OpenMRS implementers meeting 2011

I'm pleased to announce the OpenMRS implementers meeting  of 2011. 

This is an annual conference where OpenMRS contributors and implemnters meet up to share ideas, make plans and to improve ourselves and our community.

This years conference will be held in Kigali, the capital city of Rwanda.  I am able to attend because the OpenMRS project has kindly come forward to sponsor my visit. They will be taking care of my finances.

I am really looking forward to the meeting, because I will get to meet a lot of the best people in the industry. this is sure to be a great learning experience for me. Hopefully, I will also take part in the hackathon held prior to the event.

It'll be great to meet the Dev group, Ben, Darius, my dear friend Daniel, Michael and Rafa. I will also meet up with some other people from Ampath, PIH and Eldoret.

Dr. Mamlin and Dr. Biondritch will also attend. So will Dr. Hamish Fraser and Dr. Shaun Grannis of the Regenstrief institute.

It'll be great to meet all these people for the first time. 

Unfortunately, my mentor Glen McCallum will be a notable absentee at the event, so I wont be seeing him at Kigali.

PS: Not to forget Dr. Joaquin, who has a PhD from HST. He has been one of my major advisers, so I'm looking forward to meeting him as well.

See here for further details on the meeting.

A few of my early ideas

Pasted below are a few of my earliest ideas for health informatics related projects. I'm posting them here for future reference. Hopefully, one day I'll be able to implement much better ideas....

I’ve been studying syndromic surveillance recently.

I studied this [0] document, which addresses shortcomings in syndromic surveillance.  It may be rather outdated, but I feel that it raises a strong argument regarding what we’re doing wrong, and what needs to be improved.

Tradeoffs in many surveillance systems are

1. Sensitivity
2. Timeliness
3. False  positive rates

I’ve also considered,

Is it easier to work with more data, or less data? Can it be useful, or isn’t it worth the effort?

Another major objective in my mind-

You don’t need a system that will take up much resources, and finally say “ok, we calculate that you have an epidemic happening right here, right now”.  Instead, we need something that says, “I’m detecting patterns which indicate a 50% chance of epidemic proportions in location A by next Monday.  Based on these results, you need to keep an eye on Location B as well, since I’ve detected emerging patterns over there too.”

Syndromic surveillance tools should be able to identify any emerging illness pattern, not just biological attacks.

Ultimately, there is a limit to what an artificial system can do.  I believe that the final decision regarding whether or not to issue an epidemic alert should lie with a medical practitioner. The surveillance systems’ purpose it to provide the practitioner with meaningful information that will help him take a decision. Nothing more, nothing less.

Decentralization

What are the tradeoffs between decentralizing and converging of syndromatic surveillance?

What’s the impact of considering data from a wide range of locations if we’re dealing with an epidemic that’s spread across a very small demographical region? Will this help us, or will it cause problems?

In my opinion, centralizing, or converging all health data into a single processing system encourages the possibility of a single point of failure.

On the other hand, de centralizing will improve the system by offering it not one, but several chances to succeed. It will also let us create a tool that will support both pandemic and epidemic surveillance.

Another point – check out this [1] an article on ‘Rapid detection of pandemic influenza in the presence of seasonal influenza’

My question is, how do we identify if it’s just a seasonal ‘spike’ or an emerging epidemic? And how do we map symptoms to specific illnesses, and make that match before it’s too late?

So how to detect an emerging outbreak?

I came up with the following diagram to track patients and their symptoms for hospital A.
The three types of images given here depict different symptoms.


Assume the cases of Sam, Jack, Jim and Julian.

On Day 2, Sam, Jim and Julian all develop symptom A, which is associated with flu. So does this mean that we are facing an influenza epidemic? Maybe, maybe not. But on day three, Sam and Jim both develop symptom B. So now we have a pattern.

We also note that Sam and Jim go on to develop Symptom C on day 5. So this would definitely mean that Sam and Jim are suffering from the same illnesses.

But consider Jack. He developed symptom B a day later than normal. So what are his chances of suffering from the same illness as Sam and Jim?

And also, it seems that Julian has developed symptom A and C, but not B.

So based on this, and assuming that the symptoms are for flu, we could hypothetically come up with the following.

• Julian is suffering from ordinary flu
• Sam and Jim are suffering from Influenza.
• Jack is possibly suffering from Influenza.

So now we have a pattern, and we can stay alert for it. This pattern can also be used together with mathematical disease modeling to watch for outbreaks at other locations as well. This works well with the centralized (converged) vs. decentralized surveillance model I described earlier.

For example, assuming we have five data sources (hospitals) and one central surveillance unit.
If the unit fails to identify an epidemic incident in source A, it will essentially fail to identify similar cases of epidemics in the remaining four sources as well.

But if de centralized, we are improving the chances of epidemic detection by five times.

Example: assuming that our system will detect an epidemic in source A, but fail to do so in other four sources.

Once the pandemic is identified in source A, the unit will extract a set of core ‘definers’ that will be sent to the other units for further processing. Each of the other centers will do a backwards check to see if there is an indication of such a pattern in symptoms reported to them.  Not only do these centers get a second chance to check for mistakes, they also get a pre warning if an outbreak is imminent.

And assuming that a disease has only just infected the region, a different location may have only received reports of symptom A so far. If so, due to the indicators sent from other regions they will be able to make preparations for a possible outbreak depending on the emergence of symptom B as well.

We may even share the core definers with other hospitals to do a wider ranging check.


[0] http://www.rand.org/pubs/research_briefs/RB9042/index1.html
[1] http://www.biomedcentral.com/content/pdf/1471-2458-10-726.pdf

My top OpenMRS videos

OpenMRS Co- founders Dr. Mamlin and Dr. Biondritch at Google tech talks. This video explains the original idea behind OpenMRS, and how the project actually got started. Its long, but very much worth watching.

This is a good place for all OpenMRS newbies to start. Its a very basic video that runs through how to set up OpenMRS environment and get started. I only wish I had paid it more attention to it at the time I was starting up for the first time :-)

Welcome and Introduction

Welcome, dear visitor !

I plan to use this blog as a public diary on my path to improve my Health Informatics knowledge.

Health Informatics has been a long term passion of mine. Unfortunately, my home country does not have much to offer in this field, so I've decided to step out on my own, and to self educate myself to the best of my ability.

This blog is an attempt to record my 'Education', and will (hopefully) be useful to others who trod behind me.

My 'improvement' will be a community driven and long term process.

But be warned, Real work experaince will be my exams, and my friends in this industry will be my tutors

Where this journey will take me, and how far I can achieve my goals, only time will tell.