Tag: hospital improvement

A Better Way To Get To Hospital…

Here’s a clever idea, sent to us by Susan Lewis on behalf of the Formativ Health  company, the beauty of which is its simplicity. You wonder why nobody else thought of this.

If you’ve got to go to hospital – then you want the simplest way to do it. It is reasonable to assume that you are probably sick.  The one thing you don’t want is the hassle of parking the car. Hospitals have volunteers for this sort of stuff, to get you there. But what if you could just dial a ride, so to say, from someone who is already going? Have a read of this great announcement:

“Formativ Health, a technology-enabled health services company focused on transforming the patient-provider experience, announced today it has entered into an agreement with Lyft, the fastest-growing rideshare company in the U.S.

Formativ, whose technology and services support physician practices, hospitals, and health systems, will work with Lyft to integrate Concierge into its Patient Engagement Platform (PEP). Through this integration, Formativ’s 250+ Patient Engagement Specialists can schedule non-emergency Lyft rides for patients directly through its PEP platform to provide Lyft rides in 40+ States. Lyft rides can be ordered on-demand or in advance, and patients don’t need to be a Lyft user to take advantage of the service.

The PEP, which leverages the Salesforce HealthCloud, is the core of Formativ’s technology offering, enabling improved patient-provider experiences when combined with their team of highly trained Patient Engagement Specialists. Formativ’s PEP solution includes enterprise-wide scheduling functionality that enables improved appointment inventory visibility and features automated waitlist, online self-scheduling and many other key practice management capabilities.

According to a 2017 study by the American Hospital Association, nearly four million patients per year miss out on care, due to lack of available transportation options related to cost or geographic barriers. These missed appointments make it difficult for patients to get the care they need, and this partnership is one way to make it easier for provider organizations to cut that number down.

“For many patients, access to reliable transportation can be the biggest hurdle in getting them to the doctor’s office. Formativ partnered with Lyft to enable our team of patient engagement specialists to book on-demand or scheduled rides for the patients we serve on behalf of our clients, addressing some of the negative social determinants of health, decreasing barriers to care and making life that much easier for patients,” explained David Harvey, chief technology officer at Formativ.

Lyft was founded in June 2012 by Logan Green and John Zimmer to improve people’s lives with the world’s best transportation. Lyft is the fastest growing rideshare company in the U.S. and is available to 95 percent of the US population as well as in Ontario, Canada. Lyft is preferred by drivers and passengers for its reliable and friendly experience, and its commitment to effecting positive change for the future of our cities, as the first rideshare company to offset carbon emissions from all rides globally.

New York City-based Formativ Health is a technology-enabled health services company focused on transforming the patient-provider experience. Their services help health systems, provider groups, and payors respond to the rise of consumerism by combining powerful technology with an empathetic approach to customer service. Formativ helps clients enhance their patients’ experience, adapt to evolving risk-based payment models, improve financial performance, increase practice productivity, and elevate physician satisfaction and patient loyalty. For more information, visit http://www.formativhealth.com or on LinkedIn, Twitter and Facebook.

View source version on businesswire.com: https://www.businesswire.com/news/home/20180907005060/en/

Better Management of Clinical Appts and Theatre Processes.

We continue our study of better ways to reduce the costs of clinical and theatre processes, and better ways to automate the whole situation. Beatriz Agrana sent us this compelling announcement from Seattle. This is what she says:

The founders of macro-eyes, a machine learning company that personalizes patient care, today announced the introduction of Sibyl, a predictive scheduling solution that cuts the financial and operational impact from patient No-Shows without relying on patient behavior change.

We’ve all called to book a medical appointment to be told that the first available slot is in 5 to 6 weeks. That day, 10 appointments may go empty, even 20; often more. No one shows up to ~15% of all scheduled appointments. At many sites, No-Shows can constitute nearly 40% of appointments. A schedule filled with No-Show appointments prevents the greatest number of patients from accessing the care they need when they need it most.

“No-Shows and lack of optimization in scheduling costs healthcare providers billions, hits morale, strains operations and has implications on care that can cost lives. We developed Sibyl to solve the problem with cutting-edge machine learning and deliver long-needed, massive improvement in cutting the damage from No-Shows. Sibyl is AI that learns when to schedule individual patients to increase utilization overall,” said Benjamin Fels, CEO of macro-eyes. Healthcare is increasingly data-driven, scheduling is not. It’s mission-critical infrastructure, yet the decision-making that determines scheduling doesn’t benefit from data-driven insight or predictive analytics.

Sibyl is a predictive scheduling solution that machine learns the appointment times that are best-fit for both the patient and provider, increasing utilization overall. The software functions as an add-on to existing scheduling systems, showing schedulers appointment recommendations for each patient.

“It’s extremely difficult to change patient behavior,” explains Fels. “Likely the reason No-Shows continue to cost providers >$100B each year. Sibyl offers a proven approach based on solid science. We use patterns in behavior to learn when patients are most likely to show and the mathematics of optimization to build schedules that enable the greatest access to care.”

Sibyl uses macro-eyes core AI, refined over years at leading academic medical centers in NY and California, to analyze appointment histories and thousands of data points across provider, patient, location, time and type of care as well as weather patterns, air quality, traffic and transport data and state and federal data on the region where the care will occur. “The schedule is like a puzzle, and Sibyl is an expert at fitting together the schedule to minimize gaps,” Fels explains.

Sibyl works like x-ray glasses for the calendar, seeing through the chaotic schedule to understand where there are gaps that would otherwise be impossible to see. By integrating predictive analytics with schedule optimization, Sibyl provides a peerless tool for healthcare organizations, improving the bottom line as well as the patient experience.
During the software’s late-stage testing, macro-eyes worked with 20 clinics across the United States to analyze 2 million appointment records. The anonymized records contained reams of information, including scheduled appointment times, but the testing temporarily eliminated whether or not those appointments were kept. Sibyl churned through the records and generated its own recommended schedule. With that done, the real Show/No-Show results were compared side-by-side with Sibyl’s results.

The outcome? Sibyl predicted actual patient outcomes with 76% accuracy. Sibyl incorporated more than 60 factors to build each prediction. The appointment start time, the patient’s age and the zip-code of the clinic were frequently predictive. Sibyl is prediction + optimization. Sibyl demonstrated schedule optimization that would increase utilization by >20% without increasing investment to add hours or providers. For one group of clinics, that would translate to nearly $10 million in revenue.

At least one other scheduling platform exists for clinics and hospitals, but its core algorithms are rules-based. It ascertains an “average patient” profile and then makes recommendations based on this profile rather than learning, adapting, and making ranked predictive recommendations, as Sibyl does. Sibyl delivers the most accurate, effective results of any healthcare scheduling platform on the market.