Derek, that sounds like a good way to go. We have already started to break these out by the types of transactions that we currently have in Rwanda. I'm going to try get in touch with Randy (from Liz's suggestion) as he can provide us with more detailed data for Rwanda.
Here is what we have so far, working from what Shaun put together previously for RHEA: Transaction volume estimates (from Shaun) - Google Sheets
I've included Larry's suggestion that load won't be evenly spread out but we will likely encounter 'peak time load'. I've estimated this as a factor of 3 but does anyone have a better understanding of how much load will increase from the average during peak time in a clinical setting.
Cheers,
Ryan
On Tue, Mar 12, 2013 at 3:48 PM, Hannes Venter <hannes@jembi.org> wrote:
Thanks Derek, a really good thought I really like the idea of basing this on care guidelines.
And then like Ryan said; we can then perhaps focus on the stat data for some select countries such as Kenya, Tanzania, Zimbabwe and Rwanda.
Cheers
Hannes
On Tue, Mar 12, 2013 at 3:28 PM, Derek Ritz (ecGroup) <derek.ritz@ecgroupinc.com> wrote:
Hi all.
My sense is that extrapolation could be based on:
1. the care guidelines we are operationalizing
2. the size of population that is enrolled in the care plans
In this way, we would be able to estimate transaction volume profiles based on, for example:
· X pregnant women, each of whom will attend 4 ANC visits plus a delivery visit
· Each ANC visit will include an ID resolution (PIX) transaction plus Y clinical readings (weight, BP, temperature, etc.); the first visit will also include a history and a lab result transaction
· Proportion P of these women will have complications requiring R acute care referrals & visits
· Proportion H of these women will be HIV+ and will require A ARV prescription orders & fills
· Proportion T of these women will suffer from TB and will require D drug orders & fills
· Etc.
Developing a profile based on recent USAID survey figures regarding burden of disease will enable not only transaction volumes but transaction types/characteristics. Since these survey figures are typically broken out by province, it could even indicate our load profile by province (if we felt like being tricky about our estimates).
My point is that a set of algorithms which estimate transaction volumes by care guideline (maternal, HIV, TB, Malaria, IMCI, etc.) and by population and proportion could be readily scaled for any region/population for which we have survey data (www.statcompiler.com).
Just a thought…
Derek.
Derek Ritz, P.Eng., CPHIMS-CA
ecGroup Inc.
+1 (905) 515-0045
www.ecgroupinc.com
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From: openhie-shr@googlegroups.com [mailto:openhie-shr@googlegroups.com] On Behalf Of Hannes Venter
Sent: March 12, 2013 6:50 AM
To: openhie-shr; openhim-dev@googlegroups.com
Subject: SHR Transaction Volume Estimates
Hi Everyone,
We've been looking at creating some performance indicators for the OpenSHR and the OpenHIM.
One such indicator would be the estimated work load for a national SHR,
and we've been using the Rwandan transaction estimates that Shaun created
(the figure of which is around 8 transactions per second for national rollout).
One question though is should this figure be extrapolated for some "generic use case"?
In other words: would it be valid to say, we can handle 8 transactions per second for
Rwanda's 11 000 000 people, so we should test against X transactions per second
for a population of 44 000 000 (Kenya's national population for example)?
One problem we see with extrapolation though is that a national shr in Kenya (once again e.g.)
might not look like a national shr in Rwanda, they might want to partition the system by province, or something like that
(the point being we don't know what a Kenya shr would look like...).
I.e. the question is whether we can assume such an extrapolation is valid?
Or would it be the case that the minute you start scaling beyond "rwandan"-workloads, you need to think about the architecture differently?
And if this is valid: what should the "target population size" be? Is the target Kenya, Tanzania? ... USA?
Our current feeling is that we should just focus on the stats we have,
and basically focus on Rwanda as a performance test case for any SHR/HIM systems we evaluate.
(Of course this certainly wouldn't mean that scalability is never taken into account,
on the contrary, it's a very important aspect to look at)
All thoughts and opinions on this topic are highly welcome
Kind Regards
Hannes
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Hannes Venter
Software Developer, Jembi Health Systems | SOUTH AFRICA
Mobile: +27 73 276 2848 | Office: +27 21 701 0939 | Skype: venter.johannes
E-mail: hannes@jembi.org
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Hannes Venter
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