Feeding Remote Patient Monitoring data to a clinical dashboard from Atomik Server (#openEHR Platform). 👇👇👇
Why spending hours integrating data into your apps, dashboards and reports, if it can take minutes?
https://t.co/VzjjY1Pbes
Working with #openEHR is easy with the right tools. Here's how to save time creating vital signs monitoring data queries in Atomik. https://t.co/MZuJuIOXkd
@Cubotphone The speaker location for the KINGKONG X PRO is quite bad, sound is terrible when the phone is lying down, the speaker is totally blocked! I have to do this to actually listen something...
Feeding Remote Patient Monitoring data to a clinical dashboard from Atomik Server (#openEHR Platform). 👇👇👇
Why spending hours integrating data into your apps, dashboards and reports, if it can take minutes?
https://t.co/QlCmPQWxy1
Feeding Remote Patient Monitoring data to a clinical dashboard from Atomik Server (#openEHR Platform). 👇👇👇
Why spending hours integrating data into your apps, dashboards and reports, if it can take minutes?
https://t.co/VzjjY1Pbes
How to execute a #FHIR Search on top of an #openEHR server?
Here I have Atomik Server (https://t.co/x9fdgD5AC6) running, with a stored query to retrieve patients by partial names with a FHIR facade on top.
Check the video for more details: https://t.co/8j5JxkpcDD
@claudeai was generating UML diagrams using mermaid format, though it failed rendering for it was using the old notation for the relationships. Then, maybe a week ago, started generating UMLs as SVGs which do not render at all. Now I'm asking explicitly to generate just text.
This brand @Netac10 mentioning reliability and stability on their HDs. Fact: an importer sold these discs and the 960GB ones ALL FAILED, yes ALL. They changed my disc three times and tested 10 more, all failed. Then I moved back to the reliable Kingston KC600 (not the SA400 crap)
𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝘂𝗹𝗲 𝗘𝗱𝗶𝘁𝗼𝗿: 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗟𝗼𝗴𝗶𝗰 𝗳𝗼𝗿 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 I'm working on a Rule Editor for our Rule Engine that brings practical power to healthcare decision systems. Here's a look at our simple but effective logic rules.
𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝘂𝗹𝗲 𝗘𝗱𝗶𝘁𝗼𝗿: 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗟𝗼𝗴𝗶𝗰 𝗳𝗼𝗿 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 I'm working on a Rule Editor for our Rule Engine that brings practical power to healthcare decision systems. Here's a look at our simple but effective logic rules.
Is @AnthropicAI actually giving any financial support to @Wikipedia based on it using WP to train models? See the job description from AnthropicAI: "Making a Wikipedia dataset in a format models can easily consume" https://t.co/YQ8JgycKrg
Is @AnthropicAI actually giving any financial support to @Wikipedia based on it using WP to train models? See the job description from AnthropicAI: "Making a Wikipedia dataset in a format models can easily consume" https://t.co/YQ8JgycKrg
In our new #CaboLabs Rule Engine, you can get values from HTTP resources, process them and assign them to local variables, allowing all kinds of processing of data taken from REST APIs like #openEHR and #FHIR.
A small update on our @CaboLabs Rule Engine: we have the basic rule logic with HTTP data "resolvers" working, also a basic rule format and parser. Though the rule model is complete, we are working on translating that to code. As an example, a rule looks like this: