Tag Archives: Healthcare Technology

Researchers at Boston-based Massachusetts General Hospital, Ann Arbor-based University of Michigan and Cambridge-based Massachusetts Institute of Technology Are Developing Institution-Specific Models That Predict Patient’s Risk Of Acquiring C.diff. Infections

Researchers at Boston-based Massachusetts General Hospital, Ann Arbor-based University of Michigan and Cambridge-based Massachusetts Institute of Technology are developing hospital-specific machine learning models that predict patients’ risk of Clostridium difficile infections much sooner than current diagnostic methods allow, according to a study published in Infection Control & Epidemiology.

“Despite substantial efforts to prevent C. diff infection and to institute early treatment upon diagnosis, rates of infection continue to increase,” co-senior study author Erica Shenoy, MD, PhD, said in a press release. “We need better tools to identify the highest risk patients so that we can target both prevention and treatment interventions to reduce further transmission and improve patient outcomes.”

The study authors noted most previous approaches to C. diff  infection risk were limited in usefulness since they were not hospital-specific and were developed as “one-size-fits-all” models that only included a few risk factors.

Therefore, to predict a patient’s C. diff risk throughout the course of their hospital stay, the researchers took a “big data” approach that analyzed the entire EHR. This method allows for institution-specific models that could be tailored to different patient populations, different EHR systems and factors specific to each facility. 

“When data are simply pooled into a one-size-fits-all model, institutional differences in patient populations, hospital layouts, testing and treatment protocols, or even in the way staff interact with the EHR can lead to differences in the underlying data distributions and ultimately to poor performance of such a model,” said co-senior study author Jenna Wiens, PhD. “To mitigate these issues, we take a hospital-specific approach, training a model tailored to each institution.”

With this machine learning-based model, the researchers looked at de-identified data, which included individual patient demographics and medical history, details on admissions and daily hospitalization, and the likelihood of C. diff exposure. The data was gathered from the EHRs of roughly 257,000 patients admitted to either MGH or to Michigan Medicine over two-year and six-year periods, respectively.

The models proved to be highly successful at predicting patients who would ultimately be diagnosed with C. diff. In half of these infected patients, accurate predictions could have been made at least five days before collecting diagnostic samples, which would allow hospitals to focus on antimicrobial interventions on the highest-risk patients.

The study’s risk prediction score could guide early screening for C. diff if validated in subsequent studies. For patients who receive an earlier diagnosis, treatment initiation could curb illness severity, and patients with confirmed C. diff could be isolated to prevent transmission to other patients.

The algorithm code is freely available here for hospital leaders to review and adapt for their institutions. However, Dr. Shenoy notes facilities looking to apply similar algorithms to their own institutions must assemble the appropriate local subject-matter experts and validate the performance of the models in their institutions.

To read article in its entirety please click on the following link to be redirected:

https://www.beckershospitalreview.com/quality/how-machine-learning-models-are-rapidly-predicting-c-diff-infections.html

Xenex Disinfection Services’ LightStrike™ Robot With Pulsed Xenon Ultraviolet-C (UV-C) Light Technology Introduces Its LightStrike Disinfection Pod

The scientific evidence has clearly established that in the hospital environment, microorganisms such as Clostridium difficile (C.diff), Methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) are responsible for the infections that kill nearly 300
people in the U.S. every day.

Xenex Disinfection Services’ LightStrike™ Robot with pulsed xenon ultraviolet-C (UV-C) light technology is a proven solution that quickly destroys deadly viruses, bacteria and spores before they pose a threat to patients and healthcare workers. LightStrike Robots help healthcare facilities reduce their HAI rates by destroying the microscopic germs that may be missed during the manual cleaning process. Xenex robots use pulsed xenon, a noble gas, to create Full Spectrum™, high intensity UV light that quickly destroys infectious germs in less than five minutes. Hospitals using Xenex devices have published clinical outcome studies in peer-reviewed journals showing 50-100 percent reductions in C.diff, MRSA and Surgical Site Infection rates when those hospitals used LightStrike Robots to disinfect rooms.

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Now, for the first time, hospitals can utilize the power of

LightStrike Germ-Zapping Robots™ to quickly disinfect mobile equipment just as effectively as they disinfect rooms within their facility. Pathogens like C.diff, Acinetobacter baumannii, MRSA and Vancomycin-Resistant Enterococci (VRE) can travel throughout a healthcare facility on mobile equipment.

To address this gap in the infection control process, Xenex recently partnered with an industry leader in containment units, Mintie Technologies, Inc., to create the LightStrike Disinfection Pod.

Designed to quickly disinfect reusable mobile equipment such as isolettes, ventilators, pressure monitors, wheelchairs and workstations, the

LightStrike Disinfection Pod enables the power of the LightStrike Robot’s intense, germicidal light to be used anywhere in a facility.
The Pod is collapsible, mobile and can be positioned in a hospital hallway or other areas without disrupting or impeding daily workflow. Its proprietary design integrates reflective interior fabric ensuring 360 degrees of UV light coverage over difficult-to-clean equipment including anesthesia carts, ventilators, and mobile imaging machines.

To access and read the article in its entirety please click on the link below:

https://www.dotmed.com/news/story/37771

UV-C Disinfecting Takes Its Place At Thompson Hospital and the M.M. Ewing Continuing Care Center in New York State

 

UV Disinfecting

Accomplished by using  short-wave
ultraviolet-C (UV-C) light as a germicidal to destroy viruses, bacteria and other pathogens that can linger on surfaces and hide in shadows.

One piece of equipmnet can disinfect an average-sized patient room in about 8 minutes and is deployed after a room is sanitized with standard techniques and cleaning products.

In  Canandaigua, New York  a nearly 6 foot tall and wielding 20 vertical fluorescent bulbs, the R-D Rapid Disinfector robot is a formidable fighter in the war against germs.

This UV disinfecting robot is The R-D Rapid Disinfector — developed by a Rochester, New York  firm, Steriliz LLC, and is manufactured locally.

Thompson Hospital and the M.M. Ewing Continuing Care Center have begun using this automated disinfecting machine throughout the institutions to help reduce the risks of illness and infections for patients, residents, visitors and staff.

The Disinfector uses short-wave ultraviolet-C (UV-C) light as a germicidal to destroy viruses, bacteria and other pathogens that can linger on surfaces and hide in shadows. This machine can disinfect an average-sized patient room in about 8 minutes and is deployed after a room is sanitized with standard techniques. It is remotely controlled by an associate from Environmental Services.

The UV-C light fills the entire room, reaching and disinfecting areas that human hands might miss. No one is allowed inside the room when the lights are working. This no-touch cleaning system gets rid of some of the most dangerous and difficult-to-destroy bacteria, including Clostridium difficile (C. diff). Disinfectants work on the surface of non-living objects by destroying the cell wall of harmful microbes or interfering with their metabolism.

“This technology, added on to normal, regular, manual environmental cleaning, gives me a sense of ease that we are doing all we can to keep our environment clean and our patients safe,” said Thompson Health Director of Infection Prevention Michelle Vignari. “We are just now starting to see published literature supporting that the addition of UV-C technology in hospitals actually does correlate with a reduction of healthcare-acquired infections.”

This state-of-the-art robot monitors the entire disinfection process. Wireless sensors measure, record and report on UV-C light dosages delivered to specific areas in real time. The machine can be paused and repositioned to maximize efficiency, including targeting shadowed areas. The Disinfector shuts off automatically once the sensors indicate that enough UV-C light has been emitted to kill the germs.

“In a day of delivering high-reliability care, I felt very strongly that we needed a technology that we could measure and evaluate its performance,” Vignari said.

Hospital staff like the Disinfector too.

“It is pretty simple to use and seems to be working great,” said Stephanie Fowler of Environmental Services, who activates the robot after a room is cleaned with traditional methods.

The R-D Rapid Disinfector was developed by a Rochester firm, Steriliz LLC, and is manufactured locally. The Disinfector uniquely provides FDA-patented wireless sensors to measure the amount of UV-C light delivered to an area and real-time online data access and reports. Since being tried in four Rochester hospitals in 2011, several hundred of these Disinfectors are now being used in hospitals, care homes, disaster centers and government installations worldwide.

Steriliz is recognized as a world leader in UV-C disinfection.

“Improving the health and safety of patients is a blessed opportunity,” said CEO and President Sam Trapani. “The potential market for the company’s product is large and we are experiencing a high growth curve.”

To read the article in its entirety please click on the link below:

http://www.mpnnow.com/news/20170318/robot-destroys-germs-with-power-of-light

Blood Test Developed By N.C. Researchers Is Able To Distinguish Between Viral and Bacterial Infections

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In the news *

 

 

A new blood test developed by researchers in North Carolina has been shown to distinguish between viral and bacterial infections.

The blood test has been designed to measure the gene expression of certain components of the immune system, which should allow doctors to identify whether the infection a patient is suffering from is bacterial or viral.

This distinction is crucial, as bacterial infections can be treated with antibiotics, whereas viral infections cannot, and prescribing antibiotics for viral infections only adds to the growing problem of antibiotic resistance.

‘Antibiotic resistance has been described as ‘one of the biggest health threats of our time’…’

Antibiotic resistance has been described as ‘one of the biggest health threats of our time’, and bold warnings have been issued explaining that if we do not refine our use of the drugs, in the future we may no longer be able to perform routine operations or use chemotherapy, and many could end up dying from illnesses commonly treatable today.Antibiotics work by targeting properties of bacteria that are unique and fundamental to them, such as blocking their ability to synthesize proteins or damaging their cell wall. The reason antibiotics can work so well is because the properties we target have no counterparts in human cells and therefore treatment can be given with minimal side effects on ourselves.

The problem of resistance arises as bacteria mutate, and there are a number of ways in which bacteria can do this. One way bacteria can counter the effects of antibiotics is by altering the drug’s target, such as the cell wall, so it is no longer vulnerable to the antibiotic. Bacteria can also create enzymes which inactivate the antibiotic or can create a ‘pump’ to remove the drug from their cells.

It only takes a single bacterium to acquire one of these changes to result in an antibiotic resistant infection. Bacteria multiply at a very fast rate and thus if even one bacterium mutates, and the antibiotic clears every other normal bacterial cell involved in the infection, that single mutated bacterium can rapidly divide, increase in numbers resulting in an antibiotic resistant infection.

‘The overuse of antibiotics makes it far more likely that bacteria will acquire mutations that make them resistant…’

An astounding 50% of antibiotics prescribed are given to patients in unnecessary circumstances, such as in viral infection. The properties of viruses are very different to bacteria and therefore antibiotics are ineffective against infections caused by viruses. The overuse of antibiotics makes it far more likely that bacteria will acquire mutations that make them resistant, meaning our antibiotics are slowly but surely becoming ineffective.

The new blood test developed by scientists at Duke University in North Carolina managed to distinguish between bacterial and viral infection with an accuracy of 87% in a study on 317 patient blood samples.

Here in the UK, the Longitude Prize, a £10 million grant, was chosen by the public to be invested in antibiotic research with the aim to design a test that will conclusively distinguish between bacterial and viral infection.

The new blood test in question could provide a good foundation for further research to be done, allowing conclusive and accurate diagnosis of bacterial infection. Unfortunately, the blood test requires ten hours of analysis and so would be of minimal use in a GP environment where most over-prescription takes place. However, with the Longitude Prize pushing for new research into a quick and easy test to confirm bacterial infection, this new blood test has the potential to do big things for such a topical issue.

 

To read the article in its entirety click on the link below:

 

http://www.redbrick.me/tech/new-blood-test-distinguishes-viral-bacterial-infections/

C. difficile Infection – Jvion, Leader In Clinical Predictive Algorithms, Predict and Prevent Patients at Risk

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Jvion is a healthcare technology company that develops software designed to predict and prevent patient-level disease and financial losses leading to increased waste.

Jvion, the Atlanta-based leader in clinical predictive algorithms, announced the release of a Clostridium Difficile (C Diff) predictive use case for inpatient and outpatient providers. The C Diff use case is the latest in a broad compliment of predictive capabilities that span hospital acquired infections, chronic conditions, and individual illnesses. Using the firm’s RevEgis patient phenotype platform, the C Diff solution flags at risk individuals to help support clinician decision-making and reduce the likelihood of the deadly infection. These predictions can be produced in both inpatient and outpatient settings and can be integrated directly into existing electronic health record management systems.

“C.diff is a serious and deadly problem,” said Todd Schlesinger, VP for Jvion. “With this use case, we’ve extended the award-winning predictive capabilities already delivered through RevEgis to address a very pressing and lethal challenge facing all healthcare settings.”

A new report from the Centers for Disease Control and Prevention (CDC) shows that C Diff infected nearly half a million Americans. Of those infected, 15,000 died as a result of the bacteria. Anyone taking an antibiotic is at risk of developing a C Diff infection, but older adults over 65 are particularly susceptible. According to the study, people taking antibiotics are 7 to 10 times more likely to get C Diff while on their prescription and in the subsequent 30 days after treatment. The bacteria are spread through unclean surfaces and dirty hands. A recent Ohio-based study found that six out of every seven outpatient settings test positive for C Diff.

Jvion, the leader in clinical predictive algorithms, announced the development of a Clostridium Difficile (C Diff) use case to help clinicians reduce the risk of deadly infections inside and outside the hospital.

Todd went on the say that, “our goal is to help clinicians reduce the occurrence of C Diff infections while also lowering mortality rates attributed to the bacteria. We designed our solution so that it fits seamlessly into the clinical workflow and provides the additional insights that help support physicians and reduce the risk of C Diff incidences.”

RevEgis has won numerous awards including: designation as Gartner Cool Vendor for Healthcare Providers; the 2014 Intel Innovation Award; top honors from Fierce Health IT in the “Data Analytics” category, and as an overall award of “Best in Show: Fiercest Cost-Saving Solution.” These awards recognize RevEgis’s impact on the reduction of various diseases and health concerns. To learn more about Jvion, their full suite of Big Data predictive analytic solutions, and how they can help clinicians reduce C Diff infections please visit http://www.jvion.com

About Jvion
Jvion is a healthcare technology company that develops software designed to predict and prevent patient-level disease and financial losses leading to increased waste. The company offers a suite of big-data enabled solutions that combine clinical intelligence with deep machine learning to help providers protect their revenues while improving patient health outcomes. Their objective is simple—stop the waste of resources and lives by predicting and stopping losses before they ever happen.

 

To read the article in its entirety click on the link below:

 

www.prweb.com/releases/JvionStops/CDiff/prweb12562136.htm