GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK

Suki, the artificial intelligence (AI) voice assistant for healthcare professionals, launched in May to praise from health-tech innovators who hoped “Alexa for doctors” might slash electronic health record (EHR) documentation time.

If the company’s early numbers are any indication, that wish might come true.

Suki said recently that doctors who use the digital assistant have seen a 70 percent drop in time spent writing and filing medical notes.

That figure is 10 percentage points better than the preliminary results of Suki’s pilot. The finding could prove powerful, as research has suggested that physicians burn almost two hours in the EHR for every hour of patient interaction.

Suki has processed more than 12,000 patient encounters in the real world, handling 1,000 per week, according to the company. This steady stream of real-world data is poised to strengthen the technology’s machine-learning algorithm, which was trained on 250,000 patient encounters before it went live.

So far, Suki has integrated with three EHR systems. The company said that number is slated to rise.

Using voice commands, the technology pens a “clinically accurate” note that it then sends to a doctor’s EHR system.

Over the past several years, innovators across healthcare have advocated for a clinical voice assistant. But the challenges are many, from precise listening and documentation to satisfying cybersecurity concerns.

Although a clinical AI assistant might be far off for many providers and health systems, Suki and similar technologies could help solve a mounting problem in medicine: physician burnout.

Time spent in the EHR has been linked to physician burnout. On the conference circuit and in the opinion pages, doctor after doctor describes the stresses of medical note documentation — and how these demands eat up time and detract from care delivery.

Can Suki help improve workflows? It seems so. But can it go a step beyond and help physicians overcome burnout? Maybe. The answer to that question will depend on adoption rates and how Suki performs at scale.

Will Suki become a part of the clinic of tomorrow? Lets wait and see

read the unedited story at https://www.hcanews.com/news/alexa-for-doctors-claims-70-percent-reduction-in-ehr-time

At the World Medical Innovation Forum this week, participants were polled with a loaded question:

“Do you think healthcare will become better or worse from the use of AI?”

Across the respondents, 98 percent said it would be either “Better” or “Much Better” and not a single one thought it would become “Much Worse.” This is an interesting statistic, and the results were not entirely surprising, especially given that artificial intelligence was the theme for the meeting.

This continual stream of adoption of new technologies in both clinical and post clinical settings is remarkable. Today, healthcare is a technology operation. As a case in point, outside of the array of MDs and medical professionals presenting at the forum, there was clearly a strong, advanced technology thread weaved throughout the conversations of the traditional topics of pathology, radiology, bioinformatics, electronic medical records (EMR), and standard healthcare provider issues.

As an example, a panel of senior technology experts from Microsoft, Cisco Systems, Dell EMC, Qualcomm, and Google joined research and information officers from Partners Healthcare and Massachusetts General Hospital to discuss the challenges in what they called “Data Engineering in Healthcare: Liberating Value.” That is a serious title for a panel.

Data portability was clearly a key topic, as was security and the public cloud.

The underlying issue with the cloud is that the EMR was never really designed to be portable.

Health records existed with institutional walls, and were not originally intended for real time care, but more as a means of tracking costs and transactions as the patient traveled through the various systems. As the EMR has not only become more feature rich, the ability to mine that data inside of them with ML and AI methods is clearly at the forefront of everyone’s mind right now.

There was discussion of episodic systems wrapped in policy and technology – this really isn’t quite how we can gain the maximum knowledge from the healthcare version of a Digital Me. A digital object containing all of our many and varied health related attributes. The challenges of discussing how to best build a “marketplace” and healthcare data exchanges and how to integrate “data marts” with existing EMR systems was obvious.