The WAVE Predictive Algorithm Platform: Interview with Mark Koppel, CMO of Excel Medical

Frank
September 25, 2018
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Excel Medical, a medtech company based in Jupiter, Florida, has developed the WAVE patient surveillance and predictive algorithm platform. The system monitors patient physiological information, such as vital signs, in healthcare facilities. It then aggregates, integrates and displays this data in a variety of formats, including through smartphones, tablet computers and electronic medical record systems. This allows medical staff to access and appraise patients remotely, in real-time.

However, the system not only monitors patients, but also offers a predictive algorithm feature to provide an early warning for at-risk patients. The company claims that WAVE can provide over six extra hours warning to clinicians before a patient deteriorates to a critical stage, helping them to avert a serious medical emergency.

Excel Medical has recently hired a new CMO, Dr. Mark Koppel, to specifically address the issue of unexpected death in hospitals. Medgadget had the opportunity to ask Mark some questions about the WAVE concept and his new role.

 

Conn Hastings, Medgadget: Please tell us a little about how you got into this field and what attracted you to work with Excel Medical.

Mark Koppel, Excel Medical: I am a physician by training, with a background in anesthesia. I transitioned out of clinical practice very early in my career to pursue work in the commercial realm. I have held positions ranging from Medical Director to Chief Medical Officer for organizations such as GE, Wolters Kluwer Health, Pentax Medical and Exact Sciences.

I developed a deep interest in clinical decision support solutions (CDSS) while working at Wolters Kluwer Health, a company that has several highly regarded clinical support tools. As both a clinician and someone in industry, I have seen firsthand the benefit that these tools can provide. However, I have always felt that current solutions, while valuable, weren’t fulfilling the true promise of CDSS. Many solutions are static, lack patient specific context and do not have the ability to provide predictive information that allow clinicians to anticipate and intervene before adverse outcomes can occur. With the recent proliferation of patient data and technological tools that allow for the continuous acquisition and evaluation of this data, I believe we are finally capable of providing truly predictive patient surveillance at the bedside.

What attracted to me to Excel Medical was the history and legacy of working with approximately 80% of the leading academic institutions to acquire and analyze various disparate forms of clinical data such that clinicians can make more informed decisions with respect to patient care. This infrastructure, in conjunction with predictive algorithms, is really the ecosystem that sits at the heart of Excel Medical’s mission to eradicate unexpected hospital deaths. It’s the unique combination of technology and clinical content that has me very excited to work with this team to help execute the vision.  

 

Medgadget: Can you give us some background on unexpected deaths in hospitals?

Mark Koppel: The literature indicates that there are approximately 400,000 unexpected deaths in hospitals per year. In fact, this is the third leading cause of death in America behind cardiac disease and cancer. I think this is a staggering number to most people. To be fair, there are numerous causes of unexpected hospital death, some of which may not be capable of prediction or prevention. However, we believe that several causes such as sepsis, cardiorespiratory deterioration leading to arrest, and hemorrhage lend themselves to the possibility of predicting the onset in advance such that interventions might prevent unexpected deaths.

 

Medgadget: Could an early warning significantly reduce the number of these deaths?

Mark Koppel: Absolutely. We know from the literature that various early warning systems can help clinicians spot clinical deterioration before clinical signs become obvious. The early notice of clinical deterioration can ultimately lead to earlier interventions, such as the activation of Emergency Medical Teams. The earlier we can intervene, the better the outcome.

To this end, Excel Medical’s WAVE Platform is the first FDA-cleared predictive patient surveillance platform. Intrinsic within the system is the Visensia(R) Safety Index (VSI), a predictive algorithm designed to constantly analyze up to 5 clinical vital signs to help predict clinical deterioration due to cardiorespiratory causes. The VSI was shown to provide an average of 6 hours advance warning of clinical deterioration in a study conducted at the University of Pittsburgh Medical Center. In the same study, UPMC reported that unexpected deaths dropped from 6 in the 60 days prior to implementing the VSI to 0 in the 24 months after implementation.

As evidenced by this data, we know that early notice and intervention can save lives.

Medgadget: So, how does the WAVE system work to monitor patients, and transmit this information to healthcare teams?

Mark Koppel: Its best to think of WAVE as an ecosystem with a stepwise progression. First, we have to consider the numerous clinical data sources being generated from a patient encounter. Data can be generated from various medical devices such as beside monitors, ventilators, and pumps as well as within the electronic medical record of the patient. In addition, newer technologies such as remote sensors show enormous promise for accumulating continuous data from ambulatory patients, including those outside of the acute environment. Second, we need infrastructure to acquire and aggregate this data into one platform. This is what Excel Medical has been doing for years in numerous institutions via our BedMaster and BedComm solutions. These products form the base camp if you will of the ecosystem.

Once the foundation is in place, the third step is to apply advanced predictive algorithms to the data that has been captured via the technology infrastructure. In this case, the VSI algorithm logic would be continuously run in the background to determine if clinical deterioration may occur in the future. Lastly, the clinical output of the VSI calculation must be generated and delivered to the clinician at the right place and at the right time. Currently, we have the capability to provide the VSI warning on a desktop or through mobile applications including phones and tablets. We are also speaking with potential partners to expand the options for clinical access of the information.

Medgadget: What types of medical emergencies can the system predict? How does the predictive algorithm work?

Mark Koppel: WAVE has been FDA-cleared for use with the Visensia Safety Index, which can give advanced notice of cardiorespiratory clinical deterioration. The algorithm is continuously sampling and analyzing 5 different vital signs at the same time. This data is then compared against a normal data set range to determine whether the current patient or patients vital signs deviate from the normal such to a degree that is abnormal. What is unique about VSI is that it is analyzing in five dimensions simultaneously and this is level of cognitive complexity that very few humans on the planet can match.

The WAVE platform has been designed to accommodate multiple areas of clinical predictive algorithms and we are actively looking at new sources of content for that purpose. Our true north is to eradicate unexpected hospital deaths, so we are keenly focused on algorithms that can help predict such conditions as sepsis, sudden cardiac arrest, and hemorrhage among others.

 

Medgadget: How has the system been received so far? Has it helped in reducing unexpected deaths?

Mark Koppel: We are early in the commercialization process as we have only just received FDA clearance but the early indications from potential clients has been extremely positive. The feedback we have received is that providers are desperate for solutions that can provide predictive information without interfering with clinical workflows or adding to the problem of alarm fatigue.

As for the proof, we know from the UPMC research that the platform can reduce unexpected deaths.

Link: Excel Medical…

Frank’s source: https://www.medgadget.com/2018/05/the-wave-patient-surveillance-and-predictive-algorithm-platform-interview-with-mark-koppel-cmo-of-excel-medical.html

 

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