Deep dive into SENAITE laboratory information management
When I was previously tasked with implementing DHIS 2 for surveillance I must have spend hundreds of hours learning about it and playing with it, both in work and in my own time; the last several months I have been going through a similar process with SENAITE the laboratory information management system; I have been documenting things here as I go, such as installation, modifying functionality, styling printed reports and the myriad other steps involved in implementation; this week we should be “soft launching” it at the lab we are working with to see what they think and make any further changes required; I am confident we have chosen the best open source option, but there is further work to do around microbiology/AMR reporting, SMS reporting and integration of the data with DHIS 2.
SENAITE API and secure report sharing
Related to that LIMS work, I have been familiarising myself with the SENAITE API (WIP) and also working out how to send secure links for patients to download their own lab reports (using Bitwarden Send).
Revival of interest in Python
Not unrelated is the revival of my interest in Python: as well as reading a massive Python book and delving into the innards of SENAITE I’ve been playing with various Python apps.
Books and causal inference
Finally finished a massive German book that’s been on the shelves for years, and also started a good MOOC on causal inference, to follow on from a good causal inference book I finished.
Blog improvements
I did a bit of work on the blog recently, upgrading it, adding a Creative Commons licence (you can choose your own licence here) and automating the process for creating RSS and Atom feeds using RSS Anything; also added buttons so you can copy code sections.
Bayesian statistics in practice
We actually used Bayesian statistical methods in an outbreak! and it was easier in several ways than doing it the frequentist way; I’ve been supporting another research project which is using JASP, though not in a Bayesian way.
Interesting developments in data science tooling
Continuing to monitor interesting developments in the data science tooling space, such as Vapour, a “better R”; Positron, a “better RStudio” (more here); Typst, a “better LaTeX”; and hayagriva, a “better BibTex”.