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Top Five Digital Transformation Trends In Health Care

Top Five Digital Transformation Trends In Health Care | Healthcare and Technology news | Scoop.it

Technology is changing every industry in significant ways. To help frame how, I’m starting a new series discussing top trends in various markets. First up: health care.

No one can dispute technology’s ability to enable us all to live longer, healthier lives. From surgical robots to “smart hospitals,” the digital transformation is revolutionizing patient care in new and exciting ways. That’s not all. National health expenditures in the United States accounted for $3.2 trillion in 2015—nearly 18% of the country’s total GDP. It’s predicted that the digital revolution can save $300 billion in spending in the sector, especially in the area of chronic diseases. Clearly there is value—human and financial—in bringing new technology to the health care market. The following are just a few ways how.

 

Telemedicine

Even back in 2015, 80% of doctors surveyed said telemedicine is a better way to manage chronic diseases than the traditional office visit. Why? Telemedicine offers patients and health care providers both a new wave of freedom and accessibility. For the first time, a patient’s care options are not limited by geographic location. Even patients in remote areas can receive the highest quality of care, providing they have an internet connection and smart phone. Telemedicine can also save both time and money. Patients no longer have to schedule their days around routine follow-up visits (and long office waits). Instead, they can hop on a conference call to get the prescription update or check-up they need.

Nowhere has telepresence been more useful than in the mental health field. Now, those seeking emotional support can find access to a therapist or counselor at the click of a button, often for far less than they would pay for a full office visit. Internet therapies, for instance, “offer scalable approaches whereby large numbers of people can receive treatment and/or prevention, potentially bypassing barriers related to cost, location, lack of trained professionals, and stigma.” Telemedicine makes it possible.

 

Mobility And Cloud Access

Have you ever played phone tag with your doctor while waiting for important test results? It’s so nerve-racking! That’s why mobility and cloud access have been such a tremendous help in increasing accessibility for patients and doctors alike. By 2018, it’s estimated that 65% of interactions with health care facilities will occur by mobile devices. Some 80% of doctors already use smartphones and medical apps, with 72% accessing drug info on smart phones on a regular basis. Gone are the days of paper charts and file rooms. Hospitals, insurance companies, and doctor’s offices are now storing patient medical records in the cloud, with patients able to access test results online 24/7.

Given HIPAA laws relating to patient privacy, it’s probably no surprise this has also led to an increased focus on data protection and security. According to one report, “the black-market value of medical data is greater than even that of financial information.” Believe me when I say: No industry is more focused on virtualization security right now than health care.

 

Wearables And IoT

I remember the days when going into the local grocery store and getting my blood pressure read at one of those prehistoric machines seemed exciting. Imagine: A machine that helped me manage my own well-being without setting foot in a doctor’s office. Now, mobile devices as small as my cell phone can perform ECGs, DIY blood tests, or serve as a thermometer, all without even leaving my house. With help from automation, patients can even be prompted to check their weight, pulse, or oxygen levels, and enter results into mobile patient portals. Even better: They can transmit the results to my doctor in real time. Those details, when entered regularly, can help predict one’s risk for heart disease and other illnesses, ultimately saving lives. This is far more than cool. It’s life-saving.

 

Artificial Intelligence And Big Data

Big data is king in the digital world, and health care is no exception. Yes, it can be gathered to measure customer satisfaction. But perhaps more importantly, it can be used to automatically identify risk factors and recommend preventative treatment. Even more exciting: with the rise of the Internet of (Medical) Things (IoMT), mobile and wearable devices are increasingly connected, working together to create a cohesive medical report accessible anywhere by your health care provider. This data is not just useful for the patient. It can be pooled and studied en masse to predict health care trends for entire cultures and countries.

 

Empowered Consumers

All of the above have led to an entirely new trend in healthcare: patient empowerment. While many of us have come to associate health care with high costs and long waits, patients are now in the driver’s seat, with better access to higher-quality doctors, and higher satisfaction rates overall. It’s a healthy new way to look at health care, and one that holds promise for all of us with easy access to the digital landscape. My blood pressure is already lowering just imagining the possibilities.

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Presenters's curator insight, October 24, 4:16 AM
Al pensar en tecnología recurrimos a  muchos avances relacionados con la comunicación, educación... pero pocas veces nos planteamos que hay otros campos en los que también tiene una gran influencia. La industria tecnológica también está ayudando a cambiar el panorama de la salud. ¿Quieres conocer algunos de los avances tecnológicos más significativos en este campo?
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Niche Applications of Artificial Intelligence in Healthcare

Niche Applications of Artificial Intelligence in Healthcare | Healthcare and Technology news | Scoop.it

Artificial Intelligence has made its way to every field possible, steamrolling the processes along its way. One such field is healthcare. They say healthcare is a field that is not very rules based and a successful doctor is the one who leverages his/her experience to deal with complex and unseen cases. However, there are many low hanging fruits that are already being plucked by AI. This trend is being fueled by increasing digitization in healthcare data and advances in new algorithms. In this piece, we intend to give you a sneak peek into how AI is leading to improved healthcare for humanity. Below are some key examples of research areas and applications.

Virtual Slides Diagnosis

  • The tissue-based diagnosis has seen technological advancement with the introduction of virtual slides. However, virtual slides demand a lot of time and efforts than that for viewing the original glass slides from the pathologists. This is the time taken in the selection of information containing fields of view. Artificial intelligence can automate the tissue diagnosis routine work. Deep Convolutional Neural Networks are already being used in this area. Automated diagnosis would save a lot of time wasted in supervising and the pathologists can focus on the serious cases.

Diabetic Retinopathy Treatment

  • Diabetic Retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. If not caught early, it can lead to irreversible blindness. In “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs“, published by JAMA, Google presented a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients in settings with limited resources.

Skin Cancer Treatment

  • Sebastian Thrun’s lab at Stanford released an AI algorithm which detects Skin Cancer with very high accuracy. This algorithm was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of professional dermatologists.

Medical Diagnosis

AI algorithms can aid doctors in medical diagnosis.They can highlight key instances in a person’s previous health history. Incorporating AI into the medical field has the potential to change and vastly improve healthcare in its core. From improved diagnostic accuracy to better-optimized treatment plans, AI could be the key to better medical care for doctors and patients alike.

In August 2016, doctors at a hospital in Japan misidentified a 60-year-old woman’s leukemia. But IBM’s Watson examined a vast database of 20 million research papers and made a successful diagnosis in just 10 minutes. The AI-based system can be utilized to prune out the irrelevant data and help the doctor think more clearly focusing on the vital data.

Risk Prediction

The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four ‘machine-learning’ algorithms. These algorithms analyzed large amounts of data and self-learn patterns within the data to make predictions on future events which were a patient’s future risk of having heart disease or a stroke, in this case.

The results, published in the online journal PLOS ONE, showed that the self-teaching ‘artificially intelligent’ tools were remarkably more accurate in predicting cardiovascular disease than the established guidelines. This technology is a godsend for insurance companies by helping them do a more effective appraisal of health risks of a customer.

Radiology

Applying AI for Radiology is harder as compared to Histopathology and hence we are yet to see groundbreaking results here. There is, however, a lot of work going on in situations where X-rays, CTs, and MRIs can be analyzed automatically, thereby giving radiologists a quick second opinion to consult with.

AI has already been used for Chest X-rays for direct diagnosis. Some of the other areas where AI aids diagnosis significantly is segmenting hip bones and lumbar vertebra for QCT/MRI in osteoporosis screening.

A Recent release of Stanford Medical-ImageNet is likely to start a revolution like what ImageNet did for normal images.

Automating Drug Discovery

Discovery of a new drug takes years of research, its launch takes even more time and money. Automating drug discovery through AI can tremendously reduce the cost and time as well.The average biomedical researcher deals with a huge amount of new information every day. It is estimated that the bioscience industry is getting 10,000 new publications uploaded on a daily basis from across the globe and among a huge variety of biomedical databases and journals. So, it becomes impossible for the researcher to process the entire information alone. Artificial Intelligence has a vital role to play in elevating the work of drug development researchers.

  • A study published in Cell Chemical Biology reveals a big data-based approach to detecting toxic side effects of a drug before it goes to the expensive clinical testing. In the approach called PrOCTOR, researchers analyze each drug using 48 different features to ascertain its safety for clinical use. The entire process is automated using machine learning.
  • A company named BenevolentBio has been doing research into Amyotrophic Lateral Sclerosis (ALS). The AI they’ve developed incorporated in the company’s Judgement Correlation System (JACS) reviews billions of sentences and paragraphs from scientific research papers and abstracts. JACS then links direct relationships between the data and regulates the data into ‘known facts’. These known facts are used to generate a large number of possible hypotheses using criteria set by the scientists. Based on these hypotheses, possible drugs are discovered. They have already managed to identify two potential drug targets for Alzheimer.
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AI in healthcare: The unevenly distributed future is here

AI in healthcare: The unevenly distributed future is here | Healthcare and Technology news | Scoop.it

AI. Cognitive. RPA. Autonomics. Machine learning. Deep learning.

All these terms fly around in IT organizations today as CIOs, battling marketplace uncertainties and cost pressures, look for ways to enhance enterprise performance. As with most technology trends, the hype tends to overhang reality by a significant margin in the early stages of adoption, much in line with Gartner’s hype cycletheory.

 

Early this year, I wrote a piece that discussed how emerging technologies such as artificial intelligence (AI) and blockchain will drive precision medicine this year. Halfway into the year, the signs are that the use of AI technologies has definitely picked up momentum.

 

A recent study by consulting firm Accenture provides us some interesting data points. Artificial Intelligence or AI in healthcare is expected to grow more than 10x in the next five years, to around $ 6.6 billion, at a compounded rate of over 40%. AI represents a $150 billion savings opportunity for healthcare, across a wide range of applications: robot-assisted surgery, clinical diagnosis and treatment options, and operational efficiencies, to name a few. In my firm’s work with healthcare technology firms and enterprises, there is definitely a palpable excitement about the growing demand for AI in healthcare. Before unpacking what that means, it may be worthwhile defining some of the terms that are used interchangeably and synonymously with AI.

 

At the operating levels, autonomics and robotic process automation (RPA) refer to software that runs on pre-determined rules and eliminates the need for human intervention (a good example is fetching benefit eligibility information in a health plan or managing routine IT infrastructure operations). In many cases, these tools – sometimes referred to as “bots” – learn from patterns of requests and remediate/update their algorithms to respond in a more intelligent fashion over time. At higher levels of application, cognitive and AI systems aim to “mimic” humans in terms of reasoning and judgment based on techniques such as neural networks and Bayesian models that help these technologies come close to making decisions in a human-like manner. However, as IBM CEO Ginni Rometty points out, these techniques are more about augmenting human intelligence today, not replacing it (man and machine, not man vs. machine).

 

There is no doubt that these emerging technologies can transform healthcare. There is a rapidly growing body of use cases and successful applications of AI in operational and clinical areas. Here are a few examples of how AI technologies are currently being applied in the healthcare and life sciences sectors.

 

Health plans: There is considerable traction today applying RPA tools and AI technologies for improving productivity and efficiencies in health plans. By codifying workflow rules and enabling self-learning through ontological patterns and databases, these technologies are being used in areas such as provider data management, claim approvals and exception management, fraud detection, and customer service operations.

 

Health systems: AI and automation tools have found wide applications in a range of functions including revenue cycle operations, diagnosis and treatment, and population health management initiatives. IBM’s Watson Health engine, for example, has made significant strides in applying cognitive and AI technologies in the field of oncology and diabetic retinopathy, allowing the search and analysis of vast amounts of data and knowledge to provide clinicians with inputs for targeted intervention options.

 

Life sciences: Pharma companies have started successfully applying AI tools in clinical trial phases of new drugs by automatically generating content required for regulatory submissions and reviews. On the other side of the equation, these tools are being applied in pharmacovigilance for case intake and reporting on the adverse effects of drugs. There is increasing interest in the use of AI for improving efficiencies in supply chain operations. 

 

Across all of these segments, there are several commonly used applications, an example of which is the use of AI technologies for IT infrastructure operations in detecting and remediating network errors and application failures. Another example is the use of AI in patient engagement programs, especially for managing chronic conditions such as diabetes through automated alerts and interventions based on analysis of real-time data gathered through intelligent devices and wearables.

 

As the use of AI technologies gains momentum, more use cases will surely emerge. As healthcare transitions from a fee-for-service to a value-based care era, the need for advanced technologies for everything from precision medicine to increased operational efficiencies and improved patient engagement will drive the adoption rates for these technologies. Many of these initial projects are in pilot phases, and in the broader context, there is a relatively small number of healthcare enterprises that are investing in these technologies and programs. That is par for the course for new technologies in any field. Mainstream adoption may be a bit further away, and in the current environment of policy uncertainty, many of the smaller enterprises are likely to be in wait and watch mode, choosing to stay with business as usual till there is some clarity.

 

To paraphrase the sci-fi writer William Gibson, the future is already here, only it is unevenly distributed. This may be the most accurate summary of AI in healthcare at this time.

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10 Ways Artificial Intelligence Could Make Me a Better Doctor

10 Ways Artificial Intelligence Could Make Me a Better Doctor | Healthcare and Technology news | Scoop.it

Automation through AI, robotics or 3D-printing will make healthcare more efficient and more sustainable. These new digital technologies will improve healthcare processes resulting in the earlier and more efficient treatment of patients. It will eventually shift the focus in medicine from treatment to prevention. Moreover, medical professionals will get the chance to move from repetitive, monotonous tasks to the challenging, creative assignments.

AI has certainly more revolutionary potential than simply optimizing processes: it can mine medical records or medical images in order to come up with previously unknown implications or signals; design treatment plans for cancer patients or create drugs from existing pills or re-use old drugs for new purposes. But imagine how much time you as a GP would have if the administrative process would be taken care of by an AI-powered system. Your only task would be to concentrate on the patient’s problem! Imagine how much time you as a GP could spare if healthcare chatbots and instant messaging health apps would give answers to simple patient questions, which do not necessarily need the intervention of a medical professional!

She could have been a great doctor!

These were exactly the thoughts in my head when I was watching the movie Her for the second time. I was fascinated again about the scene in which the main character played by Joaquin Phoenix got his new, AI operating system and started working with it. I could not stop thinking about the ways I could use such an AI system in my life and how it actually could make me a better doctor.

Don’t get me wrong, I think empathy and great communication with patients can make a doctor better primarily, but as the amount of medical information out there is exponentially growing; as the time for dealing with patients and information is getting shorter, it is becoming humanly impossible to keep up with everything. If I could devote the time it takes now to deal with technology (inputting information, looking for papers, etc.) to patients, that would be a huge step towards becoming better.

Through the following 10 ways, AI could make me a better doctor.

1) Eradicate waiting time

You would think that waiting time is the exclusive “privilege” of patients and doctors do not have any free moment during their overpacked days. However, suboptimal health care processes not only result in patients sometimes waiting for hours in front of doctors’ offices but also medical professionals losing a lot of time every day waiting for something (a patient, a lab result, etc.). An AI system that makes my schedule as efficient as possible directing me to the next logical task would be a jackpot.

2) Prioritize my emails

The digital tsunami is upon us. Our inboxes are full of unread messages and it is an everyday challenge not to drown into the ocean of new letters. I deal with about 200 e-mails every single day. I try to teach Gmail how to mark an email important or categorize them automatically into social media messages, newsletters, and personal emails, it’s still a challenge. In Her, the AI system prioritized all the 3000 unread emails in a second. Imagine if we could streamline digital communication completely in line with our needs and if we could share and receive information more efficiently and more accurately without too much effort.

According to a recent report in the New Scientist, half a million people have professed their love for Alexa, Amazon’s intelligent personal assistant and more than 250,000 have proposed marriage to it. I have to say, I would probably do the same if it could organize my emails that way. (Also, if Scarlett Johansson were to be the voice of the assistant.)

3) Find me the information I need

I think I have mastered the skill of searching for information online using dozens of Google search operators and different kinds of search engines for different tasks, but it still takes time. What if an AI OS could answer my questions immediately by looking up the answer online?

More and more intelligent personal assistants, such as Siri on iOS or Alexa for Amazon lead us into the future, and there will be soon highly capable, specialized AI-powered chatbots also in the field of healthcare. Bots like HealthTap or Your.Md already aim to help patients find a solution to the most common symptoms through AI. Safedrugbot embodies a chat messaging service that offers assistant-like support to health professionals, doctors who need appropriate information about the use of drugs during breastfeeding.

4) Keep me up-to-date

There is too much information out there. Without an appropriate compass, we are lost in the jungle of data. It is even more important to find the most accurate, relevant and up-to-date information when it comes to such a sensitive area as healthcare. That’s why I started Webicina, which collects the latest news from the best, most reliable sources into one, easily manageable magazine.

On Pubmed, there are 23 million papers. If I could read 3-4 studies of my field of interest per week, I could not finish it in a lifetime and meanwhile millions of new studies would come out. I need an AI to process the pile of information for me and show me the most relevant papers – and we will get there soon. IBM Watson can already process a million pages in seconds. This remarkable speed has led to trying Watson in oncology centers to see how helpful it is in making treatment decisions in cancer care.

5) Work when I don’t

I can fulfill my online tasks (emails, reading papers, searching for information) when I use my PC or laptop, and I can do most of these on my smartphone. When I don’t use any of these, I obviously cannot work. An AI system could work on these when I don’t have any device in hand.

Imagine that you are playing tennis or doing the dishes at home when an important message comes in. With the help of an AI, you could respond to your boss without the need to touch any devices – a toned down version of Joaquin Phoenix’s AI, that arranged the whole publishing process of his book without the need for him to lift a finger.

6) Help me make hard decisions rational

A doctor must face a series of hard decisions every day. The best we can do is to make those decisions as informed as possible. I can ask people whose opinion I value, but basically, that’s it. Unfortunately, you would search the world wide web in vain for certain answers.

But AI-powered algorithms could help in the future. For example, IBM Watson launched its special program for oncologists – and I interviewed one of the professors working with it – which is able to provide clinicians evidence-based treatment options. Watson for Oncology has an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports that may be critical to selecting a treatment pathway. So, AI is not making the decision per se but offers you the most rational options.

7) Help patients with urgent matters reach me

A doctor has a lot of calls, in-person questions, emails and even messages from social media channels on a daily basis. In this noise of information, not every urgent matter can reach you. What if an AI OS could select the crucial ones out of the mess and direct your attention to it when it’s actually needed.

Moreover, if you look at the patient side, you will see how long is the route from recognizing symptoms at home until reaching out to a specialist. For example, in the Hungarian county of Kaposvár, the average time from the discovery of a cancerous disease until the actual medical consultation about the treatment plan was 54 days. This alarming number has been drastically reduced to 21 days with the help of a special software and by optimizing patient management practices since November 2015. Imagine, though, what earthquake-like changes AI could bring into patient management if the usage of a simpler process management tool and follow-up system could halve the waiting time!

8) Help me improve over time

People, even those who work on becoming better at their job, make the same mistakes over and over again. What if by discussing every challenging task or decision with an AI, I could improve the quality of my job. Just look at the following:

97% of healthcare invoices in the Netherlands are digital containing data regarding the treatment, the doctor, and the hospital. These invoices could be easily retrieved. A local company, Zorgprisma Publiek analyzes the invoices and uses IBM Watson in the cloud to mine the data. They can tell if a doctor, clinic or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.

9) Help me collaborate more

Sometimes I’m wondering how many researchers, doctors, nurses or patients are thinking about the same issues in healthcare as I do. At those times, I imagine that I have an AI by my side, which helps me find the most potential collaborators and invite them to work together with me for a better future.

Clinical and research collaborations are crucial to find the best solutions for arising problems, however, more often than not, it is difficult to find the most relevant partners. There are already efforts to change this. For example, in the field of clinical trials, TrialReachtries to bridge the gap between patients and researchers who are developing new drugs. If more patients have a chance to participate in trials, they might become more engaged with potential treatments or even be able to access new treatments before they become FDA approved and freely available.

10) Do administrative work

Quite an essential percentage of an average day of a doctor is spent with administrative stuff. An AI could learn how to do it properly and do it better than me by time. This is the area where AI could impact healthcare the most. Repetitive, monotonous tasks without the slightest need for creativity could and should be done by artificial intelligence. There are already great examples leaning towards this trend.

IBM launched another algorithm called Medical Sieve. It is an ambitious long-term exploratory project to build a next generation “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge. Medical Sieve is qualified to assist in clinical decision making in radiology and cardiology.

 

Many fear that algorithms and artificial intelligence will take the jobs of medical professionals in the future. I highly doubt it. Instead of replacing doctors, AI will augment them and make them better at their jobs. Without the day-to-day treadmill of administrative and repetitive tasks, the medical community could again turn to its most important task with full attention: healing.

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