By coupling machine learning with whole genome sequencing, University of Pittsburgh School of Medicine and Carnegie Mellon University scientists greatly improved the quick detection of infectious disease outbreaks within a hospital setting over traditional methods for tracking outbreaks.
The results, published in the journal Clinical Infectious Diseases, indicate a way for health systems to identify and then stop hospital-based infectious disease outbreaks in their tracks, cutting costs and saving lives.
“The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century,” says senior author Lee Harrison, MD, a professor of infectious diseases at Pitt’s School of Medicine and epidemiology at Pitt’s Graduate School of Public Health. “Our process detects important outbreaks that would otherwise fly under the radar of traditional infection prevention monitoring.”
The Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) couples the recent development of affordable genomic sequencing with computer algorithms connected to the vast trove of data in EHRs. When the sequencing detects that any two or more patients in a hospital have near-identical strains of an infection, machine learning quickly mines those patients’ EHRs for commonalities—whether that be close proximity of hospital beds, a procedure using the same equipment, or a shared health care provider—alerting infection preventionists to investigate and halt further transmission.
Ordinarily, this process requires clinicians to notice that two or more patients have a similar infection and alert their infection prevention team, which can then review patient records to attempt to find how the infection was transmitted.
“This is an incredibly labor-intensive process that is often dependent upon busy health care workers noticing a shared infection between patients to begin with,” says lead author Alexander Sundermann, MPH, CIC, FAPIC, clinical research coordinator and a doctoral student at Pitt Public Health. “That might work if patients are in the same unit of a hospital, but if those patients are in different units with different health care teams and the only shared link was a visit to a procedure room, the chances of that outbreak being detected before other patients are infected falls dramatically.”
From November 2016 to November 2018, UPMC Presbyterian Hospital ran EDS-HAT with a six-month lag for a few select infectious pathogens often associated with health care–acquired infections nationwide, while continuing with real-time, traditional infection prevention methods. The team then investigated how well EDS-HAT performed.
EDS-HAT detected 99 clusters of similar infections in that two-year period and identified at least one potential transmission route in 65.7% of those clusters. During the same period, infection prevention used whole genome sequencing to aid in the investigation of 15 suspected outbreaks, two of which revealed genetically related infections.
If EDS-HAT had been running in real-time, the team estimates as many as 63 transmissions of an infectious disease from one patient to another could have been prevented. It also would have saved the hospital as much as $692,500.
In one case study, EDS-HAT found an outbreak of vancomycin-resistant Enterococcus faecium that it traced to an interventional radiology procedure involving injection of sterile contrast that was being performed according to manufacturer instructions. Due to EDS-HAT detecting the outbreak, UPMC alerted the manufacturer to the instructions that led to faulty sterilization practices.
“In that case, EDS-HAT connected the dots between seemingly unconnected patient infections occurring in different hospital units, stopping that outbreak but also potentially preventing similar outbreaks at other hospitals,” Harrison says. “That example encapsulates the value of EDS-HAT.”
UPMC plans to introduce EDS-HAT in real time at UPMC Presbyterian Hospital and expects this innovation to benefit other infection prevention and control programs in the future. And the original EDS-HAT, which primarily focused on drug-resistant bacterial pathogens, will soon be expanding to incorporate sequencing of respiratory viruses, including COVID-19.
— Source: UPMC
A life was saved twice a week by an automated text messaging system during the fraught early days of the COVID-19 pandemic and, overall, the patients who enrolled in that system were 68% less likely to die than those not using it. These insights about Penn Medicine’s COVID Watch—a system designed to monitor COVID-19 outpatients using automated texts and then escalate those with concerning conditions to a small team of health care providers—are published in the Annals of Internal Medicine.
“At the beginning of the pandemic, we instinctually thought patients needed extra support at home, even if they weren’t sick enough or ill yet. And if they were to get very sick, we wanted to help them get to the emergency department earlier, so COVID Watch was our solution,” says a coprimary investigator of the study, Krisda Chaiyachati, MD, medical director of Penn Medicine OnDemand and an assistant professor of medicine. “Our evaluation found that a small team of five or six nurses staffing the program during some of the most hectic days of the pandemic directly saved a life every three to four days.”
COVID Watch was built on Penn’s “Way to Health” platform, accelerating its development from concept to deployment. Conceived March 11, 2020, COVID Watch enrolled its first patient March 23, 2020, only two weeks after Penn Medicine took in its first COVID-19 patient. Designed to help patients with the virus recover safely at home and keep hospital capacity available, the system uses algorithmically guided text message conversations with patients to assess their conditions. Twice a day, it sent routine questions to patients, such as, “How are you feeling compared to 12 hours ago?” and “Is it harder than usual for you to breathe?” If a patient indicated a worsening condition, follow-up questions were asked and they were elevated to the human members of a centralized team—headed by coauthor Nancy Mannion, DNP, COVID Watch’s nurse manager—who would call to check in and recommend hospitalization, if needed.
Nearly 20,000 patients have been enrolled in COVID Watch since it started.
“We did an early analysis of the system and determined that we could safely monitor more than 1,000 patients simultaneously, 24/7, with a small, well-trained team of registered nurses,” says Anna Morgan, MD, COVID Watch’s medical director and an assistant professor of internal medicine. “On top of that, those same nurses could often also take care of other COVID-related tasks such as helping patients arrange COVID testing and discussing their results, which is important during surges.”
To further assess COVID Watch’s effect on patients, researchers from the Perelman School of Medicine at the University of Pennsylvania analyzed data from every adult who received outpatient care from Penn Medicine, starting the day COVID Watch launched until November 30, 2020, a period of roughly eight months. They separated the data into those enrolled in COVID Watch and those who received the typical course of outpatient care: 3,448 patients enrolled in COVID Watch and 4,337 not in it. The analysis accounted for patients’ ages, underlying conditions, and other risks for developing severe disease.
Only three out of 3,448 patients in COVID Watch died within 30 days of their enrollment, compared with 12 of the 4,337 otherwise equivalent patients outside of the program. That meant the mortality rate outside of COVID Watch was three times higher. At 60 days after enrollment, five people within COVID Watch died compared with 16 not using the system.
These data translated to a 68% reduction in the chance of dying if a patient was enrolled in COVID Watch. Additionally, COVID Watch was credited with saving 1.8 lives per 1,000 patients at 30 days, and 2.5 per 1,000 at 60 days.
The study’s lead author and coprimary investigator, M. Kit Delgado, MD, an assistant professor of emergency medicine and epidemiology, as well as the deputy director of the Penn Medicine Nudge Unit, believes that the benefits seen by COVID Watch patients could be explained by increased access to and use of telemedicine, and more frequent and earlier trips to the hospital—an average of two days earlier for COVID Watch patients—when symptoms worsened.
Importantly, the study found that COVID Watch was equally accessible and effective for everyone.
“We saw a higher proportion of higher-risk patients and also low-income and Black patients enrolled in COVID Watch, but the fact that we measured a significant benefit associated with enrollment in the program is a good indicator that there truly is a treatment benefit for everyone,” Delgado says. “It’s crucial that we found all major racial and ethnic groups benefited because nonwhite and low-income communities have had disproportionately higher infection rates, lower access to care, and higher death rates. This implies that this model of care could have reduced disparities in COVID outcomes if it was scaled up more broadly to these communities.”
The COVID Watch team plans to see if the approach, which had originally been built off a system for keeping tabs on COPD patients, can be applied to helping people with other conditions manage their health at home. They see the nimble, algorithm-driven system as a lasting technology that will factor heavily into care in the coming years.
“Automation isn’t something that will replace human clinical care, but it is something that can extend it,” says David Asch, MD, a coauthor and the executive director of the Center for Health Care Innovation. “Without an automated system to help us watch over the thousands of COVID patients in our community, our doctors and nurses would have been stretched even thinner than they were. This is a promising model for the future.”
— Source: Perelman School of Medicine at the University of Pennsylvania
Kareo, a leader in cloud-based clinical and financial software, and PatientPop, a leader in practice growth technology, announce the closing of their merger and the unveiling of the combined company’s new name. Tebra combines leading technologies from both companies, which currently support more than 100,000 health care providers, to deliver an all-in-one platform purpose-built to drive practice success and modernize every step of the patient journey.
The consumerization of health care has finally arrived at your local doctor’s office. Patients are paying more for their health care, spending more time searching for providers online, and demanding convenient tools like online scheduling, telehealth, and two-way messaging. The COVID-19 pandemic has hyper-accelerated these trends as providers adopt digital technology to extend access to care and stay connected to patients.
“Patients today expect a seamless, digital experience from health care like they have in every other aspect of their lives. But unfortunately, not all doctors have been able to keep pace with these expectations like other industries have,” says Dan Rodrigues, CEO and founder of Kareo. “That’s why we’re so excited to join forces with PatientPop to help doctors grow their practices online and deliver a modern experience.”
PatientPop contributes technology to support practice growth, including practice websites, online appointment booking, search marketing, digital registration, messaging, and more. Kareo contributes technology to support practice operations, including a fully certified EHR, scheduling, insurance billing, patient payments, and more. Together as Tebra, PatientPop and Kareo will support the connected practice of the future and modernize every step of the patient journey.
“PatientPop helps doctors attract more patients, manage their reputation, and grow their business,” Luke Kervin, co-CEO and cofounder of PatientPop, says. “But the one thing our customers have been asking for over the years is a deeper integration with their clinical and financial system of record. Now with Kareo we can finally bring to the market the all-in-one platform our practices have been demanding.”
The name Tebra is derived from the word “vertebrae” and symbolizes the role the new company serves as the backbone of practice success, delivering digital technology to connect providers and their patients. The combined company currently has approximately 1,000 employees supporting more than 100,000 health care providers who are delivering care to more than 85 million patients in the United States.
“Tebra is building the operating system for the connected practice of the future with solutions to support practice growth, the patient experience, care delivery, and the billing and payments process,” says Travis Schneider, co-CEO and cofounder of PatientPop. “But this merger also creates a network of providers and patients, and we’re excited to bring new solutions to the market built on top of our network.”
More information and updates are available at tebra.com.
— Source: Kareo and PatientPop
Nym Health, a leader in autonomous medical coding, announces the appointment of Michael Finke to its Board of Directors. Finke has more than 25 years of experience in developing and bringing machine learning, natural language, and speech understanding technologies to the health care market, with a focus on reducing the clinical documentation burden for clinicians while automating revenue cycle processes.
Finke cofounded M*Modal in 2001, a provider of cloud-based, conversational AI-powered clinical documentation and clinical improvement solutions. Under his leadership, more than 5,000 hospitals adopted M*Modal’s clinical intelligence technology and services portfolio, supporting close to 400,000 physicians. Following 3M’s acquisition of M*Modal in 2019, Finke served as vice president of 3M Health Information Systems and led the company’s clinician solutions division.
“We’re honored to welcome Michael to Nym’s Board of Directors as we prepare for this pivotal next stage of growth,” says Amihai Neiderman, Nym’s CEO and cofounder. “Michael brings deep domain expertise, which will be invaluable to our team as we continue expanding and enriching our platform’s capabilities to meaningfully reduce the administrative burden on clinicians and other health care stakeholders, and, in turn, enable them to spend more time with patients, providing quality, personalized care.”
Powered by its cutting-edge clinical language understanding engine and explainable AI capabilities, Nym’s platform improves the speed and precision of medical billing, reducing coding-related costs for health care providers. The company’s solution for revenue cycle management transforms provider narratives in the free text within patient charts, turning them into accurate and compliant ICD-10-CM and CPT reimbursement codes in a matter of seconds, without any human intervention.
“What drew me to Nym is their relentless focus on enabling a better, deeper understanding of the patient’s story and their overall momentum in the market,” Finke says. “The unique approach Nym has taken in building truly explainable AI capabilities is key to overcoming industry challenges related to confidence in the consistent accuracy and quality of AI, which have previously prevented health care providers from implementing the technology at scale.”
Finke previously served as cofounder and CTO of Interactive Systems (later acquired by Lernout and Hauspie) and held positions at IBM Research, the University of Karlsruhe in Germany, and Carnegie Mellon University in Pittsburgh. He earned his bachelor’s and master’s degrees in computer science from the University of Karlsruhe and has authored more than 100 academic papers. Additionally, Finke holds more than 20 patents in speech recognition, natural language understanding, clinical documentation, and coding.
— Source: Nym Health