Stuart Long, Chief Executive Officer of Monarch Medical TechnologiesPoster | EndoTool Glucose Management System

By Stuart Long, CEO of Monarch Medical Technologies

During my career in healthcare, I have worked for many companies that have claimed to save lives. I was originally attracted to Monarch Medical Technologies because it was the first company to truly have that kind of impact. The value our products provide for our customers is measurable from the first dose of insulin, and the outcomes that our customers publish are ground breaking.

Recently, there have been product announcements from companies in the glucose management space promoting new tools to “predict” hypoglycemia. There is a big difference between “those products” which market hypoglycemia’s predictive ability and the EndoTool technology. EndoTool not only predicts changes in glucose levels, it also takes the next step in terms of treatment by adjusting insulin dosing recommendations for every single blood glucose (BG) check. This capability is starting to appear in all care segments, yet we feel it is especially important in an inpatient environment.

Outpatient vs. Inpatient

There are two primary “care segments” for glucose management systems – outpatient and inpatient. Most of the new product announcements are focused on outpatient prediction. Due to the sheer size of the outpatient setting, there is a great deal of attention, and regulatory hurdles are higher.  The hurdles are higher for “recommending a dose” in an outpatient setting because of the complexity of having a caregiver intervene to oversee dosing recommendations.

Stopping short of the actual dosing, when this potential is noted in the outpatient segment, the dosing decision-tree is presented to either the patient, caregiver, or a diabetes management program, which can intervene and provide treatment to the patient.

The situation for a hospitalized patient is very different.  In a much more controlled environment, bedside care-givers are present with each patient to make the best clinical decisions. In this environment, using a glucose management software that is capable of not only predicting hypoglycemic  trends, but able to recommend dosing options and ultimately prevent dangerous events via dosing adjustments is what we are all about.

In an outpatient scenario, these tools predict whether an event might occur within the next 24-hours; whereas, in the hospital setting a true glucose software predicts the expected physiologic patient response in the next BG reading to make adjustments in real time at the point-of-care. We move from predictive to prescriptive by examining historical data, predicting future individualized patient responses, and then we provide a recommended dose all while significantly reducing the risk of hypoglycemia.

The Risk Factors for Hypoglycemia

One of the greatest risks for causing hypoglycemia is the residual insulin which compounds from previous doses.  Using a standard dose for an elevated blood glucose level can result in the stacking of insulin within the patient’s extracellular space for intravenous insulin as well as the patient’s fat pad with subcutaneous insulin. This essentially creates the risk for insulin overdosing and hypoglycemia.

The challenge lies in the insulin half-life, the patient’s health and a variety of other conditions, including kidney function. Based on this function, the insulin absorption is radically different from patient to patient.

EndoTool takes into account the individualized residual insulin as a part of the dosing algorithm. Thus, the nurses or clinicians are able to dose correctly from the very beginning, using clinical variables and historical data. EndoTool enables an accurate prediction of a future event – whether it is hypoglycemia or hyperglycemia – and makes the proper and personalized adjustment of insulin.

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Factors Which Predict Hypoglycemia

In predicting hypoglycemia, the EndoTool considers a range of clinical parameters including the patient’s age, height, weight, and gender. It also considers kidney function, insulin dosing regimen (IV or subcutaneous), steroid intake, up to four previous blood glucose values, and carbohydrate intake.

Using a combination of all of these factors, EndoTool is able to predict a patient’s physiologic response to provide a personalized insulin dose.  It can also evaluate whether a one-time event, such as the patient eating a pastry or other high-carb food, is driving a glucose spike.

What This Means for a Hospital

We see this impacting two different types of outcomes – clinical and financial. The clinical outcomes are significant in terms of the reduction in manifestations of poor glycemic control. These are events that happen after the patient is admitted to the hospital and can lead to dangerous conditions, infections, and mortality.

On the financial side, there is tremendous impact from the reduction in the lengths of patient stay and operational costs. We have seen marked reduction in the number of “sticks” to the patient, which, over time, results in a reduction of nursing workload, and in supplies. This also leads to greater patient satisfaction.

When these factors are all considered, the improved clinical outcomes drive more positive financial outcomes for the hospital.

Personalized Medicine Demands Better Technology

In a hospital setting, a patient may be taking numerous medications that impact their ability to process insulin. Given the variety of interactions of these medications and the highly stressful environment of a hospital, the kinds of calculations that are required for a very individualized, personalized, predictive, and prescriptive mode of therapy – whether it is insulin or other dangerous drugs – are not only required today, yet will be continuously enhanced in the future by advancing technologies. It’s just a matter of time.

We are seeing a move toward more personalized treatments in many clinical settings and this is enhanced by algorithmic-based software like EndoTool. However, the goal of our technology is not to replace the critical skills caregivers have been trained to provide, but rather to increase their efficiency and their ability to provide patient care.

Although we hear a lot about personalized medicine and predictive technologies, healthcare tends to be slow to adopt. I think it’s only a matter of time, but is it?

What are your thoughts around this and are you using any level of these tools for clinical decision support? What’s your biggest barrier to adoption? What have you been most successful with?

If you would like to contact me directly, please click here, provide your information, and I will get back with you in a few days.

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