AI News, Health Care Providers & Services

Health Care Providers & Services

Epic's Data Science Forum in May brought more than 50 experts from the Epic data science community together in Verona for a day-long discussion focused on sharing current and emerging uses of machine learning embedded in workflows to drive outcomes.

Penn Medicine also provided an overview on how the success of a machine learning project is dependent on clinical, business, and machine learning experts working together from the moment the idea is hatched to ensure the project will provide value to the organization.

Epic Systems: Machine Learning Is the EHR Usability Solution

A public appearance by Epic Systems founder and CEO Judy Faulkner is always a big event for the health IT industry, and nothing is bigger for Epic than the electronic health record company’s annual user group meeting.

With 9000 thousand employees and around 8000 users from the US and elsewhere congregating in Verona, Wisconsin, to hear about what’s on Epic’s development docket for the coming year, Faulkner seemed committed to challenging the very definition of what makes an EHR –

This year, in a magician-themed extravaganza, Epic executives extolled the value of big data analytics for revolutionizing EHR usability and ramping up physician satisfaction as they teased a number of new developments in machine learning, voice recognition, and natural language processing.

But the focus on EHR usability isn’t just to keep Epic’s massive customer base happy.Innovations in EHR form and function are crucial for enabling a new era of patient engagement, population health management, and quality measurement.

Intuitive data visualizations, sleek drag-and-drop interfaces, and more natural input methodologies such as voice recognition are poised to change the way physicians, nurses, and patients interact with their records, she said.

has become a bit of a touchy word for clinicians in recent years, especially for those who do not believe the American Medical Association’s campaign to restore joy to medicine has thus far borne sufficient fruit.

While the poll found somewhat higher levels of satisfaction among Epic customers, segmented out for the purposes of the presentation, the number of unhappy EHR users is still worryingly high.

Epic President Carl Dvorak also pointed out that clinical notes in the United States are an average of three times longer than those in other countries, due in large part to information dumps that attempt to cover all the bases for billing and coding.

While Dvorak readily agreed that clinicians need to spend less time typing and more time focusing on patients, he did take issue with media coverage of a recent AMA study that found physicians spend around six hours per day on EHR data entry, including about an hour and a half of “pajama time”

Ambient computing devices will also play a role in patient engagement, allowing consumers to talk their way through their medication adherence issues, fitness goals, and wellness plans.

Adjusting to a longitudinal, collaborative, person-centered care environment that demands extra patient outreach and follow-up hasn’t been easy for most organizations, Faulkner said, especially those whose providers are among the 30 percent who can’t stand their EHRs.

Providers have a responsibility to retool their workflows and develop coordinated care teams to support population health and individual wellness.But the onus is on EHR developers to make sure organizations have the tools to enable practice transformation –

The social determinants of health are becoming critical data points to support comprehensive population health management initiatives, and EHRs can no longer afford to leave socioeconomic and patient-reported health data out of the clinical record.

“We won’t be able to afford to continue doing what we’re doing if we don’t change the way we look at social determinants and population health,”

While Epic is by no means the only EHR developer pushing hard to integrate social determinants data to enable more comprehensive population health management, the company believes that it has a few unique advantages that allow its big data aggregation and exchange platforms to thrive.

Data exchange through the platform has doubled in the past year, jumping from 1 million transactions per day in 2016 to 2 million each day in 2017.

she added, unable to resist taking a dig at an organization that has produced tensions in the past, although Epic tends to insist that there is no trace of competition in its relations with the Cerner-led network.

And with Epic’s recent announcement of Share Everywhere, the web-based data exchange platform that does not even require users to have an EHR system, as well as an upcoming web-based version of Epic’s population health management platform called Healthy Planet Link, seamless interoperability and collaboration can easily go universal.

“More than 1.6 million patients have had Epic predictive algorithms run on them for hypertension, diabetes, asthma, and heart failure, which helps providers create targeted care management programs.”

Natural language processing will extract meaning from free text and voice input to suggest next steps for patient care, how to best document them, and how organizations can ensure that they are getting properly paid for the services they provide.

Machine learning, alongside the integration of social determinants data into the patient record, will fuel a new generation of tools that can predict and close gaps in care.

More accurate quality and performance benchmarking will be available by individual providers or by custom groups such as a specific scheduled shift, and a personalized EHR user experience will become second nature for providers.

Vendors of all sizes and types have been gleefully embracing the idea of artificial intelligence as a panacea for everything that ails the healthcare industry, with varying degrees of effectiveness so far.

For the most part, healthcare organizations have only dabbled in machine learning pilots and cognitive computing proof-of-concept projects, said Longhurst, as a result of niche vendors only offering AI-driven tools to solve very specific problems.

Strong governance principles, a nimble and engaged organization, and attention to the details of data integrity will be crucial for organizations that wish to leverage innovative technologies to their fullest potential, cautioned Davis.

Many developers make very attractive promises about their innovations in machine learning or other cutting-edge developments, and it is important to make certain that their concept art and sales pitches really do translate into day-to-day clinical care.

UCSF, GE Healthcare Launch Deep Learning Partnership to Advance Care Globally

UC San Francisco’s Center for Digital Health Innovation and GE Healthcare have announced a partnership to develop a library of deep learning algorithms – complex problem-solving formulas – that will empower clinicians to make faster and more effective decisions about the diagnosis and management of patients with some of the most common and complex medical conditions.

The algorithms will be used to ensure providers around the world can access new knowledge and insights delivered through deep learning – a method by which machines can rapidly generate new levels of clinical and operational value from large imaging and textual data sets in ways that traditional machine learning methods cannot.

Together, we will develop tools and algorithms that will allow clinicians and researchers to identify problems and ask questions that are only achievable with vast computing power and datasets.” As algorithms are trained and the library of available algorithms expands, the associated applications will have the potential to do everything from predicting patient trajectories, to automating the triage of routine care, to improving process efficiency and enabling the development of more personalized therapies.

“By working hand-in-hand with a leading academic medical center like UCSF to design, build and verify new deep learning tools, we are defining how digital health solutions can be seamlessly integrated into care.” The collaboration is initially focused on high-volume, high-impact imaging to create algorithms that reliably distinguish between what is considered a normal result and what requires follow up or acute intervention.

Patients who previously lacked even the most basic medical services may benefit from GE Healthcare’s best-in-class technology and UCSF’s world renowned clinical expertise.” Over the course of the partnership, GE Healthcare and UCSF will expand opportunities to integrate data not only from a variety of imaging technologies such as CT, MR and X-ray, but will also incorporate clinical data sets from the electronic health record and other sources to enrich algorithm development and improve sensitivity.

Ochsner, which operates 30 hospitals and more than 80 health centers and urgent care centers in Louisiana, said the Microsoft and Epic technologies, when taken together, will enable predictive models to help its Rapid Response Team become proactive than reactive.

[Also:Ochsner deploys robots to zap germs in fight against deadly hospital-acquired infections] “By utilizing Epic’s machine learning platform and Microsoft Azure, we can detect health patterns, learn from these insights and develop a more aggressive treatment plan as a preventative measure,”

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