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. 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. Hope it helps, 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
