Now and then, a solution emerges for a large, seemingly insurmountable problem. In some instances, progress results when someone looks at a challenge with fresh eyes and sees a fix that wasn’t obvious before. More often, significant progress is born from a sustained history of incremental successes that, at some point, become a powerfully capable solution. This article describes one such solution, MedsEngine, that, in our opinion, has allowed chronic condition management to take a giant leap.
New Dr. Alisa Niksch‘s Review Notes appear at the end of the story.
One of healthcare’s biggest challenges is better management of major chronic conditions, including hypertension, diabetes, heart failure, and asthma, among others.
In 2016, chronic conditions and the downstream health and productivity events they generate consumed almost one-fifth of US Gross Domestic Product.1 These include (but are not limited to) direct medical costs for increased heart attacks, strokes, amputations, emergency visits, and hospital admissions, as well as indirect employer and patients costs like absenteeism.
In addition, University of California researchers have reported2 that the U.S. spends over half a trillion dollars per year in additional costs because chronic disease patients are being prescribed the wrong drugs. This class of conditions represents our most important health care outpatient improvement opportunity.
Efforts to manage chronic conditions have yielded despairingly weak results. Historically, measures of success have been expressed by the percentage of cases that are “controlled,” meaning patients’ metrics are within accepted limits.
Currently, only 44% of Americans with hypertension are controlled.3 Fewer than 10% of diabetics are controlled when their blood glucose, blood pressure, and cholesterol goals are considered.4 Among heart failure patients with reduced ejection fraction, only 1.1% are on all three of the most essential drugs and at effective doses as necessary to prevent further destruction of the heart muscle.5 Better control would translate to better health outcomes, fewer high-cost events, and lower overall costs.
The standard responses to poor results have been to assign blame. You’ve probably heard these excuses about why performance was subpar:
With these assumptions as a backdrop, most interventions aim to change patient behavior, typically with little success.
These reactions ignore the complex structure of chronic diseases and what it takes to manage that complexity. As it turns out, two elements are necessary for successful chronic condition management:
Let’s put aside the second, on the assumption that the right drugs deliver superior health outcomes, a reward that should easily win patients’ buy-in.
MediSync is a firm that develops and provides management services and solutions for large, high-performing primary care and multi-specialty physician practices. Over the past two decades, it sought to develop a software application for real-world medical groups. The MedsEngine application was designed to achieve nation-leading outcomes in major chronic diseases. While many provider organizations have a “blame the patient” analysis of poor chronic outcomes, MediSync’s root cause analysis led them to undertake a different approach.
The insight behind MedsEngine is that drug selection choices are more complicated than most prescribers recognize. And, physicians and Advanced Practice Providers (APPs) can achieve superior outcomes if they have access to expert assistance in evaluating and selecting the individual patients’ precise medications.
The number of variables involved in choosing optimal medical therapies generates millions of permutations. For example, there are five distinct hemodynamic causes for hypertension. MediSync has further identified several dozen demographic factors (i.e. age, race) and comorbid conditions, each of which could change the optimal medication choices.
MediSync also recognizes twelve different drug classes – with multiple drugs within each class – to improve hypertension outcomes. The problem is that most physicians are comfortable using no more than five or six drug classes, and many patients require two or more classes of antihypertensive drugs, making the permutations even more complicated.
Most other professions have come to appreciate that extremely complex calculations are best done by computers. The complexity associated with juggling multiple variables can exceed humans’ capacity to rely on memory and mental calculation.
In the end, few physicians working without an aid like the MedsEngine prescribe the best medications. And, not surprisingly, few of their patients achieve safe blood pressure levels, as reflected in the national 44% success rate in successfully managing high blood pressure.
Artificial Intelligence (AI) is an optimal solution for this type of challenge. When properly designed, it can capably and reliably handle large numbers of variables. That approach, when applied to the complexity of chronic condition management, has been MediSync’s vision.
MediSync’s chronic disease management improvement project began 20 years ago, with the realization that making the right drug choices is an impediment to improved outcomes. Working at that time in paper medical records, it developed a first generation of “paper and pencil” decision support tools to drive better medication selection. Piloted by PriMED, a progressive, 55-physician, independent primary care practice based in Dayton, OH, by 2007 this approach resulted in 93% group-wide success (BP <130/80 for hypertension with renal dysfunction or diabetes; <140/90 for all other patients) across the practice’s entire population of hypertension patients. When compared to the CDC “success rate” of 48%, this was a remarkable outcome (Table 1).
But as the number and complications of diagnoses grew, paper and pencil tools became increasingly problematic. In 2016, MediSync began to develop the MedsEngine, an AI-driven tool that deploys complex, mathematical algorithms reflecting the best current understanding of each chronic disease’s physiology and pharmacology, and the relevant interactions occurring among and between them.
Pulling patient information from the electronic health record, the MedsEngine performs several, sequential calculations. First, it calculates the hemodynamic forces that drive each specific patient’s blood pressure problem, including problems in the blood vessels, heart problems, fluid problems, or combinations of these underlying issues.
Second, the MedsEngine considers 28 comorbid conditions that, when present, can influence which classes of drugs may be contra-indicated and which may be promoted or demoted. It also addresses whether there should be a change in the order of drugs prescribed for patients who require multiple classes to gain blood pressure control.
In 2017, MediSync initiated a MedsEngine pilot with PriMED. The group’s hypertension results improved to their highest degree ever, averaging between 93% and 95% of all hypertension patients in good control each month (Table 2).
Since hypertension, cholesterol, diabetes, and the other, most common chronic diseases cause many of the higher cost events like heart attacks, strokes, kidney damage, amputations, ER visits, admissions, and readmissions, PriMED expected to see significant reductions in the total cost of care for its patients. The data strongly supported that better chronic outcomes result in lower costs of care as Table 3 below demonstrates:
This performance improvement has been validated and recognized by credible third-party groups. The American Medical Group Association (AMGA) ranked PriMED best in the US at achieving blood pressure outcomes of ≤140/90 mm Hg with 95% of their hypertension population under control.7 Similarly, the Centers for Disease Control certified PriMED as first in their Million Hearts awards program.7 It is the only large group nationally to exceed 90% of hypertension patients under control.
The same technology drove PriMED’s control of Type 2 diabetes. This program was also recognized by AMGA as first among US physician groups, achieving simultaneous control of all three major Type 2 diabetes markers: blood pressure, LDL, and HbA1c.
Within the past few years, various federal laws and rules have required that larger physician groups must measure their blood pressure control success rates. During that period, overall blood pressure control rates have declined, not improved. The ability to predictably control the major chronic diseases is a significant advance, with important clinical and financial impacts to the patient and to the US health system.
A system that allows providers to reliably achieve performance targets enhances patient care and value. Importantly, it also allows those providers to optimize health outcomes and savings. This makes them far more desirable in a value-focused marketplace. In other words, the MedsEngine can serve as a foundation for care that is more evidence-based, accountable, and predictable.
The MedsEngine’s AI-driven platform capabilities are already established for three major chronic conditions:
And, the diabetes program is currently underway. Furthermore, MediSync intends to complete clinical decision support platforms for the top 12 chronic diseases addressed in primary care within three years. They include:
Mark DeRubeis, CEO of Premier Physicians in Pittsburgh, the other MedsEngine pilot site, summed up the promise represented by this approach:
“The MedsEngine offers the opportunity for exponential improvement in chronic care management. It enables you to get the diagnosis right the first time, to prescribe the right medicine the first time. If you look at the alternative to that, it may take two or three or four times the effort, and this enables you to cut all of that out and gain an efficiency that didn’t exist prior.”
US health care reimbursement is still dominated by fee-for-service, which has been rewarded by overtreatment and so favors more, higher cost care. Value-focused arrangements that reward better health outcomes and/or lower costs are now gaining accelerating traction in the marketplace. However, for high-value vendors, progress may feel glacial.
High-value approaches like the MedsEngine are gradually being embraced by organizations at financial risk for their health care costs – e.g., employers, third party administrators, stop-loss carriers, providers in risk-based arrangements, Medicare Advantage and Managed Medicaid plans – but acceptance at this point remains an uphill battle.
The MedsEngine is the first of what will almost certainly be a flood of new digital tools that facilitate far more effective care. It was built and is continuously updated using the best current established evidence so that clinicians can feel confident in the process. It offers a highly refined, mechanized way to leverage the best guidance from the management of complex inputs. However, building these tools well is as complicated as the problems they seek to address. Fortunately, the rewards, in terms of better health and lower costs, are likely to be equally powerful.
Keywords: chronic condition, chronic disease management, artificial intelligence, hypertension, diabetes, heart failure, MedsEngine, primary care, better health outcomes, cost savings.
Financial disclosure: Both Brian Klepper and John Rodis, M.D. have consulting relationships with MediSynch, the parent company of MedsEngine.
MedsEngine exemplifies the potential of AI in the healthcare industry and the very real need for automation to reduce inefficiencies of the traditional trial and error process.
There are an enormous number of AI tools for clinical decision support which have entered the healthcare market over the last 5 years. Many of them are comprised of fairly transparent algorithms aggregating different data sets to generate an output that can guide management. In this case, an initial goal was optimal medical therapy for hypertension. The MediSync team made the smart decision to trial their algorithms in a real clinical setting.
An early partnership with clinicians who can provide a real-world test for the software, both from clinical results and workflow standpoint, is incredibly valuable. However, it is still elusive what disclaimers surround the use of the software, what percentage of recommendations made by the MedsEngine software were followed, and despite published cost comparisons, whether other patient experience was affected (e.g. increased drug costs, prior authorization denials).
While pilot studies can yield impressive results for clinical outcomes, AI must be evaluated in ethnically and geographically diverse populations to determine its ability to generate similar results on a large scale.
In fact, this is a principle the FDA requires for evaluating clinical trial data submitted in support of a 510K application – especially if there is a machine learning component to the software.
In this case, an AI platform that provides personalized treatment plans with specific drug recommendations may fit into the category of software as a medical device (SaMD). Regulatory processes provide risk mitigation and correction of any vulnerabilities which may lead to errors or misuse which could potentially harm patients. When innovators are looking to design AI platforms that inform, drive, diagnose, or treat medical conditions, evaluating a regulatory strategy with appropriate validating data is key to success.
Imaging studies are an important part of screening and diagnosis for some cancers, lung, and breast in particular. Such studies have led to more lung and breast cancers being diagnosed at a smaller size compared to what was found prior to the advent of screening programs. One important research question that is currently being explored is whether the use of artificial intelligence to aid in diagnosis can improve the performance of radiologists alone. Let’s take a look at what we know so far.
According to the American Cancer Society (ACS), approximately one in eight women will be diagnosed with breast cancer in their lifetime. It is the second leading cause of death from cancer in women.
Breast cancer screening is commonly performed on patients who have no obvious signs of disease. Many of these women are not at high risk for the disease. Nor do they have a family history.
Although many preventive health guidelines recommend screening mammograms, concerns have been raised. For example, one in five abnormal mammograms is a false positive. That means the mammogram was read as positive by a radiologist but proved not to be cancer on biopsy.
Over the span of ten years, about half of women are given a false-positive result. This usually leads to further testing, anxiety, distress, and sometimes unnecessary procedures or treatment.
Experts from Google Health and its subsidiary, Alphabet’s DeepMind unit, recently worked with Northwestern University, Cancer Research UK Imperial Center, and Royal Surrey County Hospital to examine aspects of radiographic breast cancer diagnosis. In particular, they wanted to better understand the reasons for inaccuracies in the diagnosis of breast cancer. And, they wanted to determine if artificial intelligence could help.
In order to comprehend how AI can be used to improve the results of breast imaging moving forward, it is important to have a basic understanding of how this artificial intelligence system works. This is a type of system known as “Deep Learning” which involves a three-dimensional model:
The results of this research were recently published in the journal Nature in an article titled “International evaluation of an AI system for breast cancer screening.” The study compared the results of mammography readings in an artificial intelligence model to those read by radiologists. There were close to 26,000 women from the UK and over 3,000 women in the United States in the study.
The researchers found that the artificial intelligence model reduced both false positives (when patients are told they have cancer when they don’t) and false negatives (when the disease is present, but not diagnosed).
Although in this early testing the AI caught cancers missed by radiologists, there were also cases in which it missed cancer that was caught by radiologists. This suggests that AI alone may not be the sole solution moving forward.
With approximately 160,000 deaths in 2018 due to lung cancer, it is the most common cause of cancer death in the United States. The U.S Preventive Services Task Force’s (USPSTF) new guidelines for the use of low dose computed tomography has recently been updated for individuals at high risk of having lung cancer.
Lung cancer screening using this type of computed tomography testing has been shown to reduce death by 20-40%. However, similar to breast cancer screening, one ongoing issue with the use of this screening exam has been the high rate of false positives (a result that indicates that a person has a disease when they actually do not). Although low-dose lung CTs have helped immensely in early detection, it has been found that about one-quarter of the suspected nodules are actually not cancerous.
To determine if this could be improved upon, doctors at Northwestern University and Stanford, teamed up with Google to determine if the same type of artificial intelligence, called Deep Learning, could help improve upon our current methods with lung cancer.
Researchers from Google used more than 42,000 CT scans to train this artificial intelligence system to detect cancerous lung nodules on radiology imaging. The study, titled “End-to-end lung cancer screening with three-dimensional deep learning on low dose chest computed tomography” was published in Nature as well.
Over 6,000 National Lung Cancer Screening Trial cases were tested in this study. In addition, there was an independent evaluation of a set of over a thousand cases. The performance of the artificial intelligence system was compared against radiologists who had evaluated low-dose chest computed tomography scans for patients – several of which had confirmation of cancer by biopsy within a year.
This deep-learning artificial intelligence system produced fewer false negatives (a result that indicates that a person does not have a disease when they actually do) as well as fewer false positives. When prior imaging was available, the model performed better than the radiologists (six of them) with an 11% reduction in false positives and a 5% reduction in false negatives.
The Nature study was a retrospective study that examined past cases. This type of study design is not as strong as prospective studies with randomization. Mozziyar Etemadi, MD, Ph.D., one of the authors of the study has said that “the next step is to perform a prospective study to see if the tool, when used by a radiologist, can lead to earlier and more accurate diagnosis of cancer”.
Another caveat is that it may be some time before AI with deep learning is routinely used in hospital and free-standing radiology suites. The algorithm that is the backbone of the AI-deep learning system is very sophisticated and will undoubtedly require some painstaking work to fully integrate into hospital computer systems. Further, the variability of many cancers could make new scenarios difficult for the deep learning system to interpret if they have not been seen before.
We also need to consider that although AI with deep learning improves some aspects of cancer screening diagnoses, it is not (yet) perfect. It may be that the best way to introduce AI into imaging analysis is to add it to the workflow of radiologists. This is because both have the potential to not catch something or make mistakes.
The performance of the deep learning system shows that there can be a beneficial role of artificial intelligence in cancer screening moving forward. In fact, the use of algorithms that incorporate co-morbidities and risk factors in medicine is not uncommon today. However, the use of such a sophisticated one on its own will most certainly take time. It will also require well-designed prospective studies that follow patients over time. Nonetheless, there is no denying that there will be an important role of artificial intelligence in cancer screening moving forward.
Almost 2 million people are diagnosed with cancer every year. Regardless of the type of cancer diagnosed, experts agree that early detection increases the chance of successful treatment and remission.
Luckily, there are some new technologies that may soon make it easier to detect cancer earlier. This should improve the survival rate of this often deadly disease.
More than 40,000 women have died from breast cancer in the United States alone. As mentioned above, early detection is the key to improved survival. In fact, ninety percent of women who are diagnosed in the early stages of the disease will survive.
Traditionally, doctors have relied on mammograms to detect changes in breast tissue that could indicate the growth of cancerous tissue. Unfortunately, mammograms are not always accurate, particularly for women with dense breast tissue. However, an innovative new “Internet of Things” (IoT) bra may change that sometime soon.
Known as the iTBra, this Wi-Fi enabled garment contains 16 sensors that can detect changes in the wearer’s breasts. After wearing the bra for two hours, the data is transmitted directly to the patient’s physician. This data is then paired with a predictive algorithm that analyzes the information for known risk factors.
While it’s no replacement for an annual mammogram, this IoT bra could alert patients and physicians to changes between their yearly appointments.
The product is still under development and has not yet been approved by the FDA, but Crycadia Health, the company that makes the device hopes that one day it will make early detection as easy as putting on your bra.
A cancer diagnosis generally means that the patient will pass a lot of time in hospitals or clinics. However, that is not where they’re spending the majority of their day. That place is their home.
In the future, that home may be filled with IoT-enabled devices that could transmit patient-generated health data to their doctors. The information obtained from these devices could include vitals such as heart rate, pulse ox, and respiratory rate.
In addition, IoT-enabled pillboxes, appliances, and even toothbrushes could also generate a plethora of useful data. Yet other devices will detect time in bed, falls, and even gait.
All of this information will give clinicians (and family members) a better idea of how patients are faring at home. For example, if IoT devices detect that the patient hasn’t left their bed in a number of days nor opened their pill box in a week, the system could alert their physician to take appropriate measures to check on their patient.
While current cancer treatments — surgery, radiation, chemotherapy, and immunotherapy — are generally effective. However, in some cases, their side effects may be significant. Scientists are now starting to use artificial intelligence (AI) and machine learning (ML) to formulate treatments that are just as effective while mitigating the some of the toxic side effects.
Using machine learning and years of diagnostic data, researchers have programmed an AI system to formulate a cancer treatment plan that uses the lowest doses possible while still delivering effective treatment. In a simulated trial, doctors were able to reduce the dose of a medication by 25-50% while still effectively shrinking the tumor.
It’s important to note that only one type of cancer was included in this study. And, as of the time of this writing, the trials are purely simulations. But, it is definitely a step in the right direction to use AI and ML to make cancer treatments more effective and less toxic.
Cancer treatment and prevention aren’t the only medical fields benefiting from IoT and artificial intelligence. A variety of medical apps for your cell phone could potentially help save your life in the future.
These apps will eventually be subject to FDA regulation and testing, especially if they’ll collect information for medical professionals to use. But for now, you can download GoodRX to find the best price on your prescription, RedCross First Aid to learn how to respond in an emergency, or Doctor on Demand to talk to a remote physician in the comfort of your own home.
Artificial intelligence and machine learning, when paired with a powerful supercomputer, is capable of processing more information in a week than an oncologist could sort through in their lifetime.
Medicine as a whole generates petabytes of data every year, from diagnostic information to patient demographics. AI and ML are starting to make an appearance in imaging sciences — helping radiologists analyze X-ray and MRI/CT images to create more accurate diagnoses.
By feeding the images into a machine learning system, the computer can sort through past images to find comparisons that can assist in diagnoses. It completes this task in a fraction of the time that it would take a human technician to do the same.
So why are these tools missing from cancer prediction and prevention? Part of the blame lies in the fact that this technology is still in its infancy. Bringing AI and ML into oncology offices and hospitals will require substantial investment as well as additional training to navigate the system. Many professionals are hesitant to make that leap because the technology is so new and will continue to evolve in the coming years.
Many of the technologies mentioned are still in their infancy. Most of them haven’t even reached the first stage of FDA trials. But that shouldn’t discourage us. Although these new technologies will not provide a cure for this deadly disease, they are moving us toward better diagnostics and treatments that will extend and improve the quality of life of people living with cancer.
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For nearly as long as there has been chat on the Internet, there have been chatbots. In the early days, chatbots were mainly a source of amusement. However, advancements in artificial intelligence (AI) has transformed chatbots into useful tools for across many industries, including healthcare.
The healthcare industry while still in its early stages is transitioning to more IT-based consumer-centered options. Chatbots, for example, are being leveraged to improve patient outcomes as well as customer service.
This post examines some of the many ways that chatbots are helping in healthcare.
One area where chatbots are primed to help the health industry is with patient self-diagnosis. Physicians across the board have struggled with patients, who, after reading a few articles on websites like WebMD, believe they can make their own diagnosis.
It is true that those sites can be useful to help people get a basic idea of what could be ailing them. However, some patients may be under the impression that they can establish a definitive diagnosis for themselves instead of seeking medical care. This, unfortunately, is not the case. Luckily, chatbots can be helpful here.
Chatbots can help patients make sense of their symptoms when paired with natural language processing and adaptable learning algorithms. They can also help them more accurately determine whether they need a trip to the doctor’s office or hospital. Rather than a list of symptoms, a chatbot offers something more akin to analysis.
One startup is already working on a ‘pocket doctor’ like the one described above. Your.MD is a free app that comes pre-installed on all Samsung Galaxy phones. It provides a diagnostic chatbot. The potential to improve symptom analysis is there, but to function optimally, the program requires crowdsourced information from doctors and hospitals around the globe.
Robotic-assisted surgery systems aren’t a new invention. They have been around since 1982 in one form or another.
These systems are still many years away from operating autonomously or under the supervision of an artificial intelligence program. However, integrating them with chatbots as a method of information delivery could be the first step toward that goal.
Despite open communication between patients’ families and surgeons, surgeries are still highly stressful situations for families. Chatbots can be used to deliver information to worried family members during procedures.
Bots won’t discuss specific information. But they could be used to answer general questions such as:
Telehealth is growing exponentially. It has offered a convenient alternative to more time-consuming office visits.
A good example of this growth is the connection with healthcare providers digitally for post-operative information and care makes sense in many situations. Telehealth is also useful for answering questions and making recommendations. This especially applies to those people with chronic illnesses who need care but for whom travel is difficult.
Typically, while telemedicine doctors are usually available 24 hours a day, a packed schedule can make it difficult to answer every call or question that comes across their screen. This is where chatbots become essential tools. They can streamline the appointment by gathering vital information early.
Chatbots can collect patient information, answer basic questions and get a good idea of what the patient is contacting the physician about before the appointment ever starts. In some situations, patients can get the most basic answers before seeing their physician. Examples include whether a temperature is in a normal range for their situation or if a certain virus is contagious.
It doesn’t matter what industry you’re in, sitting on hold for half an hour or more to get a basic question answered is infuriating. Chatbots are being used to circumvent some of this frustration by providing answers to basic questions such as office hours, types of care offered and other similar inquiries. For some institutions, they are becoming a preferred method of providing the initial phase of customer service.
A secure chatbots system can also be used to deliver basic patient information. This includes routine test results and prescription renewal notifications, as long as providers can ensure HIPPA compliance. Pharmacies could use similar systems to request refills from their providers.
As with any basic artificial intelligence, there are risks to be considered before implementing a chatbots system. Microsoft learned that lesson in 2016. Exposing their Twitter-based chatbot to the Internet led to it slinging racist slurs within a day of coming online.
There is also the matter of privileged patient information being shared in a networked system. While unlikely, HIPPA compliant chatbots systems can be compromised, sharing private patient information with the hackers.
There are security measures to prevent such an eventuality, like employing high levels of data encryption, making the chats difficult or nearly impossible to hack into.
Chatbots are an increasingly important part of the future of Health IT. But, we’re a long way from having a comprehensive chatbot system that will benefit patients across the board. The app created by Your.MD and other similar projects are the first steps toward turning these applications into a truly beneficial tool for the medical industry.
Dr. Leonard “Bones” McCoy, the doctor aboard the fictional Starship Enterprise on “Star Trek”, was ahead of his time. Practicing space-age medicine with a cool handheld scanner, he could get instant information about vital signs and patient health, while diagnosing and prescribing cures for unusual ailments.
Today, as technology continues to revolutionize the practice of healthcare, we have entered an exciting new frontier of state-of-the-art gadgets and high-tech communication systems. Some of these changes are made possible by our growing ability to utilize big data to improve outcomes through the field of health informatics; others by awe-inspiring advancements in medical science, telecommunications, and even robotics. Here are a few of the ways that technology is shaping the future of healthcare.
This high-tech medical advancement is still in its infancy but has the potential to create drugs, prostheses, and even human tissue and organs. Recently, scientists printed human ears and successfully attached them to the skin of mice, a huge step forward in the evolution of 3D printing. Potentially even more exciting is a development reported in Australia, where doctors successfully implanted a 3-D printed vertebrae into a human patient who had been suffering from chordoma cancer. At Vanderbilt University, The Kidney Project is currently developing a bioartificial kidney “as a permanent solution to end-stage renal disease.” This advancement could ensure that every eligible patient would have the option of receiving a transplant, not just those who make it to the top of the list.
Advancements in both artificial intelligence and robotics have led to real uses for robots in hospital settings—as surgical assistants, as delivery and transportation aids, and much more. Described as looking like something out of a science fiction movie, a bright-white cleaning robot at Thompson Hospital in Canandaigua, N.Y., uses ultraviolet light to destroy pathogens associated with hospital-acquired infections.
In addition, robots are increasingly being used in actual surgical procedures. According to the Mayo Clinic, “The most widely used clinical robotic surgical system includes a camera arm and mechanical arms with surgical instruments attached to them. The surgeon controls the arms while seated at a computer console” that displays a magnified view of the surgical site. Though robot-assisted surgery traces its roots to the 1980s, the technology continues to advance, now giving trained surgeons the ability to perform remote “telesurgery.” Other potential benefits include safety (reduced blood loss), smaller incisions, and smaller scars, along with faster recovery time.
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Wearables offer incredible potential to collect patient data, increase prevention, and improve health outcomes for users. Electronic fitness trackers and smart watches are among the best-known wearables, but there are many more on the market and currently in development that monitor everything from sleep and rest patterns to heart rate and calories. There are wearable devices that help you manage stress and high blood pressure, and one that provides data by tracking your heart as it beats around 100,000 times per day, to name just a few. Spurred by the success of smartwatch-style devices like the popular Fitbit, the global market for medical wearables is forecast to grow from an estimated $123 billion in 2015 to $612 billion in the next eight years (mobihealthnews.com).
Telemedicine involves leveraging methods of virtual communication between patient and physician. With telemedicine, doctors are able to see and treat patients through video conferencing, eliminating the need for patients to drive to a physician’s office or clinic. Telemedicine is a boon for the thousands of people who don’t have access to medical care because they live in a remote location, lack transportation options, or are not ambulatory. In recent years, some medical centers have even expanded their telemedicine capabilities to the surgical suite—remotely performing intricate robotic surgeries on patients many miles away—or even around the world.
The healthcare industry was slow to adopt the cloud, but that is quickly changing. IBM Watson is one example of a cloud-based technology that is “bringing together clinical, research, and social data from a diverse range of health sources” to advance care and speed up communication. While security and privacy of patient data have always been a concern for health centers, the cloud has introduced new challenges and concerns surrounding the possibility of cyber attacks or digital information breaches. Yet, many experts agree that the cloud is a more secure option than on-premises data storage solutions. Technology is being leveraged to dispel many of these concerns, with new tools being developed daily to protect patient information. What’s more, the government will likely be enacting stricter regulations and policies around digital health data to add an extra layer of protection for patients.
In 2009, Congress passed the Health Information Technology for Economic and Clinical Health Act, mandating the transition from old-fashioned paper records to electronic health records (EHRs). Compared to some of the dramatic breakthroughs cited above, the federal HITECH Act may seem somewhat low-tech, but the impact has been far-reaching and profound. The legislation paved the way for the growth in healthcare informatics and the age of big data in healthcare, as well as interoperability—the use of technology to easily and securely share medical data (patient records, for example) between systems, software applications, and people. EHRs offer improved continuity of care and better outcomes by ensuring that doctors are diagnosing and treating patients based on a holistic picture of their past and current health. EHRs are also expected to improve coordination of care between providers, reduce health care disparities, and streamline processes such as e-prescribing.
In addition, the widespread adoption of EHRs has given rise to volumes of health data never before imagined—creating opportunities to harness and interpret that data to improve care among both individual patients and patient populations. Though the field is still relatively new, the role of informatics in healthcare is expected to continue to expand. A workforce study undertaken by the American Health Information Management Association reveals more and more employers are looking for job candidates who possess informatics skills and training. In response, universities are helping to fill the skills gap with specific educational offerings such as a master’s degree in health informatics.
OK, it doesn’t look exactly like the one wielded by Dr. McCoy on “Star Trek”, but today the medical tricorder is much closer to being a reality—thanks in part to the $10 million Qualcomm Tricorder XPRIZE challenge. Dr. Basil Harris, an emergency room physician, and his brother George Harris, a network engineer, led a group that beat 300 teams from 38 countries to win a $2.6 million grand prize. Their tricorder, which utilizes sensors that fit over a patient’s fingers, can diagnose a variety of common ailments, including anemia, atrial fibrillation, diabetes, sleep apnea, and urinary tract infections. The Roddenberry Foundation, a charitable organization set up by the son of “Star Trek” creator Gene Roddenberry, has pledged an additional $1.6 million to help develop the finalists’ designs in the belief that such futuristic devices could one day save millions of lives.