**Transcript of Cyberseminar**

**Spotlight on Evidence-based Synthesis Program**

**Early Warning System Scores: A Systematic Review**

**Presenters:   M. E. Beth Smith, DO; Russell Coggins**

**May 13, 2014**

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

*Moderator:*Thank you, everyone for joining us for today's Spotlight on Evidence-based Synthesis Program cyberseminar series.  Today's session is Early Warning System Scores:  A Systematic Review.  Our presenter for today is Dr. Beth Smith.  She is a principal investigator at the Portland ESP Center and an assistant professor of medicine, Division of General Internal Medicine and Geriatrics, Oregon Health and Science University.  

Beth is joined today by Russell Coggins.  Russ is a clinical nurse advisor for critical care, Office of Nursing Services, Field-Based, in Washington, D.C.  He's also a nurse manager in the surgical ICU at the Asheville VA Medical Center.  With that, Beth, can I turn things over to you?

*Beth Smith:*Yes.  Thank you very much, Heidi.  I apologize to everyone.  I was sitting listening to it and couldn't get my phone unmated, and so one knew that I was actually present.  Thank you, and please accept my apologies.  

Thank you for joining for the ESP HSRD seminar this morning on early warning system scores, a systematic review that we conducted here at the Portland VA Medical Center.  Let me just make sure I can get this moving forward here.  

*Moderator:*Use the arrows and—looks like you've got it right there.  Perfect.

*Beth Smith:*Got it.  Thank you.  This report—actually, first of all, I'd like to acknowledge the report authors.  That includes myself, Dr. Chiovaro, Dr. O'Neil, Dr. Kansagara, Dr. Quinones, Michelle Freeman, Pua Motu'apuaka, and Dr. Slatore.  They're all here at the Portland VA Medical Center.  

I'd also like to acknowledge Russell Coggins, who is the chair of the Office of Nursing Services, Clinical Practice Programs, ICU Workgroup, who nominated this report.  In addition, I'd like to thank all of our expert panel and reviewers, and you can see their names noted here.

To start, I'll give you the disclosures.  This is based on research that we conducted here at the Portland VA Medical Center.  It was funded by the Department of Veterans Affairs.  However, the findings and conclusions in this document are solely the responsibility of the authors.

I'd like to start by giving a brief review of the VA Evidence-based Synthesis Program.  The program is sponsored by VA QUERI, a quality enhancement research initiative.  It was established to provide timely and accurate synthesis and reviews of healthcare topics that are identified by VA clinicians, managers, and policy makers, our stakeholders, as they work to improve the health and healthcare of Veterans.  

The ESP builds on staff expertise that is already in place at evidence-based practice centers that are designated by the Agency of Healthcare Research Quality, AHRQ.  Four of the EPCs are also VA ESP Centers located at the Durham VA Medical Center, the VA Greater Los Angeles Healthcare System, the Portland VA Medical Center, and the Minneapolis VA Medical Center.  

The evidence-based synthesis program provides synthesis on important clinical practice topics that are relevant to Veterans.  These reports help us to develop clinical policies that are informed by evidence.  

They also help with the implementation of effective services to improve patient outcomes and to support VA clinical practice guidelines and performance measures.  Finally, they help guide the direction of future research to address any gaps in clinical knowledge.

There is a broad nomination process.  For example, VA Central Office, including VISNs, as well as practitioners out in the field facilitated by the ESP Coordinating Center, can nominate online.  We are providing also the links for you to go and investigate that option.

The steering committee representing research and operations provides oversight and helps guide the program direction.  We're also guided individually with our technical advisory panel, which we assemble for each topic.  

These are individuals that are recruited for each topic and provide concept expertise.  They help guide our topic development, help us refine our key questions, and they review the data that we present, as well as the draft report.  

The external peer reviewers and policy partners also review and provide comments on draft reports.  These are invited and usually nominated by members of the technical advisory panel.  

Finally, the reports are posted on the VA HSRD website, and they are disseminated widely through the VA.  Again, we provide the links for those publications that are listed there.

Today's report on Early Warning System Scores:  A Systematic Review was published in January of 2014.  The link for the full-length report is available on the ESP website.  Note that this report is for internal use for the Department of Veteran Affairs and should not be distributed outside the agency.

Getting back, let's talk about early warning system scores.  Here's an overview of what today's presentation will be.  I'd like to give a little bit of background about the topic, discuss the scope of the review, look at the results that we found, discuss the limitations, and especially have some opportunity to talk about the future research, and then have an opportunity for questions and answers.  Russ Coggins will also be presenting at the completion of my presentation here.  

Going into the background:  early warning systems scores are tools that are used by hospital care teams.  They're based on physiologic parameters to produce a composite score.  

Things such as heart rate and blood pressure, respiratory rate, temperature—those are the primary ones that are being used.  It's looking at those physiological parameters.  Scores are given to produce, then, a composite score.  

Observational studies have suggested that patients often show signs of clinical deterioration up to 24 hours prior to a serious clinical event.  However, uncertainty exists around the utility of recognizing early signs of clinical deterioration and whether any early intervention and management actually makes a difference in patient outcomes.  

There are plans for implementing a modified early warning systems score nationally through the VA.  This system is called the MEWS, or modified.  This report is to provide evidence to the Office of Nursing Services Clinical Practice Programs ICU Workgroup to develop guidelines for implementation and to identify gaps in knowledge.  

Let's talk a little bit about the scope of our review.  We had two primary key questions.  The first question had two components.  Number 1:  in adult patients admitted to the general medicine or surgical wards, what is the predictive value of EWS scores for patient health outcomes within 48 hours of data collection, including short-term mortality, all cause or disease specific, and cardiac arrest?  As a secondary component to this question:  which factors contribute to the predictive ability of EWS scores, and does predictive ability vary with specific subgroups of patients.

*[Extraneous audio for a few seconds]*

I'm going to go on to key question 2.  We're getting a little bit of extra talk, I think.  At least I'm hearing it.  

Key question 2A:  in adult patients admitted to the general medicine or surgical wards, what is the impact of using early warning systems on patient health outcomes, including 30-day mortality, cardiovascular events, use of vasopressors, number of ventilator days, and respiratory failure?  For key question 2b:  in adult patients admitted to the general medicine or surgical wards, what is the impact of using early warning systems on resource utilization, including but not limited to admissions to the intensive care unit, length of hospital stay, and use of rapid response teams?

In the scope of our review, we have some inclusion criteria.  That's looking at what we call the PICOS:  the patient, the intervention, the comparator, the outcomes of interest, and the study designs.  Our patients—we looked at only adults and admitted to the medicine or surgical wards.  As far as intervention, we considered any early warning system score, and as a comparator, we looked at any alternate system score or usual care.  

For outcomes, again for key question 1, we looked strictly at mortality, cardiac arrest, or pulmonary arrest within 48 hours of data collection.  Again, that's looking at the predictive ability of this.  

For key question 2, we looked at 30-day mortality, cardiovascular events, such as cardiac arrest, acute coronary syndrome, cardiogenic shock; use of vasopressors; number of ventilator days; respiratory failure; and length of hospital stay.  The study designs we included for key question 1 were observational studies; for key question 2, control studies and observational studies.  

We have what's called an analytical framework just to put the scope of the review in a pictorial figure.  Again, as we come into our analytical framework, we look at adult patients hospitalized in general medicine and surgical wards, looking at the EWS.  Then for key question 1 and key question 2, we considered modifying factors such as patient subgroups or timing of use, which vital signs were being used, hospital characteristics.  

Also for key question 2, we considered intermediate actions.  These aren't actions that really are reflective of the—are really indicative of the hard patient outcomes, such as death or cardiovascular outcomes, but looking at things such as use of rapid response teams or increased surveillance by nursing staff or other forms of resource utilization.  Then the true outcomes that we have for both key question 1 and key question 2 are documented there in the final box, key question 1 again being mortality, cardiac arrest, or respiratory arrest within 48 hours.  

There were some things that we excluded.  We did not look at non-English language studies.  We did not look at the non-adult study population.  We did not look at non-medicine or non-surgical wards for key question 2.  

For the predictive ability, we did include the emergency department, as we felt that that would give us some good information.  We did not include any types of reports that had no primary data, such as editorials or case series or nonsystematic review articles, and we didn't look at any studies that included outcomes that were not within our scope.

Our methods:  we searched the databases up to April 2013.  There's an error there.  I initially thought we had updated in October 2013, but when I looked later, it was not updated at that point, so updated to April 2013.  

Data abstraction:  we looked at the population, the setting, the sample size, duration.  We looked at things such as model discrimination and calibration for the predictive ability.  Then for other health outcomes and resource utilization, we abstracted that data. 

Then for the assessment of the study quality, we looked at the QUIPS tool for observational studies.  That's the Quality in Prognosis Studies.  

We'd considered using other quality assessment tools, but as you'll see in our results, we really only found observational studies, so the QUIPS was all we needed to use.  Then we did qualitative synthesis of our evidence, as there was inadequate data to do any quantitative synthesis or meta-analysis.  

This is what our search yielded.  We yielded 13,595 titles or abstracts from our electronic databases.  Many of those were duplicate, and we came down to 9,929 total titles and abstracts, of which 9,800 were excluded.  

We brought it down to 129 articles and abstracts that we selected for full text review.  From that, we selected 6 studies of predictive value and 11 studies of the impact of EWS on interventions.  None of the rest met our inclusion criteria.  

I have a poll question.  I'd like to know a little bit more about our audience.  What best describes your professional training?  Nurse, provider, administrator, or other?

*[10 second pause]*

*Beth Smith:*Great.  It looks like—I'll give another moment.  A few more are coming in.

*[15 second pause]*

*Beth Smith:*Great.  It looks like over 70 percent are nurses, and that's what we're seeing is really the frontline use of these tools.  We have about 9 percent that are providers, and 4 percent that are administrators, and 31 percent other.  Thank you.

Another poll question:  what best describes your experience with an early warning system score?  Are you a nurse with experience using them, a provider with experience using it, a nurse or a provider considering using it, an administrator considering use of one, or other?

*[20 second pause]*

*Beth Smith:*It looks like—interesting.  It looks like we have a quarter, 25 percent or a little higher than that who are other.  I'd be interested to know what the other are.  Maybe we'll be able to get that through our question box—answers through the question box.  It does look like 36.8 percent are nurses or providers considering using EWS system score, and we've got about 15.7 percent who are nurses with experience using it.  

Wonderful.  Thank you for that.

Let's talk about our results.  For key question 1:  what is the predictive value of EWS scores for patient health outcomes within 48 hours of data collection?  As I mentioned earlier, we found six observational studies, two prospective cohort and two case-control.  

These include four distinct models reporting on death and cardiac arrest within 48 hours of measurement.  Of note, we did not receive—we did not find any studies that looked at respiratory arrest.  Three were in the U.S., two were in the U.K., and one in Canada.  

We had one study that looked at single predictors.  The rest of the models ranged from four to seven items, all including heart rate, respiratory rate, blood pressure; most including temperature and mental status.  The titles of them—the names of them:  the CART, the MEWS, the NEWS, and two forms of ViEWs; one is six-item and one is seven-item.  

I have a table that is pretty small.  I hope you can see it.  It gives an idea of what the types of measurement factors or parameters we were looking at, as well as some of the other items, such as the Rothschild also looked at diastolic blood pressure, seizures, uncontrolled bleeding, color change.  

What we found is that these EWS scores actually had a strong predictive ability for death—the area under the receiver operator curve is 0.88 to 0.93—and cardiac arrest, with the area under the receiver operator curve of 0.77 to 0.86.  The AUROC really gives us a sense of the discriminative ability of these tools.  

For instance, a score of .050 would be equal to a coin toss.  It'd be 50 percent, 50/50.  It would have no discriminative ability.  Something greater than 0.7 is considered good, and something greater than 0.9 is exceptional, so these actually did a very good job at predicting death or cardiac arrest within 48 hours.  

We did note that the lower scores were associated with a very good prognosis and that higher scores corresponded to higher rates of adverse outcome, but the sensitivity of this was actually poor.  At a specificity of 90 percent, the sensitivity ranged from 48 to 67 percent.  We found no differences based on subsets of patients in terms of sex, year of admission, age, indication for admission, and diagnosis, but this was really based on just one study that looked at these considerations.  

In part B of this study, we wanted to know which factors contributed to the predictive ability of the scores, and does predictive ability vary with specific subgroups of patients.  We really only had one case-control study that looked at this specifically.  It wasn't a very large study and was 262 versus 318 in the control group.  

They looked at cardiac arrest within eight hours.  They found that the criteria most associated with a life-threatening event included respiratory rate greater than 35, need for supplemental oxygen to 200 percent or use of a non-rebreathing mask, and heart rate greater than140 per minute.  

Multiple positive criteria were more common in cases and control.  We see that for the individuals who had greater than or equal to three positive criteria, it was very significant.  

Some of the issues around this is really the potential for risk of bias, and this limits the quality of our evidence.  That's really due to the study design types.  

Four of these six studies were derivation studies.  They're using the data from their population to create the tool that they're using, and so there's a risk of over-fitting the data to that population.  Then two were case-control studies, which are at risk of the groups receiving different exposures to the intervention, for example, vital sign measurements.

What we found, if I was to summarize it, is that the early warning systems that we looked at—so if I go back here for just a moment—the CART, the MEWS, the NEWS, and the ViEWS had strong predictive ability for cardiac arrest and mortality within 48 hours of their measurement.  As far as the predictive ability of one system over another, the evidence was insufficient.  As far as which factors contribute the most, we really found that the evidence was insufficient.  

Moving on to key question 2A:  what is the impact of using early warning systems on patient health outcomes?  To answer this question, we found 11 observational cohort studies, all using historical controls.  

Many of them were large number of patients included, anywhere from 89 to over 200,000.  They were addressing outcomes of mortality and cardiac arrest.  We did not find any studies that reported on any of the other outcomes of interest.  

The models ranged from 5 to 12 items.  All included heart rate, respiratory rate, systolic blood pressure.  Most included level of consciousness or mental status, temperature, and urinary output.  

Five were from the U.K., two from the U.S., one in Australia, and one in Belgium.  

Again, this is a busy slide, but I wanted to give an idea of what different parameters were used on these different tools.  As you can see, the first three columns, every model used heart rate, respiratory rate, and systolic blood pressure, and most of them included the temperature, the urinary output, and the mental status.  

*[10 second pause]*

What did the six studies show?  They really showed mixed results.  When we looked at overall mortality, five found a decrease in overall mortality, but only one found statistical significance of this.  

Deaths per hospital admission decreased from 1.4 percent to 1.2 percent in this one study, which found it significant.  Deaths per cardiac arrest call decreased from 26 percent to 21 percent, significant—again, just in that one study.  Deaths of patients admitted to the ICU having undergone CPR decreased from 70 percent to 40 percent in that one study.  

Five found a decrease in the overall mortality, so it was trending toward that, but really just one found it statistically significant.  Then one study found a non-significant increase in overall mortality.  They did find, however, for patients who are spontaneously breathing with a pulse at time of their code blue call, there was a significant improvement in survival, so in that one group of patients, they did find an improvement in survival.

What about cardiac arrest?  For cardiac arrest, we found three studies and again found mixed results.  This one was really quite variable.  One study found a decrease.  They were looking at two hospitals, and they found a decrease in both of the hospitals, from 0.4 to 0.2 percent and 0.34 to 0.28 percent.

Another study found no difference in patients who scored low or high but found an increase in cardiac arrest in the moderate group, five percent versus zero percent.  Then one study reported a decrease in the number of cardiac arrests among code blue calls.  Again, this was only among the code blue calls, from 52.1 percent versus 35 percent.  

Just to summarize the impact on the health outcomes, we really found that this was insufficient.  We saw a trend towards a decrease in mortality, but the six studies did have mixed results, and only one of those was significant.  Then for cardiac arrest, really insufficient.  We found mixed results.  

For key questions 2B:  what is the impact of EWS on resource utilization?  For length of hospital stay, we found three studies that again had mixed results.  One study showed no difference, one study found a decrease, and one study found an increase.  

As far as admissions to the ICU, we found five studies and again had mixed results.  Two studies found an increase.  One study at two hospitals found a decrease.  Two studies found no difference in the length of ICU stay.  

Then use of rapid response and code teams:  we found four studies.  These were consistent results.  All found at least a 50 percent increase in the number of rapid response or ICU liaison team calls.  Three studies found anywhere from a 6 to 33 percent decrease in the number of code blue calls.

As far as nursing, it just really wasn't well studied.  Three studies reported on the accuracy and compliancy of scoring, and one study found that compliance was as low as 53 percent.  

Accuracy seemed to improve.  There's one study that looked at accuracy using electronic calculations, and they found the accuracy in this group quite high:  81 to 100 percent.  

One study looked at which were the most inconsistently recorded elements, and they found that urinary output and level of consciousness were the two most inconsistently recorded elements.  One study found the most errors in the respiratory rate.  

There was one study that showed that the high scores—the EWS scores greater than five really did increase the number of observations and clinical attention given by nursing.  Then one interesting study looked at the frequency of observations per nursing shift, and they found that when the scores were elevated, that did translate into an increased number of observations during the day but not during the night shifts.  

What do we say about the impact of EWS on resource utilization?  We really say that it's insufficient evidence.  It suggests that the use of staffing may increase, while the length of hospital or ICU stay remains uncertain.

There are limitations of the evidence.  The primary limitation for this group from key question 2 is that it used historical controls, and with that there are unknown and unmeasured confounding variables that may be having an effect.  

Then there's the effects of time itself.  One of the things that we look at when grading the quality of the methodology is to really look at the rate of change pre-intervention to the rate of change following intervention.  We call this the slope of the outcome.

None of the studies reported on that, and none adjusted for pre-intervention trends in the mortality rate or accounted for any other secular changes in the care that may have been provided at their institutions.  These really limit our ability to know with confidence that the findings that we're seeing are meaningful and really reflect the true nature of the state. 

Looking at summary of the evidence for predictive value:  again, death within 48 hours, we had 4 studies that looked at that.  Positive findings.  Strong predictive value.  There is the risk of differential exposure assessment due to case-control design and risk of over-fitting the data due to derivation versus validation studies.  For cardiac arrest within 48 hours, 4 observational studies and again strong predictive value, with the same limitations.

For health outcomes, for mortality, we had six observational studies.  We had mixed results.  There's a trend towards decreased mortality but really insufficient evidence due to methodological limitations.  For cardiac arrest, we have three observational studies, very mixed results, and really insufficient evidence.  

For resource utilization, for length of hospital stay, three studies, mixed results, high risk of bias due to historical controls.  We'd call it insufficient evidence.  For ICU admissions and length of stay, four observational studies, again mixed results, similar risk of bias and insufficient evidence. 

For the use of rapid response teams, 4 observational studies, consistent results in that at least 50 percent increase in calls; 6 to 30 percent decrease in code blue calls, however.  Suggest the use of staffing increases, but, again, risk of bias due to historical controls.  

Then nursing:  three studies.  Compliance may be limited.  Accuracy may improve with electronic calculators.

As far as future research suggestions, we really need to get some randomized control trials that are looking at the use of these models, especially for the health outcome component.  We need rigorous adherence to methodological standards for the observational studies and, again, particularly for the predictive ability.  

We really need to be looking at clinically meaningful outcome measures.  For predictive ability, many of the studies were looking at ICU admission; however, vital sign changes are an indication for ICU admission or an indication for the use of a rapid response team, and those aren't the outcome measures we really should be looking at for predictive ability.  We should be considering what are the outcomes of the patient within 24 or 48 hours after using the tool.  

We also have to get standardization of cut-offs to trigger a response.  I didn't mention this, but each of the different models—their end composite score is quite variable, from as low as 5 to as high—I believe the highest one we saw was 21.  Each of them may have a different cutoff that would trigger an event or trigger increased nursing surveillance or calling a rapid response team.  

If we can get some standardization as to which cut-off would trigger a response, it will be easier to compare one tool to another.  Then again, standardization of responses.  We so strongly recommend institutions that are going to start implementing EWS to prospectively track the use of their resources.  

Thank you so much for letting me come this morning and talk to you about our study and our results.  I think at this point I will be turning it over to Russell Coggins.  I know we're running a little late and, again, I apologize for that.

*Russell Coggins:*Thank you, Beth.  I'd like to see if anybody has any questions out in the field.  Nobody has typed any in the question-and-answer.

*Moderator:*For the audience, if you do want to send a question in, please use that Q&A screen at the lower right-hand corner of your monitor.  The phone lines are lectured, so the only way to get a question in to us is to send it in through that Q&A pane.

*[15 second pause]*

*Beth Smith:*This is Beth.  Donna Miller has just asked:  has formal validity and reliability testing been established on any version of the EWS?  No, there has not been anything formal on that, the reason being that everyone wants to create their own tool.  

Even the ones that we've seen that are—the MEWS, which is a very common one— what we will see is that every institution— everyone who implements it seems to modify it a little bit.  You end not comparing the exact same one to another one, so there has not been good validity and reliability testing on any version of the EWS.

*[10 second pause]*

*Beth Smith:*This is Beth again.  Brian McGlone — I hope I said that correctly—said, "I heard you say be sure to prospectively track resources as we look to employ our T and EWS.  Is there any other advice that anyone has?  We have the opportunity to build a VA Hospital here in Orlando and are looking at the most innovative approaches."  *[Pause]* I'd open that up to anyone who is on the line to respond.

*Moderator:*That is to respond in writing, as the phone lines are locked down.

*Beth Smith:*Right.

*Moderator:*Beth or Russ, if either of you have any thoughts on that, that would be great to hear.

*[10 second pause]*

*Beth Smith:*The one thing that we saw is the use of the electronic calculators and electronically saving the data to allow for comparison actually made a difference.  I think one of the things with many of the EWS, even if they're built into the electronic medical record, is they weren't electronically calculating in real time.  It took the nurse time to pull out a calculator or add up a score.  That ended up having decrease in compliancy.  There was an indicator that having electronic calculators really made it more effective and more successful.  

I think one of the things to really think about are those different types of parameters that are not being measured as often and to really look—it's unfortunate that we don't have good comparative data, but it'd be interesting to know does taking a measurement such as the urinary output—how much does that add to the validity of this tool when it's something that's not being consistently monitored?  

Those are some of the things that I would think about as you're looking at building in an EWS system score.  If there's anything you can do to help promote the accuracy and compliancy of it, that would be optimal.

*[10 second pause]*

*Beth Smith:*Roberta Jones has just asked:  do we anticipate the VA developing a MEWS that will be utilized in all facilities.  I wonder, Russell, if you might be able to address that question.

*Russell Coggons:*Yeah, Beth.  I'd be glad to.  We're going be using the evidence from the ESP to look into the possibility of developing a MEWS for the VA, but I know that there's other VAs out there that have already started looking at things.  We would probably be reaching out to people like Brian down in Orlando because I know that they've been doing a lot of work on it, and they're right now running into some technical difficulties.  Maybe through partnering with them, we would be able to assist them in them taking it to the next level and then seeing how other VAs—and then trial it at other VA facilities and maybe even pair up with some research nurses and see if we can add to the evidence in the literature.

*[10 second pause]*

*Beth Smith:*Brian has just mentioned that there are some third-party scoring systems also being trialed now outside the VA.  He mentioned the Rothman Index.  I'm not familiar with that one.

*[10 second pause]*

*Beth Smith:*Then Roberta Jones has just asked:  are the MEWS systems used in all areas, including critical care and the ER?  They have been.  We did not include studies that were done, as far as health outcomes, that were in sites other than the wards.  

Our reason behind that is that the purpose of these MEWS is to increase the attentiveness given to the patients and increase the care given in order to reduce the chance of having a negative outcome.  In a critical care environment, such as the ICU, there is already the highest level of nursing attention and care being provided, so we really felt the most important and practical use of it is how do we detect that potentially deteriorating patient who is not having the monitoring that one does in the critical care setting.  

In the ER, we did include that for the predictive value.  Really, what we saw in the use of MEWS in the ER was as a triaging tool to determine whether this patient is going to go to the ward or whether this patient is going to go to the ICU or a higher-up unit.  That wasn't the outcome that we were looking at, but they are using them in other settings.

*[10 second pause]*

*Beth Smith:*Brian just asked:  did any of your study reference a central command center that was able to see all patients in the hospital and scores calculated automatically?  That's fascinating, Brian.  I love that idea.  

No, we didn't see anything that had a central command center.  It would be really quite interesting to see how that might be set up.  Certainly, if you're doing something electronically, where the input of the vital signs and data can be immediately reviewed and maybe triggered once a certain number arises—I think, electronically, we probably have the capacity to do that, but we haven't seen any studies that reflected that type of technology.  

Really great thought.  You had asked about innovative ways, and I think that's a very innovative way.

*Russell Coggins:*Yeah.  This is Russ.  I know that's one of the things that—I've talked to Brian before, and that's one of the things that he's really trying to lead down in Orlando—and he's really a visionary—so that they can actually see the patients that are starting to go down or starting to decline and then would be able to dispatch the rapid response team before they're really even called to see if there's any interventions that they can do before they have any larger declines.

*Beth Smith:*Yeah.  That's great.

*[10 second pause]*

*Beth Smith:*Again, that would be a great thing to study because we really don't have good studies on that, and so if you're looking at implementing it, it would be a great time to put a good study design in place.

*[10 second pause]*

*Beth Smith:*Jerry Shearer just mentioned Phillips has a monitor that will put the vital signs into CPRS and calculate the MEWS score.  The score will be totaled, and the scores will be sent to ICU, where telemetries are monitored.  

Interesting.  One of the things is—one of the ideas is that the wards that we can identify a patient early enough that we could intervene and potentially prevent even an ICU transfer or prevent subsequent decline if we can detect that early enough.  It'd be interesting if you could sort out of a way that could calculate the MEWS score and have it sent to a monitoring nurse on the ward or person on the ward, such as we do with telemetry, for instance.

*[10 second pause]*

*Beth Smith:*Brian's asking if anyone is using the Phillips system.  Do you know the answer to that, Russ?

*Russell Coggins:*No, ma'am, I do not know.

*Beth Smith:*Okay.  We'll see if anyone has any responses to that.

*Moderator:*If anyone in the audience is using it or knows of an area that is using it, there is an option on Adobe Connect.  At the top, you see a guy with his hand up.  If you click on there, you can raise your hand and we will see that pop up in the attendee list.  *[Pause]* I'm not seeing anything pop up on that.

*Beth Smith:*It doesn't look anyone is currently using it.

*Moderator:*Yes.  We actually just had one hand pop up.  Donna, if you have  any further information on it, feel free to type it in.  I'm sure a lot of people would love to hear about that.

*[10 second pause]*

*Beth Smith:*Donna has indicated that she can give the rep's name from Phillips, who can give the sites they've installed the system in.  That's great.  Brian, maybe that can be helpful to you.

*[10 second pause]*

*Moderator:*Okay.  Do we have any other questions out here today?

*[10 second pause]*

*Moderator:*If not, it looks like we may be able to wrap things up here.  Beth, Russ, do either of you have any final remarks you'd like to make before we close things out here?

*Russell Coggins:*No, ma'am.  I would just like to thank everyone for joining the call and thank Beth and her team for all the hard work that they put into this ESP program.

*Beth Smith:*You're very welcome, and I'd like to thank everyone for attending and participating and bearing with me as I struggled to get my voice heard at the beginning of the call.** Thank you so much for your attendance.

*Moderator:*The audience hung in very well.  Obviously, they really wanted to hear this cyberseminar, so we very much appreciate everyone hanging in.  

I did just put up a feedback form.  If you could all take just a few moments to fill that out, we really do read through all of your feedback, and we have definitely made changes to this program based on the feedback we receive.  You're not just shouting into a black hole.  We really do read through all of your feedback.  

At this time I really want to thank Beth and Russ for taking the time to prepare and present for today's session.  We really appreciate our presenters.  We appreciate the time and effort that you put into this.  

For our audience:  thank you, everyone, for joining us for today's call, and we do hope to see you at a future HSRD cyberseminar.  Thank you, everyone, for joining us.

*[End of Audio]*

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