Op 13-jul-2007, om 11:18 heeft Philippe AMELINE het volgende geschreven: > Hi Stef, > > You say that "it?s important to install quality assurance systems that > prove that data is trustworthy" and "I disagree with the suggestion > that > it will become an infinite loop". However, the "archetypal" example of > quality assurance, the Deming Wheel (or PDCA cycle) is essentially an > infinite loop. > > Its means that "quality" is not something you can establish, but > something you must continuously be looking for. I agree partially. It's true that you re-evaluate quality based on the feed back that you receive and changing insights (the PCDA cycle) but that doesn't mean that it's a moving target. Once a good sense of quality is established by all parties involved and the underlying insights don't change (f.i. by new lines of research), a quite stable situation can occur. Here in the Netherlands we've a lot of experience with quality protocols to asses common ailments in the GP office. Some of these protocols are stable for over 5 years now. So yes the quality cycle is infinite but the work to create and maintain such a system not. > > In a Plan-Do-Check-Act cycle, the global process is made of several > parts, for example: > - an organization who elect quality criteria and sets target levels > for > these criteria (Plan) > - an EHR to record information (Do) > - a data mining process to compare quality assurance objectives vs > actual data (Check) > - an organization who study the actual achievement and decide what > happens next (for example certify some entities and excludes some > others) (Act) > > As you can see, the EHR is one of several components here, and his aim > is "just" to store information to be sent to the data mart, and > maybe to > help knowing who is certified and who is not. > > Just to say that, unless you build a comprehensive PDCA system that > includes all these components, the EHR by itself is not supposed to > manage such a process.
I agree, the thing I'm looking/asking for is that we can store additional data required for a quality system. I realize that, especially in the beginning, quality systems wills only be used in a few (sub)processes and probably will never aply to all of them. As Ian McNicoll points out, the cost/benefit factor will kick in there, but i totally agree with Thomas that in the end it's all about quality of care we provide to the patient/citizen. > > Hope it helps, Absolutely, thanks, stef > > Philippe > > Stef Verlinden wrote: >> Thanks for all the responses. I?m glad I?m not the only one >> struggling >> with this. >> >> If I try to summarize what I?ve learned form these responses is that >> there is ?real? data (although that also can be subjective scales >> f.i. >> for pain) and there is a ?personal? interpretation that can make the >> data ?trustworthy and therefore re-useable by others. >> As far as I?m concerned my questions aren?t about accuracy itself. >> That part will be taken care of by the certifying instance. A device >> can only be certified if its accuracy is within a certain tight >> bandwidth. They?re also not about modelling data reliability. They?re >> about modelling trust by applying quality systems. >> >> My point is that if we?re not able to provide ?trustworthy? data for >> large groups of healthcare providers people will only use data >> provided by themselves or their ?trusted? peers. All other data will >> be ?rejected?. The latter will even become an argument against the >> use >> of EHR?s when healthcare providers will be forced to ?compete? for >> efficacy. In order to maintain an affordable healthcare system >> healthcare professionals will be forced to re-use data generated by >> others. This then could result in (the perception of a) lower income >> for the ?user? of that data. In such circumstances ?lack of trust? >> will be a strong argument against re-using data. If data can?t/ won?t >> be re-used what?s the use of an EHR and why should society invest >> in it. >> >> Therefore it?s important to install quality assurance systems that >> prove that data is trustworthy. Luckily there is a lot of experience >> with such quality systems, for instance in clinical research used for >> the development and testing of potentially new pharmaceuticals (GCP, >> GMP, GLP). Also in most clinical laboratories quality systems are in >> place and working well. >> >> The crux of a quality system is that al parties involved agree upon >> what the criteria are for ?something? to become reliable. I agree >> that >> it can be cumbersome to establish those criteria (although in many >> cases they?re already known, f.i. see the NHS criteria for ?self >> blood >> pressure measurement at home). I disagree with the suggestion that it >> will become an infinite loop, with the possible exception of >> experimental and non-regular processes. In a normal situation every >> aspect in a process has criteria and those can be described. If they >> can?t be described one should seriously reconsider what he?s doing. >> Without criteria it becomes a lottery: the lucky ones win something, >> but most people will leave empty handed. >> >> >> So I guess I have several questions, which I try to separate. >> >> The first part is: how/where can we add the accessory data that >> necessary to establish data quality. As Thomas pointed out, most of >> these things are already recorded. In would like to have clarity >> on to >> things: >> >> 1. device information (calibration, maintained etc, etc.). Thomas >> suggested that one should store those in the protocol part of an >> observation. That?s a possibility but then one has to fill out that >> data every time an observation is made. Imagine a GP that performs 20 >> blood pressure measurements per day. I?m certain that that?s not >> going >> to work. >> Sam suggested that you could re-use this data once it?s registered >> once (and unchanged) with a cluster. Sam can you please explain how >> that works/ could work. I don?t really understand that cluster >> concept >> well enough. >> >> My suggestion to create a separate archetype (similar to the >> demographic one) originates from the idea that (especially in >> hospitals) devices will be maintained by a technical staff (third >> party). This could result in a situation where a health care provider >> doesn?t know all this details about calibration and maintenance and >> therefore is unable to provide that data. This service person should >> be the one to record data for that device. >> On user interface/ application level the healthcare provider will get >> a reminder if (based on the data in that device archetype) the device >> isn?t calibrated/maintained properly. In that case the device >> shouldn?t be used, because its ?outcome? isn?t trustworthy. >> >> So if we can do all of this through a cluster and everybody feels >> that?s the way to go, it?s fine with me. If not I still would suggest >> creating a separate device archetype. >> >> >> 2. capabilities of a non-professional, f.i. to determine whether >> someone is capable of self-measuring x, y or z. Here in the >> Netherlands patients determine their INR by themselves at home and >> adjust their medication accordingly! One can imagine that you want to >> test the capabilities of that person rigorously on forehand. If they >> pass that test, they?re capable I my suggestion is to store that >> capability a part of the demographic archetype. Thomas stated >> ?whether >> the person who did the observation is professionally capable of >> >> doing it is another matter not really controllable by the ICT alone >> >> (other than by access control).? This remark I don?t understand >> either. Thomas can you please elaborate on that. Form what I >> understood form the demographic IM , there is a capability class in >> which ? the qualifications of the performer of the role for this >> capability?. I guess my question is, is there a role self/ citizen/ >> non-professional (I don?t know how we should call that role) and can >> we attach capabilities to that role? >> >> The second part is about the data quality criteria. Although these >> criteria are subjective, they?re real and useful for the parties who >> agreed upon those criteria. Look for instance at the ?report of the >> independent advisory group on blood pressure monitoring in clinical >> practice? by the MHRA. This is an example of quality criteria one >> could agree upon. Based on such criteria one could rate the recorded >> data as good or poor (or even an more refined scale if required) >> Now my question. Where can/ should we store such a data quality >> marker. I figured that since these are local/subjective criteria you >> don?t want to put them in the original/ root archetype that?s >> intended >> for ?worldwide? use. So my suggestion is to create a ?local? >> specialization of this root archetype in which both the criteria and >> the data quality label are added (see my previous mail). >> Is this a good suggestion and if not where can I store that data >> after >> all. I don?t want to create a second system for this and in my >> opinion >> this type of data belong somewhere in an EHR. >> >> >> Cheers, >> >> Stef >> >> --------------------------------------------------------------------- >> --- >> >> _______________________________________________ >> openEHR-clinical mailing list >> openEHR-clinical at openehr.org >> http://lists.chime.ucl.ac.uk/mailman/listinfo/openehr-clinical > > _______________________________________________ > openEHR-clinical mailing list > openEHR-clinical at openehr.org > http://lists.chime.ucl.ac.uk/mailman/listinfo/openehr-clinical
