**Transcript of Cyberseminar**

**VIReC Clinical Informatics Seminar**

**VOOGLE: A Patient Centered Data Retrieval and Visualization Program**

**Presenters: David Eibling, MD, and Augie Turano, PhD**

**November 20, 2012**

**VOOGLE: A Patient Centered Data Retrieval and Visualization Program**

**November 20, 2012**

Margaret:Welcome, everybody. This session is part of the VA Information Resource Center’s ongoing Clinical Informatics Cyber Seminar series. The series’ aims are to provide information about research and quality improvement applications in clinical informatics and also, information about approaches for evaluating clinical informatics applications. Thank you to CIDER for providing technical and promotional support for this series. Questions will be monitored, as Heidi said, during the talk in the Q&A portion of GoToWebinar and we will present them to the speakers at the end of the session. A brief evaluation questionnaire will appear when you close GoToWebinar. We would appreciate if you would take a few moments to complete it. Please let us know if there is a specific topic area or a suggested speaker that you would like us to consider for future sessions. 

At this time, I would like to introduce our speakers for today, David Eibling, MD, and Augie Turano, PhD. Dr. Eibling is Professor of Otolaryngology, Head and Neck Surgery, at the University of Pittsburgh and Assistant Chief of Surgery at the VA Pittsburgh Healthcare System. Dr. Turano is the Information Technology Director at the Veterans Informatics and Computing Infrastructure, better known as VINCI, and is also located at the Pittsburgh VA. Without further ado, may I present Drs. Eibling and Turano.

Dr. David Eibling:Thank you very much for participating at the event [sound distortion] work that we’ve been up to in the last couple of years. We’re quite excited about what we’re doing and we’re looking forward to hearing your feedback after our presentation. So I’m an old ENT doc who’s been using and creating health records for about forty years; about a third of that time, electronic health records, and I obviously have a few opinions which you’re going to hear over the next fifteen minutes or so. 

In addition to being the Director of VINCI, Augie is a longstanding IT architect and was one of the original underground railroaders, and those are the folks that actually originally wrote the code for DHCP, the forerunner of VistA. 

So Voogle is the term that we use for the web-based application that we designed that interprets user queries and returns user information. It was funded by the Greenfield Innovation Program and we’re going to talk mostly about the software today, but we’re going to briefly mention the hardware component, which some of you will probably find interesting.

So you know, despite the fact that CPRS is a decade old, it is still rated quite highly. You can see here, it’s number three in a recent Medscape survey of twenty thousand physicians. I have to admit, I was a bit surprised it was rated so highly with all of the innovation in the industry. But I suspect that this standing means more about the status of the industry as a whole than specifically CPRS. 

So you know, information systems have – we’re trying to move forward, there we go. So as we recognize, healthcare systems have always been information based. And this is sort of the root of Voogle, if you will, and how do we best provide information. So for many years, ancient writers suggested that if this, then the healer should do that. And it really wasn’t until the recent past that objective data became actively utilized in decision making. And today, physicians cannot make optimal decisions about their patients without accurate information and timely information at the point of care. And of course, it’s not just the physicians, but those responsible for resource allocation and oversight also require information.

So the next slide, we captured this image from *Time* magazine. You realize that’s fifty years ago. You see the punch cards going into the computer, you see the paper coming out. And one of the Cache individuals pointed out that this is about paper coming out of the computer hasn’t stopped and that make sure you put a shredder by every printer. So as physicians, we want to know the story about our patient. And this story consists not only of the patient experience but also, we recognize that there’s a fair amount of structured data and other information that we need in order to combine with the story to provide the information needed for decision making. And I think we recognize that with the advent of the genomic information just around the corner, these needs are going to become even more complex. And of course, we need to know about the disease and its treatment.

So what information then is needed by the others – those who are responsible for resource allocation? Well, they want to know about our patients but they’re not so interested in hearing the story. But they want to know the coded information, computable information that they can then compare and aggregate. And we recognize that treatment and patient information is currently utilized for research allocation, coding, billing, etc. But in the future, it’s obvious the outcome data is going to play an ever-increasing role in determining resource allocation. And frankly, it’s going to drive decision making over the next decades.

So why is there so much information? Well, not only do patients live longer and have more illnesses, but we now manage illnesses that in prior years were things that just happened to patients. It’s not only the care that we provide is more complex but there’s more interaction between the care that we administer and the patient’s other diseases be it medication, surgical, or other kinds of intervention. There’s more specialty care so more and more specialists know more and more about less and less, and I’m the poster child for that as I become more and more specialized over my career. And there’s more options so as a specialist, I have more and more options to choose – options that did not even exist in the recent past. So you know, scanning the pile of *New England Journal* articles next to our reading chair isn’t enough anymore. Keeping up is a formidable task for everyone and there’s estimated to be up to twenty-five thousand clinical trials a year, thousands of clinical practice guidelines, and even specialists can’t do it. 

So how do we do it? So Donald Norman – and I highly recommend his writings to those of you that haven’t encountered him before – makes the point that the success of human endeavors is derived not on the humans themselves as much as on the tools that they have built. And he points out that the power of the unaided mind is overrated. Its real powers come from devising external aids to enhance its cognitive abilities. And these external aids are here evidently displayed. You can see that antique paper health record, I haven’t seen one of those in years, books, online references, images, be they clinical images or radiographs, structured and unstructured patient information. And we use this stuff every single day and we have to go to different places to find it. 

So recognizing that information systems may not adequately support the physician needs, the National Research Council – that’s one of the national academies, it’s like, you know, this is the medicine, National Academy of Science. Well, the National Research Council commissioned a study of eight electronic health record systems back in 2008. And Stead from Vanderbilt is the senior office, it is oftentimes referred as the Stead Report. And what they found was that none of the eight systems that they evaluated actually seemed to be intended to provide support for cognitive tasks of physicians at the point of care. And I would suggest to you that even though this is almost four years ago now that not much has changed in the intervening period of time.

So what’s happened as a result? So there’s good evidence that the introduction of electronic health record systems has impacted physicians by increasing the workload required by shifting work onto the physician. And this increased workload is estimated to reduce the productivity of a physician of between thirty to forty percent initially. And even though there’s a learning curve impact, this difficulty does not completely disappear, even with time. I suggest to you that it dramatically affects the physician/patient interaction. In my personal experience – and this was done by one of my students, who actually took a stopwatch out – he found that I spent more than half of my time in an encounter looking at the computer, looking for information, than I actually spent interacting with the patient. And I suggest to you that in a typical encounter, there are two patients, there’s a computer, and there’s the live flesh and blood patient. And in my particular instance, I find it more difficult to treat the computer than treat the patient. 

So overall, there’s been some recent studies – this is one from the *Archives of Internal Medicine* just a couple of months ago – pointing out that symptoms of burnout have increased in physicians overall and that it would appear that the primary driving force is the perception by physicians that much of the work that they do is not work that is directed toward patient care but rather is, I guess you would call an excise tax on their activities. Now, I have to point out to you, however, that this may not be due to the electronic health record but rather the new administrative requirement. The electronic health record is just the interface that the physician then faces when addressing new administrative requirements.

So how is this happening? So if you look at how these systems were designed, oftentimes it would appear that the designers did not really understand the actual intended use and they tended to design for the single person with the single computer in the quiet, isolated cubicle rather than the complex environment in which healthcare is delivered every day. I would also suggest to you that to date, at least, there has been inadequate testing of much in medicine prior to implementation. Some of us jokingly state that most medical innovations are tested by... electronic health record implementations are tested by implementing after implementation rather than before. And as I mentioned before, there’s just too many administrative tasks. Here’s this poor gal still cleaning up her charts long after her coworkers have left. 

Almost all authorities would agree with the statement that the most critical role in electronic health record systems is clinical decision support. Although it’s oftentimes defined primarily as view alerts, it’s really much more than that. Decision support is providing all the information needed to assist decision making at the point of care. So it’s not just the information about this patient, but it’s information available in large databases about similar patients. In other words, it’s how do we mine large amounts of information to create support for the decision making for this patient sitting in front of the provider and of course, evidence-based recommendations, with which we’re all familiar.

So our current project is Voogle, which exists primarily – or exists only really – as a prototype and is a web-based, secure, high speed, user-centric information retrieval system that looks a lot like its namesake, Google. It accesses information from within the VistA database and then, can actually stand outside of the database. As I mentioned before, it was funded...its innovation was funded by the Greenfield Innovations program and we’re going to show you a live demo  here in just a few minutes. 

We’re really responding to the Stead Report call for a single interface through which any query can be posed. In other words, how do we obtain useful data needed for decision making from multiple data sources without adversely impacting the cognitive nodes of the patient.

So how does it work? So it’s a clean interface, data domains that are not limited to VistA, which we’ll give you a demonstration of that a little later. It doesn’t require any training because it’s very familiar to anybody who’s ever interacted with any sort of a search query. Voogle interprets national language queries. It provides direct data access without requiring a menu navigation. It facilitates the development of, or the formation of aggregated displays so you could show multiple data fields at one time – and we will demo that – as well as the ability of the user to redefine display paradigms and to create macros. Said simply, Voogle is offloading the search task.

So we see Voogle as one more step in the evolution of the computerized information system from data to understanding, with the latter best illustrated by Watson and its very advanced forms of machine understanding.

So the secret sauce of Voogle is a knowledge base. And the knowledge base knows where the data is located and whether the data is present, populated by a crawler that actually builds the map of data locations and that can be configured to find information outside of VistA, and we’ll give you a little demo of that in a few minutes. It also contains default defined display parameters, which can be overridden by user queries and we’ll demo that for you, also. 

So how does this work? Each VistA system is slightly different than others. Therefore, Voogle must configure itself to each VistA system. It does that by crawling the data and then creating a unique metadata knowledge base that is customized to that particular VistA system. Then when a query is entered by the client, a message is sent to the Voogle knowledge base, which is located on the VistA server, and it finds and retrieves the data and messages it back to the client where the display is then presented in a visualization. So Voogle is designed in such a way to minimize the package payload size, which enables high speed data return. Okay, Augie.

Dr. Augie Turano:So the way this worked was to try to maximize the code that was already in VistA. So what I did was I took a lot of the file manager API called the application program or interface call, and they’re very pattern oriented. So a file manager call usually will return data back in these four mechanisms. So single value fields or multiple values fields, pointers to other files like the State or drug files, things like that. And then with the word processing fields, where clinical notes and things like that are entered. So we wanted to support all the file manager calls. I wanted to support all the RPC calls. The RPCs are remote procedure calls, and so there’s a lot of RPC calls that are part of VistA and they’re used by CPRS and a lot of other VistA applications. And then, we also wanted to support web service calls because I wanted to make this extensible. So by making it extensible, the web service call, it’s a little bit different than an RPC. It uses an XML kind of notation and it’s also more ubiquitous through different operating systems. So we use web service calls, I wrapped a lot of the RPCs and the web service APIs. And the reason for that is that I didn’t want to use just straight RPCs because when you start developing web applications, you don’t want to make the application too chatty. So what we did was we bundled the messages up and tried to maximize the payloads that were coming back and minimize the requests going in. 

So this scary looking code is just a little bit of MUMPS. It’s typical file manager API calls. We use a lot of them, actually it looks a lot worse than it is. Once you understand what this code does, it’s actually pretty straightforward. And like I said, because of the extensive pattern use inside VistA, once you kind of understand these patterns, it’s pretty easy to use them and interpret them. The thing about Voogle is that it’s dynamic and inside the MUMPS side, which itself, there’s a lot of capability there for dynamic execution of code, and so we maximize the use of that [inaudible].

So as Dave was saying, we crawled the database, I crawled the data dictionary inside VistA, and construct a knowledge base. A knowledge base actually has more fields in it that tells the client what to do, how to render the data. It assembles the data and then it sends the data back to the client in key value pairs. So it actually looks a lot like XML and so we maximize the CPU cycles on the client. So there’s a tremendous ability inside a client to render this data so we don’t have to do it on the server, so this keeps the server free to just deliver data.

So as we stated before, it handles direct queries do you don’t have to memorize a bunch of menus. It’s adapted to the user so they can direct what the output should look like. So they can say show me medications but send it to a PDF. We’re hopeful that it might decrease medical errors because it’ll keep the physician focused and not waste cognitive ability and just trying to remember where data is located. Since it’s simple to use, there can be practically no support or training cost. And we support auto completion so as you type in – just like Google does – we can refine search mechanisms [inaudible].

So how does it work? I take an input string, I tokenize it, and then I go look for data fields for quantifiers. So you can say things like show me glucoses greater than five hundred so it separates the entities and looks for quantifiers and kind of does pattern-oriented query construction. It then looks in the knowledge base for those entities, figures out where the locations are, and then assembles the data to be returned back to the client. 

So the knowledge base will tell the client requestor what the data is and it might do that using file manager calls, RPC calls, a mixture of both. It really depends on what the request is. And then inside the knowledge base, there’s a default display field so it’ll say this data’s probably best displayed as a chart or a [inaudible]. The user can override that at any time.

So one of the things – I’m an old time VistA guy, Cache guy – and so I was interested in actually looking for a way to make an excuse to try to demonstrate the architecture in unique ways to try to speed up the VistA performance and things, but also minimize cost. And so we decided to use commodity processors and mainstream operating systems so this thing runs on Windows and [inaudible]. The knowledge base is built actually inside the MUMPS database and so that knowledge based is used to locate the data. But I think one of the things that’s unique about this is that we used Infiniband, as well [inaudible] Ethernet in combination here. The Infiniband actually was giving us disk access performance that was ninety percent of the speed that we could get with direct attached disk. So what that means is that I can access data on a remote disk drive on a remote server with ninety percent of the speed that I would get if it was locally accessed, so that’s a pretty big deal. And then one of the other things that we did was we used solid state disk drives. The very nature of MUMPS, there’s a lot of disk access and some things that go on there. With having a solid state disk, it roughly delivers data about ten times faster than a regular disk-oriented system. We didn’t want to use a high priced SAN and so what we did was this distributed architecture with the modern disks. 

So the reason for doing this was we knew we wanted to use web services because they’re more ubiquitous throughout the industry and we wanted to open up the APIs that we were going to develop for getting reminders, getting allergies, getting medication, all the clinical indicators that normally a physician would use. So we packaged them up a lot like [inaudible] did and some other applications. We homogenized them, made the data coming off very easily parsed and readable, and we used these commodity processors, we used the dual networks. In Cache we used objects, which made the programming for me a lot easier. We used AES 128 bit encryption going back and forth and because of the architecture, we actually prepared some of the database servers for high availability.

So what is the system actually constructed of? We used twelve Nehalem commodity servers, SATA disk drives with JBODS. JBODS stands for just a bunch of disk connected to RAID controllers, all standard stuff, all actually pretty inexpensive. We used seventy-nine dollar one-terabyte disk drives for the storage. We use Windows and Centos. Centos is the red hat open source version of LINUX and like I said before, we used the combination of Infiniband and Ethernet and I have a little diagram that I’ll show you. 

What’s nice about this system is that it’s horizontally scaleable. And so even with the way our system was, which was very inexpensive, I mean, we paid ninety-eight thousand dollars for all the hardware including the switching. We were able to get close to two point four million global references per second. The average VistA system during peak load time during the day does about two hundred fifty thousand global references a second. So this thing is really pretty scaleable. 

This is a picture of what it looks like. Again, we didn’t buy blades. Blades were more expensive. We just used CPUs, stacked them together with some disk drives to [inaudible].

So this is the overall architecture. I’m not going to spend too much time on this. There’s a combination of both Ethernet and Infiniband. We used Infiniband mainly for IO traffic to get to the data and Infiniband more for the client to actually go to application servers and actually run the application.

So one of the things that we had to do was we thought it would be best to use just the sign-ins so we still have regular VistA access and verify codes that you use. But we mapped that stuff to a new security paradigm where we have a very simple system that’s a numeric based system and every field in the knowledge base could be protected with a certain level of authentication, which is used by Voogle to go back and forth to and from the database. So you can restrict who’s able to see a particular field, just like [inaudible]. 

We use an encrypted session identifier to go back and forth because a web application is a stateless environment but we really need to maintain state. We just use this encrypted session identifier to go back and forth, mainly for speed.

So overall, the Voogle application, it’s simple, powerful, it’s web based. We use this natural language interface so that it’s really flexible as far as what you can access on the data side of things. Any entity in the database is instantly accessible from the command line. We’re not limited to just the sources – you know, we can go out to the web or we can go to a SQL database or just do calculations, which we’ll hopefully, we’ll get a chance to show you here. The knowledge base, if you want to add more entries to the system, you can do that very simply by just adding to the knowledge base. And you can redirect the visualization of the data, which we thought was a pretty important thing. So how the data’s returned, you can say we’ll put it back as a data grid or bring it back as a chart and so it’s user directed.

So at this point, I think I can actually show you this stuff. So we’re going to actually go in and show a demo here of what this actually is.

[PAUSE]

Dr. David Eibling:It looks like we have to reset the program. We ran it through just a few minutes ago. So you can see the screen that Augie is working with here. It allows you to select a patient or you can create a query. Once the query is created, it moves actually to the upper corner of the screen. It’s the clean real estate... to free up real estate for the display. So we’re going to go ahead and just display demographics for Patient Eibling as our first query. Watch what happens now with the query box. It’s going to move into the upper left hand corner, freeing up real estate and in a few minutes you’re going to see – yeah, there he is, there I am. And you can see you had some of my social security number but it doesn’t have the whole number. 

Let’s look at glucose. Display glucose for Patient Eibling. And whoa, here we have a problem and we have a security restriction. So those of you that have been watching realize that we didn’t log in. So Augie’s going to log in as himself and he has an adequate security restriction so now let’s rerun the query one more time. And now we’re going to see glucose for Patient Eibling depicted as a chart over a period of time. So let’s look at glucose greater than five hundred, just display glucose greater than five hundred. We don’t need to rewrite the patient because we already have the Patient Eibling up. And there we have the glucose greater than five hundred. How about the last glucose? Display last glucose. And we could do first glucose. So once again, what’s happening is the query is sending a message to VistA which then returns the data, which is then painted. 

Let’s look at multiple pieces of information. Let’s display demographics, allergies, medications, and let’s do it as a data grid. Allergies, medications as data grid. So we’re going to change the display, as well as seeking numerous pieces of information. So here we have the demographics at the top, allergies, and medications. And you can actually collapse these screens up and down and you can also scroll backwards to previous screens, as Augie will demonstrate here, so that the information doesn’t disappear. You don’t have to regenerate the query. We’ve also integrated some novel forms of display. So let’s display Chem4 as radar. I had this bright idea that we might be able to link together linked information by creating novel displays, but this is a radar plot of the Chem4 on four different dates. I think it’d be kind of cool to do this as a 3D, but we don’t have that capability.

Let’s look at notes. Let’s look at notes as a timeline. So one of the powerful attributes of Voogle is the ability to generate these timelines. So here’s the clinical notes as a timeline – this takes a little while because it’s going out and retrieving all of the notes. And here we go, there’s a box above and a box below. The box above, you can change it. And then the box below is what is in the focus point of the slider, and then you can click on those and get additional information. We don’t yet link this directly to the notes, although that should be a pretty straightforward innovation. It does give you information about the note.

Let’s do notes as a book. So you know, with this interest in using mobile devices and the idea of being able to use your finger to rapidly scroll through notes, here we have notes as a book coming up and you can sit there and scroll the pages back and forth, similar to any of the text readers that you may be familiar with.

We can also look at the notes as a PDF, let’s do notes as a PDF, so what this will do, this actually takes the TIU note and converts it to a PDF, which then enables you to search it as you would a PDF. So let’s search for taco. I know we have taco in here. So let’s see, it should be there. There we go, there it is right there. So if a patient is fearful of Taco Bell, don’t give him any Taco Bells.

Now let’s show some of the... One of the things I just wanted to say with this is this doesn’t look as impressive as it did three years ago before the iPhone came out. [Laughter] 

Dr. Augie Turano:We were able to do this [inaudible] some of these graphics things, and they looked a little bit more impressive. 

Dr. David Eibling:But it’s certainly a capability that we think that individuals will look for, and that is the ability to rapidly scroll through pages of text. Let’s display Chem Eibling. So what we did is we defined a group of requested information, so Chem Eibling, and it should pop up right there, Chem Eibling. 

Dr. Augie Turano:You can choose from the pick list here. I didn’t want you to type it in [inaudible].

Dr. David Eibling:So what this is now is aggregated information. So we have the glucose, we have... Is the agent there someplace? The Chem and the... [interruption] yeah. So let’s demonstrate now how to show what is in the group. So let’s show Group Chem Eibling. So we defined this, we won’t take the time today to show you how to do it but it’s a simple one-line command to define groups of data. So here to demonstrate, here the popup tells us it’s age... 

Dr. Augie Turano:Glucose is a chart. 

Dr. David Eibling:And Chem as radar. So we mentioned earlier that Voogle will go outside of VistA. So we’re going to ask it to display Easter for 2018. So the knowledge base says whoops, Easter is not in VistA but I know where to find it. So it goes to a computational library, calculates out Easter, and we can actually do Easter for 2015 to 2022. [Inaudible] Pick a number, there we go. And here is, once again, the query generates an external call. 

So the knowledge base knows what it knows and it also knows what it doesn’t know, and it knows where to find what it doesn’t know if it’s been so configured. 

Dr. Augie Turano:So let me display PSA.

Dr. David Eibling:PSA, yeah. So here we go. No data for prostate specific antigen. So this is kind of cool. Those of us that have spent, sometimes it seems like hours looking for information that doesn’t exist in VistA, the Voogle notes that that data does not exist.

Dr. Augie Turano:Right, so the fields that it knows it does look up on. The fields that it can’t find, like I just put in a kind of nonsense word here, it knows that it’s not there and so it just moves on.

Dr. David Eibling:So I think what we’re going to do is we’re going to move back to the PowerPoint. We’ll leave this up and maybe someone maybe will ask a question later. But we’re going to go back to the PowerPoint. You know, one of the challenges in electronic health records is what do you do with unstructured text. And as we were working on Voogle, we recognized that the ability to search for structured information from unstructured text was going to be a challenge. So we came upon iKnow, which is actually going to be incorporated at no cost into the Intersystems Cache as of 2013. So this should be accessible to many VA people in the very near future.

Dr. Augie Turano:Yeah, I just wanted to interject that the structured data is easy to get to and we can show you, we can display the notes with the PDF and the book. But really, what we were really trying to get at was the concepts inside the notes and so that led us to the software. I mean, that was a pretty complex undertaking. And we found this commercial software from Intersystems that allows the concept extraction from bodies of text. So again, it was very convenient because it was based on Cache and making different web service calls to it seemed very easy. So incorporating this, wiring this into Voogle is something that we could do very easily with external web source.

Dr. David Eibling:So one of the problems with templated information is that it loses that richness of the narrative. One of my partners, who has been using EffiCare now for a couple years, complains bitterly about the fact that all his patients look the same. So he’s taken to writing a paragraph on each patient in free text just so that he knows the story of the patient. So that in this free text, of which there are enormous quantities in VistA in TIU Notes, there are incredible amounts of unstructured information, which if we can create structured knowledge from that becomes then useful for a variety of purposes. 

Dr. Augie Turano:So one of the things that iKnow does is it’ll take a sentence or whatever the text is and determine what the concepts are and then the relation between the concepts. So for this instance, when it looks at the sentences, okay, there are two concepts here – the brown dog and the mailman and the relationship that connects them is the fact that it barked. So the reason we really liked this, we started looking at a bunch of different kind of NLP systems but a lot of them required the use of building dictionaries or building ontologies and that was time consuming and neither one of us really had the time to build large dictionaries or descriptions of what we were looking for. So iKnow was appealing because you didn’t really have to do that. You can still build dictionaries if you want to, but you don’t have to. 

Dr. David Eibling:So one of the problems, of course, with just pure word searches is the ambiguity of words in the English language – bank of a river, bank in the road, where you keep your money, bank of computers. And sentences that are, when removed from context, lose their meaning – breast cancer in women mushrooms or in women mushrooms. And I guess probably teachers would like to strike idle students, although that was not the intent of the author of that sentence. 

Dr. Augie Turano:So the way iKnow works, there’s a classic approach to NLP that we have here in the red, so you take the sentences, you split them into words, use a dictionary or some other syntactic clues to try to figure out which ones go together, and then use a dictionary or ontology to determine what the results are. That’s a classic approach. It’s tried and true. It could be very effective if you don’t like training the system. But Dr. Eibling and I really didn’t have the time or the patience to do that. So this iKnow approach seemed to give us pretty good results without having to do that work. So the iKnow approach basically says I’m going to take those sentences, I’m going to split out the element using some linguistic algorithms or whatever to look for a connecting word, and then we can optionally use dictionaries if we want to, to continue down that path. 

So really, what it does is when we go through an iKnow processing sequence, basically, we read the data in, it does a smart indexing with the concepts. And then we can also do something call smart matching. So smart matching just is a way to take those concepts and then map them to some external source if we want to, or not. But again, going through the smart indexing, so we can take a sentence like for example, here, we have two patients are suffering from congestive heart failure. We scan the line, iKnow scans the line, it finds out that the concept is two patients. The patients are suffering from, a second concept is congestive heart failure. So we have concept detection and we have relation detection. So one of the things that you can do with this smart matching capability is to look at those concepts and then do a lookup to an external database like SNOMED to find out that well, if there’s a SNOMED ID that corresponds to that. And then, you can also look for the way the concepts are. And there’s a qualifier on congestive heart failure that says it’s an acute heart failure, and so that modifies the SNOMED code. 

So what we were thinking as we were doing testing – and we’ll continue to test and refine our processing of the text notes – is to see if it couldn’t aid in ICD-10 mapping and UMLS mapping, which is the library science.

Dr. David Eibling:So here’s a dashboard that Augie and his collaborators have put together that enables the physician to access the data. And this happens to be a compilation of a hundred thousand notes. And to give you some idea of the speed – the hundred thousand TIU notes – of the speed, they were able to index a million notes in just about three hours. So this is a fairly rapidly functioning processor. So what it does is it then generates this list of concepts and here we have the top concepts in the lower corner here. I think you pointed to those. And for example, there’s thirty-six thousand instances of patients in the hundred thousand notes and of those, it then appeared in five thousand of those notes.

Dr. Augie Turano:Documents.

Dr. David Eibling:Oh, documents, I’m sorry, of documents, not just notes. And then you want to mention the hypertension? Here you go. Go for it.

Dr. Augie Turano:So we typed in hyper-T with a T and so it did a look up through the index of all the concepts that it knew about with hypertension. So you see hypertension, hypertensive heart disease, cardiovascular disease, but also hyperthyroidism popped up, which we really weren’t that interested because it didn’t refine that because I didn’t type enough characters in to eliminate some of those things. So in doing this, it tells you – again – the same concept – relationships of the frequency and the spread through the documents across those. 

Dr. David Eibling:So what was sort of amazing to me was how fast the screen refreshed. I literally did not think the dashboard was working because if you type hyper-T and then type the E, the screen refresh in this particular prototype dashboard was almost instantaneous.

Dr. Augie Turano:Well, and it’s instantaneous because it’s already pre-indexed so you can call this data up really quickly. It also gives you the ability, this iKnow interface, to drill down to the particular sentence where the concepts occur and also, it gives you an ID for the actual text. So this view of it shows the actual reference to the text file and this is just a PowerPoint. The reason we just showed this part as a PowerPoint is because most of this was done on live patient data, PHI data, and I just didn’t want to risk having something pop up that shouldn’t. So if you double click this, a window will pop up that actually shows the actual text.

So the whole point of this is we can do the indexing on the unstructured text, extract the concepts relatively quickly, very fast, the algorithms pretty fast. And then you can match those concepts to any external databases or dictionaries that you have. What’s also nice about this is that it’s extensible to any domain. So of course we’re interested in medical domain, but you could use it with just about any kind of freeform text or anything like that that you wanted.

All these capabilities are callable for web services. You just need to do a little bit of programming on the internal side of iKnow. And the text size that you feed into it can vary from just simple sentences or paragraphs to entire bodies of text as the index. And like I said, it’s very fast in processing that data. Then what’s nice about it is the ATIs are all described and exposed. So if you’re a developer, you can make some nice applications that utilize this unstructured text. And Augie and I believe that this will be so rapid in the indexing that patient notes can be indexed literally in a minute or so after the patient is called up in the record, enabling real time use in actual patient care of iKnow indexing.

So in conclusion, we all recognize the information-rich characteristics of healthcare and how the quantity and complexity is increasing, how these information needs far exceed human cognitive abilities, and why we need to build new tools. That our current tools have not done a very good job at matching our needs for information with the actual tasks at the point of care. And we need to focus on using information systems to decrease physician work by simplifying information access to physicians at the point of decision making.

So with that, we’re going to close and ask if there are any questions.

Margaret:Okay, this is [sound distortion]. Thank you very much. I have a few questions here [sound distortion]...

Heidi:Margaret, can you hear me? We’re getting really bad feedback on your audio. 

Margaret:Okay. Is it any better now?

Heidi:Right now it is, but by really bad, I mean people are not going to be able to hear if it comes back on.

Margaret:Okay.

Heidi:So let’s give it a try.

Margaret:Well, you give me some feedback, Heidi. If it’s terrible, you’ll have to read the questions, if you don’t mind.

Heidi:Not a problem.

Margaret:Okay, first question. How do you use the knowledge base to locate data?

Dr. Augie Turano:A call comes in from the web-based client from the input text box. The program picks apart that request and then it does a lookup of knowledge base. So mainly, it looks up entities. So an entity might be glucose or sodium or demographics or whatever. It takes that value, it takes that editing, does the lookup in the database, the knowledge base, and then those instructions, that lookup provides the directions on how to retrieve the particular data. So for Easter, it was like a calculation. So we said okay, go out, call the C# program that’s going to calculate Easter or for glucose or for any kind of VistA data, then it knows to go to the VistA database. And that is all handled internally by the Voogle program by utilizing the directions inside the knowledge base to actually do a retrieval.

Margaret:Okay. An iPhone user wants to let you know that she is still impressed with Voogle. Next question. Does Voogle account for misspelling in text notes?

Dr. Augie Turano:No, it doesn’t. Misspellings will come up as separate concepts. That’s something that actually, as a developer, when I see a concept like that, that’s a good case for using a dictionary and being able to do the auto correction. Now we did with... I just started tinkering with that stuff so we have the [inaudible] algorithm for word matching and things like Google does. Sometimes you type a misspelled word inside the text box and it’ll correct. I actually built the algorithms and put them in Voogle but we’re not actively using them at this point in time. That’s something I can do actually relatively easily. I just haven’t gotten around to doing it.

Dr. David Eibling:And that’s really cool because I’m a horrible typist. So if we can actually integrate that into Voogle from the query standpoint, that would be really, really nice. “Did you mean” would be very helpful.

Dr. Augie Turano:Yeah, it doesn’t do that right now. That’s a great question.

Margaret:Can you build a more easy-to-use metadata report for CDW data?

Dr. David Eibling:[laughter] This is an Augie question.

Dr. Augie Turano:Sure. There’s certain data conventions like taxonomy that was used in the CDW. Some of them are confusing. Usually we use descriptive text to describe what the names of the fields are. It’s certainly doable to make that extension be more realistic. In fact, that’s one of the things that we used was the knowledge base inside Voogle is... a lot of the names inside VistA had kind of oblique or not really direct names. We can give them common names and synonyms, too. Actually, it shows the synonym capabilities. Yeah, so it is possible to do that so that you can access terms and entities using a more common dialogue or common [inaudible].

Margaret:Okay, next. Great tool. Curious if an addition to the application, if you are making available the platform APIs. Does that make sense?

Dr. Augie Turano:Yeah, [interruption] the APIs, we developed some of the APIs on the VistA side, particularly, for the display of appointments, reminders, lab tests, demographics, medications and things like that. Basically, because I’m an old MUMPS guy and a VistA guy, it was pretty easy for me to kind of wrap a lot of the single call RPCs up into more complete kind of clinical descriptions once that initial request came in from the web service client. And actually, I don’t know if we mentioned that we’ve covered the front end with Silverlight. So it’s a combination of Silverlight and C# on the client side and then on the back end, we sit there monitoring web service coming in from TCP to actually do the processing.

Margaret:Okay.

Dr. David Eibling:Voogle actually resides on the server.

Dr. Augie Turano:Well, the database processing mainly happens on the server. The rendering and the visualization happens on the client. So the timeline... We wanted to maximize the CPU site that was actually on the client so it wouldn’t be taxing to a VistA system.

Margaret:Million dollar question. What is the next step for Voogle?

Dr. David Eibling:Ah, a million dollars. [Laugh]

Dr. Augie Turano:Dave’s a surgeon and I’m the director of VINCI. So we both have day jobs, which really keeps us from doing a lot. I mean, we work at night sporadically, so that’s the main restriction that we have. We’d like to scare up some funding, I guess, to try to get some help.

 Dr. David Eibling:We’d love to get this to the point where we could actually install this on a production system and turn providers loose. Just let them... just go in to the system and see how they can use it and make recommendations to us for ways to improve it.

Dr. Augie Turano:Yeah, that’s really the next step – to find a site – a beta site or an alpha site or whatever that would install it. 

Margaret:Okay. What company makes the iKnow software?

Dr. Augie Turano:Intersystems. It’s part of Cache, the MUMPS language software. And they’ve been very helpful giving us demo copies and we actually have been working with the developers to figure out how the system works and go through refinements and iterations to try to get it to do what we need it to do.

Dr. David Eibling:Yeah, we’ve been helped quite a bit by them, as well as by, of course, the Greenfield Innovation program of the VA.

Margaret:Okay. Have you reached out to VA library network to incorporate VA’s existing online resources such as commercial abstracting databases?

Dr. David Eibling:Not yet, but that capability certainly exists within Voogle.

Dr. Augie Turano:Yeah, and PubMed and external databases. We just haven’t really done that. The knowledge base really isn’t limited. Anything that we can crawl, we can put in there. We haven’t done that yet. It’s mainly just VistA and proof of concept things like doing calculations or going out to particular databases, go hit a SQL database or something like that.

Dr. David Eibling:Pull up a document or something.

Dr. Augie Turano:Yeah, pull up, yeah.

Dr. David Eibling:But we haven’t done that yet but it’s certainly in the dream.

Margaret:Next, I see that the program can map free text to SNOMED concepts. Does it map to any other standardized terminologies; e.g., LOINK, HL7?

Dr. David Eibling:It certainly could.

Dr. Augie Turano:Yeah.

Dr. David Eibling:Because once it’s been indexed, it’s just a matter of using a lookup table.

Dr. Augie Turano:Right, you just tell it what dictionary you want to use and just let it do the matching. So it’s very flexible as far as that goes. We’ve only played with that aspect of it a little bit but the capabilities there are really, to use just about anything.

Margaret:Okay. How do we get access to use this system?

Dr. David Eibling:Good question.

Dr. Augie Turano:...money. No, right now, it’s just a prototype. We run it in a sandbox. I mean we really need to take it to production. I’m actually thinking about putting it up on VINCI. Right now, it’s just restricted to the sandbox so it’s really not accessible to the larger population at this point.

Dr. David Eibling:Yeah, we suspect that once others have the opportunity to interact with it that there’d be a lot of ideas and suggestions on how to improve it and how to make it even more useable.

Margaret:Okay. Where can I learn more about iKnow computational approach? I googled it but found nothing.

Dr. David Eibling:If you google iKnow space Intersystems, it should come up. Or you can go to the Intersystems website. It’s www.Intersystems.com. Or send me an email and I can send you references. They have a very nice user manual, as well. The version that we’re using is actually the 2013 version so we’re running data [inaudible].

Margaret:Okay, we are just about at the top of the hour. There are more questions. If you two and Heidi can stay on for another five minutes, shall we do that?

Dr. David Eibling:We’ll be glad to.

Margaret:Okay?

Heidi:Yeah, I’m fine with another five.

Margaret:Okay. Five minutes and then for those of you who do not have your answers – sorry – your questions answered, we will follow up with you and send out answers to the questions that are not addressed today. Next question: Can you re-aggregate VistA information that is collected at one encounter; e.g., health  factors, to a complete set?

Dr. Augie Turano:Yeah, you can take whatever you consider those health factors; in other words, I’m thinking that they mean like a list of fields or entities. You can aggregate those things and then say okay, for Patient XYZ, deliver all those values in kind of one call. So for me, probably the easiest way to do that... You can do it by specifying what those sub fields are or a lot of times, one of the things that I find is if it’s something that’s going to be done repetitively, I’ll write three or four lines of MUMPS code and then tell the program that for those entities or whatever, go out and grab those particular fields, and I’ll return those values back. And that’s actually sometimes much faster. If I can write the code in MUMPS, it executes much faster than if I have to use a file manager or API just because of some of the mechanics that goes on inside MUMPS.

Margaret:Okay. Will Voogle in the future help create a patient summary from multiple visit notes using the timeline technique you showed? 

Dr. David Eibling:We certainly believe that that’s feasible. We think that’s very feasible, and especially when we incorporate iNote to pull out specific concepts and problems and then use that to generate a timeline. For example, I do head and neck cancer. So the idea of being able to generate a summary of all the head and neck cancer visits for my patient – and that includes not just visits to me but also to Radiation Therapy and Palliative Care and Hematology/Oncology – the ability to generate that kind of a timeline would be extremely powerful. We think it’s very feasible.

Dr. Augie Turano:Yeah, I mean, I might move away from Silverlight and use HTML5 because there’s some tremendous capabilities there that we can do. We’re also toying around with these different zoom technologies so that we can zoom in and zoom out on the notes, as well as the searches.

Margaret:Okay. Is Voogle an HSR&D project or OI&T?

Dr. Augie Turano:Well, it was funded by Greenfield and Dave is a VHA employee and I’m an OI&T employee so I don’t know. I guess if you divided it in half [laughter]... But the funding came from OI&T, yeah.

Margaret:Great application. Curious as to how many developers, QAs, and team members it took to develop this and for how long.

Dr. David Eibling:[Laugh] Great question. Thank you for asking that. Augie, you’re on. 

Dr. Augie Turano:No, I worked on it in large part. All the MUMPS programming I did by myself. We had a contractor come in and help us with the UI part of it, that’s engineering. We had a short one-year, one-person contract so everything that you saw was just David, myself, and that’s engineering that had one guy.

Dr. David Eibling:And I didn’t do a whole lot other than advise. I mean, Augie’s the real brains behind this. And he and I worked together at night to sort of say here’s what we ought to do, let’s try this. And then the contractor helped us with the Silverlight science side but they kind of ran out of... we ran out of time and money. So it’s not quite to the point where we would like to see it.

Margaret:To what extent can Voogle assist in surgical clinical appointment management and the generation of related reports – clinic utilization, no-show rates, etc.? At present, VistA is not a robust enough system to meet the needs of my facility.

Dr. David Eibling:So we actually haven’t addressed that. I mean, we haven’t really looked at scheduling. I mean, we can certainly pull up appointments and so forth. We have the ability, again, to use a timeline so you could actually portray this stuff on a timeline on a different display than in VistA. But from the perspective of preparing for a surgical clinic visit, I think it could be extremely powerful to pull up the information that is actually needed for that particular patient at that particular visit rather than as I do personally, you know, roll the computer into the room, say hello to the patient, and then start pulling up information. To have that information already, shall we say, indexed and accessed, would be extremely useful to me in real time. 

Dr. Augie Turano:Yeah, I don’t think we really could do that. We could certainly show data in the VistA database but as far as actual input, we don’t really do this. Right now, it’s just basically [inaudible].

Dr. David Eibling:Oh, yeah. 

Dr. Augie Turano:We can write to the database. I just don’t do it because it’s not considered polite.

Dr. David Eibling:We have sort of adopted the idea that there’s a whole host of people working on data entry and we’ve sort of confined ourselves to looking at data extraction rather than data entry.

Margaret:Okay. We should stop and how about one last question and I’m going to combine two of them, okay? Is that okay with you guys?

Dr. David Eibling:Yeah.

Margaret:What are the real barriers to making Voogle accessible to everyone? And the other question that’s kind of related is was this a level three initiative and are you advocating for level two status. You can sort of...

 Dr. David Eibling:Well, in answer to the second question first, no, it’s not really a level three. It was a funded innovation project by the Greenfield Innovation program, which was separate from level three software development program. So it’s a different paradigm. Regarding what it would take, in its current state, the implementation, the uploading of the program is fairly tedious. 

Dr. Augie Turano:Well, only on the server side. Let me qualify that. On the VistA side, there’s maybe – I don’t know – ten or twelve routines on the VistA side, the actual MUMPS routines. On the server side because of some of the rendering that we did, we used a couple of external libraries. I had to set up some web services so I had to go into IIS and some specifics that probably aren’t that interesting to the general population here. But it did take a fair amount of configuring just because of some of the libraries we used for rendering the data on the fly. But actually, part of the work ahead actually is to simplify that deployment. So right now, I would go into IIS – the Internet Information Server – and have to do a little bit of configuration, install the MUMPS routines, and then on the client, there’s really nothing to do because as long as you have the Silverlight plug-in, you’re good to go. And that’s a free download, right?

Dr. David Eibling:Yeah. So it’s not yet one-stop shopping, if you will, to configure the server. 

Margaret:Well, we should stop. Thank you both so much. We really appreciate the time you’ve taken. I just want to tell the audience that remains our next session is Tuesday, December 18. Dr. Stephen Lindley is presenting. The topic is Using CPRS to Facilitate Evidence-Based Treatment of PTSD. Thank you all, happy Thanksgiving, and bye bye.

Dr. Augie Turano:Thank you.

Dr. David Eibling:Thanks, everybody. Thank you.

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