**Transcript of Cyberseminar**

**Consortium for Healthcare Informatics Research**

**Systems for Helping Veterans Comprehend Electronic Health Record Notes**

**Presenter: Hong Yu, PhD**

**January 14, 2014**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact Hong.Yu@umassmed.edu*

Moderator:And we have reached the top of the hour so at this time I would like to introduce our speaker. We have Dr. Hong Yu presenting for us. She is a research health scientist at the VA Central Western Massachusetts and a professor in the Department of Quantitative Health Sciences in the Department of Computer Science at the University of Massachusetts Medical School. So we are very grateful for Dr. Yu for joining us and I will turn it over to you at this time. 

Dr. Hong Yu:Thank you so much and it is a pleasure to present at VA. So today as I said we are going to present some of the pilot work we have done for our system for helping patients and Veterans in comprehend electronic health records notes. So this is a collaborative work and so I am—my student Balaji Ramesh who have done most of this implementation so we collaborate with Cindy Brandt and the VA connected healthcare system and Sowmya Rao and Julia Volkman and Tom Houston from the Bedford VA and also Donna Zulman from the VA Palo Alto Healthcare System. And also we collaborate with Kathy Mazor from University of Massachusetts Medical School. The first I will—this is the outline so present some of the background and information and then I describe our NoteAid system and I describe three pilot evaluations and finally with a conclusion and future work. 

So it has known that patients reading their electronic health record notes and have the potential to enhance medical understanding and to afford improve of healthcare management and outcomes. And so the Department of Veteran Affairs (VA) and has developed system called Blue Button which enables Veterans to view, print, and download their own electronic health records and including clinical notes and which include progress notes. And so as of April, 2013 over seven hundred fifty thousand registered users and millions of Blue Button download requests and have been posted and since have been made available. 

So but then what are the challenges? So physician’s notes are usually difficult to comprehend because physicians, they write notes for their own communications. So in addition to that Veterans and as long as all the Americans in and many of them have limited health literacy. So for example, this is a progress note, randomly selected so the patient will be scheduled for a repeated EGD in one year for surveillance purposes of Barrett’s esophagus. And from a GI standpoint we recommend to proceed with bariatric surgery. However, he will need to continue daily PPI administration to maximize acid reduction. Otherwise there are no additional recommendations. The patient was treated with myocardial infarction. 

So here not only there are medical terms and but also their abbreviations which were difficult and for non-medical and lay people, patients, to understand. So what we try to do is that we try to develop a system called NoteAid is to assistant—to assist patients and to helping patients and comprehend their electronic health record notes. And basically what it does is automatically identify clinically relevant concepts from the notes and link the notes to their definitions and translate the medical jargon from notes into lay language the patient understands and link notes to other education materials. 

So here I—the system—the pilot system we developed is fully implemented and here I will actually do a demo, demonstration and so Molly, can you send the link? 

Molly:Yes, I will turn it over to you now, one second. 

Dr. Hong Yu:So here is—basically that if you go to www.clinicalnotesaid.org it will show a system called NoteAid. And if you click that and so if you copy the page and [paste the text] and so here is the—the progress note we show—we have seen earlier and so the click onto the simplify, and the system would output... would provide output. So in the output the system automatically identified those abbreviations and concepts and linked to those and to, for example, the application to the full expanded format and the full form. And also the definition. And so the other education material we have not implemented currently, only link to the definition. So can we go back to the presentation? 

Molly:Yes, one second. 

Dr. Hong Yu:But now I would like to have some poll questions and because I want to know a little bit about audience and who because later on I am going to describe our systems and also some of the evaluations. So I would like to know that about audience so I can , you know, for some area I probably have more focus. So what is your primary role in VA? And the second poll question is that which best describes your research experience? So it is informatics research or your research relates to natural language processing. 

Moderator:We still have the first poll question up so we will give people just a few more seconds to get those in. it looks like most of the responses have come in. So in our audience today we have about nine percent student trainee or fellow, six percent clinicians, keeps changing, make that eight percent clinicians, about forty-three percent researches, four percent manager or other policymakers, and thirty-seven respond as other. So thank you to those respondents. And we will go to the second poll question which as Dr. Yu was saying, what best describes your research experience: you have not done informatics research, have some informatics research experience, have some informatics and some NLP research experience, have extensive NLP research experience. 

And it looks like our responses are still streaming in so we will give people a few more seconds to reply. Just click the circle next to your response and you can submit that. It does help our presented gear the talk towards your level of experience. All right it looks like most of our audience has answered. We have right around thirty-eight percent who have not done informatics research, about half our audience have some informatics research experience, about eight percent have some informatics and some NLP research experience, and six percent of our audience identify themselves having extensive NLP research experience. So thank you again to those respondents. 

Dr. Hong Yu:Thank you. So the next step that I am going to describe very quickly in what is the NoteAid system.  Only three people—three percent of the audience have extensive NLP so I am just going to describe at a very high level what the NoteAid system does. So the NoteAid system concept we have implemented has two components. So one is what we call the knowledge resource. So the knowledge resource [inaud.]. And here is the NLP approach. This one and so here when presented with a note right here and we showed earlier this note and we presented it into the system. So the first the system identified a concept and in the note and then the second step would link to the concept and to the knowledge resource and research the links, the concept, and so basically link the concept to the knowledge resource. So here our goal is not only just provide the definitions but also link to other education material that patient will be interested in. 

So our long term goal is to provide personalized... the system tailored to patients’ needs. So because we believe that electronic health records of this specific to a particular patient so that is pretty much like a fingerprint of the patient, it is a blueprint of the patient in terms of the medical problems that patient has. So and by tailor the information to electronic health record for each patients we will be able to tailor their specific health needs and the clinical needs and so that is our long term goal is provide this called personalized and healthcare delivery and a system to help patients and Veterans and to improve the healthcare outcome. 

But our current step and we just started this project. And so we have developed a pilot NoteAid system. So it consists as we said earlier, consists of two parts one is a knowledge resource and another part is this NLP system. for the knowledge resource, that country we have explored MedlinePlus and we use the knowledge resource, we try to get knowledge resource from trusted and also open access the resource that is available. So the first thing they are looking to is MedlinePlus which is National Institute of Health, the website for patients and for their families and friends. And the second resource we look into is the Unified Medical Language System which is files and software integrating biomedical vocabularies. So particularly for those in the knowledge resource UMLS we are looking to two components, one is called Consumer Health Vocabulary, which has been extensively researched by Qing T. Zeng along with other colleagues and they added UMLS Consumer Health Vocabulary. And also UMLS some of the concepts have definitions which are provided by the National Library of Medicine. And also we are looking other resources such as NIH resource including NCI and [NSLPI?] resources which provide a lot of education material for patients as well and also other consumer health resources we look into. The second component we have is NLP approaches. So for the NLP approaches we have the two—it is a path line system so country will implement just two components only and so at the prototype system. so the first component is concept identify and you see here it is highlight component one is concept identify. 

And what it does is that it process input the notes and which is the—in the text format. And it then automatically clean code relevant concept. As you see here says concept identify and has three subcomponents which are described quickly later on. The second component and is the definition locator. So it is a path line it is after we identify the concept and the definition location and it links the concept and links to the knowledge resource and fetch definitions from MedlinePlus, UMLS and other knowledge resources and then provide a definition. So for the concept identify and first subcomponent that when the input is a note which is the—is a text and the first task is that the system would identify the sentence boundary, so identify—split the text into sentences. And then come back and using the parser and to identify the known phrases and so on so identify the concepts. 

So once concept identified we need to identify—we need to match to the clinical relevant concepts and so we call it concept filter. So and after that and it goes to definition, the components and the final—the component, too, which is definition locator. So the system was—we demonstrate early on was fully implemented and so again if this is a pilot system that we put together. So we want to evaluate—our evaluation focus on comprehension. We want to find out whether the people who use the patients who use our system would improve their comprehension for the original notes. So therefore we have a _____ [0:15:04] evaluation which I describe later on and now. 

The first we evaluate is lay people self-reported and comprehension. So what does it mean? So basically that we give a note and to a person so here we call that layperson and laypeople. So we do not call them patients in this case because we—everyone is a patient, right, because we all see these physicians at some point. We all get sick and so we all consider ourselves patients. But a difference between lay  people and a patient in our definition is that so the patients will be reading their own notes and what if if lay people would read notes, the identified notes of our other patients. So we call them lay people. So we—for a lay person we give him some notes and so this note and would be the note, just original note without anything, or the note with our NoteAid implementation. So in the implementation that is the concept and abrogations will be linked to the education material definitions and so on. So we would ask them to report self-report comprehension just ask them that to what extend it you understand the note so we ask them to give us a score one to five. One means it is impossible to understand the note and five is understand the note completely. 

So and the we—and for the evaluation we implement four NoteAid in the pilot system. So the major difference is that because the knowledge resource difference. The first is Medline Plus which means that it is NLP where the knowledge resource is solely from the Medline Plus. So the reason we are doing that is that Medline Plus is developed National Library for Medicine for consumers. So therefore we believe that those is first the information is trustworthy and also it is consumer friendly and the website and so information so therefore that will be easier for patient to understand. So the UMLS is also a medical knowledge resource and we count it as accurate. So the third implementation we tried out is Wikipedia. This is unconventional and the reason we try Wikipedia is that we found that fifty percent of physicians actually using Wikipedia and fifty-three person US population use Wiki. It is out there. It is a lot of content and so we as a pilot evaluation we also implement that which is a link and a note to the information at Wikipedia. 

We also, for the fourth implementation report hybrid which is we aggregate the—all the resource we have: Medline Plus, UMLS, and Wiki and provide a link to them. So one important concept I would like to introduce is the readability of the note because not only that it is important like we said before people have to have certain health literacy to be able to understand notes. And also that the—whether the note is difficult and easy to understand that is also important criteria. So we evaluate the readability of a note using a standard grade level which has been widely used and for education. So as example that for the concept of hypothyroidism. And so for the National Cancer Institute provide a definition is that it is too little thyroid hormone. Symptoms include weight gain, constipation, dry skin, sensitivity to the cold and also called underactive thyroid. 

So in this case that the grade level of this definition is nine which corresponds to US ninth grade education. So in contrast another NIH resource provide a different set of definition. So in this case that as you can see this definition contains a lot of medical jargons and so it is much more difficult to understand what is definition. So in this case that the score and is twenty-five which indicates it is much more difficult to understand. 

So the evaluation data is twenty randomly select de-identified progress note and we target and at the first batch people with chronic disease diabetes. So for those twenty progress notes and some statistics and of these progress notes are shown in this table. So the average number of sentence per note is about twenty to twenty and sentences for each note. And the average number of words for each progress note is two—about two hundred fifty and the readability of the note is between nine or ten which means that you need minimal -- the ninth or tenth grade level, that you—people actually can read those notes. 

So when we recruit subjects so we used also unconventional way to recruit subjects. So the subject we recruit—so by the way—so all this study has been approved by the IRB board by the University of Massachusetts Medical School. So we went to the Amazon Mechanical Turk and to recruit the people. So the reason we used that, we have shown recently in reliable people has using these online resource and we found that they are reliable for medical education and also for a lot of evaluations people have found actually quite very useful and resource and for subjects and for the evaluation. And the quality has been considered to be very good. And also just much easier to recruit people and do these resources. So we recruited twenty-five subjects and so again we have five systems. So we already said there are four implementations which is that we implement and we include knowledge resource from Medline Plus only and from the UMLS and from the Wikipedia and also with the hybrid system. 

And that the another system which is the—without NoteAid which means that we just giving the subject a note only and ask them to give us a score. So averages about five subjects per system, that is what we designed. And although that we found only twenty-one subject complete evaluation. So the demographic information of the subjects participate in the evaluation study is more than fifty percent male and so it turn out that though the Mechanical Turk is a good resource to recruit subjects. Unfortunately those subject population is not a typical US population because they are tend to be better educated than average people. So all of them have certain level of education and at least high school. And so thirty-three percent have high school and there were many of them had a college degrees and so it is more than fifty percent have college degree so which indicates that this group actually is reasonably educated. And so we follow the evaluation process and again, so we have five systems. So basically that each subject is randomly assigned to one of the five—one of those ... is randomly assigned to one of those five systems. 

So they only assigned to one of them so they do not see two systems. So and they given twenty progress notes one note at a time. So after they given to one progress note we ask them to give us a score from one to five as we said for earlier. So we first and would when the resultss come back and we first we check out to find out whether they are self-reported and comprehension score it is reliable. So how to do that? So record that we have the grade level and the score, readability score, the grade level. So the hypothesis that if the notes, clinical note, is more difficult to comprehend and based on these grade level score. And so we would see that the self-comprehension score should be lower. So we did find such positive—such actually negative correlation. So which means that the lower this grade level and the higher comprehension score. So and they are statistics [inaud.] so this result shows that and their self-reported comprehension score actually can be reliable to indicate the level of their comprehension, of laypeople’s comprehension.

So of the five systems and so note alone and Medline Plus and implementation UMLS and Wikipedia and hybrid system. So here is report self-comprehension score. Recall that the higher score, the better comprehension. So the best score is five and the lowest score is one. So when presented with notes alone the self-reported comprehension score is below three and when with all the other systems so the improved substantially. So the improvement is all statistically significant. So what we conclude here, we conclude here is that in the NoteAid improve and comprehension of laypeople. So based on the self-reported comprehension score, however, that our study, pilot study, has limitations because first of all it is self-reported comprehension score. So the question we have just is that do they actually really understand the notes. So whether they understand correctly, whether they understand incompletely, and those things are questions are that we do not know. 

And also of course they are laypeople, not patients, and perform the evaluation. So the second step and we did is that we usually called paraphrase notes. So what does it mean? So basically we just present a note to the layperson and we ask them rather than asking them to give us a score indicate their comprehension we ask them to paraphrase and to write a summary about the main content using their own language about the content of the note. So we recruit—we hear using—we from the previous twenty progress note and we randomly select five in the progress note and we recruited forty subjects and from the Amazon Mechanical Turk and so for each subject and we present when we assign notes. Of course one of the time and either with a noted implementation or without. 

So and then we ask each subject for each note and to paraphrase the main content of the note. So we found that thirty-nine subjects complete evaluation so then each one has write five notes so we about one ninety-five total the summary and available and—So here is example. So a subject here is present a note and without our NoteAid implementation. 

So here is a progress note, it is also de-identified progress note so the patient is doing OK today and he has some complaints of discomfort and at a tracheotomy site and but otherwise there is no complaint of chest pain and shortness of breath and abdominal pain, nausea, or vomiting. And for the vital signs currently stable and the cardiac normal and no murmurs and rubs or gallops are present. The lungs clear to auscultation and bilaterally. Abdomen is soft, not tender, and not distended. And normoactive bowel sounds present. For the labs and there are no new labs today. For the assessment and plan it is preoperative assessment from the previous note and hyponatremia likely to be SIADH related. Continue to monitor now and leukocytosis and so likely to be reactive. No clear infection source. Thrombocytopenia. And it is probably chronic in nature, appears to be stable. Although we are continuing to monitor this for now as well. 

So for the—so this is one example of a typical example of paraphrased note. So written by the layperson. So it says the patient seems to be doing all right despite some minor discomfort. And his vital signs stable. He has not got any lab today. The plan going forward is to keep monitor patient. 

So we found that this [inaud.] was that is just a typical summary, written by—we observe this is typical because so we have—so it is typical because the summary does not actually incorporate any of the clinical terms from the concept. So it is very high level summary and that avoiding all the clinical terms. So we found there is another second typical summary generated by the laypeople is that so in this example it has more content. And but then the content we found the laypeople just copy and paste some of the clinical concepts and a lot of them with us—with or without some proper understanding of the meaning of the concepts. 

So in contrast so here is the—a system summary paraphrased note and the laypeople generated. And when presented with the NoteAid system, so the NoteAid system that you see here is that all these concepts have been linked into the definition. And so this all be linked to the definition. And so we found that almost all the paraphrased notes or the summary generated by the laypeople when presented with NoteAid they actually using the definitions and incorporate it into the—these are low sodium contents as hyponatremia and high white blood cells and leukocytosis and so on. So therefore that—if in fact they actually and the people actually read the definition and recall that our system you cannot just copy and paste the definition because we present system just like pop out window, right? So when you move over the mouse cursor those pop-up windows disappear. So the people really cannot copy and paste. And so they really have to really understand a little bit more and to incorporate those and into their—the paraphrased notes. 

So we simply count them, so we concept recorded we had total of one hundred ninety-five summaries generated by the laypeople is twenty progress note—five progress notes and about thirty-nine subjects participated and each subject is five—write up five notes either with the NoteAid system or without NoteAid system. So we just—simply just count. And so with the NoteAid system—so the number of medical jargon terms in lay language which is two hundred sixty-eight. And without NoteAid it is only sixty-eight. So there is a substantial increase in with the NoteAid system. And the number for medical jargon term copy and paste in contract without NoteAid is one hundred sixty-nine and with NoteAid it is only seventy. So and we found for each note we did statistic analysis the all statistic is significant. And so we presented—so what our conclusion—so our sudy is very rich so we still process analyze more details of study. 

So but then we still have because these were the laypeople, right, paraphrasing the notes. So we actually—we did a patient interview and which is the next pilot study. So again this is approved by the University of Massachusetts Medical School and so so far we have recruited three patients and we feel like on the path line for new patients, more patients. So turn out that we went to the Amazon—so how we recruit patients, we just basically just told advertisement and fliers and put in the hospital and people contact us. They have to call us and or email us. 

So unfortunately we found that all the three patients we recruit they all college graduate so we need have other methods to recruit like less educated people. So we are working on it. So for those three patients we recruit patients from diabetic clinics at The University of Massachusetts Medical School. We found that there is one diabetic patients and two type II diabetics in the patients recruited. So those three patients, they have been being treated for five to fifteen years and at the clinic. So we present the—for each patient and we present with one or two their own progress notes of the most recently clinical visit. So we asked what we do not understand. 

So for the patient populations that there are two males and one female. So we asked what they do not understand. And in the note first—and then afterwards that would present our NoteAid system. And so we asked them to think about and to share us anything about this NoteAid system. And then we—afterwards we asked them a list of questions. So for each patient we present his note, his progress note, and so we ask people to tell us what information they do not understand in their note. It turned out all of them, they told us, that they really do not understand abbreviation concepts in his own notes. They do not understand all of them. They understand some of them because they have been diabetes for a long time and they are well educated. So they understand some of them but still average is between five or ten concepts in their progress note they do not know. So they want to know. 

So example that is including FT4 and some of them is not directly related to diabetes such as auscultation and also some of them relate with diabetes. And then afterwards that we asked them that do you want—we show them the NoteAid system so we ask them do you want to—the question, a little question, and so where is that? So the question said do you want to read your electronic health record notes? So all of them said yes they would like to read them. So when do you want to read your notes? And they said, all of them said, before end of the clinical visit. So is there a specific part of notes that you want to read? So two of them just said they want to read all notes. And so one person said oh I just want to read the current plan. So would you be willing to share your notes with your families or family members? So very surprisingly because we thought they would like to share but actually they said all of them say no but one person said oh, only my wife. So it is interesting they do not, I mean, they not intend to share their notes with others. 

So if a NoteAid helpful they all said yes. They all like NoteAid system. And do you want to use NoteAid? They said they all of them said yes. So we further asked that do you—I mean, what is your recommendation how to improve NoteAid? So all of them they all said they want more concept so more concept to be included because currently NoteAid does not [define] all clinical concepts only some of them. And so because they really think that our system is limited to those knowledge resource we indicated like Medline Plus. Only if we have the definition we would provide the definition. So NoteAid does not generate definition itself. And also that they want to provide NoteAid provide a definition but also those definitions readable. So because a lot of the definitions currently link to the knowledge resource be very difficult to understand. So they want to easy to read definitions and they also want more education material, not only just the definitions. They also want to know and so what does it mean? And if I have this test. And what is the prognosis and I mean, what is this test and what are the discomforts of this test and so they want to know these kinds of things. And so other related education material. 

So here that just is the conclusion that we have done three all pilot evaluations and so but then so our next step then which is the interview Veterans. And so but however we are waiting because we still have not got approved, IRB approval, from the VA. So we have—for the pending IRB approval. Once approved we will—interesting interview on the Veterans. So our study is limited because the main limitations that is all pilot studies and sample size is small. And so we do not have any Veterans in population at present but is our goal to do that in the future. 

So conclusion. So the conclusion is that we found our NoteAid system even with current the implementation is not the most sophisticated implementation so because we study for this project in only about one year’s history. And so we found actually even with the current implementation we found it improved electronic health record notes self-recorded comprehension. And we found that NoteAid it did help patients and comprehend their own notes. So the future work that we have a lot of work to do. So we need to—lots of work we need to do NLP which is improved concept coverage and concept—clinical concept recognition and improve the quality of the definitions and also add education materials. And we also need to improve our evaluation process which is to have more quantitive ways to measure comprehension and also we need to evaluate the system in which the Veterans read their own electronic health record notes. 

So here is the references and we have published two articles. And as a result of this work and one article was the—won the best student paper and presented by my students and Balaji Ramesh. So here these questions and thanks and here is the system is can be accessed and from this website called http://www.clinicalnotesaid.org and give a try and you can copy and paste any the clinical text into the notes and into the NoteAid system and try out that—what concept that the system recognize. And here is my contact information and here I am giving to Molly for the question and answer. 

Molly:Great, thank you very much Dr. Yu. For those of you that joined us after the top of the house if you would like to submit a question or comment just use the Q&A box in the upper right hand corner of your screen. We do have a few questions. Will the NoteAid system be linked to the Veterans Health Library recently released by the NCP?

Dr. Hong Yu:Well, I do not—I would say I would like to but I do not know about this library system yet so I will have to have more knowledge about this library system whether we are able to know that. So this is really the pilot project that we just started and so we hope that eventually it will be used for—it will be integrated into the VA system. 

Molly:Thank you for that reply. The next question has two parts. Is NoteAid able to handle misspelled terms or abbreviations?

Dr. Hong Yu:Yes, so we are able to handle abbreviations and so for the misspelled terms so currently we do not have the capacity to handle misspelled terms. So we have a component called disambiguation so which will improve performance so the path is to use contextual features to identify the meaning of those particular abbreviations which is the full extension expansion. So for the misspelled terms in technically is the same principle and so we—but we have not—we have not focused on that yet. So currently we do not—we have not done any evaluation in terms of the ability to handle those misspelled terms. 

Molly:Thank you for that reply. The second part of the question: does NoteAid recognize medical note slang such as two slash two meaning secondary to? 

Dr. Hong Yu:What does it mean? Can you repeat that? [laughter]

Molly:Does it recognize medical slang, for instance two slash two which means secondary to?

Dr. Hong Yu:Oh so, I do not think—I do not know that, I mean, we need to try out [laughter] the system. you can try out—go to our website and so what we do is that if it is very unconventional like uncommon the such slang used and so I do not think currently has the ability to do that. But we will certainly that we are—this is a pilot that was used. So we intend to make it as comprehensive as possible. But in theory that we would if we continue to improve the system we should be able to handle a substantial amount of the medical jargon. 

Molly:Thank you. The next question: in the sample notes that you used in your pilot studies did you include any notes about mental health or substance abuse disorder treatment? Do you think there are specific challenges to adapting NoteAid for these conditions? 

Dr. Hong Yu:Well, we currently we focus on chronic disease like diabetes, hypertension and so we have not look into mental illness at present. But we heard that lots of patients who, Veterans, who reading their... you know through the patient portal of Blue Buttons and so they are reading their notes, a lot of them are mental patients, mental health patients. So it is our goal—we are interesting looking into—we just now because of pilot study we have not done anything with the mental health patients. But its definitely that we may be that we may be interesting looking further. But for the challenges and I am not a physician. So I do not know that would be the challenge means for the patients or for the physicians but from the technical challenging from the natural language processing point of view I do not think there is a significant challenging in terms of comparing with diabetes hypertension patients in terms of—but no, we provide this facility to help comprehension. So I do not know whether that address the question or not. 

Molly:Thank you, they are always welcome to write in for more clarification. The next question: has there been instances where NoteAid linked to the wrong definition? 

Dr. Hong Yu:We actually in this presentation but we did—we did not want to see that whether actually the definition provided are correct or not. So we asked a nurse practitioner to read with the twenty progress notes we have and to find out to.... evaluate the definitions. And so yes, absolutely. For example, Wikipedia and some of the definitions is not correct. So we found—so with the Wikipedia it is about eighty, more than eighty percent correct. So there—but this is very pilot. So with a pilot study you know,  a good study would be where you have two people and you have [inaud.] agreements and so we have not done that so that is why we have not really published this landmark result yet. 

So here is the mistake—there are two ways to make mistakes. One is natural language processing make mistakes. The second mistakes would be the knowledge resource if not correct content. So any of the path line is potentially have—it could potentially have wrong.. But the thing is that whether it provides those content to what extent it is wrong or whether it is relevant, for example like Google, I mean, people—lots of people use Google, right. But if you look at accuracy of Google and people have found it is only forty percent. So but still Google has been—it is generally useful. So the question stands whether not perfect because we can never have computer system can never be perfect, right? Because you always have your knowledge resource is not complete and so then even with a non perfect system can it be useful, can it be helpful? 

So we believe it can be useful and can be helpful even with such a, I mean, you know our pilot implementation and our evaluation has demonstrated that. 

Molly:Thank you for that reply. The next question: will it be possible in the future for researchers to add their own definitions to the software either in a local database or in a cloud-type database?

Dr. Hong Yu:Absolutely. I think that is something that we are, I mean, we are keenly interested in. There has ability that where the users can expand their own knowledge resource. So that is the whole idea of Wikipedia. That is why Wikipedia is powerful, right, because people, they can add in the resources, yes. So we are interested. 

Molly:Thank you. The next question: is NoteAid the only software that provides this service? My question is why did you decide on this particular software other—over others and can the VA justify purchasing this software to include in My HealtheVet instead of doing some market research? 

Dr. Hong Yu:Well, I mean, to our knowledge we do not have other—we do not know that whether there is a commercial system available out there provide what we try to do now. So I cannot really comment on. And then of course the system we develop is free so it is not commercial system. So the research—for the research community we are not aware of any other software that has been developed. So if you aware of something we know there are lots of related research and published research but there is no software to our knowledge has been developed and be substantially evaluated as we have found in our pilot study. So but if you note anything that we do not know and please email us and we would like to look into that. 

Molly:Thank you. The last question we have: who developed the definitions? I just put a recent note into the system and the word died is defined as cessation of all bodily functions but that does not seem easier to understand. [crosstalk]

Dr. Hong Yu:Right [laughter] so we actually​—now we did not develop any of the definitions. So as we said this is our pilot implementation. So the—all the definitions coming from Medline Plus and from the UMLS so and so it is one of the those large resource that provide the definitions. So again this is a pilot study so [laughter] so it might—so we obviously we need to improve that, for example, that should not even provide the definition. So we need to have again with that in the future work we need to improve the filtering which is identify those aspects, the clinical concepts that we still need to provide definitions, yes. [laughter] But thank you for using the system. 

Molly:Great, many people wrote in and said thank you, it was a great presentation. So I am going to ask our attendees to just hold on for a few seconds. I am going to close the meeting out in just a second. And you will be able to fill out a feedback form, just a few questions. It is your opinions that help guide what presentations we do have presented. So first up I want to thank Dr. Yu very much for lending your expertise. It was a great presentation. And thank you to our attendees for joining us and now I will put up the feedback form. Thank you Dr. Yu, have a good day. 

Dr. Hong Yu:Thank you. 

[End of audio]

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