Tag: artificial intelligence

Artificial Intelligence Can Help with These 3 Public Sector Challenges

In recent years, the public sector has become increasingly aware of the multifaceted potential of big data. These days, government agencies around the world collect vast amounts of data from people’s activities, behaviors, and interactions—a virtual treasure trove of information that the public sector can utilize and turn into actionable insights, and later, into actual solutions.

The big challenge, however, is that government agencies are falling under more and more pressure to find relevant insights from complex data while relying on limited resources and technologies with inadequate capabilities. And with the sheer size of data being generated nowadays, it’s becoming more difficult to extract meaning from what the data hides beyond their colossal façade.

Thankfully, there is one critical technology that is redefining the way public sector agencies study and utilize data, and this is artificial intelligence. Today, artificial intelligence solutions that make use of technologies like machine learning and topological data analysis can automatically process big data and discover patterns and anomalies no matter how complex and seemingly disjointed these different points of data are.

Precisely because of these advantages, artificial intelligence solutions are now being used by numerous public sector agencies and institutions around the world.  In this article, we’ll fill you in on the basics of three important areas of the public sector in which AI is currently making waves.

Financials

The financial industry is one of the biggest producers and exchangers of data, and as such, it stands to benefit immensely from artificial intelligence technologies. Every day, government-owned banks and other financial institutions buy, borrow, and trade currencies and financial products, generating massive amounts of data from customers, partners, and other stakeholders.

By using artificial intelligence solutions for financial services, institutions can develop an intelligence framework that will allow them to cater to customer needs, understand and manage regulatory risks, and proactively prevent money-laundering activities. This way, public sector financial services are able to assume a position of strength over increasingly complex challenges.

Population Health

In many jurisdictions over the world, value-based care is being adopted as an alternative healthcare model that focuses more on accountability, and on the type and quality of service provided to patients instead of the volume of care provided. With this increased emphasis on value, government-aligned health providers and insurance systems have begun using artificial intelligence software in order to become more agile and proficient at managing the risks of patient pools.

This way, even with the immensity and complexity of patient data, public sector providers and health payers are able to better understand clinical variations, as well as automatically predict individual and subpopulation risk and health condition trajectories. Through insights gained from these data, clinicians and other personnel across the healthcare spectrum can then better determine the best and most affordable courses of care, in addition to being able to confidently recommend the most appropriate health programs for their governments to implement.

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7 Ways AI Is Changing Medicine As We Speak

Artificial intelligence is a topic that should interest us all, as it changes the world with every second. And the healthcare system is one of the areas that AI has already started to revolutionize. These are the main ways in which that is happening.

  1. Precision Medicine

Due to the introduction of personalized diagnosis and precision medicine, now doctors can treat a patient’s condition, by taking into account his/her background, as opposed to merely treating the disease. This is accomplished by using proteomics, which is a type of DNA mapping, as well as advanced AI machine learning.

  1. Killing Occam’s Razor

Occam’s Razor is also known as the Law of Parsimony, and it refers to providing a range of solutions to a given problem. Also, according to this principle, the simplest solution is, most of the time, the correct one. Considering that both machine learning and AI doesn’t have the human assumption element, their capacity of reading and analyzing amounts of data can significantly increase the accuracy of the diagnosis.

Accordingly, this can be really helpful in diagnosing elderly patients, in particular, as they are more likely to suffer from various diseases at the same time.

  1. Google Can Spot Eye Disease

DeepMind is a Google-owned AI company that has come up with a way of diagnosing eye disease. After assessing and attentively analyzing the medical records of a significant number of patients, it has created machine learning technology that should help doctors diagnose eye illness earlier. This merely outlines that, even though AI is innovating almost every field, it still relies on human help.

  1. Automated Cancer Treatment

It appears that AI can also play an important role in treating cancer, which affects more and more people. Accordingly, the CareEdit tool can be utilized by oncologists for crating practice guidelines. To be more specific, the tool analyzes considerable amounts of data such as past treatment regimens, aiming at comprising a clinical decision support system that should help physicians treat each patient. This can significantly enhance the rate of survival, while cutting down the costs associated with the treatments.

  1. Virtual Health Assistant

Interestingly enough, at the time being, there are apps that carry the roles of personal health coaches. This functions the same way as a customer service representative at a call center. What is more, the digital assistant can do as much as take notes, ask questions, even provide specific advice while streaming the information to the healthcare provider. This has the role of simplifying the process.

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Health IT Startup: Vyasa Analytics

VYASAVyasa Analytics provides a highly scalable deep learning platform for organizational data, enabling conceptual querying and collaborative analytics to help inform key decisions derived from your most valuable information assets.

Elevator pitch:

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations

Founder’s story:

Christopher Bouton, Ph.D.
Christopher Bouton, Ph.D.

Dr. Christopher Bouton earned his Ph.D. in molecular neurobiology from Johns Hopkins University and sold his first big data software company, Entagen, to Thompson Reuters in 2013. Living in India for four years as a boy, he developed a great respect for Vyasa – an important Hindu figure, storyteller and compiler of information – and believes that AI approaches will help us better compile and gain insights from our data systems. In 2016, he founded Vyasa Analytics to apply AI in life sciences and healthcare.

Marketing/promotion strategy:

Vyasa engages with life sciences and healthcare organizations to educate the industry about deep learning technologies, including speaking alongside executives at conferences and events. Dr. Bouton is also a frequent contributor and commentator to industry publications.

Market opportunity:

In 2016, the pharmaceutical industry spent some $157 billion on research and development. This figure is set to increase to more than $180 billion by 2022. The healthcare analytics market was $8.69 billion by 2016 and is estimated to reach $33.38 billion by 2022.

Vyasa is positioned to capture hundreds of millions of dollars in these markets by allowing organizations to conduct analytics on data relevant to their research. Other analytics companies in the space experiencing rapid growth include Lattice.io (acquired by Apple for $200 million), BenevolentAI (valued at $1.7 billion) and Exscientia (recent deals with GSK for $43 million and Sanofi for $273 million).

Who are your competitors?

While there are many deep learning companies, Vyasa is the only one applying deep learning to life sciences and healthcare specifically.

How your company differentiates itself from the competition?

Focusing in the life sciences and healthcare verticals is a key differentiator for Vyasa. In partnership with life sciences and healthcare organizations, we build software to help design better therapeutics, free up researchers for higher-level thinking and solve problems that matter for humanity.

Business model:
Vyasa has a B2B business model. Every project is a blend of software licensing and services, provided to the life sciences or healthcare organization to advance their research goals. We are projecting upwards of $3 million in revenue in 2018.

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The Four Trends Shaping Digital Transformation of Healthcare in 2018

By Rahul Patel, EVP of digital products and services, Persistent Systems.

Rahul Patel
Rahul Patel

As we launch into 2018, questions remain about the healthcare policy environment and how it can impact many healthcare initiatives. As Yogi Berra said, “It’s difficult to make predictions – especially about the future.” I feel confident, however, about some fundamental trends in the healthcare landscape. These include a steady shift toward value-based care, an increased focus on data and analytics as a core enabler for digital transformation, and the all-consuming focus on the patient experience.

Here are my four key predictions for the healthcare IT trends that will transform the industry in 2018:

Patient Satisfaction Takes Center Stage

The era of healthcare consumerism is here. Patients are bearing increasing financial responsibility for healthcare costs, and seek improved experiences as a part of the value-for-money equation. In response, providers are taking a 360-degree view of patients, employing better analytics to leverage patient data such as demographic information, lifestyles and individual preferences, to personalize interactions and treatment.

Artificial Intelligence (AI) Becomes Entrenched in Clinical Settings

Despite the overuse of the term AI to describe many types of technology-enabled solutions, the adoption of AI has been steadily gaining ground in a wide range of settings. Deep learning algorithms will increasingly be used in clinical settings to support medical diagnosis and treatment decisions, predict the likelihood of patient re-admissions and help providers better leverage the data that has been accumulating in electronic health records. According to the 2017 Internet Trends Report by venture capital firm Kleiner Perkins, medical knowledge is doubling every three years, and the average hospital is generating more than 40 petabytes of data every year.

While all this accumulated information empowers more informed physicians, the growing range of data and knowledge sources creates a challenge as well, since physicians and clinicians must manage and stay on top of this information on specific conditions, especially in fields such as oncology. AI technologies are enabling time-constrained and overworked physicians to make sense of the vast amounts of data, helping them uncover hidden insights and supporting their medical diagnoses and decisions with timely and relevant input at the point of care.

Open Source Finally Takes Hold

Healthcare organizations have been conservative when it comes to open source technologies, largely due to concerns about data security and privacy. With the growing adoption of cloud-enabled solutions and a gradual shift of enterprise IT workloads to the cloud, they no longer have to worry about risks to the IT environment and can rely on mature cloud service providers, such as Amazon Web Services (AWS) or Microsoft Azure. And, open source architecture can now incorporate robust technology components with rich functionality. Our current collaboration with Partners Healthcare to build a digital platform for clinical care is based on an open source architecture. As the industry shifts rapidly to value-based care, cost pressures will force healthcare enterprises to transform their technology strategies, turning to open source solutions to rapidly build new solutions cost-effectively.

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10 Things You Can Expect From Healthcare In 2018

Guest post by Abhinav Shashank, CEO, Innovaccer.

Abhinav Shashank
Abhinav Shashank

With 2017 in the rear-view mirror, it is time to look forward to 2018 and how healthcare will evolve in this year. The last year has been an eventful one for healthcare, from the uproar in healthcare regulations to potential mega-mergers. Needless to say, it’s a time of transition, and healthcare is in a very fluid state- evolving and expanding. There are certainly going to be new ways to keep healthcare providers and health IT pros stay engaged and excited, and here are our top 10 picks:

The future of the GOP Healthcare bill

The Republican healthcare reform bill gained immense traction this year. In their third attempt at putting a healthcare bill forward, the senators and the White House officials have been working round the clock to gather up votes, but somehow, the reservations persist. The lawmakers have insisted that Americans would not lose their vital insurance protections under their bill, including the guarantee that the plan would protect those with preexisting conditions. However, as it so happens, even these plans have been put to rest. Perhaps sometime in 2018, the GOP may pass a budget setting up reconciliation for tax reform, and then pass tax reform. Then, they would pass a budget setting up reconciliation for Obamacare repeal, and then pass that- it all remains to be seen.

The ongoing shift to value from volume

Despite speculations, healthcare providers, as well as CMS have pushed for more value-based care and payments tied to quality, but it’s been going slow. Although providers have been slightly resistant to take on risk, they do recognize the potential to contain costs and improve quality of care over value-based contracts. And perhaps as data assumes a central role in healthcare, the increasing availability of data and smarter integration of disconnected data systems will make the transition easier and scalable. Notably, with a $3.3 trillion healthcare expenditure this year, there has been slow down the cost growth. 2018 is expected to be much more impactful as it builds on the strong foundation.

Big data and analytics translating data into real health outcomes

Big data and analytics have always brought significant advancements in making healthcare technology-driven. With the help of big data and smart analytics, we are at a point in healthcare we can make a near-certain prediction about possible complications a patient can face, their possible readmission, and the outcomes of a care plan devised for them. Not only it could translate to better health outcomes for the patients, it could also make a difference in improving reimbursements and regulatory compliance.

Blockchain-based systems

Blockchain could arguably be one of the most disruptive technologies in healthcare. It is already being considered as a solution to healthcare’s longstanding challenge of interoperability and data exchange. Bringing blockchain-based systems will definitely require some changes from the ground up, but 2018 will have a glimpse of by innovation centered around blockchain and how it can enhance healthcare data exchange and ensure security.

AI and IoT taking on a central role

2018 can witness a good amount of investment from healthcare leaders in the fields of Artificial Intelligence and Internet of Things. There is going to be a considerable advancement in technology, making the use of technology crucial in healthcare and assist an already unbalanced workforce. AI and IoT will not only prove instrumental in enhancing accuracy in clinical insights, and security, but could also be fruitful in reducing manual redundancy and ensuring fewer errors as we transition to a world of quality in care.

Digital health interventions and virtual care to improve access and treatment

In December 2016, many were suggesting that wearables were dead. Today, wearables are becoming one of the most sought-after innovation when it comes to digital health. And, the market is quickly diversifying as clinical wearables gain importance and as several renowned organizations integrate with each other. Not only wearables- there are several apps and biosensors that can assist providers with remotely tracking patient health, engage patients, interact with them, and streamline care operations. As technology becomes central to healthcare, 2018 will be the year when these apps and wearables boost the patient-physician interaction.

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Axway 2018 Healthcare Predictions

Guest post by Joanna Gorovoy, senior director product and solutions marketing, Axway.

Joanna Gorovoy
Joanna Gorovoy

Healthcare organizations need to unlock the value of their data
In 2018, the healthcare industry will accelerate its shift toward value-based healthcare as the industry struggles to address challenges associated with rising cost burdens, an explosion of data and increased mobility. Along with evolving government policy, organizations across the healthcare ecosystem will face a rise in healthcare consumerism as patients bear more risk, face higher out of pocket costs, and demand more value.

Unlocking the value of a wealth of patient data will be key to improving patient engagement, delivering more personalized healthcare products and services, and improving collaboration and care coordination across the patient journey – all critical to enabling value-based care delivery and improving outcomes.

In 2018 AI goes from science fiction to reality in healthcare
Population health and precision medicine are among the initiatives where AI is expected to have the greatest impact. Based on a recent HIMSS study: About 35 percent of healthcare organizations plan to leverage artificial intelligence within two years — and more than half intend to do so within five. Focusing AI investments on population health, clinical decision support, patient diagnosis and precision medicine supports the industry shift toward value-based, personalized care models and reinforces the use of AI to augment intelligence and skills of physicians and drive efficiency in diagnosis and treatment.

Some current use cases include: Enhancing speed and accuracy of diagnosis medical imaging, supporting surgeon workflow and decision-making during (e.g. spine implants), virtual assistants to enhance interactions between patients and caregivers to improve the customer experience and reduce physician burnout, and digital verification of insurance and claims information.

Healthcare Will Stagnate Without AI

Guest post by Ripal Vyas, president, Softweb Solutions Inc.

Ripal Vyas
Ripal Vyas

The healthcare sector is one of those that has always embraced emerging technologies to make better use of technological innovations. And now artificial intelligence (AI) is gradually making its way into the healthcare market with all its power to disrupt.

The annual investment in artificial intelligence for healthcare will grow tenfold in the next five years, becoming a $6 billion industry by 2021 – estimates Frost & Sullivan. They have also forecasted that by 2025, AI systems could be involved in everything from population health management to digital avatars capable of answering specific patient queries.

In healthcare, the opportunity for AI is not just limited to making doctors and medical providers more competent in their work; in fact, it’s about saving lives and making the lives of the patients better. Whether it is for improving the standard of treatment, patient outcomes, healthful behavior, new drug development, weight loss advice or cost reduction, the possibilities of artificial intelligence in the healthcare industry are enormous.

Six amazing use cases of artificial intelligence in healthcare sector:

AI for effective treatment

Although, healthcare generates a huge amount of data due to record keeping, patient care, and compliance & regulatory requirements, it struggles to efficiently utilize the flood of data and convert it into useful insights to improve the value of care. Artificial intelligence helps in making sense of the huge data streams gathered from hospitals and health IT systems by identifying the relationships and patterns between patients, symptoms, and more to provide the right treatment at the right time.

AI for the patient’s caregivers

A lot of modern healthcare providers have adopted AI-driven apps for scanning the findings of a patient’s laboratory tests, as well as drug orders, and sending relevant updates, alerts, and reminders to patients. This application interacts with patients just as a human would to understand the mental condition of the patient and have an impact on monitoring patients when clinicians are not available. For example, AiCure is a clinically authenticated artificial intelligence platform that visually confirms whether the patient has consumed the prescribed medicines on time.

AI for smart drug development

According to figures from a Tufts University study and the U.S. Food and Drug Administration, developing a new drug costs an average of nearly $2.6 billion and can take as long as 14 years. This lengthy process covers identifying the demographic information, multi-gene interaction, proteins, environmental effects, optimizing the molecule for effective delivery to patients, carrying out clinical trials, drug efficacy testing and more. The latest innovations in AI can greatly aid in converting a drug discovery idea from initial inception to a market-ready product rapidly by predicting the therapeutic use of new drugs before they are put to test. This might sound like a small thing to some, however, for researchers it a huge one, who otherwise would have to make these predictions after conducting various tedious experiments. For example, Johnson & Johnson and Sanofi are using IBM Watson to discover new targets for FDA approved drugs.

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Divining the Future of Healthcare with Predictive Analytics

Guest post by Sanjay Govil, founder and chairman, Infinite Computer Solutions

Sanjay Govil
Sanjay Govil

It’s impossible to see the future with certainty, but one branch of technology is playing a leading role in helping institutions and industries predict, on the basis of empirical research, the future behavior of participants and the outcomes of their decisions.

This relatively new branch of tech – predictive analytics (or PA) – has made inroads at a steady clip in the marketing, manufacturing and financial services industries. It is now gaining traction in healthcare as well.

Although debates around its ethical applicability to healthcare persist – the debate around data privacy, for one – the consensus emerging across the board is that with the right skills and in the right hands, PA has the power to effectively address challenges in the healthcare ecosystem in ways that human intelligence alone cannot.

Let us examine a few recent examples.

The power of PA

The Gold Coast Health Hospital in Southport, Queensland, Australia, dramatically improved patient outcomes and hospital staff productivity by applying a predictive model that was able to project with 93 percent accuracy emergency admissions before they happened. By analyzing admission records and details of sundry circumstances that led to patient admission to the ER, hospital staff were able to know how many patients would be coming in, on any day of the year, what they would be coming in for and methodically plan procedures that were now for all purposes elective rather than urgent.

Similarly, the El Camino hospital in California was able to drive a dramatic turn-around in its high rate of patient falls by collaborating with a tech company. The company, Qventus, linked patient EHR to bed alarm and nurse call light usage to derive an algorithm that was able to alert nurses in real time about the high-risk patients under their care and the exact times when they were most likely to be vulnerable. The result was a whopping 39 percent reduction in falls, improvement in patient health outcomes and a dramatically improved reputation for the hospital.

In fact, it isn’t only hospitals that are alive to the potential of analytics. Tech companies too are cognizant of how some of the newest technologies being developed under their roofs have immediate relevance to healthcare outcomes. In a paper published earlier this year, researchers associated with Google demonstrated how deep learning algorithms were able to correctly identify metastasized cancer tissue with nearly 90 percent accuracy as compared to just 73 percent when done by a human pathologist.

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Artificial Intelligence In Healthcare

Guest post by Dhiren Patel, Digital Health Informer.

Dhiren Patel
Dhiren Patel

Being born with a heart condition I have had a chance to see how healthcare has evolved or stagnated in innovation because of inherent risk to the bottom line. Reducing revenue, patient risk and pressure from big pharma and insurance has kept the status quo. It’s crazy to think that we can order food from our phones and yet can’t even schedule our appointments online at most physicians offices and hospitals. We have the most expensive and least effective healthcare system in the world, it’s broken so we need to fix it.

There is a lack of technology in healthcare as a whole. Think about when you go into a doctor’s office and you tell them what’s wrong or if you go to a hospital and nurses are tracking your symptoms, they still write it on a piece of paper at most hospitals and physicians offices! Well, what happens when the nurse or doctor can’t read what’s been written or worse what if that paper gets lost. To put that in perspective, hospital errors are the number three leading cause of death in the U.S.

Where there is some technology it is often difficult to use and is not standardized so if you go to an emergency room that doctor will likely have to spend time trying to get your primary care doctor on the phone to better understand how to care for you. It’s happened to me before, the ER doctors spent hours trying to track down my cardiologist to get a rundown on what medications or tests need to be run on me, all the while I was lying there in pain waiting for care. Standardization of basic medical protocols needs to happen. Even better, a shared database of all the different medical protocols and AI can run through to find the right match or machine learning like autocorrect and predictive typing on your phone.

Too much data

Today’s doctor and healthcare providers receive copious amounts of data, whether that’s from your daily activity data, your daily measurements, data from scans, DNA testing data, etc., that they must go through in order to properly diagnose a patient. Sometimes there’s too much data for the doctors to consider and so they cut bait with some of it to rank all the clutter. On top of all that data they are looking into a system to find how that data correlates with your back pain, sleep issues and whatever another symptom you are looking at then finding the proper medication for you. All of this takes time away from the doctor to properly develop a relationship with the patient and better diagnose patients problems. Let’s dive into how machine learning and AI’s can help with this.

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Rethinking EHRs: Actionable Insight Increases Usability (Part 2)

Guest post by Matthew Douglass, co-founder, SVP Customer Experience, Practice Fusion

Matthew Douglass
Matthew Douglass

In part 1 of this series, we reviewed the history of digital health tools and discussed why they are not yet fully satisfying the needs of many physicians.

If you think of the U.S. healthcare system as a vast nationwide transportation network, current electronic health record (EHR) functionality is the basic highway infrastructure. The American Recovery and Reinvestment Act of 2009 provided the incentives for those highways to be built and put in place the structure for ONC-certified EHRs to define the rules of the road via regulatory standards. The roads are now mostly in place: certified EHRs all offer roughly the same base functionality for use by physicians, store clinical information in standardized ways, and have the capabilities to securely communicate with each other.

Sixty-seven percent of medical practices in the U.S. are now using EHRs to run all or part of their daily operations. Patients’ vital signs are stored as discrete values for each visit. Encrypted messages between physicians and their staff are transmitted reliably. Chart notes are being digitally documented and can be shared confidentially with patients. Physicians that have chosen cloud-based EHRs can securely prescribe and refill medications from the convenience of their mobile phones.

Despite having this digital highway system in place, we haven’t yet reached a destination where use of EHRs achieves better patient outcomes or improved clinical experiences. Physicians want more from digital tools than simply receiving, storing, and displaying data values about each patient visit. Rather than devoting too much of their already limited time to data entry and retrieval, physicians want to provide the best patient care possible, and they expect technology to help them achieve this goal.

There is such a thing as too much data, which physicians are reminded of each time they open a digital chart. Clinicians very often are left swimming in more data than they can adequately process, which can erode the crucial patient-provider human relationship.

To address data overload and dehumanization challenges, software partners must go back to the drawing board and visualize dramatic innovations that can be built on top of the nationwide EHR foundation. Significant cognitive overhead is required to distill hundreds of disparate pieces of clinical data into a salient picture of an individual’s overall health. The vast amount of data now available in a patient’s chart is quite often far more than any medical professional, no matter how clinically experienced, can consistently and reliably assimilate.

Physicians and their staff need intuitive technology to be their always-available, intelligent assistant, from start to finish during a patient’s visit.

When a patient’s record is displayed on the computer screen, physicians shouldn’t have to dig for relevant information about that visit. Instead, the EHR should be able to display the pertinent clinical data and health insights for the physician to review and assess a patient’s health condition more quickly and effectively. For example, lab values and vital signs relevant to that patient’s chief complaint are likely already stored as discrete values in the patient’s chart. An EHR that learns along with the physician’s workflow preferences should display only the most relevant data through easily digestible visualizations.

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