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In a previous post, Disaster Response Applications: New Lessons From The Fall of Kabul and Severe Weather Events, we featured mobile apps designed as multi-sided platforms useful in the aftermath of a natural disaster, political crisis, or in the case of an ongoing crime incident or safety emergency. These platforms function in larger ecological and industrial ecosystems – hurricanes, wildfires, flash floods, earthquakes, power outages, sanitation, street closures, and supply chain disruptions – all integrated with many variables that need to be maintained and addressed in order for business operations, public works, or civic life to gradually stabilize after a disaster.
Disaster and emergency management ecosystems are unique in their temporality, ephemerality and marked by finite windows of time in which mission-critical information flows are the difference between, rescue and safety, life and death. The “evacuation phase” of a disaster scenario, for example, is one of these time windows: evacuating is never easy, as these four people who fled Hurricane Ida can attest. If we apply some of the lessons learned from the crowdsourced community and personal safety platforms like Ehtesab, Citizen, and Protect, we can envision a future when the evacuation phase of a crisis is efficiently self-managed by evacuees.
Or possibly the emergence of a queue-based system with state-mandated evacuation complete with scheduled times for departure assigned by zip code. This approach, of course, is dependent on a high percentage of the local population accepting that a mandatory evacuation managed by the state or federal government is an acceptable solution (over yet another chaos-as-usual evacuation: stuck on the road, in traffic, in an unregulated, random, inefficient fashion). The recent behavioral psychology lessons learned from vaccine hesitancy do not make this centralized, state-mandated queue-based approach a clear winner, however, as an innovative solution to decision-making support and situational awareness during the evacuation phase of a disaster.
Industrial control systems’ monitoring operates on another time horizon entirely – ideally no-fault, 24/7 operations with system redundancies. Such was apparently not the case in the aftermath of Hurricane Ida, as 15 state-operated air monitoring systems were out of service. Air quality was a unique phenomenon in the aftermath of Hurrican Ida, as the oil refineries and petrochemical plants in the river basin between New Orleans and Baton Rouge are powered, ironically, by electricity – and during the aftermath of the storm went into a ‘running flares’ mode and had to burn off oil and petrochemicals as a safety measure. The EPA tried to measure air pollution at the industrial plants, showing some platform-based resiliency with mobile air monitoring systems deployed at a couple of oil refineries.
System outage reportage for real-time decision-making support is also, ideally, a stable variable during a crisis. The Stock Market has the Dow Jones and other indices. Hollywood has the weekend Box Office report. Yet the national electricity grid and cellular tower network do not have a national index for regional reportage of mission-critical outages during a disaster or emergency. This platform should be recognizable, easily accessible, and ubiquitous for the general public. Such a national standardized system would be helpful for managing the time window for the safe return home of evacuees. If they can and are able, people want to get home.
Disaster conditions will clearly be more impactful – Hurricane Ida’s impacts on infrastructure were unprecedented, bringing down more powerlines than Hurricanes Delta, Zeta, and Katrina combined – and more frequent due to the impact of climate change. The domestic terrorism threat stateside is becoming a constant, with the impact and frequency of growing domestic U.S. political instability and public safety incidents to be determined.
We will need systems that are monitoring these temporal, ephemeral ecosystems and providing insights and recommendations for real-time decision-making support and situational awareness analysis, designed with redundancy and zero fault mechanisms in mind and not dependent on the current electrical grid.
The following is a case study from another industry vertical which grapples with rescue and safety, life and death as real-time, mission-critical business issues.
The trial by fire crisis conditions created by the current public health disaster has forced the healthcare industry into an acceleration of artificial intelligence innovation and implementation of applied technologies that will “make healthcare more efficient, more accessible, and more human in a post-pandemic world.”
They call it “ambient clinical intelligence” and Dr. Greg Moore of Microsoft recently spoke to its potential while on a panel entitled “How AI is Transforming Patient-Focused Care in a Post-Pandemic World,” on Day 1 of the recent Boston Globe – Global Summit 2021:
“With conversational AI and ambient AI, what seemed wildly ambitious two decades ago…now seems to be at hand for us. Cloud computing and AI-enabled cognitive computing capabilities like speech recognition and computer vision and the ubiquitous proliferation of sensors give us a great foundation and really sets the stage for what is the art of the possible in terms of patient care. It has profound implications for how we practice medicine and how we care for patients.
One example, the ICU is the most complicated and indeed the most expensive place to be in healthcare. Let’s take something that seems as simple, but as dangerous, as falls for ICU patients. Falls can contribute to doubling your one-year mortality rate. The non-tech solution is the observation of the patient by a caregiver. We have seen that in early work in ambient clinical intelligence, it becomes possible for technology to monitor patients with computer vision techniques, like muscle atrophy, predicting the likelihood of a fall. Early studies are showing that ambient sensing can decrease the likelihood of a fall by 90% as they access patient mobility and muscle strength. So interesting applications are emerging as we combine ambient AI and sensor technology in the cloud.”
While it does not specifically fit all the design and system requirements of a future-perfect public safety platform for real-time decision-making support while managing disaster conditions, the creativity and design innovation of this ambient clinical intelligence solution is impressive – and instructional.
Joe Petro, CTO and Executive Vice President of Research and Development of Nuance, expanded on Moore’s comments:
“This is a super exciting area for us, it’s a very thorny technology problem.”
“There is ambient clinical intelligence, and above that, there is ambient intelligence. I think starting there might be helpful. If you think about ambient intelligence, we are literally trying to make technology invisible. This is a Star Trek use case, where you are pushing the technology back away from the experience, but the experience itself surfaces so that you can do what do are doing on a day-to-day job without technology getting in the way.
Ambient Clinical Intelligence is an extension of that. About 7 years ago a client was talking about “wouldn’t it be a really good idea if the room could listen to the conversation between patient and physician and actually generate accurate documentation.” I walked out of the meeting thinking it was a great idea but thinking that we were not even close to having the technology you would need available to do something like that.
But it spurred on what Nuance now calls Ambient Clinical Intelligence in healthcare – an AI-based voice-enabled solution at the point of care. That colloquial conversation between patient and physician now generates accurate documentation from that conversation. The whole thing is based on AI, and it is accurate and efficient, and you can use it right at the point of care. You can use this technology today. It is in production. It is cloud-based and can be used remotely. It really does enhance the quality of care. There is also a smart device that can be mounted on the wall, it has seventeen different microphones on it, so you can detect the various speakers in the room. Today the technology is already integrated into all the telehealth platforms.
Computer vision is also something that is coming our way at some point in time in the future. We will have the ability to measure gait, measure a range of motion, when the physician points to the shoulder, “does it hurt it?’ we can figure out that he is talking about the left shoulder.
Nuance has played a very foundational role in the productization of speech recognition in healthcare and enterprise and in the auto industry, but the Nuance platform for Ambient Clinical Intelligence is a super significant advancement in the state of the art. It really is a very unique approach. It is fueled by massive data feeds, which we work out from a contractual point of view with our clients. It is a closed loop, so we put a human in the loop to correct the AI drafts to make sure they are high quality. And the humans that are in the loop are not physicians, they work for Nuance as part of a training loop and supervised learning loop.
Our conversational AI, our technology, is happening at the time that matters while that conversation is happening. We then harvest that conversation; integrate it with knowledge databases – and provide real-time decision-making support to the physician at the point of care.”
Ambient Clinical Intelligence feels like a logical market precursor to the emergence of an Ambient Public Intelligence ecosystem of AI-enabled voice recognition and computer vision sensors platforms, along with information flows provided by drones, satellites, geospatial data analytics, etc.
For the business executive or public safety official concerned with disaster and emergency management, the lesson right now is that solutions can and should be found anywhere – In Kabul, Afghanistan, or in another vertical industry – grappling with the future role of technology-enabled decision-making support tools for disaster, crisis, and emergency management.
Of course, start by determining use cases for the future of real-time decision-making support and situational awareness analysis in your industry vertical or public sector space. But stay really nimble and open to innovative ideas.
Next, create sensemaking capabilities within your organization for the evaluation of solutions in far-flung locations, disciplines, domains, and industry verticals that manage disasters and emergencies with finite decision-making time windows. Productization of decision-making and situational awareness tools based on AI and ML is no longer on the horizon, but clearly available for assessment and technology implementation now.
For the full conversation on Ambient Intelligence, Ambient Clinical Intelligence, and “How AI is Transforming Patient-Focused Care in a Post-Pandemic World,” see The Boston Globe – Global Summit 2021 – Day 1.
For the previously mentioned post on innovative disaster response applications in use during recent crises, see Disaster Response Applications: New Lessons From The Fall of Kabul and Severe Weather Events.
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About the Author
Daniel Pereira is research director at OODA. He is a foresight strategist, creative technologist, and an information communication technology (ICT) and digital media researcher with 20+ years of experience directing public/private partnerships and strategic innovation initiatives.
Informing your decisions with actionable intelligence
Informing your decisions with actionable intelligence