Determining the relevance of a sentence when compared to a specific document is essential for many different types of applications across various industries. In this post, we focus on a use case within the healthcare field to help determine the accuracy of information regarding patient health.
Frequently, during each patient visit, a new document is created with the information from the visit. This information often consists of a medical transcription that has been dictated by either the nurse or the physician. Such a document may contain a brief description statement (also known as a restatement) that explains the main details from that specific patient visit. In future visits, doctors may rely on previous visits’ restatements to quickly get an overview of the patient’s overall status. Such restatements may also be used during patient handoffs. However, this introduces the potential for errors to be made during patient handoffs to new medical teams if the restatements are difficult to understand or if they contain inadequate information (Staggers et. al. 2011). Therefore, having an accurate description of the patient’s status is important, because the cost of errors in such restatements can be high and may negatively affect the patient’s overall care (Garcia et. al. 2017).
This post walks you through how to deploy a machine learning (ML) model that aims to determine the top sentences from the document that best match the corresponding document restatement; this can be a first step to ensure the accuracy of the patient’s health records overall by determining the relevance of the restatement. We emphasize that this model determines the top ranking sentences that match the restatement; it does not generate the restatement itself.
When creating this solution, we were faced with a dual-sided challenge. Beyond the technical challenge of actually creating an AI/ML model, several surrounding components complicate actually using such models in the real world. Indeed, the actual ML code may be a very small part of the system as a whole (Sculley et al. 2015). This is especially so in complex architectures frequently deployed in the context of the healthcare and life science space.
We focused on one particular challenge: creating the ability to serve the model so that others (applications, services, or people) can use it. By serving a model, we mean to grant others the ability to pass new data to the model so they can get the predictions they need. This post provides a broad overview of the problem, the solution, and a few points to keep in mind if you plan to use a similar approach in your own use cases. A full technical write up, including a readme and a step-by-step deployment of the architecture, is available in the GitHub code repository. For more information about approaches to serving models, see Build, Train, and Deploy a Machine Learning Model With Amazon SageMaker and AWS Deep Learning Containers on Amazon ECS.
Background and use case
In the medical field (as well as other industries), documents are frequently associated with a shorter restatement text of the original document. We use the term restatement, but in fact this shorter text can be a summary, highlight, description, or other metadata about the document. For example, an after-visit clinical summary given to a patient summarizes the content of the patient visit to a physician.
For illustration purposes, the following is an example that’s unrelated to the medical industry.
On Monday morning, Joshua ate a large breakfast of bacon and eggs. He then went for a brisk walk. Finally, he returned home and sat at his desk.
Joshua went for a walk.
In this example, the restatement is just a rewording of the highlighted sentence in the full document. This example shows that, although the use case that we focus on in this post is specific to the medical field, you can use and modify this approach for many other text analysis applications.
Let’s now take a closer look at the use case for this post. We used data taken from MTSamples (which we downloaded from Kaggle). This data contains many different samples of transcribed medical texts. It includes documents with raw transcriptions of sample notes, as well as shorter descriptions of those notes (which we treat as restatements).
The following is an example from the MTSamples dataset.
HISTORY OF PRESENT ILLNESS: , I have seen ABC today. He is a very pleasant gentleman who is 42 years old, 344 pounds. He is 5’9″. He has a BMI of 51. He has been overweight for ten years since the age of 33, at his highest he was 358 pounds, at his lowest 260. He is pursuing surgical attempts of weight loss to feel good, get healthy, and begin to exercise again. He wants to be able to exercise and play volleyball. Physically, he is sluggish. He gets tired quickly. He does not go out often. When he loses weight he always regains it and he gains back more than he lost. His biggest weight loss is 25 pounds and it was three months before he gained it back. He did six months of not drinking alcohol and not taking in many calories. He has been on multiple commercial weight loss programs including Slim Fast for one month one year ago and Atkin’s Diet for one month two years ago.,PAST MEDICAL HISTORY: , He has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, difficulty walking, high cholesterol, and high blood pressure. He has asthma and difficulty walking two blocks or going eight to ten steps. He has sleep apnea and snoring. He is a diabetic, on medication. He has joint pain, knee pain, back pain, foot and ankle pain, leg and foot swelling. He has hemorrhoids.,PAST SURGICAL HISTORY: , Includes orthopedic or knee surgery.,SOCIAL HISTORY: , He is currently single. He drinks alcohol ten to twelve drinks a week, but does not drink five days a week and then will binge drink. He smokes one and a half pack a day for 15 years, but he has recently stopped smoking for the past two weeks.,FAMILY HISTORY: , Obesity, heart disease, and diabetes. Family history is negative for hypertension and stroke.,CURRENT MEDICATIONS:, Include Diovan, Crestor, and Tricor.,MISCELLANEOUS/EATING HISTORY: ,He says a couple of friends of his have had heart attacks and have had died. He used to drink everyday, but stopped two years ago. He now only drinks on weekends. He is on his second week of Chantix, which is a medication to come off smoking completely. Eating, he eats bad food. He is single. He eats things like bacon, eggs, and cheese, cheeseburgers, fast food, eats four times a day, seven in the morning, at noon, 9 p.m., and 2 a.m. He currently weighs 344 pounds and 5’9″. His ideal body weight is 160 pounds. He is 184 pounds overweight. If he lost 70% of his excess body weight that would be 129 pounds and that would get him down to 215.,REVIEW OF SYSTEMS: , Negative for head, neck, heart, lungs, GI, GU, orthopedic, or skin. He also is positive for gout. He denies chest pain, heart attack, coronary artery disease, congestive heart failure, arrhythmia, atrial fibrillation, pacemaker, pulmonary embolism, or CVA. He denies venous insufficiency or thrombophlebitis. Denies shortness of breath, COPD, or emphysema. Denies thyroid problems, hip pain, osteoarthritis, rheumatoid arthritis, GERD, hiatal hernia, peptic ulcer disease, gallstones, infected gallbladder, pancreatitis, fatty liver, hepatitis, rectal bleeding, polyps, incontinence of stool, urinary stress incontinence, or cancer. He denies cellulitis, pseudotumor cerebri, meningitis, or encephalitis.,PHYSICAL EXAMINATION: ,He is alert and oriented x 3. Cranial nerves II-XII are intact. Neck is soft and supple. Lungs: He has positive wheezing bilaterally. Heart is regular rhythm and rate. His abdomen is soft. Extremities: He has 1+ pitting edema.,IMPRESSION/PLAN:, I have explained to him the risks and potential complications of laparoscopic gastric bypass in detail and these include bleeding, infection, deep venous thrombosis, pulmonary embolism, leakage from the gastrojejuno-anastomosis, jejunojejuno-anastomosis, and possible bowel obstruction among other potential complications. He understands. He wants to proceed with workup and evaluation for laparoscopic Roux-en-Y gastric bypass. He will need to get a letter of approval from Dr. XYZ. He will need to see a nutritionist and mental health worker. He will need an upper endoscopy by either Dr. XYZ. He will need to go to Dr. XYZ as he previously had a sleep study. We will need another sleep study. He will need H. pylori testing, thyroid function tests, LFTs, glycosylated hemoglobin, and fasting blood sugar. After this is performed, we will submit him for insurance approval.
Consult for laparoscopic gastric bypass.
Although the raw transcript document is quite long, only a few of the sentences actually appear to be related to the restatement “Consult for laparoscopic gastric bypass.” We highlighted two sentences within the document that you might intuitively think best match the restatement. The approach we deployed quantifies the similarities and reports the sentences in the document that best match the restatement. We did this by using a pretrained BERT language model trained specifically on clinical texts (published by Alsentzer et. al. 2019). The model itself is hosted by HuggingFace, a platform for sharing open-source natural language processing (NLP) projects. We used this model to calculate sentence-by-sentence similarities using the sentence-transform Python library.
It is important to note that in this example and in this solution, we are performing the sentence ranking without explicitly extracting and detecting the medical entities. However, many applications rely on explicitly extracting and analyzing diagnoses, medications, and other health information. For detecting medical entities such as medical conditions, medications, and other medical information in medical text, consider using Amazon Comprehend Medical, a HIPAA-eligible service built to extract medical information from unstructured medical text.
More information about this approach is available in our technical write-up.
In this section, we go over the architecture diagram for this solution at a very high level. For more details and to see the step-by-step framework, see our technical write-up.
In the model development and testing phase, we use Amazon SageMaker Studio. Studio is a powerful integrated development environment (IDE) for building, training, testing, and deploying ML models. Because we use a prebuilt model for this solution, we don’t need to use Studio’s full ability to train algorithms at scale. Instead, we use it for development and deployment purposes.
We created a Jupyter notebook that you can import into Studio. This notebook walks you through the entire development and deployment process. We start by writing the code for our model to a file. The model is then built using an NGINX/Flask framework, so that new data can be passed to it at inference time. Prior to deploying the model, we package it as a Docker container, build it using AWS CodeBuild, and push it to Amazon Elastic Container Registry (Amazon ECR). Then we deploy the model using Amazon Elastic Container Service (Amazon ECS).
The final result is a model that you can query using a simple API call. This is an important point: the ability to query models via an API capability is an essential component of designing scalable, easy-to-use interfaces. For more information, see Implementing Microservices on AWS.
After we deploy our model, we create a graphical user interface (using Streamlit) so that our model can be easily accessed through a webpage. Streamlit is an open-source library used to create front ends for ML applications. After we create our webpage, we deploy it in a similar way to how we deployed our model: we package it as a separate Docker container, build it using CodeBuild, push it to Amazon ECR, and deploy it using Amazon ECS.
By creating and deploying this webpage, we provide users with no programming experience the ability to use our model to test their own documents and restatements. The following screenshot shows what the webpage looks like.
After the user inputs their restatement and corresponding document, the top five results (the five sentences that best match the statement) are returned. If you deploy the entire solution using our original MTSamples example, the final result looks like the following screenshot.
The solution reports the following results:
- The top five sentences within the document that best match the restatement.
- The similarity distance between each sentence and the restatement. A lower distance means closer similarities between that sentence and the restatement sentence.
In this example, the best matching sentence is “He wants to proceed with workup and evaluation for laparoscopic Roux-en-Y gastric bypass” with a distance of .0672. Therefore, this approach has correctly identified a sentence within the document that matches the restatement.
Like any algorithm, this approach has some limitations. For instance, this approach is not designed to handle cases where the restatement of the document is actually high-level metadata about the document not directly related to the text of the document itself. You can solve such use cases by using Amazon Comprehend custom models. For more information, see Comprehend Custom and Building a custom classifier using Amazon Comprehend.
Another limitation in our approach is that it doesn’t explicitly handle negation (words such as “not,” “no,” and “denies”), which may change the meaning of the text. AWS services such as Amazon Comprehend and Amazon Comprehend Medical use deep learning models to handle negation.
In this post, we walked through the high-level steps to deploy a pre-built NLP model to analyze medical texts. If you’re interested in deploying this yourself, see our step-by-step technical write-up.
For more information, see the following references:
About the Authors
Joshua Broyde is an AI/ML Specialist Solutions Architect on the Global Healthcare and Life Sciences team at Amazon Web Services. He works with customers in the healthcare and life sciences industry at all levels of the Machine Learning Lifecycle on a number of AI/ML fronts, including analyzing medical images and video, analyzing machine sensor data and performing natural language processing of medical and healthcare texts.
Claire Palmer is a Solutions Architect at Amazon Web Services. She is on the Global Account Development team, supporting healthcare and life sciences customers. Claire has a passion for driving innovation initiatives and developing solutions that are both secure and scalable. She is based out of Seattle, Washington and enjoys exploring the PNW in her free time.
The future of e-commerce: Trends, tips, traps to avoid
Amazon is approaching its 30th anniversary, set to mark the milestone in 2024. The World Wide Web hits 35 the same year. E-commerce, the buying and selling of goods and services over the internet, has grown up — and it has gotten big. Worldwide e-commerce sales for the retail sector alone exceeded $4 trillion in 2020, according to eMarketer. The research firm expects the figure to hit $5 trillion in 2022. Global B2B e-commerce sales, meanwhile, hovered around $6.6 trillion in 2020, according to research firm Frost & Sullivan.
As the value of e-commerce has risen, so has the complexity of online transactions. E-commerce today means more than simply processing electronic payments and enabling internet sales. It’s also more than knowing your customer, crucial as that is. E-commerce sales in 2021 depend upon the robust performance of just about every aspect of modern enterprises, from operations and supply chain to delivery services and customer loyalty programs.
Organizations must harness all the power of integrated back-office systems in tandem with intelligent customer insight systems to deliver personalized, seamless digital transactions that — in the lingo of the age — delight the customer. Personalized, seamless transactions must happen whether the customer is an individual consumer buying his or her first product, or a global business ordering for the 100th time under a multiyear procurement contract. Buyers demand as much — whether they’re ordering from their computer or their smartphone, via Alexa or through another connected machine.
“E-commerce transactions are becoming ubiquitous, and expectations are going up. People have expectations that it’s always going to be as easy as using Uber to get a ride,” said Mike Welsh, chief creative officer at Mobiquity, a digital consulting agency.
Amid rising customer expectations, however, many organizations are falling short on their e-commerce operations. A Gartner report on the COVID-19 pandemic’s impact on digital commerce predicted that “through 2020, 50% of large organizations will have failed to unify engagement channels, resulting in a disjointed and siloed customer experience that lacks context.”
The bar is high, said Lisa Woodley, vice president of customer experience at NTT Data Services. “E-commerce [covers] every stage, from acquisition to loyalty and advocacy. It’s your customers telling their friends, ‘I had a great experience; go do business with this company.’”
In this look at the future of e-commerce, we examine the evolution of buying and selling over the internet — from the early corporate websites that functioned as online brochures to today’s powerful, concierge selling sites that can be accessed through multiple channels. We offer expert analysis of the impact of COVID-19 on digital transactions, delve into the challenges enterprises face in meeting customer expectations in 2021, and provide detailed advice on overcoming those challenges.
From ‘product-centric’ to ‘solution-centric,’ e-commerce evolves
A combination of factors is driving the evolution of e-commerce. At the core is the internet.
Companies once mostly competed on the so-called four P’s of marketing: place, price, product and promotion. But the web’s search function and the internet’s reach neutralize one or more of these differentiating factors. Shopping online, a customer can easily get the same or similar product at the same or lower price with comparable shipping times and costs.
As a result, other factors are emerging as key differentiators, with personalization being the catch-all term for the new elements that drive buying habits in the digital realm.
“The concept of e-commerce is shifting from online sale transactions [and a] static webpage to a personalized and interactive experience,” said Eleftheria Kouri, a research analyst with the tech market advisory firm ABI Research.
“Customers have access to a wider range of capabilities when visiting an online store, including product virtual try-on and gaming and interactive storytelling concepts that increase engagement and educate the consumer about products [and] brands.”
Penny Gillespie, vice president at Gartner and a fellow in its customer experience/digital commerce team, said that in the e-commerce marketplace of 2021, companies must figure out how to deliver the product and the solution to a customer’s problem. To do that, they must understand the online customer’s intent.
For example, a retailer serving a customer searching for a black dress should be capable of using digital tools, as well as general and personal data, to understand that the shopper doesn’t simply need a dress but rather needs an outfit for an event. In fact, the color of the dress may in this case be irrelevant — with black dress being nearly synonymous with cocktail dress.
“Understanding intent is part of personalizing an experience,” Gillespie explained. A retailer that understands this concept can ensure the products in the search results actually match that customer’s needs, guaranteeing the sale of a dress and other relevant items (e.g., accessories) — and ensuring repeat business.
Customer intent is relevant in B2B transactions as well. Here, it could mean understanding a customer’s unique procurement process by, for example, automatically displaying any special prices specified by an existing procurement contract, facilitating any approval requirements, and anticipating needs based on past ordering histories.
“It’s a work in progress, with some sellers being much better at it than others,” Gillespie said.
In both the B2C and B2B spaces, online selling has gone from being reactive to being proactive and participatory, said Gillespie: “It’s a move from being product-centric to solution-centric.” She used the sale of an exercise bike online as an example.
“It’s not just selling an exercise bike online, but rather delivering it to the buyer’s house, setting it up and then helping them maximize its value through use,” she said. “The bike is a product; when it’s in my house and working, it is the solution.”
COVID-19 pushes companies and customers into the digital realm
The evolution of e-commerce from static webpages to interactive customer “solution” sites was enabled by sophisticated technology, but it took a global health crisis to make the future of e-commerce the new normal. The shift to online-everything in 2020 due to pandemic-induced social restrictions and quarantine orders pushed physical transactions into the digital realm.
According to findings from consulting firm McKinsey & Company, e-commerce as a percentage of overall retail sales in the U.S. grew 3.3 times more in 2020 than the average annual rate in five years before COVID-19. E-commerce sales as a share of overall retail sales grew 4.6% in 2020 vs. an average of 1.4% growth in previous years.
“Consumers are demanding more digital access than ever before,” said Nicole West, vice president of digital strategy and product at Chipotle Mexican Grill.
In November, the restaurant chain opened its first “digital-only restaurant,” the Chipotle Digital Kitchen, in Highland Falls, N.Y. The location offers pickup and delivery only, a prototype the company said will allow Chipotle to enter more urban areas that don’t support its full-size restaurant concept. The new restaurant requires customers to order in advance via Chipotle.com, its app or through third-party delivery partner platforms.
Providing an exceptional digital experience has become a priority for the 28-year-old chain, West said. She added that Chipotle is “relentless when it comes to UX and making it fast, easy and convenient” to place digital orders.
She cited the company’s 2020 rollout of Unlimited Customization. A feature in the Chipotle app and on the company’s website, it allows customers to customize orders, just as they do when ordering in person at a restaurant. Earlier in 2020, the company launched ordering on Facebook Messenger and a Group Ordering feature, which allows multiple people to participate in the ordering process simultaneously on the Chipotle app and Chipotle.com. And it’s now testing Chipotle Carside at 29 restaurants in California, an in-app feature that lets customers have their Chipotle orders delivered to their parked cars.
Chipotle’s digitalization efforts have shown real-world business value. Digital sales for Chipotle have grown 177% year over year, West said, and they accounted for 49% of sales in the last quarter of 2020. More than 19 million people joined the company’s customer rewards program via digital sales, West added, noting that the company’s digital pickup orders are currently its most lucrative transaction type.
There is no denying that the COVID-19 crisis and the at-home new norm have reshaped consumer behavior and boosted e-commerce/online shopping, which is expected to continue growing after the end of the pandemic, ABI Research’s Kouri said.
The technology powering these e-commerce trends also continues to evolve rapidly, Kouri noted, citing technological advancements in smartphones — such as high-resolution cameras and displays — enhanced connectivity, mobile-friendly websites and the rise of social media shopping.
Amazon, of course, has continued to make online shopping easier with innovations such as its Add to Cart and Buy Now buttons. The Home Depot and Lowe’s are often lauded for their use of instructional videos that give customers confidence to make purchases, as well as for apps that help customers navigate their stores. And the use of various technologies to let customers see how their items will look on them or in their homes before they buy is becoming standard practice.
From the customer’s perspective, Gartner’s Gillespie noted, the benchmark for all digital transactions is “the last great experience they had.” Keeping up with that moving target will require a panoply of technology and continuous technology innovation.
Although the internet was the enabling technology for e-commerce, it is far from the only technology needed to deliver the experience that customers expect now and moving forward. Some of these broad technology capabilities include the following:
- Customer-facing capabilities. Sites must be easy to navigate and user-friendly as well as quick and responsive. Sites should have the features that matter most to the target audience and be able to interact with other sites — social media sites for young consumers, for example, or company procurement systems for corporate customers.
- Data-related technologies. Organizations must be capable of collecting and using their own data as well as data from outside sources. This allows the organization to anticipate a customer’s needs even when it has little or no data on that specific customer; the company can use its other data sources to compile an understanding of what that one customer needs based on its interactions with similar customers.
- Automation technologies such as RPA. Robotic process automation can speed and streamline processes that service the customer by minimizing errors in data collection, enabling self-service by providing access to back-end systems.
- Customer journey orchestration engine software. This class of tools help organizations analyze real-time data of individual customers to predict future interactions with that customer, using predictive models, decision trees, matrix rules and machine learning.
- Augmented reality. AR lets customers bring products into real lifelike situations and virtually try on or fit items before purchasing. “The introduction of digital tools, such as augmented reality, in e-commerce platforms or apps not only assists brands to differentiate from the competition but transfers static websites/2D images to interactive and personalized experiences,” Kouri said.
- Artificial intelligence. Organizations can use AI to offer personalized online experiences. A cosmetics brand, for instance, could use AI algorithms to provide skin analysis and recommend suitable products.
- Back-end systems. Companies need modern infrastructure and current IT architecture that can support all these other capabilities. Typically, this means moving from legacy systems to cloud computing and SaaS applications to quickly enable scale and speed when needed; leveraging microservices to increase agility and flexibility; and breaking down silos through integration and the use of APIs. “There’s actually a lot more on the back end needed to reach our goal of making the front-end experience as seamless as possible,” Woodley said.
Specific tools, such as geofencing platforms that provide location-based services to help organizations and customers pinpoint their locations, and payment systems also have an important role in an e-commerce strategy, as do the technologies and processes companies use to optimize their warehouse and supply chain management.
“Building competitive e-commerce experiences requires the synergy of numerous technologies and tools, from AR to AI and secure payment systems,” summed up Kouri.
Bringing all these parts together to work consistently and flawlessly is, not surprisingly, a significant challenge.
“Personalization is not a one-size-fits-all approach. You really need to consider your business model, value proposition and customers before you create your strategy. Once those pieces are solidified, you can then begin to seek out the right tools and technologies needed to be successful,” said Britt Mills, senior director of customer experience at Mobiquity.
Organizations also need the data experts, technologists, marketing team, logistics workers, supply chain personnel and other executives and supporting staff who can competently contribute to that vision.
“Stores can rush to market with a new technology to enable customer safety and convenience, but they shouldn’t do so at the customer’s expense,” Mills said. Training employees to use the new technology is essential. “If your associates can’t support this new expected experience, your customers won’t be satisfied. It doesn’t matter how great the technology is.”
In addition, companies must have a strategy for dealing with emerging data use laws that put more control over personal information into the hands of individuals. And they must be able to mitigate against escalating cybersecurity risks.
These capabilities and safeguards are hard to achieve. Experts have acknowledged that the difficulty of developing and implementing know-your-customer processes — from collecting the necessary data to analyzing it to turning it into action items — has been oversimplified and glossed over in many conversations.
It’s not shocking, then, to learn that most organizations are struggling to develop the capabilities required to deliver seamless, personalized service, especially as the number of engagement and delivery channels have increased.
Research from Verint, a provider of customer engagement management products, found that 82% of the nearly 2,300 business leaders it surveyed said the challenges of managing customer engagement will increase in 2021, but only 50% said they’re well prepared to support customer engagement priorities moving forward. The vast majority of those surveyed pointed to nearly every aspect of customer engagement as challenging for their organizations, indicating the following problems:
- understanding and acting on rapidly changing customer behaviors (94% cited);
- managing the growth in volume of customer interactions (88%);
- achieving a unified view of customer engagement and overcoming data silos (79%);
- using customer feedback to improve experiences (78%); and
- building enduring customer relationships (77%).
Customer journey mapping
How do traditional organizations become as competitive in the digital sphere as they were in the brick-and-mortar heyday? It starts with mastering customer journey mapping, according to Peter Charness, vice president of retail strategy for UST, a digital technology and transformation IT services and solutions firm.
“[Organizations] need to ensure the digital journey is well aligned to a shopper’s needs, using a high degree of personalization and creating relevant interactions and conversations,” Charness said. He laid out six areas where organizations need to benchmark their capability:
- Interest generation. Determine how successful your organization is at getting potential customers to its website or store.
- Research and decision influence. Examine whether it’s easy for the user to find products of interest and “gather the information and confidence they need to move that product into a shopping cart,” Charness said.
- Decision confidence. From browsing to buying, companies should make it easy for the shopper to say yes to a purchase. “Organizations should have this part of the conversation with their shoppers and build their confidence to press ‘buy,’”Charness noted.
- Delivery/collection. “Speed of delivery (with ease of return implied) comes next, and the cost to deliver or collect a product becomes one of the most relevant associations any retailer will have to profitability and customer satisfaction,” Charness said. Assess your supply chain and fulfillment capabilities, and benchmark them to competitors and best-in-class companies.
- Post-sales service, resales and loyalty. “Your conversation with your customer doesn’t end with the shipment,” Charness Consider what else you can say to or advise your customer on to keep the relationship alive and productive.
- Personalization everywhere. “Put yourself in your shopper’s shoes, and play back the conversations you’ve had during the entire shopper journey,” Charness said. “Was it always relevant, interesting and useful? Or did you communicate with mass marketing techniques, treating everyone the same?” Develop a strategy for using AI and machine learning across “the end-to-end interaction chain” with customers to enhance personalized service.
The future of e-commerce
Many businesses have been on their e-commerce journey for years, adapting business processes to the customer predilection for digital transactions. However, few were well prepared for the rapid and wholesale shift to digital transactions driven by the pandemic. A record-breaking 11,100-plus stores closed in the U.S. last year, and 40 major retailers filed for Chapter 11 bankruptcy protection, according to CoStar Group, a collector of retail real estate data. More stores are expected to shutter in the upcoming years, with some analysts predicting 100,000 stores — mostly apparel retailers — could close by 2025.
Yet, despite their struggles and challenges, many organizations are on their way to success. The awareness that challenges must be faced and addressed indicates that organizations understand that data-driven, personalized and secure customer transactions are the future.
How these transactions happen — whether online, via a mobile device, through some combination of digital and physical channels or by some augmented reality lens not yet imagined — will depend on circumstances and customer preferences, but they will increasingly involve digital technologies.
Indeed, Gartner has advocated replacing the term e-commerce with digital commerce to better reflect the convergence of all the digital systems that go into transactions today.
As customers increasingly decide that the frictionless experiences they have when buying online from leading digital vendors are the norm, the semantic distinction between e-commerce or digital commerce or any other kind of buying and selling transaction will disappear.
“When it’s all said and done,” Gillespie said, “we’ll just call it commerce.”
France’s Shift Technology, an SaaS Provider of AI based Decision Automation for Insurance, Secures $220M via Series D
France-based Shift Technology, an SaaS provider of AI-enhanced decision automation and optimization solutions for the insurance sector, recently revealed that it has finalized a $220 million Series D funding round.
Shift Technology‘s latest investment round brings total investment in the company to $320 million along with a market valuation of over $1 billion. This investment reportedly marks Advent’s sixth growth equity investment in 2021. Shift’s round was led by Advent International, via Advent Tech, along with contributions from Avenir and other investors.
Previous Series C investors Accel, Bessemer Venture Partners, General Catalyst, and Iris Capital also took part in Shift’s Series D round.
With this latest funding, Shift said it would use the capital to expand its business operations into the US, Europe, and Asia as well.
In the United States, the firm will be penetrating the property and casualty (P&C) insurance sector and will also expand into the health insurance industry, an area in which the company sees a great opportunity.
The funds raised by Shift Technology will also be used to support researach and development (R&D) work in the implementation of new solutions to cater to innovative decision automation and optimization needs for insurers.
Initially known for its fraud detection and claims automation solutions, in January 2021 Shift Technology launched its Insurance Suite to enable insurance providers to leverage AI-powered decision automation and optimization tech to a wider array of critical processes (related to policy lifecycle, including underwriting, subrogation, and compliance).
The firm currently serves over 100 clients in 25 countries and has reportedly analyzed almost 2 billion claims so far.
Thomas Weisman, a Director on Advent’s technology investment in London, stated:
“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance.Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”
Jeremy Jawish, CEO and co-founder, Shift Technology, remarked:
“We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment. We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”
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Longevity startup Gero AI has a mobile API for quantifying health changes
Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.
The ‘longevity’ startup — which condenses its mission to the pithy goal of “hacking complex diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.
A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health, is the contention. Gero’s AI has been trained on large amounts of biological data to spots patterns that can be linked to morbidity risk. It also measures how quickly a personal recovers from a biological stress — another biomarker that’s been linked to lifespan; i.e. the faster the body recovers from stress, the better the individual’s overall health prognosis.
A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.
Another paper, due to be published in the journal Nature Communications later this month, will go into detail on its device-derived measurement of biological resilience.
The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).
Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early stage fund, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to Russian drug development firm, Valenta. (The pharma company itself is not an investor).
Fedichev is a theoretical physicist by training who, after his PhD and some ten years in academia, moved into biotech to work on molecular modelling and machine learning for drug discovery — where he got interested in the problem of ageing and decided to start the company.
As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to stress of humans — via sensor data captured by mobile devices.
“Health of course is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number then, for a lot of people, the biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”
Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modelling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).
Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the wider research mission — where it hopes to progress anti-ageing drug discovery, working in partnerships with pharmaceutical companies.
The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.
“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.
“We developed these capabilities because we need to understand how ageing works in humans, not in mice. Once we developed it we’re using it in our sophisticated genetic research in order to find genes — we are testing them in the laboratory — but, this technology, the measurement of ageing from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.
“Ageing is this gradual decline of your functional abilities which is bad but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. Which means that when you’re [biologically] stressed you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience it means that they’re not robust anymore and the same level of stress as in their 20s would get them [knocked off] the rails.
“We believe this loss of resilience is one of the key ageing phenotypes because it tells you that you’re vulnerable for future diseases even before those diseases set in.”
“In-house everything is ageing. We are totally committed to ageing: Measurement and intervention,” adds Fedichev. “We want to building something like an operating system for longevity and wellness.”
Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with Pepsi Co.
He sketches a link between how it hopes to work with insurance companies in the area of health outcomes with how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensor data in the driving seat, if you’ll pardon the pun. (“Essentially we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.)
But the nearer term plan is to raise more funding — and potentially switch to offering the API for free to really scale up the data capture potential.
Zooming out for a little context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then a small but growing field of ‘longevity’ startups has sprung up, conducting research into extending (in the first instance) human lifespan. (Ending death is, clearly, the moonshot atop the moonshot.)
Death is still with us, of course, but the business of identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.
The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking health, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery.
Longevity has also seen a bit of an upsurge in interest in recent times as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically.
Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and/or drug discovery.
Plenty are also — like Gero — trying to use AI and big data analysis to better understand and counteract biological ageing, bringing together experts in physics, maths and biological science to hunt for biomarkers to further research aimed at combating age-related disease and deterioration.
Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI ‘longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).
Gero AI is taking a different tack toward the same overarching goal — by honing in on data generated by activity sensors embedded into the everyday mobile devices people carry with them (or wear) as a proxy signal for studying their biology.
The advantage being that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their own health. Instead our personal device can generate proxy signals for biological study passively — at vast scale and low cost. So the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.
And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to try to stay one step ahead of death, such high end services simply won’t scale to the rest of us.
If its digital biomarkers live up to Gero’s claims, its approach could, at the least, help steer millions towards healthier lifestyles, while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).
The insurance industry is naturally interested — with the potential for such tools to be used to nudge individuals towards healthier lifestyles and thereby reduce payout costs.
For individuals who are motivated to improve their health themselves, Fedichev says the issue now is it’s extremely hard for people to know exactly which lifestyle changes or interventions are best suited to their particular biology.
For example fasting has been shown in some studies to help combat biological ageing. But he notes that the approach may not be effective for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise or eating or avoiding certain foods).
Again those rules of thumb may have a lot of nuance, depending on an individual’s particular biology. And scientific research is, inevitably, limited by access to funding. (Research can thus tend to focus on certain groups to the exclusion of others — e.g. men rather than women; or the young rather than middle aged.)
This is why Fedichev believes there’s a lot of value in creating a measure than can address health-related knowledge gaps at essentially no individual cost.
Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s measurements of biological age and resilience. But of course it hopes to go further — as it ingests more data.
“Technically it’s not properly different what we are doing — it just happens that we can do it now because there are such efforts like UK biobank. Government money and also some industry sponsors money, maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.
“Imagine that for every diet, for every training routine, meditation… in order to make sure that we can actually optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.”
“When we will have 1M tracks [half a year’s worth of data on 1M individuals] we will combine that with genetics and solve ageing,” he adds, with entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”
Fitness and health apps are an obvious target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science.
“We expect that these [apps] will get lots of people and we will be able to analyze those people for them as a fun feature first, for their users. But in the background we will build the best model of human ageing,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)
“What we are doing is we are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have lots of signals — including from available devices which produce something like a few years’ long windows on the human ageing process. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us.
“We cannot own the treatments because we cannot patent them but maybe we can own the personalization — the AI that personalized those treatments for you.”
From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.
7 Ways Artificial Intelligence is Improving Healthcare
Emerging technologies have the potential to completely reshape the healthcare industry and the way people manage their health. In fact, tech innovation in healthcare and the use of artificial intelligence (AI) could provide more convenient, personalized care for patients.
It could also create substantially more value for the industry as a whole—up to $410 billion per year by 2025.
This graphic by RYAH MedTech explores the ways that technology, and more specifically AI, is transforming healthcare.
How is Technology Disrupting the Patient Experience?
Tech innovation is emerging across a wide range of medical applications.
Because of this, AI has the potential to impact every step of a patient’s journey—from early detection, to rehabilitation, and even follow-up appointments.
Here’s a look at each step in the patient journey, and how AI is expected to transform it:
Wearables and apps track vast amounts of personal data, so in the future, AI could use that information to make health recommendations for patients. For example, AI could track the glucose levels of patients with diabetes to provide personalized, real-time health advice.
2. Early Detection
Devices like smartwatches, biosensors, and fitness trackers can monitor things like heart rate and respiratory patterns. Because of this, health apps could notify users of any abnormalities before conditions become critical.
Wearables could also have a huge impact on fall prevention among seniors. AI-enabled accelerometer bracelets and smart belts could detect early warning signs, such as low grip strength, hydration levels, and muscle mass.
3. Doctors Visits
A variety of smart devices have the potential to provide support for healthcare workers. For instance, voice technology could help transcribe clinical data, which would mean less administrative work for healthcare workers, giving them more time to focus on patient care.
Virtual assistants are expected to take off in the next decade. In fact, the healthcare virtual assistant market is projected to reach USD $2.8 billion by 2027, at a CAGR of 27%.
4. Test Results
Traditionally, test results are analyzed manually, but AI has the potential to automate this process through pattern recognition. This would have a significant impact on infection testing.
5. Surgery / Hospital Visits
Research indicates that the use of robotics in surgery can save lives. In fact, one study found that robot assisted kidney surgeries saw a 52% increase in success rate.
Robotics can also support healthcare workers with repetitive tasks, such as restocking supplies, disinfecting patient rooms, and transporting medical equipment, which gives healthcare workers more time with their patients.
Personalized apps have significant care management potential. On the patient level, AI-enabled apps could be specifically tailored to individuals to track progress or adjust treatment plans based on real-time patient feedback.
On an industry level, data generated from users may have the potential to reduce costs on research and development, and improve the accuracy of clinical trials.
7. Follow-ups and Remote Monitoring
Virtual nurse apps can help patients stay accountable by consistently monitoring their own progress. This empowers patients by putting the control in their own hands.
This shift in power is already happening—for instance, a recent survey by Deloitte found that more than a third of respondents are willing to use at-home diagnostics, and more than half are comfortable telling their doctor when they disagree with them.
It’s All About the Experience
Through the use of wearables, smart devices, and personalized apps, patients are becoming increasingly more connected, and therefore less dependent on traditional healthcare.
However, as virtual care becomes more common, healthcare workers need to maintain a high quality of care. To do this, virtual training for physicians is critical, along with user-friendly platforms and intentionally designed apps to provide a seamless user experience.
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