RICHMOND, Va. — While the coronavirus pandemic closed schools throughout the country, students at a Virginia Catholic school have turned to manufacturing personal protective equipment for health care workers.

Using 3D printers, middle school students at St. Bridget School in Richmond, guided by their robotics teachers, produced much-needed equipment to make the job of the workers easier to navigate.

Eric De Boer, one of the school’s two robotics coaches, saw posts on social media about 3D printing helping in the response against COVID-19, the illness caused by the novel coronavirus.

“After the diocese announced we would not be physically returning to school for the rest of the year, I thought we could put our 3D printers to work during this pandemic,” he told The Catholic Virginian, newspaper of the Richmond Diocese. “Our school values service, and this seemed like a way for our students to serve our community.”

De Boer sent emails to his students and their parents looking for volunteers. Quickly, eighth grader Bridget Plank, 14, and seventh graders Hayden Veech and Anthony Pennock, both 13, replied. Turning their homes into makeshift factories, they have produced hundreds of items for health care workers.

The students said they were inspired to act by their faith.

“Jesus said, ‘Do unto others as you would have them do unto you,’” Bridget said. “So if I were a health care worker during this time, I would want people to help me and my co-workers in any way they could.”

Hayden added, “My faith taught me to treat others as you want to be treated. It would be wrong to do nothing.”

With their team assembled — virtually — they got to work. De Boer knew the school’s printers were too small to print large PPE gear like face shields and masks, but he was also sure that workers on the front line could still benefit from their help.

He connected with #MakeItThru Alliance, a group of engineers that has been coordinating efforts to produce and distribute 3D printed materials to local health care personnel and essential workers such as bus drivers and customer support staff.

Jean-Etienne LaVallee, an alliance leader, welcomed the students with socially distant open arms. He told De Boer there was a need for small items such as “door grabbers” and “ear savers,” which could be printed on smaller printers.

Door grabbers are small plastic devices that hook over door handles, allowing people to open doors without actually having to touch the handles themselves. The tool helps reduce spreading germs.

Ear savers are designed to reduce irritation brought on by wearing face masks. Each elastic ear band on a regular surgical mask hooks on to one end of the ear saver, alleviating the pressure on the ears themselves.

The ear saver strap, which can be adjusted for size, sits at the back of a person’s head. This provides relief to hospital workers who wear masks for several hours at a time.

While the robotics students have experience planning and producing objects, the effort marked the first time they have had the responsibility of manufacturing products on their own.

Each student has a printer at home. De Boer used video chat to give the students instructions for completing their projects. Once a print job is finished, each product is sanitized and placed into a plastic bag. They leave the bag in a “drop zone” at the school and members of #MakeItThru pick it up.

Hayden said health care workers appreciate the students’ work.

“They loved that we were helping out with simple tools that made working much easier,” he said.

After De Boer posted about the group on social media, several health care professionals expressed interest.

“In addition to the practical support this provided, there is also the emotional support felt when strangers reach out to help,” De Boer explained. “Knowing others around you care for you and are with you can provide a great sense of hope and encouragement.”

3D printing is relatively inexpensive. Each spool of filament — the printer’s version of ink — costs about $20, and each spool can make dozens of ear savers or door grabbers.

St. Bridget teachers are chipping in to help cover the costs. It takes one hour to print a set of two door grabbers and a half-hour to make an ear saver.

“It feels just as good to give to others as to receive things yourself,” Bridget said.

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Artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome today’s barriers to adoption, compliance, and implementation.

Here are key obstacles to consider and how to handle them:

Developing regulatory frameworks. Over the past few years, the U.S. Food and Drug Administration (FDA) has been taking incremental steps to update its regulatory framework to keep up with the rapidly advancing digital health market. In 2017, the FDA released its Digital Health Innovation Action Plan to offer clarity about the agency’s role in advancing safe and effective digital health technologies, and addressing key provisions of the 21st Century Cures Act.

The FDA has also been enrolling select software-as-a-medical-device (SaMD) developers in its Digital Health Software Precertification (Pre-Cert) Pilot Program. The goal of the Pre-Cert pilot is to help the FDA determine the key metrics and performance indicators required for product precertification, while also identifying ways to make the approval process easier for developers and help advance healthcare innovation.

Most recently, the FDA released in September its “Policy for Device Software Functions and Mobile Medical Applications” — a series of guidance documents that describe how the agency plans to regulate software that aids in clinical decision support (CDS), including software that utilizes machine-learning-based algorithms.

In a related statement from the FDA, Amy Abernethy, its principal deputy commissioner, explained that the agency plans to focus regulatory oversight on “higher-risk software functions,” such as those used for more serious or critical health circumstances. This also includes software that utilizes machine learning-based algorithms, where users might not readily understand the program’s “logic and inputs” without further explanation.

An example of CDS software that would fall under the FDA’s “higher-risk” oversight category would be one that identifies a patient at risk for a potentially serious medical condition — such as a postoperative cardiovascular event — but does not explain why the software made that identification.

Achieving FDA approval. To account for the shifting FDA oversight and approval processes, software developers must carefully think through how to best design and roll out their product so it’s well positioned for FDA approval, especially if the software falls under the agency’s “higher risk” category.

One factor that must be considered is the fact that AI-powered therapeutic or diagnostic tools, by nature, will continue to evolve. For example, it is reasonable to expect that a software product will be updated and change over time (e.g., security updates, adding new features or functionalities, updating an algorithm, etc.). But given the product has technically changed, its FDA approval status could be put at risk after each update or new iteration.

In this case, planning to take a version-based approach to the FDA approval process might be in the developer’s best interest. In this approach, a new version of software is created each time the software’s internal ML algorithm(s) is trained by a new set of data, with each new version being subjected to independent FDA approval.

Although cumbersome, this approach sidesteps FDA concerns about approving software products that functionally change post-FDA approval. These strategic development considerations are crucial for solutions providers to consider.

Similarly, investors must also have a clear understanding of a company’s product development plans and intended approach for continual FDA approval as this can provide clear differentiation over other competitors in the same space. Clinicians will be hard pressed to adopt technologies that haven’t been validated by the FDA, so investors need to be sure the companies they are considering supporting have a clear product development roadmap — including an approach to FDA approvals as software products themselves and regulatory guidelines continue to develop.

AI is a black box. Besides current regulatory ambiguity, another key issue that poses challenges to the adoption of AI applications in the clinical setting is their black-box nature and the resulting trust issues.

One challenge is tracking: If a negative outcome occurs, can an AI application’s decision-making process be tracked and assessed — for example, can users identify the training data and/or machine learning (ML) paradigm that led to the AI application’s specific action?. To put it more simply, can the root cause of the negative outcome be identified within the technology so that it can be prevented in the future?

From reclassifying the training data to redesigning the ML algorithms that “learn” from the training data, the discovery process is complex — and could even result in the application being removed from the marketplace.

Another concern raised about the black-box aspect of AI systems is that someone, either on purpose or by mistake, could feed incorrect data into the system, causing erroneous conclusions (e.g., misdiagnosis, incorrect treatment recommendations). Luckily, detection algorithms designed to identify doctored or incorrect inputs could reduce, if not eliminate, this risk.

A bigger challenge posed by AI systems’ black box nature is that physicians are reluctant to trust (due in part to malpractice-liability risk) — and therefore adopt — something that they don’t fully understand. For example, there are a number of emerging AI imaging diagnostic companies with FDA-approved AI software tools that can assist clinicians in diagnosing and treating conditions such as strokes, diabetic retinopathy, intracranial hemorrhaging, and cancer.

However, clinical adoption of these AI tools has been slow. One reason is physician certification bodies such as the American College of Radiology (ACR) have only recently started releasing formalized use cases for how AI software tools can be reliably used. Patients are also likely to have trust issues with AI-powered technologies. While they may accept the reality that human errors can occur, they have very little tolerance of machine error.

While efforts to help open up the black box are underway, AI’s most useful role in the clinical setting during this early period of adoption may be to help providers make better decisions rather than replacing them in the decision-making process. Most physicians may not trust a black box, but they will use it as a support system if they remain the final arbiter.

To gain physicians’ trust, AI-software developers will have to clearly demonstrate that when the solutions are integrated into the clinical decision-making process, they help the clinical team do a better job. The tools must also be simple and easy to use. Applying AI in lower-stakes tasks initially, such as billing and coding (e.g., diagnostics, AI-assisted treatments), should also help increase trust over time.

At the industry level, there needs to be a concerted effort to publish more formalized use cases that support AI’s benefits. Software developers and investors should be working with professional associations such as the ACR to publish more use cases and develop more frameworks to spur industry adoption and get more credibility.

Lower hurdles in life sciences. While AI’s application in the clinical care setting still faces many challenges, the barriers to adoption are lower for specific life sciences use cases. For instance, ML is an exceptional tool for matching patients to clinical trials and for drug discovery and identifying effective therapies.

But whether it’s in a life sciences capacity or the clinical care setting, the fact remains that many stakeholders stand to be impacted by AI’s proliferation in health care and life sciences. Obstacles certainly exist for AI’s wider adoption — from regulatory uncertainties to the lack of trust to the dearth of validated use cases. But the opportunities the technology presents to change the standard of care, improve efficiencies, and help clinicians make more informed decisions is worth the effort to overcome them.

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Artificial intelligence is quickly taking root in the healthcare ecosystem by supporting better clinical and financial decision-making.

Providers and executives now have access to much more actionable insights derived from natural language processing (NLP), deep learning, neural networks, and other advanced machine learning techniques.

These strategies aren’t just revolutionizing the way providers make decisions.  Artificial intelligence is also fundamentally altering the way patients interact with their caregivers, turning a more-or-less passive relationship into an opportunity to make informed choices and when, where, and how to seek out services.

As a result, the role of the patient is being completely rewritten, says Gregg Meyer, MD, MSc, Chief Clinical Officer at Partners HealthCare, and the consumer experience must follow suit.

“Patients are becoming wiser consumers, and much more empowered consumers,” explained Meyer to HealthITAnalytics.com ahead of the Partners World Medical Innovation Forum in Boston on April 8-10, 2019.

“Consumers are paying drastically more for their care out of their pockets, and that is creating a much stronger pressure to get their money’s worth, in their opinion.”

“At the same time, AI is enabling them to optimize the things they couldn’t in the past, such as choosing care based on quality or making decisions about price,” he continued. “As a result, they have much more control over the experiences they have, and that means the health system is also under pressure to adapt to what patients want.”

The traditional paradigm of forcing a patient to squeeze an appointment into a packed clinical calendar, leave their job or find a babysitter, and relinquish half their day to the waiting room before being seen by a physician for five minutes is no longer sustainable.

In the era of chatbots that can triage patients in minutes and algorithms that display price lists at local facilities on demand, said Meyer, providers have to do better to meet modern expectations of convenience and self-service – something that consumers routinely enjoy in other industries but haven’t yet found in healthcare.

“Think about how much we have embraced automated, streamlined digital experiences in other areas of our lives.  When was the last time I interacted with a bank teller?  Or a travel agent?  I can’t remember because those industries have created experiences that make it more convenient for me to do the admin myself,” he said.

“The consumer gets control, convenience, and immediate responsiveness that we don’t typically find in healthcare.  If we are going to harness what has been proven successful in other sectors, we need to embrace AI.”

Meyer recognized that the transition to a more patient-driven industry is bound to bring some growing pains, most of which are likely to affect physicians as the health system reorients itself to the patient-centred point of view.

Many providers remain wary about integrating artificial intelligence into the care process, either because they don’t yet trust AI to perform correctly or because they fear being made redundant.

“AI is certainly going to make many of us uncomfortable, because it will require delivering care differently,” Meyer acknowledged.  “And there are certainly issues that we need to address as we bring AI into the care process.  But in the long run, it’s going to be great for patients, and it could be great for providers who embrace it, too.”

“There are more and more opportunities to use AI to provide convenience and efficiency to the patient and to the provider at the same time.”

Using AI to reduce the time devoted to administrative tasks such as documentation and coding can benefit providers and patients simultaneously, he explained.

“I don’t go home at the end of the day saying, ‘Wow, I had a really great time doing my coding today – I made a real difference in someone’s life by doing that,’” he said. “If I could outsource that to a bot or an algorithm, I’d be a much happier doctor with much more cognitive and emotional bandwidth to tackle the complex patient-facing aspects of my job.”

“I don’t view a net-benefit like that as a threat.  I view it as something that can free me up to deliver better care to people, so my patient gets my full attention and I can exercise my skills at the highest level.”

Routine chronic disease management and preventive care processes are also likely to be among the first targets of automation, and offer a low-risk way to free up a time to focus on more complex issues, he continued.

“A patient doesn’t need to come in just to have their blood pressure checked anymore,” asserted Meyer.

“There’s simply no reason they can’t take their pressure at home with one of the high-quality devices available to them, and no reason we can’t use an app with an AI algorithm that suggests adjustments to their medication based on the results.”

Leveraging the power of advanced analytics in such a manner could help to ensure that patients are matched with the right therapies much more quickly and efficiently than current processes allow.

“If you come in with high lipids, it could take between twelve and eighteen months to find the right combination of diet, exercise, and medication that effectively gets your values under control,” he said, drawing on his experience as a primary care provider.

“But imagine being able to use an algorithm that synthesizes your clinical history, assesses your genetic likelihood of responding to a range of potential therapies, and models predicted outcomes so that you can get on an effective therapy in a matter of weeks instead of months at a much lower cost.  That isn’t fantasy – that is where AI is heading, and it’s getting there very quickly.”

Meyer predicted that it will take less than two years for similar AI-driven experiences to be available for a wide range of common conditions.

“I can see why some people might feel threatened by that, because it’s changing the relationship the provider has with the patient,” he said.  “It does require giving up a little bit of perceived control over that relationship.”

“But as a physician, you can’t hold on to everything and then complain that you’re overworked and that you’re not operating at the top of your license.  Something’s got to give.  And that doesn’t have to be a bad thing.”

At the same time, Meyer said, he doesn’t want to see clinicians “parking their brains” and letting artificial intelligence completely take over the patient experience.

“There will always be a need for human clinicians to oversee decision-making and ensure that AI is doing the right thing for our patients,” he stressed.

“A patient might think it’s really great that an app prescribed them an antibiotic for their sore throat, for example, but if we are not very vigilant about the parameters we’re setting for those prescriptions to make sure antibiotics are truly warranted, we could be doing more harm than good in terms of antibiotic stewardship.”

“We have to be actively engaged as clinicians and ensure that the technology we’re using is consistent with evidence-based guidelines – guidelines that are changing all the time.  We need to be mindful about using AI as an augmentation to human cognition, not as a short cut.  Technology still requires real, sustained engagement from the provider community, and that isn’t going to change.”

What consumers are truly seeking in their updated experiences is the optimal blend of data-driven convenience with human empathy and involvement, Meyer emphasized.

“The single most important thing that physicians and medical students need to do is retain their humanity,” he said.  “Patients will always need a physician who doesn’t just know what’s the matter with them, but also knows what matters to them,” Meyer said.

“An algorithm can help get someone’s high blood pressure under control, but AI isn’t going to help them feel any better if the person’s marriage is falling apart, they’re about to lose their job, and they just need someone to understand the stress they’re going through and help them manage it.”

“The very best providers of the future are the ones who will be able to take empathy and humanity to the next level – and they will be able to do that because artificial intelligence is taking care of the routine grunt work and taking the admin off their plates.”

Meyer will continue to share his insights on optimal strategies for deploying artificial intelligence into the clinical environment at the World Medical Innovation Forum.

He will be joined by experts from across the care continuum as they explore the challenges and opportunities of architecting an AI-driven consumer experience.

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To understand the benefits that artificial intelligence can bring to the world of human medicine, consider the case of Ayako Yamashita, a 60-year-old Japanese woman, whose condition befuddled doctors in 2015.

Yamashita was thought to be suffering from acute myeloid leukemia. But after several unsuccessful treatment attempts, her doctors decided to search for another answer to her condition. They turned to IBM’s Watson, an AI system capable of analyzing vast amounts of data.

The computer reviewed nearly 20 million previously-published oncological research studies and cross-referenced data points. Watson’s analysis suggested the woman had a rare form of leukemia not detected through conventional methods. This led to a change in treatment and doctors crediting Watson for saving  the woman’s life.

The analysis of such huge amount of data is next to impossible for a human mind, but it’s like a walk in a digital park for AI. And it shows what may be one of the valuable things that AI can do for us. It is “the most practical application in the field of medical and healthcare for artificial intelligence,” said Seiji Yamada, of the National Institute of Informatics and chairman of the Japanese Society for Artificial Intelligence.

The global artificial intelligence market is expected to reach $19.47 billion by 2022, according to the research firm Allied Market Research. As AI is marking its presence, tech giants are working to capitalize on new opportunities. The healthcare sector is a natural fit, according to Sanjay Gupta, managing director, South Asia and Middle East for NICE, a technology firm based in Israel, in an interview with ETHealthworld:

“The development of automation enabled by technologies including robotics and artificial intelligence in healthcare sector brings the promise of higher productivity with increased safety.”

Saving lives and time

Among Google’s many AI ventures is an effort to develop new products targeting the health sector. The company is focusing on applications for life preservation, preventive care and improving health care services.

The company plans to launch a trial in India to test an AI system that scans a person’s eyes to look for signs of diabetic retinopathy. The company aims to license the technology to clinics. The system already has proven itself adept at detecting high blood pressure, or risk of heart disease or stroke, according to a study published in early 2018.

From a story published in the Washington Post:

“This may be a rapid way for people to screen for risk,” Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email. “Diagnosis is about to get turbo-charged by technology. And one avenue is to empower people with rapid ways to get useful information about their health.”

Jeff Dean, the Chief at Google AI, outlined for Boss Magazine how this system will enable doctors to better diagnose and treat patients for a range of diseases. Moreover, this system will also track key events in the patient’s past (including hospital stays) to help doctors more effectively.

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