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Montefiore Health Makes Big AI Play

Posted on September 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

I’ve been doing a lot of research on healthcare AI applications lately. Not surprisingly, while people find the abstract issues involved to be intriguing, most would prefer to hear news of real-life projects, so I’ve been on the lookout for good examples.

One interesting case study, which appeared recently in Health IT Analytics, comes from Montefiore Health System, which has been building up its AI capabilities. Over the past three years, it has created an AI framework leveraging a data lake, infrastructure upgrades and predictive analytics algorithms. The AI is focused on addressing expensive, dangerous health issues, HIA reports.

“We have created a system that harvests every piece of data that we can possibly find, from our own EMRs and devices to patient-generated data to socio-economic data from the community,” said Parsa Mirhaji, MD, PhD, director of the Center for Health Data Innovations at Montefiore and the Albert Einstein College of Medicine, who spoke with the publication.

Back in 2015, Mirhaji kicked off a project bringing semantic data lake technology to his organization. The first pilot using the technology was designed to find patients at risk of death or intubation within 48 hours. Now, clinicians can also see red flags for admitted patients with increased risk of mortality 3 to 5 days in advance.

In 2017, the health system also rolled out advanced sepsis detection tools and a respiratory failure detection algorithm called APPROVE, which identifies patients at a raised risk of prolonged ventilation up to 48 hours before onset, HIA reported.

The net result of these efforts was dubbed PALM, the Patient-centered Analytical  Learning Machine. PALM “represents a very new way of interacting with data in healthcare,” Miraji told HIA.

What makes PALM special is that it speeds up the process of collecting, curating, cleaning and accessing metadata which must be conducted before the data can be used to train AI models. In most cases, the process of collecting data for AI use is largely manual, but PALM automates this process, Miraji told the publication.

This is because the data lake and its graph repositories can find relationships between individual data elements on an on-the-fly basis. This automation lets Montefiore cut way down on labor needed to get these results. Miraji noted that ordinarily, it would take a team of data analysts, database administrators and designers to achieve this result.

PALM also benefits from a souped-up hardware architecture, which Montefiore created with help from Intel and other technology partners. The improved architecture includes the capacity for more system memory and processing power.

The final step in optimizing the PALM system was to integrate it into the health system’s clinical workflow. This seems to have been the hardest step. “I will say right away that I don’t think we have completely solved the problem of integrating analytics seamlessly into the workflow,” Miraji admitted to HIA.

Health Leaders Say Automating Patient Engagement Efforts Will Have Major Impact

Posted on March 12, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Over the last few years, many vendors have rolled out products designed to engage patients further in their care. According to a new study, these solutions may be just the tip of the iceberg. In fact, many healthcare executives see patient-facing, engagement-enhancing technology as critical to the future of healthcare, according to a new study.

The study, by the World Business Group, focuses on technology that can link patients with care in between visits to their primary care center. Patient engagement technologies, which they call “automated care,” have the potential to bridge such gaps by providing AI-based assistance to consumers.

The survey, which was also backed by Conversa Health, drew on direct interviews and survey responses from 134 health execs. The researchers looked at how those execs viewed automated healthcare technologies, how these technologies might be used and whether respondents plan to adopt them.

Respondents were clearly very enthusiastic about these tools. Nearly all (98%) said they believed automated healthcare will be important in creating a continuous, collaborative relationship with providers. The survey also found that 87% of respondents felt that automated healthcare will be helpful in getting patients to engage with their own care.

The next step, of course, is throwing resources at the problem — and it’s happening. Seventy-nine percent of survey respondents said they expected to work on integrating automated healthcare in their organization within the next two years. Also, 44% said that they had a chief patient experience officer or other executive with an equivalent title on board within their organization. This development is fairly new, however, as 40% of these organizations said that the role has existed for two years or less.

Meanwhile, several respondents felt that automating patient healthcare could generate a positive feedback loop. Forty-nine percent of those surveyed reported that they were either integrating or have already integrated patient-generated health data, which they expect, in turn, to integrate into the patient experience efforts.

Among organizations working with patient-generated health data, 73% were gathering patient health histories, 64% treatment histories, 59% lifestyle or social data, 52% symptoms data, and 32% biometric data.

Thirty percent said they were beginning to integrate such data and collect it work effectively, 28% said they could collect some PGHD but had trouble integrating with their systems, 14% said they were just beginning to collect such data and 9% said they were not able to collect this data at all. Just 19% reported they were fully able to collect integrate PGHD and use it to improve patient experiences.

All told, it appears we’re on the cusp of a major change in the role patient services play in provider outreach. It will probably be a few more years before we have a good idea of where all this is headed, but my guess is that it’s heading somewhere useful.