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