large space

based on logic service (ARDEN Syntax) which became usable in that last here.

used by Paul's CHICA (?) System

Martin (AMPATH) is working on taking critical summarires and driving them through the logic service and provide clinicians with information

Paul: Clinical reminders are transformations of data
	eg: Hemogloben of 8, MCV of 5(?) for a child means iron deficient

	Reminders can be string of text, other values or a combination of both

Burke: Goals are to allow the system to treat

	Birdge the gap between computers and clinicians/researchers

	Answers to real questions are almost always derived concepts

	Is asmathic? answers: asmatic diagnosis? been to astma clinic? are they on treatment? peak flow below threashold?

	Idea: define a derived concept which encapsulates the rules

	Make derived concepts as available as standard concpets

	Make it possible for non-programmers to define rules

	Diagnosis suggestions

Burke:

	Decision support can be thought of broader as just logic service

	Aim to help the users along the way

	HTML form entry validation is a form of decision support

	Conflicting prescriptions (counter indicaitons) alerts 

	Prescription suggestions is a form of decision support (makes things easier)

		e.g. In Burke's hospital the changed a default dosage of Antibiotics from twice to once a day when the literature changed, and had the desired effect

	Decision support also means prividing the correct options to guide care practices

	Need to think about how we maximize our effect


Martin (clinician) about changing behaviour in Eldoret:

	Use systems to collect data

	Has not happened: clinicians getting the data back

		-> They want to see the information as a flow sheet or clinical summary

	Challenge: Show data to clinical !! (The report vertically fine)

	Next step to use data to improve care

	Look at standards and protocols and use system data to provide alerts

		e.g. Haven't got CD4 could, prescription contra indications, various logic rules

	REASON: Mostly clinical officers and NOT clinitians, so decision support WILL improve care

	AMPATH: Coded deision rules in java

		Found -> rules aren't followed
			e.g. no CD4 count for many patients (or old CD4 counts)

		Privided clinical summaries based on hard coded rules

			Included alerts
	
		when from 35% complience went up to 60-70%

		bring providers to the same leve

	Easier to hard code for a few rules

		for 200 rules not so easy?

		and there are many rules that could be applied to improve care

	
	Thinks can prove to the world to increase patient outcomes, and reduce the cost of care and increase quality of care even for non-clinitians

	
	Need to decouple from concept_id's to make more robust and reusable

Paul:

	Martins work is part of clinical trial

	Now have full-time developer (Win?) working on logic service

	Challenges: when clinicians approach developer to write a rule they think about coarse idea's

		seems on the surface to be simple, but defining things depend on how ppl practive medicing

			e.g. finding ppl who are HIV+ may be difficult
				could have a diagnosis, could be based on treatment, might be assumed for everyone (e.g. in an HIV clinic)

		necessary to consider the complexities

		need a way to document within the system

	Paul has a grant and other funding to improve logic service, with is being used in production

	Making it easy to write the rules is difficult

		Don't want clinicians to have to learn Java

	ARDEN is a language specifically used to represent medical logic rules

	Collegue made a parser that converts ARDEN to Java


Roger Friedman:

	[CDC] Used to having well-defined cases and dont want fuzzy case logic

Paul:

	Need to match logic to vagueries of how people document medical care

	AMPATH: Clinitians taking care of 60 patients in a half-day

		Difficult to document acurately always : high burden

	Have to make a assumptions have vagueries

	Trying to extract clinical knowledge is where rubber hits the road

	Similar problem for indicators -> rule is written as OR's (e.g. for ppl on antiretrovals)

	Would like to use rules to compensate for human recording bias

Burke:

	"Life is hard" - Burke's mentor Clem

	Simple rule: every woman over 45 should have a mamogram has complexities

		e.g. had mamogram in external system, had bilateral masectomy, only has 3 months to live

	Have to accept things don't happen according to plan

	So we are trying to define the rules and apply as best as possible and realising that sometimes there are going to be exceptions
		with the goal of providing clinicians with useful information


	Loop will be self-correcting because if rules are broken, hopefully the pattern on care wil be correct in the next iteration


Chris:

	Interested in how treatment failure will be affected by this

	Based on clinicians decisions to change regimens

	Requesting whether decision support speaks to this (genotyping)?


Martin:

	Thinks logic service should help by alowing you to write rules to define
	criteria for treatment failure


	Logic should be generic framework

	Can use every piece of data stored in the system (adherence, drugs, labs, etc)

	You can use this data to write the ruels


Paul:

	Key for genotypic inforation: get into the database like other data

	Then write rules taking this into account

Chris:

	Can you do data mining?

	
Paul:

	Maching Learning?

Chris:

	Ben building SVM to look at outcomes and genotypic information

Paul:

	Dont have machin learning, but can use ouput of something like that


Isaac:

	Asked about statistical approach, maching learning?


Evan:

	Was asked about Boabab and Mateme for (NDH and PIH)


	Knew workflow of creating a new treatment takes long

	
	So they asked clinicans to link treatments to diagnosis

	Show list or treatments ordered by frequency

Martin:

	Frequency is good in some cases -- unless a lot of them are doing it wrong


Evan:

	Want to be able to remove things from list
	
	Want to be able to move to something that is editorialised

?:

	OMRS focused on person level

	Epidimiology looks are person, place, time, agent

	Family doctor able to make decision while also consider context (e.g. context)

	Asked how context information can be used to assist decision support

		to make brige between family doctor style and mass care

Burke:

	First step is to get the necessary data
		
		interview with social worker or nurse to get context

	PRojects in place to get aggregate data to systems that are good at epidemiological analysis

?:

	So can logic serive do this stuff with the data?


Paul:

	Local context has large influence on how health-care is practiced

	Local variance is key to how things are done

	Rules are difficult to generalize outright

		Local tweaks will NEED to be done for treatment success

	Thats why local capacity is important

Hamish:

	1.	Machine LEarning

		Working on collecting data on TB drug resistance

		Will be doing machine learning

		Will include geographical and sociological factors

		Illustrates overlap of clinical treatment and other factors

		Challenging to collect data thats why nurese or social workers are used


	2. When buiding DS systems

		Subtle art to scale up from a few rules to many rules

		ppl get annoyed with lots of rules and then ignore them

?:

	If we have default concepts can we get nice trusted rules by default

	
Paul:

	Thought about this

	if ppl share conpepts, derived concepts will work

	higher level rules will be human understood (hiv positive)

	will still have to add local context

	will have to define where customization needs to be done

	even if tweaking is needed, still worth sharing

	sharing is good because it improve the generality of the ruels

MAtrin:

	Problem of sharing rules and reminders is pretty international


	There are funded projects which try to come up with ways to share ruels and reminders

	
	we would like to use those standed


carol(?):

	we need a holistic view

	contextual factors are critical to have usefule DS system


	OMRS value: access to the knowledge

	need transfer across languages

	transfer data through language and DS we will see clinical care improvement

	DS can be confusing

	will be nice to get help from DS from developed world


Darius:

	CAn we hide the complexity?

	can WHO (or others) help define a few crucial rules

Burke:

	We are trying to do that, and also with concept (OCC) and other standardisation attempts

	using collaboration and crowd sourcing

	sharing is needed

	when creating forms or concepts, first see what exists and try and use it

	rules should folllow

Andy:

	this discusiion is about symantic interoperability

	OCC allows us to define internal standard

	Using reference maps (SNOMED) need to be used for external interoperability



?:

	Philedelphia uses DS to provide clinical feedback

	can be aggregated up


chris bailey:


	last year talked about convening a meeting about clinical ontologies

	should consider it this year

	to make sure what we're doing is aligned with other efforts