Machine learning is possibly the most disruptive technology of the 21st century. Good machine learning systems will, with limited interaction from a doctor, will be able to analyze almost any kind of information so that doctors can make better decisions.
But since a machine learning system is trained on data from humans, it reflects their internal bias, and can magnify them. Hospitals need to focus not only on what advantages these systems are bringing but also the biases that they encourage. WebText, a solution used to train natural language processing to analyze new articles and documents, was trained on posts from Reddit.
Reddit is almost 70% men with more than half of the users are from the US. The majority of the users are under the age of 35. These biases in the data create machine learning solutions that reflect these biases. One criticism, for instance, when asked “A man is a doctor, as a woman is, too” responded with “nurse.”
AI systems used in hospitals have already shown to be able to do incredible things from being able to diagnose disease from a simple, but also accomplish human mistakes at the speed of a machine; for example, an AI system rejected black medical students because data it was trained on was principally white students.
To prevent these effects companies need to carefully monitor these new artificial employees and make sure they are meeting the standard of governance reflecting the values of the company and the law. This can only be accomplished through specific tools that allow you to enter the mind of these artificial employees and understand how they think.
There is interesting research in Forbes’ recent article named “How E-Commerce’s Explosive Growth Is Attracting Fraud”. According to the statistics, e-commerce retail sales achieve 209% year-over-year revenue growth. It is expected that this indicator will grow, which means it will seduce fraudsters even more. This is a completely obvious pattern – big money always means a lot of attraction for attackers.
Is there a way to protect your business from these risks? We are sure there are. In this article, we will tell you how eCommerce fraud prevention and detection system work being powered by machine learning and artificial intelligence.
What Is Fraud in e-Commerce?
E-commerce and retail fraud mean any activity aimed at deceiving sellers or buyers in order to seize goods or money. In the e-commerce industry, all fraudulent transactions occur online. As for retail, fraud at the physical point of sale of goods is also possible. AI and ML solutions to encode expertise for detecting fraud in eCommerce and retail can handle both tasks.
What Are the Three Types of Fraud?
In fact, it is possible to identify more types of fraud depending on the nature of the crime, the level of damage, tools for committing fraud, and even unrealized criminal plans. Basically, it is possible to distinguish three main types of fraud.
Mobile Fraud
This is a fairly logical phenomenon – the more users began to use mobile phones for instant transactions, the more scammers thought that these devices might have hidden potential. The infographic below illustrates the current situation with mobile fraud.
Moreover, this is the case when businesses and individuals are equally at risk. Very often, mobile fraud becomes a way of illegally making purchases on behalf of another person.
Identity Theft
Identity Theft is the second type of fraud that is very popular. Who do you think are the most frequent victims of this type of fraud? These are Millennials, the most active users of social networks and the most solvent category of the population at the same time.
The thing is that social networks allow you to make a portrait of a person and collect all the necessary data very easily. It’s not even necessary to hack anything. And comprehensive personal data also easily provides access to e-mail, and other applications, including financial ones.
Last month saw the rollout of the latest upgrades to Amazon’s Echo speaker line: earbuds, glasses, and a ring that connect to Amazon’s personal assistant Alexa. These new products are just three examples of a growing trend to incorporate technology seamlessly into our human experience, representing the ever-expanding frontiers for technology that have moved far past the smartphone.
These trends and others are going to make a big impact in the healthcare space, especially as providers, payers and consumers alike slowly but surely recognize the need to incorporate tech into their workflows to meet the growing consumer demand for digital health tools. At the same time, the data-hungry nature of these innovations is creating its own problems, driving a discussion around privacy and security that is louder and more urgent than ever.
Here are three trends to look out for in the coming year:
Artificial Intelligence (AI) and Machine Learning are growing into themselves
In 2020, we will continue to see AI and machine learning push boundaries, while at the same time mature and settle into more defined patterns.
With the adoption of technologies like FaceID, facial recognition technology will be an important player in privacy and security. It can be leveraged to simplify the security requirements that make multi-factor authentication a time-consuming process for healthcare professionals — on average, doctors spend fifty-two hours a year just logging in to EHR systems. On the patient end, this same technology has the ability to detect emotional states of patients and anticipate needs based upon them, and the success of startups like Affectiva, the brainchild of MIT graduates, shows its tremendous promise..
Meanwhile, FDA-approved innovations from Microsoft and others claim the ability of computer vision for assisting radiologists and pathologists in identifying tumors and abnormalities in the heart. While robotic primary care is a long way off, some view AI as a rival to more niche clinical positions.
Artificial intelligence is poised to make a major impact on healthcare and healthcare technology. Investment in the healthcare AI sector alone is predicted to reach $6.6 billion by 2021. By 2026, that number will balloon $150 billion. And there’s no doubt about the transformative power of artificial intelligence, however, in terms of healthcare, its restorative effects are truly life changing.
Today, there’s a term in healthcare called the “iron triangle.” The iron triangle refers to three combined factors that can have negatives trade offs: affordability, access, and effectiveness. Though closely interlocked, improving one area without neglecting another is very difficult—even in modern times. With AI, the healthcare is much better equipped to tackle these conundrums. Here’s how artificial intelligence will impact the future of healthcare tech:
Prevention Intervention
One of the biggest benefits of AI in healthcare is the ability to predict potential issues and eradicate them before they become too serious. Machine learning is a major part of prevention intervention. With machine learning, computer systems are handed data and use statistical techniques to identify patterns over time and “learn” more about the information it processes. Doctors can use these targeted analytics to make more accurate diagnosis, spot potential issues before they arise, assess risks, and offer better treatment plans.
By Sachin Kalra, vice president of customer success, Infostretch.
Rapid advances in technology mean the chatbot market
is now one of the fastest-growing segments in healthcare, with the market
expected to be worth more than $314 million by 2023.
Sachin Kalra
In some ways, this growth is not surprising. Combined with the commercial benefits for healthcare providers, there is a genuine appetite for more advanced technologies to form part of patients’ healthcare. A recent study in the US revealed that more than half of consumers would use an app for remote general consultation if given the option, while research in the UK found that apps would be used by 47 percent of patients to book appointments, and 42 percent to manage prescriptions.
Before long, it is likely to be very commonplace for prescriptions to be re-ordered through your smart speaker, for medical appointments to be made by Alexa, and for medical disclaimers and drug side effects viewed in augmented reality (AR) via Google Home. In fact, chatbot applications such as these already exist as proof of concept projects and even, in some cases, as deployed systems in the US. The providers that successfully deliver systems like these which make the lives of patients fundamentally easier will inevitably gain mind share and market share, as the good news of the improved service spreads.
Beyond these immediate applications, the potential of
chatbot systems in healthcare is virtually endless, limited only by the
imagination and needs of physicians and their patients.
The three types of chatbot
Whether they’re employed in healthcare, customer
service or simply for general consumer use, there are three main types of chatbot.
The first of these is the task-oriented chatbot,
designed to deal with specific scenarios such as placing orders or scheduling
appointments.
The second type are information-oriented chatbots,
which are more focused on the generative aspect of a conversation. Relying on
AI and expert systems, they’ll offer answers as creatively as possible,
avoiding repetition and attempting to keep the conversation interesting for the
person they’re chatting with. In the context of telemedicine, for example,
these chatbots could be used to explain side effects or to discuss concerns
over drug interactions.
The third type of bots are either open-domain,
designed to retrieve information for questions such as what the weather will be
like in a week’s time, or closed-domain. Also known as domain-specific, these
chatbots operate with regard to a particular area of interest, aiming to give
answers to narrow scenarios such as offering guidance through a museum by
providing visitors with very specific types of information.
Most healthcare chatbot apps would typically fall into this latter, closed-domain category. Whatever the category of chatbot, so long as they provide users with an improved quality of experience, healthcare providers will be able to deliver a better service to more people at a lower cost.
Applying artificial intelligence
The performance of these chatbot apps – especially
their ability to adapt as required – can be largely impacted by AI and machine
learning technology, the application of which can enhance a number of areas.
By eliminating human bias from interactions, natural
language processing can widen the topic of conversation, and increase the
number of valid responses available to a chatbot. Of course, being able to
answer a wider range of specific questions and provide more information will
only make these apps more useful.
AI can also improve business performance for
internal-facing bots which, in turn, will improve the customer experience for
both practitioners and patients. Automating patient/admin interaction will
enable more flexible scheduling options, for example, while the ability to more
thoroughly convey information on side effects and conflicts from drug
interactions will only improve patient outcomes.
What’s more, chatbots known as cognitive bots can use deep/machine learning to continually learn from their ongoing interactions, in order to provide more tailored responses to a patient’s needs. Accessing massive data sets and rapidly extracting insights from them is a task much better suited to AI versus humans who are limited by time. Longer term, cognitive bots will deliver improved healthcare outcomes for more patients at a lower cost to the provider.
Adoption and appetite
The growth in the adoption of chatbot technology is likely to be organic. With each success that is achieved, a wider set of needs will be recognized and the technology developed further to address them. Its adopters will range from the largest healthcare innovators, where we would expect to see such innovation, to the smaller rural healthcare facilities who are set to benefit most from the resource and cost efficiencies it offers.
Within the last few years many of us have become accustomed to using Alexa, Siri and Google Home in our daily routines. As the healthcare industry continues to embrace chatbots, it won’t be long before we think nothing of asking them for medical advice, to carry out administrative tasks, or even to speak directly with our doctors. And given the rate at which this technology is evolving, who knows what the next few years might hold?
By Abhinav Shashank, CEO and co-founder, Innovaccer.
Abhinav Shashank
The Johnsons were blessed with twins the day before; two healthy baby boys, haphazardly named Jill and John in the health records. Definitely, this marks the start of pediatric services in the family. Hospital records set for the twins hardly mark any difference, gender, weight, parents, address; all records read the same. The only visible difference is a skin allergy with the second baby.
Their names were changed to Jack and Ross in a
month, and records got multiplied by two. Vaccinations done within the first
month were registered in the records of Jill and John, while Jack and Daniel
got registered under fresh EHRs.
Is the
pediatric space ripe enough for Machine Learning?
How should the healthcare industry deal with
data redundancy or data hop, and maintain data integrity to ensure reliable
records? This is a real serious concern for pediatric organizations.
However, to our rescue is machine learning technology aiding the critical issue of record matching and streamlining medical procedures in child healthcare. ML has the potential to revolutionize the pediatric care ecosystem and assist the major challenges in healthcare operations of the young population.
With the global healthcare market estimated to reach a sweeping $11,908 billion by 2022 and fast-growing problems in the younger population, there is certainly a vast frame of exploration for pediatric focus and care delivery for the young. Being a continuously evolving age group with tailored and sensitive healthcare needs at different stages of growth, the pediatric population is most challenged when it comes to successful reforms and insights.
How are
EHRs doing injustice to the future of healthcare?
Kids from their birthdate are expected to face the EHR duplicity that scatters their record and essential medical data. The key facts of a newborn like weight, height, allergies, among others, are stored in an EHR that is occasionally hopped a month later, with a permanent name signing in.
Once a new EHR is registered with the new name, all medical information of the previous few months gets disconnected. This has a challenging impact on the entire care protocol. The critical notch here is incoherent vaccination and immunization information of the growing baby. Not only does it lead to seemingly real care gaps, but also ripples out to erroneous procedures and increased health costs.
Machine Learning is transforming the way
services are delivered globally. Detecting the minutest of factors in an
outcome, and cascading the learning over huge data, it can provide us with
crucial considerations which are evidently present but still go unnoticed by
us. ML is helping to deliver accurate algorithms for all domains. Applying ML
to pediatric care is sure to transform the current scenario of care delivery
for the younger population.
What
are the major challenges pediatric organizations are facing?
We need strict adherence and care, not only to
ensure healthy children but also to ensure optimized care procedures for them
in the future. However, there are a lot of shortcomings in understanding and
implementation of the medical requirements of the population aged 0 to 18.
The major challenges in this regard are:
Most pediatric organizations today do not have precise and distinct health measures to evaluate the younger population. We need measures that can efficiently assess the patients on their growth-specific checkers, respectively.
Patient records at different stages are difficult to merge, with inadequate data-merging proficiency.
Data hop in EHRs during record matching or establishment. This is of critical concern for babies and toddlers who need consistent care episodes.
Lack of customized reach to parents for time-sensitive immunization and vaccinations. This leads to missed appointments, which leads to complications and increased costs over time.
Care plans including uncertainties to manage intelligent adherence. This will enable strong network functionality and improved care.
Flexible and optimized timeline for care delivery.
Currently, about 50 percent of children under five years of age attend out of home care. Throughout childhood, children receive care at daycares, check-ups at community places, have physician visits at different pediatric facilities, among others.
It becomes essential to compile entire patient data at a single place to avoid redundant and erroneous procedures. According to the American Health Information Management Association, an average hospital has about a 10 percent duplication rate of patient records. A study by Smart Card Alliance in 2014 projected that about 195,000 deaths occur yearly in the US because of medical error, with 58 percent of them being associated with “incorrect patient” errors.
Does
Machine Learning truly have the answer?
An article in the AAP News and Journals Gateway mentions that only 71.6 percent of young children in the United States have completed their primary immunization series. Moreover, evidence suggests that 10 percent to 20 percent of young children receive more than one unnecessary and extra immunization. Evidently, scattered records lead to a lack of timely, accurate and complete immunization. This can have serious repercussions on the health and care protocol of the patient, in addition to increased medical costs.
Machine Learning can nourish the split needs
and resolve the errors of pediatric healthcare in different domains:
Automatic Triggering for Episodes and Immunization: ML algorithms can be developed to track and prompt parents for necessary episodes and immunization. This will ensure timely care episodes.
EMPI Matching: Enterprise Master Patient Index is a database of medical data across departments and healthcare organizations. Machines trained in pediatric EHRs can develop a robust algorithm to match patient records across hospitals and unify them.
Streamlining Vaccinations: ML algorithms can regularize time-sensitive vaccination arrays for different pediatric categories as decided by the World Health Organization.
Scanning Data Hops: ML algorithms can detect data gaps in procedures, and point out critical consequences enforcing timely merging of EHRs.
Predicting Episodes and Costs: ML algorithms trained with localized pediatric data can detect underlying factors for an episode and predict the average costs for unforeseen episodes.
The
road ahead
The pediatric population is foundational to a
healthy nation and demands our attention to reform its split functionalities.
Machine Learning can bring about unimaginable amendments in our current
pediatric care management and delivery. Data, which is foundational to all
ventures in the healthcare industry, can be merged with ML to close all care
gaps and invest in a healthy tomorrow.
By Jeff Springer, senior vice president of healthcare solutions, CitiusTech; and Fernando Schwartz, vice president of data science and consulting, CitiusTech.
As consumers, we experience artificial intelligence
(AI) every day. In fact, we’ve come to expect the consumer experience it
enables, such as Amazon’s highly personalized suggestions for additional items
we might like. Now is the time to gain AI’s rewards in healthcare as well. Dr.
Eric Topol, founder and director of Scripps Research Translational Institute,
put it this way: “If properly and humanely deployed, AI has the potential to
restore efficiency to a wide array of burdensome healthcare processes, freeing
up physicians to treat their patients in the way they deserve. The path won’t
be easy, and the end is a long way off. But with the right guard rails,
medicine can get there.” [1]
While there are significant challenges with
interoperability, data sharing, and data access, organizations are taking
several approaches to overcome them. With an incremental approach to data
management and analytics, healthcare organizations can reap the benefits of AI
– specifically machine learning (ML) and natural language processing (NLP) faster,
enabling them to overcome challenges and achieve success in value-based care.
AI
and Its Subsets
First,
it’s important to have a shared understanding of AI concepts. AI is a term used
to describe the ability of machine intelligence to imitate human intelligence
through cognitive functions and behavior. Both ML and NLP are subsets of that broader concept. ML applies
algorithms and statistical models that effectively perform a specific task or
make predictions using patterns and inference. NLP enables computers to
understand, interpret and manipulate human language using computational
linguistics. At the highest level, these tools can be applied in healthcare to
help find the answers to questions and identify root causes—leading to workflow
improvements at a massive scale.
Technology
Advances Speed AI Adoption
Advances in data and analytics technology are making
it possible to significantly reduce implementation time for AI projects. From a
data management perspective, five years ago, it would have taken two years to
implement a strategy and infrastructure for data management needed for AI.
Hence, many organizations applied a project-by-project approach and didn’t take
advantage of the opportunity to reuse data for multiple projects. The results
were data silos and missed opportunities to leverage data across the
enterprise.
Today, new technology and new data approaches
expedite projects from the data, analytics and learning perspectives.
Specifically, today’s late-binding architecture enables organizations to take
only the data needed for a question, metric or pattern, and then curate
additional data as needed. This opens the door for going beyond the standard
healthcare sources, such as claims, HL7, and CCDA, to also include historically
cost-prohibitive data, such as social media, benefit information and
unstructured data. These data sets can be leveraged to determine the most
effective engagement models, risk patterns and communications. Today, if organizations
start by asking the right questions before implementing a new data set, they
could have answers in as little as three months, rather than years. By
leveraging existing infrastructure and data in conjunction with new technology,
organizations can begin to see success more quickly, while building out an
incremental, long-term strategy.
Putting
AI to Work Answering Healthcare Questions
When
organizations take advantage of the new technologies that enable sophisticated
data analytics, they can more easily apply ML and NLP to specific data sets.
Start with a question and determine what data can best provide answers. The
examples to follow illustrate questions organizations might ask to improve
clinical outcomes, revenue assurance, or operational efficiency.
Clinical Best Practices: Using
ML, healthcare organizations can more effectively analyze treatment patterns by
asking questions, such as: What interventions during a clinical encounter help
avoid sepsis? Or how can an ED visit be prevented? Once there is an
understanding of what contributed to positive outcomes, organizations can embed
care protocol improvements within clinical workflows.
Population Health: Again,
using ML, organizations can thoroughly analyze populations, treatment patterns
and results by asking questions, such as: Which segments are having trouble and
how can they be addressed? Are issues arising that are related to geography,
benefit structure, or disease pattern? ML can be applied to sift through many
dimensions and identify root causes. For example, a member of a diabetes
population segment lives within a certain zip code, sees a certain provider,
and is more likely to be readmitted. By asking the right question, variations
in care can be identified, and protocols and workflows can be adjusted to
improve outcomes.
Utilization
Management: Healthcare organizations, especially
ACOs, strive to improve quality while tightly controlling costs. Using ML,
organizations can look for variations in practice patterns across providers,
patients and conditions. From there, organizations can implement protocols that
operationalize utilization management. For example, duplicate exams, such as
expensive MRIs, can be avoided by making recent results available to providers
at the point of care. And providers can be guided to the most cost-effective
location for a given procedure or exam.
Data Aggregation: Even with
standard EMR implementations, there can be tremendous variation in how certain
types of data are captured. For example, data points needed for population
health management may be captured in data fields and as unstructured text.
Using NLP, healthcare organizations can now parse many different types of data
from many sources. This enables access to data for risk-based contracts, such
as social determinants of health like transportation, weight loss, food
insecurity, and electricity.
Apply
AI to Gain Quick Wins in Quality Improvement
With today’s technology, healthcare organizations
can move more quickly to take advantage of AI, especially ML and NLP— seeing
results in months rather than years. With modern data and analytics approaches,
organizations can proceed incrementally to identify questions, metrics or
patterns that deliver quick wins in clinical, financial and administrative
outcomes. At the same time, these achievements contribute to a successful
long-term strategy to continuously improve quality and outcomes while assuring appropriate
payment in today’s value-based care environment.
Recent research was published by the Washington Post about malware that was created to disrupt medical imaging equipment and networks. This is yet another wake-up call for the healthcare industry that been underinvesting in security for the last decade. Quite simply, there is a misconception that hospitals’ internal networks are a safe harbor from external cyberattacks. This is despite the fact that the real-world data has repeatedly shown that healthcare is one of the top industries under attack for the last five years. While previous attacks mainly focused on stealing personal health information, this research demonstrates how serious or even deadly an attack to healthcare can be.
There are a few reasons why cyberattacks in healthcare today can have devastating consequences.
Medical device vulnerabilities
Many medical devices inside hospitals are running decade old operating systems and applications that have many well-known vulnerabilities. In fact, it may be a surprise to many that the vast majority of imaging systems run on Windows OS. Further, recent Zingbox research shows that today, 1 out of 4 imaging systems run on OSes that are no longer supported. By next year, 85% of imaging systems are expected to run on End-of-Lifed OSes as Microsoft terminates support for some of their popular Windows OSes.
To make matters worse, most medical device manufacturers lack strong in-house cybersecurity expertise. While their expertise lies in device reliability and accuracy, which continue to be top requirements for connected medical devices, the lack of cybersecurity expertise puts the device reliability and accuracy into question. The lack of cyber-specific expertise also limits manufacturers’ ability to “bake in” cybersecurity measures on the device.
One might think that patches and upgrades are the answer. Unfortunately, no. FDA certification and other requirements pose significant hurdles for manufacturers to apply patches or upgrades to devices already deployed at hospitals.
Tools designed for IoT
Many hospitals lack the tools to monitor life-critical devices with 100% assurance of uninterrupted service and care. Such tools must be completely transparent to the device and in no way interfere or hamper its operation. Yet, organizations continue to rely on traditional IT security solutions for IoT. Such network security tools like firewalls and antiviruses result in security gaps that hackers can easily exploit.
Vulnerabilities that stem from inadequate IoT security tools:
Most network security solutions often cannot discern a PC from a CT scanner, whereas such a distinction is critical for cybersecurity.
CT scanner’s communication is almost never encrypted, device access doesn’t require basic authentication, and given the mobility of typical CT scanners, the devices can be connected to any internal network, according to Zingbox’s research findings.
Connecting a device to any network breaks the basic micro-segmentation policies IT teams have been encouraged to deploy for cybersecurity.
Last month saw the rollout of the 
