites

    • of implementation folks employed to support sites

    • of data managers employed to support sites

    • Etc
  • Costing evaluations – ROI
    • This ones a bit tricky, but Starley Shade at UCSF (my PI on the Mozambique work) has done extensive studies in this area in multiple countries with OpenMRS. So that might be useful to bring her into this type of discussion

There are two discussions identified so far as needing to happen in order to do these metrics:

  1. Metrics for evaluating the impact that OpenMRS is making on healthcare / patient lives
  2. How to gather and what to gather for metrics evaluating individual systems that could ultimately contribute to #1

Do we need to go through both of these discussions now, or can we prioritize one over the other for now?

For the second discussion, “how to gather and what to gather at the individual system level”:

@michael Just as a note, we are hoping to deploy a system as shown at for tracking internal community metrics. This system could also host and visualize impact metric data as well, if it can be continually collected (or probably even so if it is more periodic). So feel free to take a look at the above demo to whet your appetite and imagination on the types of reports we could to get long-term trends over time.

@hamish It is a challenge for OpenMRS to have such a limited knowledge of what people are doing with OpenMRS beyond the well known and usually long standing partners. I have a book on evaluation that said something like: “when you have no evaluation data even a simple study can give you a much better idea what is happening”. I found the views and downloads metrics that Michael ran for me a couple of years ago very helpful. They may have helped get me my fellowship :- ) “In the last 12 months there were 32,550 downloads of OpenMRS from 177 countries and in the last 30 days (August 2014) there were 31,714 web visits from 184 countries.” Michael and Burke and others have tried to get people to give us feedback on what they are doing but responses and rates seem limited. It probably requires a more active and hands on approach to surveying sites by email, phone call or site visit. This is the approach we have taken in Rwanda and Tanzania with EMR users. It will be interesting to see how many sites are functioning well when we install the server, usage and data quality monitoring software in Rwanda in a month or so.

@lober: can come in part from “instrumenting” openmrs instances - that’s a really good idea. . One thing we’ve had in Haiti from early on was a sense of how well the systems were being used. One think we’ve lacked in Kenya (despite planning for something better than we’d done in HT) was the same - direct measurement of performance and usage indicators. We built a module and visualization framework to do that, but always good to move to something someone else is developing and maintaining.

@janflowers Just to add to Bill’s comment about monitoring… @pascal and I worked together mentoring GSOC students over the past 2 years to create a module that measures performance and usage in the Mozambique implementations. We’re just getting ready to pilot that in an upcoming release and upgrade to the implementations. Maybe those types of tools can feed into what Michael is creating? On the flip side – I think atlas was intended to do some of that, but the data in atlas seems to be pretty inconsistent and unreliable as a concise measurement of implementations. Maybe I’m wrong about the intention of the atlas tool though…

@lober: We had a framework of metrics in Haiti.We improved it for Kenya and created a hierarchy of metrics - this is relevant to @jteich ’s comment. that hierarchy started with low level metrics - “can I ping the system?”, “is the system up?” I created 5 levels, as I recall, that went up to the kinds of measures Jonathan mentioned (level 4 - health services delivery). I think level 5 was outcomes. I can try to dig that up - it might be in the “PUMP” tools documentation though I’m sure it’s in a grant proposal. @janflowers has a handle on what tools were actually developed - I think we concentrated on level 1 and level 2 metrics.

@lober, @terry, @hamish, @jteich, @michael, @paul, @darius - more to add from our discussion so far, or is this a good enough summary to continue the discussion from here?


At least some of these 20+ articles and these 900+ results would qualify as impact too. I’d guess the number of research papers based on OpenMRS data is in the hundreds or thousands. It might be worth considering if we could get a sense of OpenMRS-specific and OpenMRS-powered publications. Perhaps a shared bibliography with an occasionally student’s effort to discover & add new entries?

The distinction between “actively treated” vs. “ever treated” is fine & important, but leaving out the “ever treated” would be a mistake, IMHO. Patients ever treated is easier to obtain, easier to define, less labile over time, and doesn’t vastly underestimate the impact of OpenMRS.

1 Like

I feel strongly that we should discuss the what first, before getting distracted by the how. To that end I created a different Talk topic about the how at Technical approaches to measuring OpenMRS impact.

I think these are a great start. And while I would like to eventually get to the clinical impact measures suggested by @jteich, I think we need to crawl before we can walk, and I would suggest focusing on the MVP of just the very broad statistics.

I think that this is useful information for us to know, but I think our KPI should be number of patients records managed in the OpenMRS platform (optionally/eventually: “in a good-quality installation”). For purposes of judging if we are doing well or poorly we shouldn’t be judging that KenyaEMR is better/worse than the OpenMRS Reference Application.

I do think it’s also worthwhile to have a second-priority KPI like “% of known OpenMRS patient records that are stored in a modern OpenMRS version” (meaning it’s a supported version of the OpenMRS platform, rather than an EOL version).

I agree with Burke that we shouldn’t try to get too clever with “active”. I would look at something as simplistic as “# of patient records with an encounter in the last 12 months” (and I would make this lower-priority than “# of patient records”).

2 Likes could always use some care & feeding! :heart:

I would also point out that as we push out common dictionaries which we should be able to monitor updates to, that we might 1) be able to use that data and 2) set up some standard queries which might identify some basic frequency distribution of use… for example the malaria concept was used in OBS 11200 times last year in x implementation…

The Google Scholar articles list is impressive, it digs out all sorts of interesting stuff a lot of which I am familiar with but other good stuff. Definitely worth using as a n evaluation metric. I would be interested in working with others in the community to analyze them and classify them.

I suggest that there are several dimensions of evaluation that are relevant to OpenMRS. I would not just focus on one but develop initial metrics for:

  1. deployment, usage, patient numbers, developer, modules etc.
  2. clinical usage and user experience
  3. clinical impact studies
  4. research impact both published studies regarding OpenMRS as a tool and studies based on data from OpenMRS (maybe harder to determine)

Analyzing the types of articles should give us a basic idea/starting point on (4)