Noise about AI-driven healthcare
I guess I’m more of a warrior than a worrier. And I’m excited about the challenges that are coming to Healthcare.”
For all the noise about AI-driven healthcare, however, today’s reality lags stubbornly behind the grand vision. Many of the products now available will disappoint users expecting miraculous results from AI genies. That’s a letdown, for sure, but it also gives us some time to think about what kind of healthcare we want machines to do for us, and what roles we should be reserving for human beings.
There are Mainly three different stakeholders to consider. many other areas exist but I want to declutter this article by focusing on these main three players.
Patients
Providers
Payees
WordPress, Squarespace, Weebly, and Wix—content-management systems that took most of the code out of web design a good while ago.
nothing similar to
Yet this data-driven software, Christopher explained, wasn’t easy to develop. The first challenge was to extract data from health records and hospital call-light systems. “Getting data out of hospital systems can be incredibly challenging,” he said, commenting on how they had to “do a lot of manual work.”
What’s more, for this project, there were no APIs to help software applications talk to each other without user intervention. Once they could access the data, Christopher and his team had to comb through the dataset and clean it. “Data in healthcare is not the most pristine, clean, and beautiful thing in the world,” he lamented
The next step was to build and train a model that would predict the likelihood of a patient falling, but the ability to make meaningful predictions came with its own set of complications.
“We had to pay a lot of attention to underlying factors and ensure that it didn’t pick up any frivolous information,” Christopher said. “For instance, we didn’t want a model that would predict something like everyone who had a pain medication in unit 12 and called at 10 am is likely to fall down in the next 12 hours. Our predictions needed to be more specific and meaningful.”
Turning Data into Action
Since the Qventus team was delegating complex decisions to the AI algorithm, they worked hard to ensure that there was no bias in how the AI software made predictions. Typically, bias can creep into machine learning algorithms from parameters unconsciously designed to promote a certain outcome or match some preconceived notions, such as older people are more likely to fall. There can also be limitations in datasets, such as overrepresentation of certain populations.
“We began by developing a model internally and went back to the drawing board after talking to people actually working at the hospital,” he recalled. “It took a few iterations before we got the prediction process right.”
It’s this iterative process of building models and aligning them with the real problems people face that can transform organizations like El Camino Hospital. Or, as Christopher puts it, it’s this process that “turns data into action.”
As for Qventus, which received $30 million in a Series B funding in a round led by Bessemer Ventures Partners in May, it is time to expand their platform. “We are exploring a number of solutions, from streamlining patient flow to improving pharmacy management and building staff schedules, to helping simplify how hospitals operate and relieve operational bottlenecks and challenges in emergency departments, perioperative areas and the pharmacy,” Christopher explained. In the end, of course, it comes down to how technology can best serve the people in need.
so this all looks more like Humans spoon feeding the AI rather than the other way round
1. AI-assisted robotic surgery
people going gaga over theses results'
Differences in the amount of retinal microtrauma between the two groups were statistically insignificant, yet dissection (robot)took longer with robotic surgery (median time: 4 min 55 s) than with manual surgery (1 min 20 s). We also show the feasibility of using the robot to inject recombinant tissue plasminogen activator under the retina to displace sight-threatening haemorrhage in three patients under local anaesthesia
Virtual nursing assistants week end call hell
I guess I’m more of a warrior than a worrier. And I’m excited about the challenges that are coming to Healthcare.”
For all the noise about AI-driven healthcare, however, today’s reality lags stubbornly behind the grand vision. Many of the products now available will disappoint users expecting miraculous results from AI genies. That’s a letdown, for sure, but it also gives us some time to think about what kind of healthcare we want machines to do for us, and what roles we should be reserving for human beings.
There are Mainly three different stakeholders to consider. many other areas exist but I want to declutter this article by focusing on these main three players.
Patients
Providers
Payees
WordPress, Squarespace, Weebly, and Wix—content-management systems that took most of the code out of web design a good while ago.
nothing similar to
Yet this data-driven software, Christopher explained, wasn’t easy to develop. The first challenge was to extract data from health records and hospital call-light systems. “Getting data out of hospital systems can be incredibly challenging,” he said, commenting on how they had to “do a lot of manual work.”
What’s more, for this project, there were no APIs to help software applications talk to each other without user intervention. Once they could access the data, Christopher and his team had to comb through the dataset and clean it. “Data in healthcare is not the most pristine, clean, and beautiful thing in the world,” he lamented
The next step was to build and train a model that would predict the likelihood of a patient falling, but the ability to make meaningful predictions came with its own set of complications.
“We had to pay a lot of attention to underlying factors and ensure that it didn’t pick up any frivolous information,” Christopher said. “For instance, we didn’t want a model that would predict something like everyone who had a pain medication in unit 12 and called at 10 am is likely to fall down in the next 12 hours. Our predictions needed to be more specific and meaningful.”
Turning Data into Action
Since the Qventus team was delegating complex decisions to the AI algorithm, they worked hard to ensure that there was no bias in how the AI software made predictions. Typically, bias can creep into machine learning algorithms from parameters unconsciously designed to promote a certain outcome or match some preconceived notions, such as older people are more likely to fall. There can also be limitations in datasets, such as overrepresentation of certain populations.
“We began by developing a model internally and went back to the drawing board after talking to people actually working at the hospital,” he recalled. “It took a few iterations before we got the prediction process right.”
It’s this iterative process of building models and aligning them with the real problems people face that can transform organizations like El Camino Hospital. Or, as Christopher puts it, it’s this process that “turns data into action.”
As for Qventus, which received $30 million in a Series B funding in a round led by Bessemer Ventures Partners in May, it is time to expand their platform. “We are exploring a number of solutions, from streamlining patient flow to improving pharmacy management and building staff schedules, to helping simplify how hospitals operate and relieve operational bottlenecks and challenges in emergency departments, perioperative areas and the pharmacy,” Christopher explained. In the end, of course, it comes down to how technology can best serve the people in need.
so this all looks more like Humans spoon feeding the AI rather than the other way round
1. AI-assisted robotic surgery
people going gaga over theses results'
Differences in the amount of retinal microtrauma between the two groups were statistically insignificant, yet dissection (robot)took longer with robotic surgery (median time: 4 min 55 s) than with manual surgery (1 min 20 s). We also show the feasibility of using the robot to inject recombinant tissue plasminogen activator under the retina to displace sight-threatening haemorrhage in three patients under local anaesthesia
Virtual nursing assistants week end call hell
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