The Patient-Centered Outcomes Research Institute (PCORI) has approved a new policy that will encourage data sharing among researchers with the goal of accelerating big data analytics and secure health information exchange.
The new policy strengthens PCORI’s commitment to open science by allowing researchers to verify and build on past findings from PCORI-funded studies and generate new evidence for healthcare decision-makers.
Research teams that have received PCORI funding will place the data generated from their studies, as well as documentation for how that data was produced, into a repository designated by PCORI.
The data, which could include deidentified participant information, full protocols, metadata, and statistical analysis plans, can then be made available for other research teams for additional analysis. PCORI will also provide funding to researchers so that they can prepare the data and other materials for sharing.
“Through this data sharing policy, we’re taking a major step in advancing open science,” said PCORI Executive Director Joe Selby, MD, MPH.
“By supporting how others may use information generated by the studies we’ve funded, we’re helping to enhance the quality and increase the quantity of evidence for healthcare decision making. We’re also reducing redundancy in collecting clinical data sets, which can speed research and the production of more useful evidence.”
PCORI will also require that all personal health information is de-identified to protect the privacy of study participants.
Additionally, informed consent from study participants is required to permit the reuse of data. PCORI will review requests before granting access.
The new data sharing policy is part of a series of initiatives from PCORI that aim to support research transparency and ensure broad availability of high-quality health data assets.
The organization’s policy on peer review and public release of research findings ensures that all results from PCORI-funded studies undergo a review and are made publicly available on PCORI’s website in a final research report.
In addition to the reports, PCORI offers brief summaries of studies and their findings that are posted as public and professional abstracts on the website.
The Institute also has a public access policy in place to cover the costs for journals to make papers presenting the results of PCORI studies freely available to the public.
By approving this new data sharing policy, PCORI expects to expand on these past initiatives and accelerate healthcare innovation.
The Internet of Things (IoT) is expected to combine with the power of artificial intelligence, blockchain, and other emerging technologies to create the “smart hospitals” of the future, according to a new report by Frost & Sullivan.
The IoT – also commonly known in the healthcare industry as the Internet of Medical Things (IoMT) – consists of any and all medical devices, patient monitoring tools, wearables, and other sensors that can send signals to other devices via the internet.
These tools generate massive amounts of data that must be stored, integrated, and analyzed in order to generate actionable insights for chronic disease management and acute patient care needs.
IoT data is a valuable addition to other clinical data sources, such as the electronic health record (EHR), that allow providers to monitor patients on an ongoing basis or predict changes in an individual’s health status.
“Escalating demand for remote patient monitoring, along with the introduction of advanced smartphones, mobile applications, fitness devices, and advanced hospital infrastructure, are setting the stage for establishing smart hospitals all over the world,” says the report.
Predictive analytics strategies are beginning to rely on the availability of data from wearables and IoT devices both inside and outside of the hospital.
Predicting patient deterioration or infection in the inpatient setting requires continuous feedback from bedside devices, while home monitoring tools such as Bluetooth-enabled blood pressure cuff, scales, and pill bottles can keep patients adherent to chronic disease management protocols outside of the clinic.
According to a recent analysis by Deloitte, more than two-thirds of medical devices will be connected to the internet by 2023, compared to just 48 percent of devices in 2018.
The uptick in connected devices will lead to the availability of more data for analytics, which will in turn require novel methods of extracting meaning from raw datasets.
Artificial intelligence and machine learning strategies are ideally adapted to managing and analyzing continuous data streams in large amounts, says Frost & Sullivan, and will be critical for ensuring that actionable insights are presented to providers without overloading their workflows.
“Sensors, artificial intelligence, big data analytics, and blockchain are vital technologies for IoMT as they provide multiple benefits to patients and facilities alike,” said Varun Babu, Senior Research Analyst, TechVision.
“For instance, they help with the delivery of targeted and personalized medicine while simultaneously ensuring seamless communication and high productivity within smart hospitals.”
The potential to improve efficiency, engage patients continuously, and get ahead of adverse events has created a significant commercial opportunity for device manufacturers, software vendors, and analytics developers, adds a separate report by MarketersMedia.
Currently, the global IoT market is valued at $20.59 billion, and is anticipated to grow at a 25.2 percent compound annual growth rate (CAGR) until 2023 to reach $63.43 billion.
The market includes implantable tools, such as cardiac devices, as well as internet-connected ventilators, imaging systems, vital signs monitors, respiratory devices, infusion pumps, and anesthesia machines, MarketersMedia says.
Frost & Sullivan also anticipates that emerging categories of IoT devices, including adhesive skin sensors, will contribute to the financial opportunity, while developing technologies, such as blockchain, will enhance the security, interoperability, and analytics potential of these tools.
In order to succeed, providers and developers will need to collaborate on creating and deploying data standards and shared protocols to ensure the seamless exchange of data across disparate systems.
“The main objective of IoMT is to eliminate unnecessary information within the medical system so that doctors can focus on diagnoses and treatment,” said Babu.
“Since it is an emerging technology, technology developers need to offer standardized testing protocols so that they can convince hospitals of their safety and efficacy and make the most of their massive potential.”
If there was ever an industry in dire need of increased efficiency, cost containment and improved outcomes, health care tops the list. Despite consuming 18 percent of our nation’s GDP—equal to $3.4 trillion in annual expenditures—it is responsible for nearly 250,000 deaths due to medical errors, poor record keeping and a dismal lack of shared data among doctors about patients in their care.
From blockchain technology to surgical robots, medical experts worldwide agree that big data and artificial intelligence (AI) will play a key role in vastly improving health care quality and delivery. Aided by advances in sensor capabilities, computational power and algorithmic ingenuity, the pace of medical innovation is accelerating rapidly.
To be sure, AI and big data are not the next best thing, they are here and now. Digital medicine is currently tracking down and destroying mutant cancer cells faster than ever before. It is also commonly used in operating rooms by doctors tapping into pools of data accumulated from previous surgeries to receive guidance from computers systems that have analyzed learned procedures that can be scaled up in order to make appropriate recommendations before, during and after treatment. So instead of depending on one or two local practitioners determining the course of lifesaving treatments, patients now have access to a knowledge base of thousands of doctors worldwide.
AI—versus natural intelligence used by humans to power up their brains—is akin to a jigsaw puzzle that deploys algorithms to draw together seemingly unrelated dots of information to paint a clear picture of the underlying data. It has changed dramatically since the concept was first introduced at Dartmouth College in 1956. Today’s man and machine AI is being aided by neural networks and deep machine learning methodologies powered by quantum computers and sophisticated algorithms that can crunch raw data into meaningful and actionable analyses.
A recent Wall Street Journal article titled “The Operating Room of the Future” is a case in point. Verb Surgical Inc., a recent startup formed by a partnership between Alphabet and Johnson & Johnson, is designing neural networks which enables robots to learn from one another by connecting each of them to the Internet to create machine-learning algorithms. Called “Surgery 4.0," it is the next logical step after traditional open procedures, minimally invasive surgery and the introduction of robotics. Using machine learning methodologies, computer programs study past procedures to identify best practices and potential errors. Verb’s technology has rendered the da Vinci surgical robot ancient by today’s standards despite the latter performing more than five million surgeries worldwide since 2000.
Another area ripe for AI is mental health. Researchers are developing new drugs and pharmaceutical combinations using machine learning to assess chemical reactions of anti-depressants among individual patients. They then tailor them to closely match an individual’s unique biochemical makeup. The results thus far are promising. In addition to detecting when a patient is veering off into a bipolar episode even better than a psychiatrist could ever imagine, these drugs are mitigating some of the wrenching side effects associated with traditional serotonin re-uptake inhibitors. Taken one step further, Stanford University has created chatbots to combat this debilitating disease. Patients feeling an aura can tell their chatbot how they are feeling that day. Using predictive analytics, the bot can quickly suggest coping strategies drawn from numerous cognitive behavioral therapies. Again, the results are impressive, reducing depressive symptoms by 20 percent.
AI and big data can also predict patient falls resulting in head traumas, bone fractures and other injuries costing on average $30,000 per incident. Businessweek recently featured California-based Qventus, a company that developed a program to help nurses overcome alarm fatigue and sensory overload from the constant beeping sounds and alerts found in hospital environments. In many cases, this results in medical staff missing critical and life-threatening alarms altogether. Qventus’ software extracts and analyzes data to recognize patterns from call lights, bed alarms, electronic medical records, patients’ prescriptions and age and other fall indicators. In turn, this has reduced injuries by 13.5 percent.
Today there is no such thing as TMI when it comes to data capture now that we have the tools to make sense of it all. We are far from Star Trek’s tricorder ability to instantly detect what ails us, but we are moving in that direction. Even in its embryonic stage, AI outperforms dermatologists in spotting skin cancer, helps pharmacists predict more effective drug combinations, and spots nuances on x-rays far better than radiologists.
Quantum computers uncovering newfound data have provided medical professionals with keen insights into disease mapping and prevention, rendered speedier diagnoses and treatments for patients, accelerated scientific discovery aimed at curing the leading causes of death in our country, and have also played a major role in predictive analyses and detection.
Last month, Oakridge National Laboratory rolled out the world’s most powerful supercomputer, Summit, capable of 122.3 petaflops (or 200 quadrillion) calculations per second. Comparatively speaking, the human brain clocks in at 10-100 petaflops per second. However, computers do not yet match the human brain in areas like reasoning, perceiving and intuition. AI will never replace humans or lead us to the dreaded robopocclypse of lore. Considered by many as an idiot savant, AI is well versed in single, closely supervised tasks but out of its element performing wider, more complex calculations. Equally important, big data and AI are only as good as the data fed to it by their mere mortals (you and me) whom program neural networks and plug and play algorithms that run the risk of being inherently biased or, worse yet, a victim of groupthink.
Yet, the opportunities that big data and AI present in vastly improving health care and the quality of life for ailing patients far outweigh the challenges. Together, man and machine are teaming up to exploit unprecedented amounts of medical information churned out by powerful computers and advances in integrated software technologies. According to Kevin Lasser, CEO of JEMS Telehealth, “we are at an inflection point now and will soon look back and realize that today was only the beginning of a major revolution in medicine.