>> Desney Tan: It's my pleasure today to introduce our speaker, Dr. Justin
Gatewood. Justin graduated from the University of Washington School of
Medicine some number of years ago. I won't say how many I guess. So he's -and he's got family around the region, so he's plenty familiar with the beautiful
weather we have this time of year.
Justin did his residency at the University of Chicago Hospitals where he was also
education chief resident and is now an attending physician at the emergency
department at Washington Hospital Center out in DC.
I met Justin about a year ago when I was out visiting the Microsoft Medical
MediaLab out there. In fact, Liz is out here from there. And since then we've
been collaborating on several projects, mostly to do with patient facing
information displays. It's been a blast working with Justin, I must say, both
because of his breadth and depth of expertise and interest in the medical domain
but also in technology, which is sort of a difficult combination to come by.
Justin's here for the day. I've got a couple of open slots later in the day if anyone
would like a slot catch me after this meeting.
And you know, hopefully these exchanges start to foster more collaboration both
with Justin, but also with Washington Hospital Center and the MedStar unit as a
whole as well as the various health solution group labs out there.
Without further ado, I'm going to hand the mic over to Justin.
>> Justin Gatewood: Great. Thank you. Thanks, Desney. And you know, just
to kind of get sentimental a little bit here, Microsoft has been great. Washington
Hospital Center and Microsoft have kind of an established relationship I think
starting back from the development of Azyxxi, which clearly we know now is an
Amalga by Dr. Craig Freied and Dr. Mark Smith. And so that's just sort of
continued to roll, and things have been great and we're kind of forging ahead and
I'm asking some interesting questions. So thanks for having me here. I
appreciate it.
So again, Justin Gatewood. And I'm an attending physician in emergency
medicine. And this is sort of your chance to pick the brain of an ER doc to get
under the hood of a working emergency department. And I want to kind of take a
systems view of the of the emergency department. It can be sort of apparently
chaotic place on the outside, but it actually makes sense. There's a method to
the madness and I want to kind of elucidate some of that.
So the objectives for today -- and we've got the room until 12, I want to spend
roughly half of that kind of talking -- I don't want it to be too didactic, and then I
want the other half to just be a lot of sort of question and answer discussion,
brainstorming of potential collaborations. Please feel free to stop me at any time
to ask questions.
A couple disclaimers here before we start. I don't have any formal training in
systems theory or in computer science or mathematics. Sometimes I may flirt
with terms that -- and feel free to say, hey, that's not -- you know, we're purists
here, that's not the correct usage of the term.
Another disclaimer, I'm a physician. I have a very physician centric view of
workflow, so that bias is inherent as well. With any talk about workflow, there's
going to be lots of flow diagrams. This is not up to UML convention. I didn't use
Visio. I should have. But that being said, hopefully you'll be able to grasp some
of the concepts. They're quite -- they're quite straightforward. So we'll
deconstruct the flow in the emergency department, looking at two systems, one
the flow of patients and also the flow of information. Sometime they can be one
and the same. And a very high volume active emergency department.
And then I want to examine some of the concepts that underline medical decision
making. Because I hope that our discussion can move past just examining
workflow but also get towards really understanding how a physician's mind works
and how decisions are made. And then I want to take a second to highlight some
existing MSR and Washington Hospital Center collaborations and then get into
areas that we can work together.
So a bit about my hospital. MedStar Health is a large health organization. We
have several hospitals in the Washington, Baltimore area of which had
Georgetown is also one. Washington Hospital Center is the largest, it's the
largest in the District of Columbia. It's got 926 beds. It's a very large hospital.
It's also a major teaching hospital. We accept patients from all over the area, so
it's a tertiary care referral center. It's also the level 1 trauma center and the only
burn center in the area. And a plug for the hospital, consistently ranked by US
News and World Report in several areas.
About the department itself, we provide care, emergency medicine and trauma
care for adult patients. There's a children's hospital directly across the parking
lot. So we don't see -- we don't see kids. Sometimes they'll wander in. We see
about 85,000 patients a year. That breaks into, you know, as much as 300
patients a day on a very busy day. We have a very -- a population that reflects
the District of Columbia. It's largely African-American although we have a
surprisingly diverse international population. There's several universities near by.
More than a third of the patients that we serve are illiterate, and from that I'll
deduce have health illiteracy as well. The 37 percent is something that was put
out by the DC government to describe actually all of DC functionally illiterate all
of DC.
And as far as payors go, the majority have public assistance Medicaid. As far as
a degree of the acute of patients, we see -- we roughly admit to the hospital -and this is a -- this is sort of a proxy for how sick our patients are, we roughly
admit about a quarter of our patients. 10 to 15 percent of those admissions are
about two to three percent of the entire people that we serve end up in the critical
care unit.
So just to kind of give you a benchmark. This is a -- we see really, really sick
folks.
The department itself is -- and I'll go into the layout. It's 42 beds. At any time
there can be up to six attending level emergency physicians. An attending
physician is just a physician who has finished their residency training. Anywhere
from 10 to 20 emergency trained nurses. We also are a emergency medicine
residency training site. And we have scads of great Georgetown medical
students as well.
So first I just want to talk about the principles of information flow. As you sort of
-- as we go through the flow of patients and information you'll see a couple things
emerge here. First of all in really the classic sense of the term, they're both the
deterministic and a nondeterministic system. You know, some things are
measured output for every input, and other things you put a patient in the system
and you really don't know what you're going to get. And this is really important to
realize. You put in a therapy, you administer a medication and you really don't
know what you're going to get. And that's something inherent in emergency
medicine and in medicine itself.
Another concepts, there is asynchronous flow, but you have these parallel sort of
pathways. Patients go if the system at different times they converge at different
nodes, whether it be in a radiology suite, labs. They're all different bottlenecks,
and they all sort of manage their way out of the system. And we'll go through
that as well.
Another thing is this is not just a linear pathway. You will see that patients will
come back to different nodes. We are constantly reevaluating the patient. The
machine sort of will go, start and stop and will do different iterations of therapy.
So it's not just we're not going to straight through here. And another thing is it
can be very chaotic. And I mean this in, you know, the lay sense of the word.
You walk in and it can be very chaotic. And sort of more of the system sense of
the word, it's chaotic in that there are identifiable patterns within what appears to
be chaos. And hopefully you can make some connections there as well as we go
through.
How many people have ever been to the emergency department? I think most
folks have been to the emergency department. And how many of those people -how many kind of understood what was going on? Okay. Great. That's
fantastic. That's fantastic. And kudos to that emergency department that they
were able to have it make sense. I wish we were always able to do that.
A lot of people I think see the emergency department as a black box. I mean
patient in, patient out. The same could be said for a hospital as well. And I'll use
the words of my mentor Marc Smith and also Dr. Craig Freied who wrote a
manuscript back in '99 looking at the emergency department as a complex
system.
In a sense, it really looks much like a hospital. People go in, they're evaluated,
they're treated, tests are run, decisions are made, hopefully cures occur and then
the patient comes out on the other side.
So the emergency department is kind of a small temporally compressed version
of that. In fact, though, the emergency department looks much more like this.
And we'll fill in each of these boxes. So if you can kind of keep this framework at
the front of your mind as we're walking through flow, I think it will be kind of easy
to understand where things fall into place.
So you know, the first kind of space in flow is a prehospital space. It's not even
in the department itself. Second is when people come, they register and they're
triaged. We'll of course walk through this. Evaluation and management is by far
the most complicated, in a sense the most chaotic and it's a place where we'll be
able to explore some of the decision making. And then lastly, the disposition;
that is, where does the patient go once we're done with them?
The diagram itself looks like this, and we'll walk through each part. So keep that
in mind.
Starting from the beginning. So the prehospital setting is anywhere that the
patient comes from. So patients arrive by ambulance, they arrive by helicopters.
We run MedStar Transport, we have several helicopters and ground units that fly
and pick patients up. They go to scenes, get people off of the freeway, they get
them from other hospitals. So they arrive by ambulance, helicopter. DC Fire
Department brings them. Sometimes they're transferred via private ambulance.
All those people are funneled in.
Clearly they walk in as well and are dropped off by friends. And at times we will
get people from the hospital. These are patients who are in the cafeteria visiting
a family and who become acutely ill and are brought to the emergency
department.
So once a patient is -- enters into the physical space of the emergency
department, the first thing that happens is that they're registered. We have to be
able to generate a chart for them, a computer chart or an electronic chart. We
generate a medical record number and a visit number. And that happens when a
patient presents to a front desk and states their chief complaint.
The chief complaint I wish it would be something like well I have Chess pain of
cardiac etiology. But often it's I don't feel well. So -- and we'll see how that can
be kind of confusing in terms of codifying why people are actually in the
emergency department. Their demographics, how they're going to pay of course,
we have to get paid. And then that's when the paper chart is generated.
I won't get into all disparate electronic medical records but in our department
currently we have paper charts that are filled out and then scanned into an
electronic medical record.
So after the chart is generated, then they'll be triaged. So a triage, if anybody
speaks French is French to sort. And what triage does is when a person enters,
it's a way of determining essentially how sick you are. You'll hear an emergency
medicine of sick or not sick. A lot of what we do especially is determining -certainly we want to find a diagnosis, but we want to know are you sick; IE, are
you likely to die in X amount of time or are you not? And by assigning a triage
priority that's a way initially of sort of deciding how quickly a person should be
seen and what resources that they're likely to need.
This is done by a nurse. And it's based on symptoms, age, vital signs that are
taken initially. That's just heart rate, blood pressure, temperature, oxygen level.
And just getting a quick past medical history. There are two ways of doing this.
There is an ESI algorithm, couldn't even tell you what it stands for. But this is an
algorithm that basically you can go through that will then assign you a triage
number. I will tell you seasoned nurses can look at a person and say you look
like you're about a two. You look like you're a four. One is the most acute and
then four are, you know, sprained ankles, hang nails, that sort of thing. Okay?
And then patients are sequentially assigned to the treatment teams. We have
four treatment teams. We have the blue team, the red team, the green team.
The color has no significance at all. They are just geographic areas. And then
we have the ambulatory care area, which is like an urgent care or fast track.
Most, if not all, larger emergency departments have these areas.
From there, the patients will often go through a triage team. This provides a lot
of confusion for patients and for people who aren't familiar with the flow of the
department. And I'll try to make this as clear as possible. The triage team is sort
of an optional team that's in place through which all patients are funneled on the
way to the teams to which they're actually assigned. The purpose of the triage
team is to do just sort of a rapid evaluation to sort of get the ball rolling and to
anticipate any diagnostic procedures, any diagnostic tests that the team
physician on the other end would likely order. A quick example would be
someone who comes in and has Chess pain. Instead of -- after their triaged,
instead of putting the patient in the waiting room you can say well, Dr. Gateway is
going to see you on the green team, the wait may be a while, but in the time
being we'll go ahead and draw some blood and sent to you chest x-ray so that by
the time all of these things are back Dr. Gateway will be able to make a more
informed decision.
The triage team at our hospital emergency department is only operates between
10 a.m. and midnight. So actually the majority of the -- you know, around the
clock we do not have the triage team. But we do during the busiest times. And
we've actually seen that that does sort of increase throughput, depending on how
you measure it.
So patients wait in the waiting area until they're seen by the respective team after
they see the triage doctor, okay. So you go, triage doctor says hi and then you
still sit out in the waiting room. It can be several hours on busy day until you're
actually able to see the physician to which you're assigned.
Medical decision making is incredibly complex. It takes a period of observation.
It takes some thought. And it takes getting a very full history of the illness and to
go a good physical exam in a period of observation. So the evaluation by this
doctor in triage is not a substitute for a full examination on the other respective
teams. That's something that patients often have a hard time with, they say well,
a doctor already saw me, why can't I go home? And it's sometimes difficult to
explain that it's not always that simple.
So, after they've waited, patients are placed in rooms on the respective
geographic teams as they become available. They're first placed in order of
acute and then duration of weight. This causes a degree of duress because
patients don't necessarily understand that well, this guy is -- if I have an 85 year
old with chest pain who has had medical heart history stints placed and has a
history of a heart attack, I understand that you've been here for five hours with a
sprained ankle and that this patient just came in. She's much more acute. She
has a higher triage number or a lower number, higher acuity, so she needs to
come back first. And then after acuity of course it's then placed by duration of
wait.
As I mentioned, the team colors have no relation to acuity. And the least acute
patients during the hours at least at our hospital of 10 a.m. and midnight go to the
ambulatory care area, okay? And so they're sort of fast tracked through.
So during the evaluation, this is where the patient physician relationship really
begins. This is when the doctor takes a full medical history, performs a physical
exam and begins the workup or thinking about the diagnostic therapy, the
diagnostics and the therapy. That's when they're initiated. And this is really
when medical decision making begins.
Some of it can begin back around where the triage doctor is. But all that is is to
anticipate the medical decision making that will take place here.
Any questions thus far? I think that's kind of a good place to -- okay. Yeah?
>>: Yeah. You might get this -- is it your -- so you're describing how it kind of
works at the abstract. At least in some places it -- it actually opportunity always
work that way in the sense that the nurse -- that people may be assigned an
emergency [inaudible] and next number, it's not actually -- it doesn't actually
correspond to the one that's in the book.
>> Justin Gatewood: Absolutely.
>>: Right.
>> Justin Gatewood: And this is part of the -- that's part of the chaos that can
happen. As you mention, the ESI algorithm is not 100 percent, clinical gestalt by
even the most seasoned of nurse is not. Patients will often declare their true
acuity. And remember acuity can change because the natural course of disease
can change. And so bad outcomes can happen in the waiting room someone
who comes in with chest pain then begins to have abnormal vital signs and may
they're themselves as being more acute.
So the idea is that we can get people back to see doctors as soon as possible so
that someone doesn't start off a four and then a three and then a two, all this
happening within the waiting room.
And that's why we've sort have put the triage team out front, to be able to kind of
capture some of that. But that's a very good point. This is best case scenario
how it's happened. And I'll actually go through a couple of scenarios as well.
So after the -- we'll talk just briefly about physician documentation, how it
happens at our hospital in our department. Physician documentation happens
around the patient-physician encounter. So after taking a good history and a
physical exam, the physician will either handwrite the note at the bedside, will go
out depending on an individual sort of microworkflow, write it outside of the room.
Sometimes they'll busy -- they'll just keep it all in their brain. They'll go do other
tasks.
An hour later they'll come back and hand write the note or type the note.
Currently notes are not being typed bedside. Workflow in the environment just
doesn't facilitate that. Handwritten notes and also the typed notes are
sometimes scanned, and then into the electronic medical record. Now we have
Paladin which is sort of a module on to Amalga, which will allow you to
electronically sign.
And so we've got different physicians refer to handled documentation differently.
And that's only in our department.
So after the evaluation, which is when the workup begins. And a workup
basically is just, you know, the diagnostic tests. So patients and their appropriate
specimens flow to the appropriate laboratory or imaging suite. So what happens
is a physician comes out and says this person could have this, I want to order
this. They go, they write down the order, they give it to a clerk, the clerk inputs it
into the system and then the sort of ancillary diagnostic suites are activated so
the CAT scan will say we see an order here for CAT scan of the brain, then we
will -- when time allows, send patient transport out to get that patient. The patient
will go over, get the CAT scan an then go back.
Urine is sent, blood is sent, spinal fluids, et cetera, are sent to the labs. What
happens is the clerk -- and this is getting pretty granular, but the clerk will print
out a sticker to go on the tube with the patient's name, the type of test ordered.
That will be sent down to the lab.
So the physician orders the says and takes this -- the nurse takes the specimen
which is sent either to the hospital laboratory or also the emergency department
itself has its own laboratory that does sort of quick, you know, time critical tests.
Sort of ultra time critical. Everything is time critical in the emergency department.
Those results, if they are critical, will often be called directly to the physician for
really high values or really low values depending on the test. Once those tests
are run, they'll also put into the local database that's sort of specific to the lab,
and then the electronic medical record, Amalga is able to locate those from
disparate databases and then pull them into one field, which the physician can
view. And then the physician views the electronic medical record.
Radiology happens similarly. The physician orders the tests, either performs -sometimes them self will perform some bedside imaging techniques in the case
of ultrasound more often they will go to the respective imaging suite. That
information is then put into a local database, which is then viewed by the
radiologist. So emergency physicians are credentialed to read certain types of
imaging studies but we can't read them all. The radiologist will then call back the
results to us or dictate them and have a written report and an electronic medical
record.
Often in the case of a simple x-ray, the emergency physician will just look at the
image in Amalga and then make a decision. Yeah?
>>: Is the quality of the in and out of the department tests -- assumed to be
roughly the same, or is there also a discrepancy, the trading off time for quality?
>> Justin Gatewood: No. There are certain standards that are established by
the department of laboratory medicine, and so although the tests are run in
different ways, you are -- you can hang your medical decision making hat on the
tests that you get. It's -- we would love to do everything in department but sheer
volume prevents us from doing that.
So therapy. Once you've decided a workup, you start to administer therapy, the
appropriate IVs, medication, oxygen, sometimes procedures are performed and
then often consultants or specialists are involved, depending on the case.
Now, all of this happens at sometimes simultaneously. Some processes are
dependent on other things. In terms of therapy, often you are administering
therapy and then you are going back to reevaluate the response to your therapy
based on that decision sometimes you'll order another x-ray, another CAT scan,
order more plod or urine tests. So these things are interdependent, often they're
also happening in parallel and there are multiple feedback loops happening.
This is for one patient. So, you know, superimpose this same flow -- you know,
as an attending emergency physician it's not uncommon that on a very busy
Monday afternoon I will have 25 people at a time assigned to my team. So
imagine that big nasty diagram 25 folks deep and, you know, you've only got one
lab. You've only got one -- you know, two or three CAT scan machines. You've
only got one orthopedic surgeon in house available for consults. So things can
get -- that's really where the bottlenecks occur.
And then lastly we'll talk about disposition. Clearly certain medical conditions
and responses to therapy dictate where the patient goes. If they are being
discharged, it implies usually that they're going home. Sometimes they're being
transferred to another institution. Often they will leave against medical advice,
which is unfortunate, but sometimes what we deem as being sort of a priority
does not always -- and if you've got five dogs at home and no one's going to feed
the dogs, then maybe you just don't want to stay to get that other test. And so
sometimes, you know, that's important for the patient.
Others will just sort of disappear and leave AWOL, and then unfortunately some
patients do die as well.
If the patient is not discharged, they're then admitted. So we admit patients
based on how ill they are based on the need for extra more tests, more
interventions, things that cannot be accomplished while they're in the emergency
department. And there are several areas in the emergency department to which
they're admitted. And that really depends on how ill they are and what resources
they need.
So when someone is just admitted to the floor, this is just sort of a general
medical or surgical bed. These patients are sick enough to stay in the hospital,
but, you know, they don't -- they're not critically ill. Critically ill patients stay in the
ICU. We have different ICUs depending on specialty. Sometimes patient meet
criteria to be in the intermediate care, which is sort of in between. And I won't get
into that because it just goes into a lot of medicine. And then some folks go to
the operating room.
So here again is that big nasty flow chart. And one thing I want to point out is
you'll hear -- there's several metrics that we track and are easy to calculate. The
door to doctor time is probably the one that you'll hear the most. And that's
basically how long it takes you to see a doctor.
Door to decision time is the time that they enter the door until the time that a
decision to either discharge them or admit them is made. After they're admitted,
they may reside in the emergency department proper for some amount of time.
That's the boarding time, and it's completely -- it is motion dependent upon what
else is happening in the hospital. And then of course the length of stay is from
the time they enter the door to the time they physically leave the department.
So we talked a little bit about, you know, kind of individual flow of patients. And I
wanted to talk about sort of stepping back and viewing the department and all the
chaos. And here's sort of a face based diagram of the emergency department
kind of the state of emergency department. And you could probably -- there are
probably several different types of states. But the one that I wanted to talk about
is sort of the progression from normal operations to disaster. It's great you get
there at seven o'clock in the morning. There may be some intoxicated folks
sleeping it off from last night. Someone slips on the ice in the morning. The
department's slow. Folks are not that acute. You're down here, hey, great.
Normal ops we're getting people through.
And on this axis here is the number of patients. This is basically just the
measure of acuity. And so when there's a time when you have a lot of patients
and really none of them are that acute but there's still so many, that can be
classified really down kind of in the disaster zone. In fact, when you talk about
emergency preparedness and disaster medicine, the majority of people who
present even after a major bus crash or a dirty bomb detonation are people who
are not very acute. It's usually the walking wounded, people who are just freaked
out and they just want to get checked. People who have -- you know, rolled their
ankle and aren't very sick.
But occasionally, let's say an example where like we all heard unfortunately in
Lakewood yesterday where the four police were shot, it was early in the morning.
They may have been taken to a local emergency department where there were a
few patients, but four shooting victims are all very acute, could be classified as a
disaster.
The funny thing is, and just sort of think about this, is at some point we say this is
a disaster and we flip a switch and we make a call to the head of the hospital and
we go into disaster mode. And when we do that, we open these pelican boxes,
we put on -- we put on these vests, we have walkie-talkies and we sort of get into
disaster mode and we follow, you know, sort of a different flow of patient care.
And we change some of our resources and we change the way that we look at
patients a little bit.
>>: [inaudible].
>> Justin Gatewood: Once every couple of years probably. At our hospital it
happened I think during the anthrax scare, it happened during 9/11.
>>: Those cases where the large [inaudible] Y disaster tons of people coming in
because they were scared?
>> Justin Gatewood: I wasn't there. But I will tell you that when I hear them
talking about them, especially the anthrax, you're here. Now, this can actually -if you take this and you look at it over time as well, you can have several weeks
that will sort of stay around here. For example, Monday afternoon you have a lot
of people who have flu like symptoms. None of them are very sick, but they all
want to know dock, check me out. Tell me if I've done Swine flu. So we sort of
hover around here.
And so it's just sort of a kind of abstract question. When do we pull the trigger?
And ideally we wouldn't pull a trigger. Ideally our operations would be scalable
enough that we could just adapt to, you know, a time when you're up in the red
zone.
I wanted to do this in 3D to show you that you can easily put another parameter
or another axis, and that is that the rate at which patients present. A classic
example would be you are -- it's eight o'clock in the morning, nothing's
happening, and all of a sudden there's a bus accident full of special needs adults.
And they just have to get checked out. Nothing's wrong with any of them. And
they drive them in. And all of a sudden the bus drops off. You know, 40 people.
You know, then you're up around here. So that the just another sort of
parameter.
But a very interesting question, and one that hopefully we can explore later as
well in discussion. When do you -- what do you call disaster and why do you
change your operations and what happens to workflow? So, let's talk about
some of the types of information. It's hard to actually show a separate flow of
information. But I wanted to talk about the types of information and where they -where they're generated and the pattern in which they're generated.
So demographics just to sort of review occurs in registration, and it's a one-time
thing. People's demographics don't change during the visit. The medical
database really rear to consists of the chief complaint, the history of present
illness, basically the story behind their chief complaint, their past medical history,
the vital signs and then the physical examination. So the chief complaint as you
can see happens up front sort of in lay terms. It's recorded by a clerk up front.
It's often reiterated in triage. And then really the doctor figures out -- their job is
to figure out really why are you here? You say you're sick, you say you're here
because you just don't feel well, but it turns out that you've got, you know, the
worst chest pain of your life and you're wife just made you come in because
you're a pretty stoic guy. And often this is not explicit, the chief complaint. And
it's our job to make sure that we find it.
The history of present illness is expanded upon in the physician encounter. And
it actually is kind of ongoing as well. Patients will add things. Hey, doc, I forgot
to tell you this. So it's not a one-time thing.
Past medical history is usually a one-time thing because it's available from the
medical record. Vital signs are taken by the nurse, and they are taken serially,
depending on how ill the patient is.
The physical exam is ongoing. It's often goes through a period of reevaluation. If
you are classic example is someone with abdominal pain. You're not too sure if
they need a CAT scan, but they've got enough risk factors that it could be
something bad so you did you have them some pain medicine, do some lab
work, go back fill their belly again go back before you're able to make a decision.
So -- and when something like that happens, the documentation of that may not
end up in the medical record until the patient leaves the department. Until I'm as
a physician able to say okay, this is what I want to put into the medical records to
describe this patient's exam.
Laboratory results come from the laboratory either after being put into the
medical record or often if it's a critical result by phone that's ongoing the same as
we discussed with radiographic images and the dictations.
And then consultants come down and help us decide how to best take care of
patients. Sometimes they'll come down and follow a patient through the course
of the stake and be ongoing, or sometimes they'll come and say, no, I don't think
they have an appendicitis and they can go home.
And then also, this is really big, is kind of the nonverbal and subjective things that
are -- can be inferred from communication with other staff members can be
inferred from talking to the patient. And sometimes ways that other consultants
or physicians will write a note in the medical record you can sort of read between
the lines and the subtext may tell you something that's not explicitly stated.
And that's very important as far as medical decision making as well. So let's talk
about principles of decision making. The first is at its core decision making is
sort of hypothetico-deductive, and that is you present with some chief complaint,
I will then initiate a battery of tests that will aid me in sort of proving my
hypothesis wrong or proving myself right.
They can also be algorithmic. And this really depends on how ill the patient is or
how serious the chief complaint is. If someone comes in and says, you know, my
belly just hurts and I've had a fever, there really isn't any strict algorithm for
abdominal pain and favor. If you are 65 and you twist your ankle and it hurts
here, there is, however, a clear algorithm for how to take care of those patients.
In reality it's often a mix of this hypothetico-deductive decision making and an
algorithm.
So this is sort of getting under the hood and into the brain of an emergency
physician's mind. So crossed with this algorithmic and hypothetico-deductive
sort of framework and approach is time dependence. The emergency
department is intensely time -- and that's why I love it. What you do matters.
Everything that you do matters. You have to anticipate the next step because we
don't have all day to take care of patients. We have other patients waiting.
Some illnesses and some disease processes will reveal themselves as I
mentioned earlier over, you know, varying degree of time. And so we're doing,
number one, you know on an X axis and so things need to be -- things are very
time dependent as well. And time is we're always trying to sort of get ahead of
the curve of the natural progression of this disease process.
And I'll detail this in an example here is the idea that diagnoses can either be
presumptive or confirmed. If it quacks like a duck, it often is a duck. However, if
we have the CAT scan that says it's a duck, well then it's a duck. And often we'll
treat those two things like ducks.
As far as treatment is concerned, we treat things empirically or we use sort of
more evidence-based approach. And sometimes they're not mutually exclusive.
A good example is a patient who comes in, has an x-ray that has a confirmed
pneumonia, but we know based on the patient's age and other things that this is
likely a community acquired pneumonia, that there are ive to seven different
bacteria that will cause that pneumonia. We will initiate empiric treatment of a
particular antibiotic that we know will take care of all of those bacteria.
And then as I mentioned before, we're constantly reevaluating the patient,
making sure that we're measuring the response to our therapies and ordering
any additional tests. Taken in the context of several patients, sometimes up to
25, we may need to reshuffle our priorities. Sick patient take precedence. It's
very frustrating, but also understandable when you come out of a room and the
patient who two hours ago you just said I'm sorry, I know you don't feel well, I'll
be right back, they're calling me, you run in and you spend the next, you know,
hour or two hours running a code, resuscitating a dying patient and then you go
back in and you say I'm so sorry, where were we?
It's born out of necessity. It can be very frustrating. And it's hard sometimes to
let other patients know that, hey, I was in there with a dying patient for the last
two hours, you know, give me a break, I'm sorry that that happened. That's
certainly a part of the job.
And then any intervention or any interpretation needs to be taken in its
appropriate context, both in terms of the individual or the group. A group sort of
context would be in a disaster situation where you say, hey, I got to let the
walking wounded kind of just term and often the disaster is we will turn them
around and send them home. For the individual, a patient who has a blood level
of X who also we know had a blood level of X for the last 10 years and has a
reason for it, you don't really get too excited about blood level X, although it's
below normal. However, a healthy male or female who comes in, who we
assume had a blood level of Y that was normal two weeks ago and now has a
blood level of X, we know that that's abnormal.
So deciding how we interpret labs and imaging results completely needs to be
taken in context, and that provides -- it can be sometimes tough as well.
I'm going to quickly go through two examples, and then I think that's about it, and
we can open it up. So to display some of these concepts.
The first is a 43 year old guy. He calls the ambulance after twisting it while
stepping off of a curb. By 7:22, he's in the department. He provides his
demographic and insurance information. After hearing his chief complaint and
you know looking him over, they say, hey, you're level 4, go to ambulatory care
area. He's taken by a wheelchair.
The patient waits there for, you know, a little more than half hour to be seen by a
doctor. The doctor looks at him and says, great, want to make sure you don't
have a fracture. Let's get an x-ray and here's a percocet for your pain.
He gets the percocet, he goes over to x-ray, the doctor reviews the x-ray and
says you don't have a broken ankle, asks the nurse to splint it. The nurse gives
the patient some -- a wrap, some crutches and the patient's discharged.
Easy-peasy right? So that was problem a very deterministic way of approaching
that system. Depending on how many people were there, there was that
converging note of radiology. Maybe the nurse was busy with some other
patients. And not particularly chaotic, and there was no reevaluation. That was
as I mentioned very algorithmic decision making process. Time was still of the
essence. And we had to anticipate get the patient out in a timely manner so that
we could see other patients.
We confirm that diagnosis by looking at an x-ray, and we use some evidence
based practices that say ankles heal better if we wrap them. And there is really a
broken ankle is a broken ankle.
Case 2 is a little more difficult. And this is kind of more what we live for. It's a 25
year old woman. She didn't call the ambulance like the other guy. She walked
into the emergency department. She's 25. She says I've got a little chest pain.
She provides her demographic information. She was a three on the emergency
sever it index. She had normal vital science, kind of a reassuring story. But, you
know, chest pains don't usually go the fast track because more can go wrong in
the chest. But in any case, she was assigned a 3, and she was sent over to the
blue team.
Before going to the blue team, she was seen by a triage doctor who said well,
you got chest pain, you look pretty well, but you know, chances are that the doc
on the blue team is going to want an x-ray and EKG and I'll order some blood
tests that they probably will find pertinent. The lab is drawn by the nurse and
sent to the lab.
After seeing the triage doctor, the patient goes out into the waiting room for
another two plus hours and then waits. At 5:47, finally a room opens up on the
blue team and the patient's taken back. And the physician starts the
patient-physician encounter, takes a good history, and performs a physical exam.
You know, after that, the patient notes that, you know, doc, I'm actually having a
bit more pain and the doc says, okay, well, we'll order some pain medicine and
the nurse gives it to her. And then she's tape off to x-ray to get this -- to get the
chest x-ray.
So at 6, though, the patient returns from the x-ray. The nurse takes another set
of vital signs and says looks like the heart rate is a little elevated, and it looks like
her oxygen level had dropped a little bit. The doctor is at the work station and
reviews the x-ray and says that it's normal. But then also reviews the -- reviews
the lab work that was done that was actually sent by the triage doctor and says
well, I see here that you have an elevated D-dimer. D-dimer is a blood test that
checks for degradation products of blood clots. So it's not very specific. A lot of
things can give you blood clots. But the utility of this test, and I won't belabor it,
is if it is normal, you essentially have excluded the possibility of any blood clots.
In this case, it was sent to rule out a blood clot in the lungs.
He notes that it's high though. He or she notes that it's high, orders a CAT scan
of the chest to rule out a blood clot in the lung for this patient. That's at 6:07.
And then at 6:10 the nurse goes to the doctor and says, you know, these vital
signs actually look a little worse. So the doctor seeing that and then seeing that
the D-dimer is evaluated says well, let's presumptively treat this patient for a
blood clot. So the doctor orders an injection and a blood thinner Lovenox, just to
prevent the clot from further forming. And then the nurse administers this.
Well, at 6:21, the patient not great vital signs but still stable, goes over to the
CAT scan where the CAT scan of a chest is performed and then comes back to
the emergency department. When the patient comes back, and I hope your
heart is just fluttering right now like mine is, in anticipation of what's going to
happen to this patient, the blood pressure actually is dangerously low when the
patient comes back. The doctor thinks this is probably a worsening blood clot,
gets the cardiologist to come and perform an ultrasound of the heart to see if this
blood pressure is caused by that.
At the bedside the cardiologist performs this echocardiogram and notices that
there's strain on the heart, presumptively from the blood clot. And then the
doctor then orders TPA, which is a clot butting sort of clot chewing medication.
This is administered by the nurse. The doctor then calls -- requests a bed up in
the cardiac care unit, critical care unit. Yeah?
>>: So you're waiting on results of the CT scan, you're treating presumptively for
embolism that the CT scan can pretty much confirm or deny?
>> Justin Gatewood: The CT scan as soon as it's performed and is in the local
database and then is in the electronic medical record and that takes a while to
happen ->>: How much pressure do you have to -- for a case like this with the CT, you're
already treating presumptively for some things is pretty critical for how much
pressure can you exert on the radiologist or on whatever other [inaudible].
>> Justin Gatewood: As much as the system will allow. The radiologists often
are very overwhelmed. May be you know, four gunshot wounds just came in
and, you know, they're booked up and they say well, we got a bunch of trauma
cases ->>: They have their own internal sort of triage system that's not necessarily first
come first serve, they also have a severity system roughly for other parts of the
hospital so ->> Justin Gatewood: Imaging from some areas of the hospital take precedence.
So CAT scan -- you know, we have dedicated critical care radiologist who only
read our scans and scans from the trauma unit. But in a smaller hospital, all the
routine daily x-rays and CAT scans often are read by the same radiologist. So
often we need to make time sensitive decisions without confirmed -- yeah. And
that's absolutely the point of that.
And then as you can see, then at 7:07, after we've already administered this
TPA, the clot busting mechanics, the doc calls and says -- or the radiologist calls
and says hey, I just want to let you know that your patient has a blood clot. And
we say yeah, thanks. We kind of, you know, we needed to act on the information
that we had, and the patient's now tee'd up for a CCU bed. And then at 9:12,
after more than two hours boarding and a length of stay of more than six hours
the patient is then transported up to the critical care unit.
That is much more typical of kind of what we're dealing with and patient by
patient, day to day. That does represent a very sick patient. Remember what I
said. About three percent of the patients that we see are admitted to a critical
care unit. So. And then we talked about clearly this is deterministic and non
deterministic flow. You know, the asynchronous and parallel flow. We talked
about the feedback loops in terms of maybe administering pain medicine and
going back and seeing what effect it has. And clearly there's chaos involved in
that, especially if you sort of zoom out and you see nurses running and phone
calls being made. But it all is embedded within a relative -- you know, relatively -I don't want to say easy, but defined system. And then more or less most of
these decision making concepts are utilized here.
So quickly we'll talk about currently projects. And I just kind of want to open it up,
and we can chat. One thing that we're doing and this is -- I've been working with
Greg and Desney and Dan -- yeah?
>>: Just before we transition, you said that the patient was randomly assigned to
the blue. Do you literally mean randomly, or it just so happened that based on
availability ->> Justin Gatewood: I shouldn't have said randomly. Honestly it's very low tech.
How it happens is there is a rack for blue team, for red team, for green team.
The triage nurse after triaging the patient takes the chart. There is a clip. Dan
was there, saw it. Des was there, saw it too. There is a clip on the rack,
whatever clip is on the rack you put the chart in that rack, you move the clip to
the next rack. So not randomly, it's just sort of sequentially.
>>: [inaudible].
>> Justin Gatewood: But the point is that there's no relation to acuity.
>>: Sure. I ->> Justin Gatewood: Yeah.
>>: Just randomly sounded like you were doing some sort of trial to figure out
which team was the best and the patient might wait [inaudible] just so that you
can do that.
>> Justin Gatewood: Embedded in that flow chart I have all of the colors going
from all of the teams to all of the other ones. And that is because, come on, if -you know, if the green team doc is having a bad day and is very slow and they're
piling up, it makes sense to do a little reshuffling. And although we set up the
system so that shouldn't happen, often that happens.
>>: The teams are not fixed to people, the physicians are randomly assigned to
teams, also, so it's not really like you would evaluate one thing at a time in that
sense?
>>: Yeah, it just seemed a little odd for it to be random.
>> Justin Gatewood: Yeah.
>>: [inaudible].
>> Justin Gatewood: Any other quick questions just about the flow before we
talk about quickly the projects that we're working on so far? And they'll be plenty
of time for questions afterwards as well.
So one thing that we wanted to do was in working with Dan and Greg and
Desney on this is how do we effectively model throughput, you know? There are
quite a few papers out there on neural networks. There's some work out of
classic machine learning stuff. But people have been doing kind of neural
networks, artificial neural networks of simplified and small emergency
departments for quite a long time, for the last, you know, 10, 15 years or so.
And this particular project we're using various machine learning techniques, none
of which I'm at all qualified to talk about, although they sound pretty cool. And
the training data that we're using is from Microsoft Amalga-generated
timestamps. So most of what happens in the department requires a click by
somebody that generates timestamps. They all reside on a SQL server database
and we can utilize those and feed them through fancy SQL server algorithms.
And so we're able to predict with and increasingly so all the time with reasonable
accuracy a lot of different milestones in the visit. The one that we're utilizing
most in trying to display is trying to predict wait times based on these models and
display the wait times in a way to patients waiting out in the waiting room. This is
important because patients often wait -- I mean, you can wait six hours pretty
easily on a busy Monday afternoon.
And the idea behind this is that we are given these kind of receding flood bars to
monitor progress. The problem that we're finding -- and this I think is actually
one of the more interesting problems is, you know, on one side the patient is
given a ton -- is given no information, kept in the dark. And the other side if we
give the patient too much information and our model is incorrect or is not
accurate enough, we're setting up unachievable expectations for the providers
where the patient will come up and say hey, you said you were going to see me
at 2 hours, 37 minute, at 2 hours 38 minutes what's going on.
So this is just a screen shot of what is -- embedded in this is a dynamic model
that will, you know, display these receding flood bars. And there really is no time
anchor for this. And this is one of the things that we're trying to work on is how
do we display this information so that it's -- it provides the appropriate amount of
information. Yeah?
>>: How often do you see patients lie about [inaudible] in order to get prioritized
[inaudible].
>> Justin Gatewood: It is not uncommon that patients will -- especially those
who know the system will manipulate the system for secondary gain, whether
that's pain medication, whether that's to be seen more quickly. It's very common.
>>: And I assume -- is it safe to assume that the more information you give them
about weight time such the more likely they are to do that?
>> Justin Gatewood: Yes. To an extent. Often our triage nurses are great and
we can say, well, I understand that you say you're having the worse pain in your
life. We see here that your vital signs are totally normal, and we know that
people who exhibit -- who are in true pain usually have an elevated heart rate.
And there are kind of ways of -- but it's dangerous. A lot of patients know very
keywords they can say that will get them straight back.
But generally patients are -- patients wait a long time and often don't say
anything. Yeah?
>>: So perhaps a slightly different direction of that is [inaudible] the system in the
other way. So you talked about like Monday afternoon I'm feeling a little sniffly,
I'm going to, you know, appear at the ER or whatever. The flip of that is so
before we start it sounded like a bunch of people this weekend probably you
know put up holiday declarations and it's Thanksgiving weekend and I fell off my
roof and whatever. I'm wondering if like we know that there are rush hours for
car traffic and like we know that even the DMV says don't show up to renew your
driver's license on a Wednesday because we have higher than normal volume on
all Wednesday. Do you -- is there something interesting that can be abstracted
so like for example I didn't know that there was on Monday afternoon -- didn't
occur to me there was a Monday afternoon phenomenon in the ER.
When should I put my holiday lights up? I could -- if I could do it Wednesday
morning [laughter] and like then I fall off my roof [inaudible].
>> Justin Gatewood: So, you know, unless you -- that's a great ->>: [inaudible] as much as I can schedule the things that cause me to get
damaged, can people? Is there anything interesting there? Or is that ->> Justin Gatewood: So essentially.
>>: [inaudible].
>> Justin Gatewood: You know, using as a parallel the traffic on a map program,
is there any way to adjust to your commute or seeking care? Yes. That could be
done. The problem is I think what makes that a little difficult is unless you are
using the emergency department a lot.
>>: Right. As your primary care provider.
>> Justin Gatewood: As your primary care provider. Or even if you're chronically
ill but no hey I'm going to go to Atlantic City this weekend and I'll probably eat a
lot of salty food and come back and my congestive heart failure will be acting up,
you know. But I think that's very useful.
Another thing is we do not want to dissuade for two reasons anybody to come in
the emergency department. Number one, the more patients we see, the more
people we can take care of, and the more money we can make. Right? Number
two is it gets very tricky when you're putting anything out there that would ever
dissuade a person from seeking care. And that's one thing that we -- a risk that
we run with that and we'll monitor that is I understand that you have chest pain,
you've been made a triage level two, but the wait for a two, believe it or not, is
still two hours because we're so busy. Assume that the other twos are either
sicker twos or got there before you did. I understand that two hours is a long
time, but at least you're in our sights and we can see you. We've got some vital
signs. We would hate for anybody to come and say oh, man, my wait's this, well,
I'm going to go somewhere else, or I'm just going to come back tomorrow.
>>: Yeah, you might keep them in there. It's great to give them data when
you've got them on the facility but if they're at home like the wait is three hours,
I'm going to just hang out here, oh, my God, I'm dead.
>> Justin Gatewood: Absolutely. And that's a huge fear.
>>: A pulmonary embolism, your wait is four hours. I'll just hang out here.
>> Justin Gatewood: And it's much better for the natural course of that disease
process to really manifest while you are in a waiting room or monitored bed
where things can still be done than to happen at home when you're by yourself.
>>: [inaudible] my weird outlier I fell off my roof because I was doing this you
know largely self-inflicted thing.
>> Justin Gatewood: Yeah.
>>: Almost no value to exposing that data outside the hospital.
>> Justin Gatewood: Well, plenty of healthcare systems have displayed average
wait times on bill boards like along the highway or on websites. And what they
found is there's obviously sort of a redistribution. But we don't want to
redistribute patients. We want to see everybody and anybody that comes to our
emergency department. But we want to do it quickly and safely so that we can
make room for the next person who's coming in behind you.
>>: I see ->>: Tell him to put up the Christmas tree ornaments before Thanksgiving so you
might beat the rush. [laughter].
>>: I assume relative wait times would be relatively harmless to the patient and
potentially harmless to the bottom line. If you are at a hospital system where the
same hospital system -- where the money all went to the same place ->> Justin Gatewood: And the experiment that I mentioned were in those
settings.
>>: Well, I think on the flip side, your idea capturing the wait times and time of
year and weather is how do you stack. You know you're going to have a --
>>: Yeah, yeah, yeah, that's actually great.
>>: And then you know stack for those things. And then if we collect the right
data, since Amalga can take data from anything, data is data, then it would be
very interesting to see is there actual causation or just correlation in an
assortment of data.
>> Justin Gatewood: And just so that you know, when I gave those ranges of
staffing, we staff for averages and the only variations that we -- we have a couple
less docs during the nighttime. And we ramp up during the day. But -- and
sometimes some of the actual like the triage team shifts we don't have on
weekends because weekends actually tend to be pretty slow. Besides that, there
really are kind of just staffer averages.
But the idea is that the system is scalable that we can, you know, take -- that we
can work quickly in a surge situation. Unfortunately that doesn't always happen
because, you know, union laws govern some of these things. Just plain old
ethical laws govern some of these things. You cannot have a nurse, even
though it just takes me -- it may take me 15, 20 minutes to go see a patient, write
a documentation, and then order some stuff, well a nurse has to do that and he
or she may have another critically ill patient that is boarding in the emergency
department for two hours because the hospital's so full that there's not a room
upstairs, and so you can't assign although there's no law against assigning, you
know, a physician to N number of patients. The nurses and the nurse
management, and I think rightfully so, once they start getting four, five patients,
they're like it's not safe.
>>: But that is just data too. And presumably some, you know, future or maybe
current version of Amalga can consume that same type of information, thou shalt
have this number of minimum level of nurse care.
>>: I think that you can also add patient acuity to that. You have a nurse with
five patients and they're all the worst acuity, that would be a lot harder to manage
than five patients that have sprained ankles. And so we could do some sort of
algorithm and then add in staffing with historical on Christmas Eve you know
somebody's going to call in sick. And so then staff, those sort of staff fluctuations
you might stand a better chance of really evening things out.
>> Justin Gatewood: One thing I hope that you'll find too the more we can work
together is -- and Dan and Desney especially having subjected to this, you know,
there's a certain culture in the emergency department. There's a certain
hierarchy within nurses, within physicians, within the technologists within the
clerks. And between those as well. And so certain cultural potentially cultural
attitudes often make some of these things difficult. As we all know culture sort of
lags behind in medicine often what makes sense. I mean, it's only until recently
that residency programs, training programs have put caps on work hours. Before
that, you know, working 120, 140 hours as much as you needed to in a week was
the norm, and the idea was, well, you know, tough, it's sort of a right of passage.
So the first thing I think would be to prove that there are bad outcomes because
staffing levels are low and then that would give you the leverage to be able to
use that data to fix things. But it's incredibly interesting. And I -- you know, I
invite you all to come out and hang out, and you're all welcome any time to get
an idea of how subtle of these cultural nuances shape implementation and also
valuation of the data.
>>: Are there approximate simulators that have been built to try to figure out
what happens, how long it takes for these pipelines of -- how long it takes to do a
CT scan and how long it takes to do each thing where you'd say okay, if we threw
another CT scan in here, how would that change is workflow? Do you know if
this has been done.
>> Justin Gatewood: I don't -- I'm sure that there have been -- I glanced over a
couple papers that related to using neural networks to show if you perturb the
system in this way how well your outcomes for example heart attacks or -- but in
-- as far as resource utilization, I have not. I mean, I think there have been ->>: There have been [inaudible] if you could add another machine or if you could
add more staffing on that. There's a lot of literature, but they tend to be ED
specific sometimes, so they can ->>: [inaudible].
>>: So I looked at some of the simulation picture and [inaudible] and one of the
problems in the literature is that the models that [inaudible] doesn't take into
consideration the culture there [inaudible]. Do you have any idea about how you
could incorporate that [inaudible]?
>> Justin Gatewood: As far as -- well, I think there are two ways. There is -- you
try to modify -- you essentially try to change culture or at least modify what you
can to streamline it. As far as trying to codify that and having it be another
parameter in your model, to account for it, one thing that you may be able to do is
if you were -- right now a doctor is a doctor, right? A nurse is a nurse. You could
look at differences in throughput or care given by different providers and then
sort of, you know, weigh -- well, Dr. X is a little slower, Dr. X is a little faster, this
nursing team -- even these nurses when together are more efficient and use that
as sort of a proxy. I would love to do that.
I would tell you that you have to be very sensitive with that data. No one wants
to -- you end up putting people in competition.
>>: This is a small end but I've looked at a hospital system in Atlanta and huge
cultural difference between teaching hospital and non-teaching hospital. So if
you were starting down that path, that might be one ->> Justin Gatewood: Yup.
>>: And it leads to first of all staffing differences and then other cultural
differences as well. So.
>> Justin Gatewood: And you may be able to actually use that, though, if it is a
teaching hospital it's a lot easier to evaluate residents and medical students and
nursing trainees than it is to say to the 30 year emergency attending veteran
we're going to be watching you, you know.
>>: Do you think number of years in the field could be a determiner or do you
think that it's based -- once you're an attending? Because I think there's a clear
line between residency and attending that once you're an attending that years of
experiences ->> Justin Gatewood: Some of our residents see more than our senior
attendings. Some of our senior attendings are so fast it's -- you know, are faster
than some of the young bucks. I mean, it's really ->>: It's all over the map?
>> Justin Gatewood: I think it's all over the map. And I think things would
ultimately would kind of center towards the middle. Which then you beg the
question, well, is that even -- is that even a pertinent factor.
I wanted to just get your brief idea. I'll show this as well, something we're
working on with Lauren Wilcox. She's out at Columbia working on her PhD. And
she was here as an intern. And we wanted to find out -- she wanted to find out is
there any benefit to having an in-room display that may potentially be
manipulated by the patient or that they would have access to at the bedside that
could show, you know, what's the care team information, what are some of the
results of my diagnostic tests, you know, things like, well, what's next. And then
also what patients and what the physicians and providers deem would be
relevant or appropriate information to put on one of those displays. And so we
were able to complete a complex paper prototype, a big poster that was actually
assembled by Desney and Lauren and Dan in realtime using information from the
physician and the electronic medical record. I'm sorry, I wish I could make that a
little bit bigger. But we short of just finished kind of a informative study, and
we've got a lot of other questions. But I think a great step is just received really
well by hospital administration. Very sort of high profile thing that was
implemented relatively easily. I mean, you guys put a lot of work into it and has
received -- and most importantly the patients absolutely love it and felt quite
empowered by it.
So as far as potential projects, things that I've been thinking about and that have
been mentioned as well by other physicians is interest -- and Dan and I are -and I read your -- is there a way to sort of intelligently automate these patient
displays; that is, do we have physician defined rules as to what is okay to display,
taking into account that the interpretation of labs and images is contextual based
on that individual person if that person has never been there before, been there
once, you have relatively sort of shallow pool of data to choose from to be able to
make those decisions.
But I think that's a ton of fun to think about. And then also, you know, right now
our documentation kind of impedes workflow. I mean, you're sitting there. And if
you're like me, you're writing madly in the room, but how much eye contact am I
making with the patient? Well, you could also talk to the patient and then go
outside and sit down and then type your note and stay after your shift or type
your note and then nurses and other docs are coming up to you. I mean really
we just don't have a way to seamlessly sort of capture that encounter. And is
there a way sort of passively of doing it. You know, perhaps using voice
recognition to sort of populate, sort of predetermine templates that would kind of
listen in and then populate sort of like an e-Scribe.
Right now there are some places that have scribes. We're usually medical
students or pre-med students who listen and take notes and then the physician
will review those before they enter the medical record. But that -- that would be
incredibly useful. And the question would be, you know, how does that improve
efficiency and also what impact does it have on the patient-physician relationship
and ultimately patient satisfaction?
>>: [inaudible] just stay on that idea just a minute. And when you were talking I
visualized you standing by the bedside with a voice-activated device like a
recorder. Based on your prompts ->> Justin Gatewood: This would be ->>: [inaudible].
>> Justin Gatewood: Yeah, and again, I don't know what technology exists. This
would not be just simply a dictation machine that's voice activated that will turn
on, but you know, if you had -- if you had some device that had preloaded
templates so I understand you're here for chest pain. Opens up chest pain. And
then so how long -- you know, what duration of your chest pain. Listens to
duration and may populate that portion. Because when we document, we
document for three reasons. To relay medical information between providers.
We document for medical legal purposes, to make sure that we're doing the right
thing if we get sued. And then also we document for billing. So a lot of the
templates that exist in these documentation systems that are already in the
market, they exist to capture sufficient information for documentation.
And I think using Protex someone in medical MediaLab has already used some
natural -- Pete was telling me using natural language processing to sort of parse
out relevant things for billing. But if we could do that using some sort of voice
system, I think, you know, that may be a pipe dream but it seems like ->>: No, no, I think it's very doable. If you had a template that you could hit a tab
on the keyboard and then it record your conversation and then pull out through
the natural language processing pull out the relevant information, once it then
comes to you, you do a little cleanup and sign it so that you then captured all the
voice interactions between you and the patient, which we can leave as voice tags
in the actual document.
>> Justin Gatewood: You know, that would be something ideal. But ->>: I don't think that's beyond ->>: I wonder if there's even a -- the systems that are regularly used in particular
[inaudible] where the medical professional is someone who may or may not
actually have specific medical training and they're walked through a very form
laic kind of flow chart thing, you could imagine a similar kind of tool that, you
know, the doc is working from and so that because they're walking through
ostensibly a logical template, the system then knows I'm here, I'm there, and you
can enter the tab on the little thing on the screen that says I'm going down this
branch and so then you have some contextual awareness presumably about the
kinds of data that the system will be expecting. Would you describe the chest
pain as mild or really not at all mild? Okay. And then that becomes data rather
than nearly a string that somebody needs to text ties as well as keeping your -you know, the voice stuff around and somebody says but this doesn't make any
sense and then they turn back.
>>: Reporting [inaudible] voice that says what is the duration of your chest pain?
>> Justin Gatewood: I just pulled out the [inaudible] [laughter].
>>: Or like with the tablet that you have, you know, you could stand at the
bedside and click that, record everything as being said, click the next button,
record it.
>> Justin Gatewood: Now, there are T systems that exist, they're called T
systems that exist for documentation that allow I to sort of electronic systems
where you just sort of check boxes. And that could be. But it still requires kind of
deflecting your attention.
>>: Right.
>> Justin Gatewood: And it also doesn't leave room for -- there's nuance
verbiages that -- verbiage that often needs to make its way into electronic
medical record. So you need to have a space to be able to edit that afterwards.
And I by no means am an expert on all of those products that exist. But we can
keep talking about that. And then also using sort of fuzzy text classification
schemes to be able to make better use of the text inputs that we all right have
now. So program, the chief complaint field is often misspelled, may have some
loose association with what it really -- what the chief complaint truly is, and that is
a ton of data that right now we're just not able to use. And if we were able to
incorporate that into our model, clearly chest pains look much more -- look like
kind of going down the pipe than does a chest pain and an abdominal pain even
though they both may be a two, may come in at the same time and be the same
age. So we think that there's probably some wait in that as well.
So those are things that we've brain stormed and hopefully we'll continue. I know
we're at 12 now, and I want to be cognizant of time, but is there anything that
anybody -- I hope this was, you know, somewhat useful to kind of give an idea as
to kind of what goes through our brains. Great.
>>: Very interesting. Thank you.
>> Justin Gatewood: Well, thank you so much for your time.
[applause].