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.
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.
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.
Guest post by Matthew Douglass, co-founder, SVP Customer Experience, Practice Fusion
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.
Guest post by Torben Nielsen, senior vice president of product at HealthSparq.
Significant policy changes are inevitably on the horizon for health care in 2017. Though the question marks about what is next for our industry seem endless, Americans are wondering how health care costs will change, and if their insurance carrier will continue to provide them with the coverage they need. One thing we know for certain is that health care industry disruptors will continue to innovate in a way that we can’t ignore. That’s why it’s important for health plans and hospitals alike to embrace the technology that could simplify the way people interact with the health care industry.
To that, here are my five predictions for the industry in 2017:
Artificial intelligence innovations will help people navigate the healthcare system.
From robots and chat bots, to increasing telehealth options, we’re expecting significant innovations in 2017 for both doctors and patients. On the hospital side, chat bots have the potential to streamline the processes that people often get caught up in when visiting their practitioner, or when dealing with insurance protocol. The chat bots of the future will be able to have meaningful conversation that will help people navigate the system, instead of confusing them. A member could say to their health plan, “I’m looking for a cheaper MRI,” and artificial intelligence can help with a more guided search.
Virtual reality will continue moving into the hospital side of healthcare.
With technology like Oculus Rift and HTC Vibe on the market, people around the world are getting used to the idea of virtual reality in health care, too, and we don’t expect that interest to die down anytime soon. Surgeons are already utilizing virtual reality to practice upcoming surgeries, and patients are beginning to see the benefits of this technology, too. For example, at the University of Southern California combat veterans experiencing PTSD are being treated using virtual reality gaming as a healing mechanism to help process trauma. As these tools continue to get smarter, both hospitals and patients will continue to see virtual reality extend into their care practices more regularly in the coming year.
Personalization of healthcare technology will help data transfers happen easier.
Block chain technology has potential to help secure EHR data and health plan member information in a way that streamlines the health care journey for both the patient and the provider. Healthcare processes and experiences can feel very stifled and complicated to all parties in the system (that’s why HealthSparq created #WhatTheHealthCare!) because hospitals and health systems are sitting on so much data that is not connected or easily shared. Data fluidity is a goal for the industry, and with new applications of block chain technology, the health care ecosystem may now see data transfers and fluidity happen much more simply, giving everyone a more holistic view of health care status, options and improvement opportunities.
Guest post by John Barnett, project coordinator at Iflexion.
With evolving requirements for care value and quality, caregivers turn to technology to handle emerging challenges related to patients’ health outcomes, care costs and CMS reporting. Each year, new tech-driven solutions arise to assist providers in complying with changing circumstances.
The upcoming 2017 will be even more interesting technology-wise, since after Donald Trump was elected the new President, it’s now possible to form a very different perspective on healthcare. With this in mind, let’s look into market analysts’ predictions for growing trends to watch next year.
3D imaging, augmented and virtual reality
Currently, MRIs and CT scans allow viewing patients’ body parts, organs and tissues in 3D. 2017 may uplift care delivery by harnessing 3D imaging and improving it with augmented and virtual reality.
Caregivers can adopt 3D imaging for patient education and engagement, as well as for treating mental health disorders, such as phobias and schizophrenia.
Surgeons, physicians and nurses might use 3D and enabled glasses for further education and training – for example, to simulate complex microsurgeries. Augmented reality can be harnessed during live surgeries as well, allowing more precision to locate organs and blood vessels accurately, reducing possible damage to healthy tissue.
For instance, eye and brain surgeries imply working in limited spaces, using high-powered microscopes, and making cuts sometimes smaller than a millimeter (e.g., in retina surgery). 3D cameras can widen the picture and allow the whole team to see the target area. When 3D view is coupled with enabled glasses, this may also reduce surgeons’ fatigue from constantly looking into a microscope and keeping an uncomfortable posture with bowed heads and strained necks.
Artificial intelligence (AI)
While physicians have remarkable capabilities to analyze patients’ symptoms and make deductions, still humans can process quickly only a limited volume of information. This is where technology comes into play to support experience and proficiency.
Particularly, artificial intelligence software development is anticipated to become one of the widespread trends of 2017, with such headliners as IBM, Google, Amazon and many others.
AI encompassing machine learning and big data analytics evolves to make multiple healthcare processes faster and more effective. Some of the examples of future benefits are:
Automated diagnostics based on medical images
Predicted disease progression with chances to develop complications and further admissions / readmissions
Predicted reaction to chemotherapy in cancer patients
Calculated groups of at-risk patients (such as chronic patients with multiple conditions) according to their vitals, heredity, prior diseases, passed procedures and more to enroll them in specialized connected health programs
Predicted care results and patients’ health outcomes according to established treatment plans, allowing to intervene timely and improve care delivery
Many of future solutions will support natural language processing, as big data in healthcare usually comes in big chunks of unstructured information. If surgeons, physicians and nurses are able to input information directly with their voice, this will also reduce time, effort and, ultimately, costs.
Guest post by Santosh Varughese, president, Cognetyx.
Since cybersecurity healthcare threats on hospital EHR systems have become a topic of nightly newscasts, no longer is anyone shocked by their scope and veracity. What is shocking is the financial damage the attacks are predicted to cause as they reverberate throughout the economy.
In the 30 days of June 2016, more than 11 million patient EHRs were breached, making it the year’s worst incident according to a study by DataBreaches.net and Prontenus. For comparison, May had less than 700,000 and 2016’s former breach leader (March) topped out at just over 2.5 million.
While traditional security filters like firewalls and reputation lists are good practice, they are no longer enough. Hackers increasingly bypasses perimeter security, enabling cyber thieves to pose as authorized users with access to hospital networks for unlimited periods of time. The problem is not only high-tech, but also low-tech, requiring that providers across the healthcare continuum simply become smarter about data protection and privacy issues.
Healthcare security executives need to pick up where those traditional security tools end and investigate AI cybersecurity digital safety nets. IDC forecasts global spending on cognitive systems will reach nearly $31.3 billion in 2019.
CISOs are recognizing that security shields must be placed where the data resides in the EHR systems as opposed to monitoring data traveling across the network. Cloud deployment directly targeting EHR systems data is needed rather than simply protecting the network or the perimeter.
Pre-cursors to AI are also no longer that reliable. Organizational threats manifest themselves through changing and complex signals that are difficult to detect with traditional signature-based and rule-based monitoring solutions. These threats include external attacks that evade perimeter defenses and internal attacks by malicious insiders or negligent employees.
Along with insufficient threat detection, traditional tools can contribute to “alert fatigue” by excessively warning about activities that may not be indicative of a real security incident. This requires skilled security analysts to identify and investigate these alerts when there is already a shortage of these skilled professionals. Hospital CISOs and CIOs already operate under tight budgets without needing to hire additional cybersecurity guards.
Some cybersecurity sleuths deploy a variety of traps, including identifying an offensive file with a threat intelligence platform using signature-based detection and blacklists that scans a computer for known offenders. This identifies whether those types of files exist in the system which are driven by human decisions.
However, millions of patient and other medical data files need to be uploaded to cloud-based threat-intelligent platforms, scanning a computer for all of them would slow the machine down to a crawl or make it inoperable. But the threats develop so fast that those techniques don’t keep up with the bad guys and also; why wait until you are hacked?
The Mix of Forensics and Machine Learning
Instead of signature and reputation-based detection methods, smart healthcare CSOs and CISOs are moving from post-incident to pre-incident threat intelligence. AI innovations that use machine learning algorithms to drive superior forensics results and deploy pre-incident security are just what the IT doctor should be prescribing.
In the past, humans had to look at large sets of data to try to distinguish the good characteristics from the bad ones. With machine learning, the computer is trained to find those differences, but much faster with multidimensional signatures that detect problems and examine patterns to identify anomalies that trigger a mitigation response.
Guest post by Donald Voltz,MD, Aultman Hospital, Department of Anesthesiology, Medical Director of the Main Operating Room, Assistant Professor of Anesthesiology, Case Western Reserve University and Northeast Ohio Medical University.
As Halloween approaches, the usual spate of horror movies will intrigue audiences across the US, replete with slashers named Jason or Freddie running amuck in the corridors of all too easily accessible hospitals. They grab a hospital gown and the zombies fit right in. While this is just a movie you can turn off, the real horror of patient data theft can follow you.
(I know how terrible this type of crime can be. I myself have been the victim of a data theft by hackers who stole my deceased father’s medical files, running up more than $300,000 in false charges. I am still disputing on-going bills that have been accruing for the last 15 years).
Unfortunately, this horror movie scenario is similar to how data thefts often occur at medical facilities. In 2015, the healthcare industry was one of the top three hardest hit industries with serious data breaches and major attacks, along with government and manufacturers. Packed with a wealth of exploitable information such as credit card data, email addresses, Social Security numbers, employment information and medical history records, much of which will remain valid for years, if not decades and fetch a high price on the black market.
Who Are The Hackers?
It is commonly believed attacks are from outside intruders looking to steal valuable patient data and 45 percent of the hacks are external. However, “phantom” hackers are also often your colleagues, employees and business associates who are unwittingly careless in the use of passwords or lured by phishing schemes that open the door for data thieves. Not only is data stolen, but privacy violations are insidious.
The problem is not only high-tech, but also low-tech, requiring that providers across the continuum simply become smarter about data protection and privacy issues. Medical facilities are finding they must teach doctors and nurses not to click on suspicious links.
To thwart accidental and purposeful hackers, organizations should implement physical security procedures to secure network hardware and storage media through measures like maintaining a visitor log and installing security cameras. Also limiting physical access to server rooms and restricting the ability to remove devices from secure areas. Yes, humans are the weakest link.
Medical data theft is a growing national nightmare. IDC’s Health Insights group predicts that one in three healthcare recipients will be the victim of a medical data breach in 2016. Other surveys found that in the last two years, 89 percent of healthcare organizations reported at least one data breach, with 79 percent reporting two or more breaches. The most commonly compromised data are medical records, followed by billing and insurance records. The average cost of a healthcare data breach is about $2.2 million.
At health insurer Anthem, Inc., foreign hackers stole up to 80 million records using social engineering to dig their way into the company’s network using the credentials of five tech workers. The hackers stole names, Social Security numbers and other sensitive information, but were thwarted when an Anthem computer system administrator discovered outsiders were using his own security credentials to log into the company system and to hack databases.
Investigators believe the hackers somehow compromised the tech worker’s security through a phishing scheme that tricked the employee into unknowingly revealing a password or downloading malicious software. Using this login information, they were able to access the company’s database and steal files.
Healthcare Hacks Spread Hospital Mayhem in Diabolical Ways
Not only is current patient data security an issue, but thieves can also drain the electronic economic blood from hospitals’ jugular vein—its IT systems. Hospitals increasingly rely on cloud delivery of big enterprise data from start-ups like iCare that can predict epidemics, cure disease, and avoid preventable deaths. They also add Personal Health Record apps to the system from fitness apps like FitBit and Jawbone.
Banner Health, operating 29 hospitals in Arizona, had to notify millions of individuals that their data was exposed. The breach began when hackers gained access to payment card processing systems at some of its food and beverage outlets. That apparently also opened the door to the attackers accessing a variety of healthcare-related information.
Because Banner Health says its breach began with an attack on payment systems, it differentiates from other recent hacker breaches. While payment system attacks have plagued the retail sector, they are almost unheard of by healthcare entities.
Guest post by Santosh Varughese, president, Cognetyx.
The U.S. healthcare industry is under siege from cyber criminals who are determined to access patient and employee data. Information security think tank Ponemon Institute’s most recent report on healthcare cyber security, published in May 2016, revealed some sobering statistics:
In the past two years, 89 percent of healthcare organizations – and 60 percent of their business associates (or BAs) – experienced at least one data breach, with 79 percent experiencing two or more breaches. The most commonly compromised data are medical records, followed by billing and insurance records. These breaches have not declined since Ponemon began tracking them in 2010.
The average cost of a healthcare data breach is about $2.2 million.
Criminal attacks, from outside the organization or from malicious insiders, account for half of all healthcare data breaches, the other half being due to mistakes by employees or BAs.
The majority of respondents (69 percent of healthcare organizations and 63 percent of BAs) feel that the healthcare industry is at greater risk of breaches than other industries. Despite these concerns, the majority of respondents reported that their organizations had either decreased their cyber security budgets or kept them the same.
Another study conducted in April by IBM, found similar problems, as well as insufficient employee training on cybersecurity best practices and a lack of commitment to information security from executive management.
With only about 10 percent of healthcare organizations not having experienced a data breach, hackers are clearly winning the healthcare data security war. However, there are proactive steps that the healthcare industry can take to turn the tide in its favor.
Data Security Starts with a Culture of Security Awareness
Both the IBM and Ponemon studies highlight an issue that experts have been talking about for some time: despite increasing dangers to information security, many healthcare organizations simply do not take cybersecurity seriously. Digital technologies are relatively new to the healthcare industry, which was very slow to adopt electronic records and when it finally did so, it implemented them rapidly without providing employees adequate training on information security procedures.
Unfortunately many front-line employees feel their only job is to treat patients and that information security is “the IT department’s problem.” These employees fail to grasp the importance of data security, and are not educated on the dangers of patient data breaches, reflected in Ponemon’s findings that employee mistakes account for half of all healthcare data breaches.
The healthcare industry needs to adjust this attitude toward cybersecurity and implement a comprehensive and ongoing information security training program, and cultivate a culture of security awareness. Information security should be included in every organization’s core values, right beside patient care. Employees should be taught that data security is part of everyone’s job, and all supervisors – from the C-suite down to the front line – should model data security best practices.
Additionally, organizations should implement physical security procedures to secure network hardware and storage media (such as flash drives and portable hard drives) through measures like maintaining a visitor log and installing security cameras, limiting physical access to server rooms, and restricting the ability to remove devices from secure area. Continue Reading
Since 2011, more than $870 million have been invested in more than 65 healthcare artificial intelligence (AI) startups. These startups concentrate on various areas, from nursing to drug discoveries, where AI’s potential can be put to best use. This is where the world’s heading towards and the future of healthcare lies.
The roots of AI may have been from some science fiction storytellers, but now, the reality is that AI plays a major role in our everyday life. Beginning with the IBM Watson supercomputer defeating the longtime Jeopardy champion, Ken Jennings, the world started taking notice about what artificial intelligence can do.
With Google and IBM making tremendous progress with their AI initiatives and the other tech giants (Like Apple, Dell, Facebook) trying to catch up, it makes us wonder what will happen when one day we have robots running around doing our everyday chores.
But, the main question should be what will happen when AI does fully breach our day to day lives: Will we embrace this reality and let robots take us over? And do we really need or is it desirable to have self-driving cars and artificial intelligence? Should computers acquire enough data and knowledge to replace our existing doctors?
Maybe we do or maybe we don’t, but let’s stop before we get ahead of ourselves.
AI should not be perceived as “artificial intelligence” but rather as “augmented intelligence.” It has the potential to process data and make cognitive decisions, which an average human can take many months to process. AI has truly opened numerous opportunities in the field of healthcare, which was humanly impossible just a few years ago.
Getting into the facts, the main advantage AI has over a normal human being is the ability to process a gazillion data points within seconds.
So let’s imagine a patient walks in with a flu – even to diagnose and treat this common illness with the right medication can take a while. There are some cases where the patients don’t even react to the medication. These are common scenarios, as each body reacts differently to different medicines leading to an increased treatment time. Whereas, if the diagnosis is powered with an AI backed system to help, doctor’s will be armed with all the right data and can diagnose and prescribe the right medication within minutes.
How’s that for a game changer?
Yes, AI is the perfect medical assistant to healthcare professionals.Through an iPad based electronic medical record, even the patient genome studies could be integrated into their electronic medical reports. Armed with this data, AI has enough information to make a better analysis and provide accurate treatment plans based on the patient’s medical history, genetic conditions and other medications they are taking for other illnesses.