The role of telehealth during the pandemic
Telehealth or telemedicine could be a useful option at any time, but it is especially important to find out what it is during a public health emergency. Telehealth protects you and your doctor against the possible spread or infection by the COVID-19 virus.
Telehealth is no longer just a nice thing to have, but a must have for patients and healthcare professionals during these uncertain times caused by the COVID-19 pandemic. While we all wish it hadn’t taken a pandemic to boost telehealth, for better or for worse, it has.
Since a significant portion of the US has stay-at-home orders to help flatten the curve, people still need their doctors, and access to telehealth can help make this possible. As with all technology, there are limitations, but telemedicine has an important role to play not only in detecting COVID-19 symptoms, but also in keeping up with routine needs and follow-ups. It is even used to keep seeing and treating cancer patients .
In a recent survey , nearly half (45%) of those surveyed said their mental health is being affected by the coronavirus pandemic, and the use of virtual visits for mental health needs has reached record levels. The current situation has drawn attention to the value of access to telemental health both for existing patients and for the influx of new ones.
“In addition to the exacerbated anxiety people feel, I have seen an increase in relapses and domestic violence, as well as a growing number of clients requesting psychiatrist referrals for the first time. At first my clients and even I were resisting telemental health (which was new to me), but within weeks the training wheels came loose and I am treating clients efficiently and effectively.
Additionally, an announcement by the Federal Communications Commission (FCC) has established a $ 200 million COVID-19 telehealth program to help eligible healthcare providers continue to treat patients with the help of telehealth technology. This should help make technology more accessible to patients who might not otherwise be able to access telehealth services.
For now, telehealth is playing an important role during the COVID-19 crisis. Adoption by both patients and healthcare professionals will help lay the foundation to secure your place in the future of continuity of care. As well as the idea of robot-assisted surgeries, it seemed a bit too futuristic not too long ago, they are here to stay. To turn a blind eye to the value of telehealth is to reject the untapped potential for the immediate and long-term future of healthcare. While no one really knows what the post-pandemic world will look like, it is clear that telehealth technology will continue to evolve and become a permanent part of our daily lives.
With the help of technology, we will be even more connected to our own health. But it has the potential to help track and identify those who may have been in contact with someone infected with COVID-19 and can help predict outbreaks before they get out of control.
Until a vaccine is developed, we are all likely to continue to exercise caution. We will continue to choose telehealth services that reduce risk and reduce unnecessary interactions.
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Machine learning in mental health care
Artificial Intelligence and specifically the rise of Machine Learning applications in the mental healthcare sector are giving great hope to the human race to achieve greater capacities to diagnose and treat diseases. One of the leading industries today that is being revolutionized by machine learning is undoubtedly the healthcare industry.
What is machine learning?
Machine learning is a concept mainly in Artificial Intelligence and thus in computer science. It is also an area that is related to, or adjacent to, computer vision and pattern recognition, among other things. Machine learning means that you teach computers to perform tasks based on data without them having to be programmed specifically. It is a way to, so to speak, train computers to solve a task. But the computer is not programmed to specifically solve that particular task, but must instead solve tasks itself based on different so-called “training data” data, mainly set up as different premises. Machine learning systems use complicated algorithms to learn from huge volumes of data. The more “training data” the algorithms have access to, the better the system learns to learn more.
Machine learning in relation to AI
Machine learning is considered part of AI. An “intelligent” computer thinks like a human being and performs activities on its own. One way to train a computer to mimic human thinking is to use a neural network, which is a series of algorithms modeled after the human brain.
The artificial intelligence allows improvements huge qualitative in the field of health, from improving early diagnosis of serious diseases such as breast cancer or melanoma , to predict kidney failure days in advance , to applications in telemedicine.
The algorithms of machine learning, mainly, are the culprits of these advances in medicine, few advances that can improve the quality of life of hundreds of thousands of patients and potentially save many lives.
Machine learning and mental health
There are many ways machine learning can help us better understand and possibly treat mental health conditions.
The human brain is most complex object in the universe and the problem with complex things is that they are difficult to solve. Billions of neurons vibrate incessantly across billions of synapses. This makes it extremely difficult to eavesdrop effectively. It also means that a lot of things can go wrong. In fact, our big brains can make us more susceptible to problems like schizophrenia and bipolar disorder .
So machine learning/AI can help us better understand the genetic, environmental, and brain structural relationships of mental health and in doing so, potentially provide us with some predictive/preventive/diagnostic tools.
First of all, artificial intelligence / machine learning can reduce existing barriers to seeking help. An example is catboats with machine learning conversational strategies as a useful first point of contact. As more and more people use these services, speaking strategies will become more refined and adaptable to individual patients.
Another treatment option that can be improved through artificial intelligence/machine learning is personalization of medical treatment. For example, not everyone responds to antidepressants. Combining brain scans with machine learning can predict who will (and won’t) respond . This can prevent people from experiencing unwanted side effects unnecessarily.
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Future of Telepsychiatry
The development and growth of the technological era demands the exploration of new opportunities in the provision of health services. Telepsychiatry is a clear example of the development in technology and constitutes a valuable contribution to the solution of specific problems, such as the provision of specialized mental health services in regions with difficult access.
Telepsychiatry will be one of the four great fields that technology will open in the future of this medical practice, at a level almost as important as the development of psychotropic drugs during the 20th century, says the study The Future of Digital Psychiatry, published in the journal Current Psychiatry Reports in August this year. According to this research, development in these areas will benefit patients in aspects such as early diagnosis and personalized treatment.
But perhaps the most important revolution that telemedicine raises for mental health has to do with the ease of support that the specialist can carry out throughout the patient’s evolution, given the ease of access that digital communication offers.
For a psychiatrist it is possible to use telepsychiatry to diagnose the conditions that most frequently affect people today, such as sleep disorders, anxiety, depression, among others; and remotely carry out precise monitoring of both the therapy and the treatment required for each case.
Although this practice has gone almost unnoticed until today in our country, already in 2014 it was stated at the World Congress of Psychiatry that this form of telemedicine allows to facilitate access to mental health for people with reduced mobility, or who are in areas of difficult access (which is particularly relevant given the geography of our territory. Telepsychiatry can also be a highly effective complement to a traditional psychiatric treatment, freeing up clinic time and reducing costs for both the patient and the doctor.
Due to cultural factors such as the lack of emotional education, or the notion that we must “be strong” in an adverse situation, many people in our country tend to ignore or underestimate their mental health problems, which, if not treated properly , end up affecting our family and work life, among other complications. Telemedicine in mental health can help overcome insecurities when going to the psychiatrist.
The great goal of the digital world is ultimately to erase the distances that limit interpersonal communication, and telepsychiatry has been able to interpret this notion and bring it into medical practice. As it is a state-of-the-art tool, current and future research will undoubtedly contribute to perfecting its use, but its use is already very promising, especially for our local reality.
Telepsychiatry must act in a complementary way to traditional psychiatry
Although telepsychiatry improves the quality of care offered to the patient, it must be used in a complementary way to traditional psychiatry. This is still essential to address the most complex cases where the professional and the patient must maintain direct contact.
Despite the great advances that telepsychiatry brings, it is still necessary to clearly determine what type of patients could benefit from this type of psychotherapy and which ones should be treated in a conventional way.
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Mental Health Parity and Addiction Act (MHPAEA)
The Mental Health Parity and Addiction Act (MHPAEA) is a federal law that requires group health plans to provide mental health and substance abuse (MH/SUD) benefits commensurate with the medical and surgical benefits offered. This legislation does not require health plans to provide mental health and substance abuse (MH/SUD) benefits, but if such benefits are provided, they must be at least as restrictive as the medical/surgical benefits of the plan.
How does MHPAEA work?
The MHPAEA was originally set up to prevent health insurance companies from imposing more treatment restrictions (such as the number of visits) or financial requirements (such as the amount of co-pay) on mental health and substance abuse services. Prior to MHPAEA, a health plan may provide an unlimited number of medically necessary appointments to a dermatologist, but it only covers five psychiatric treatments per year. When MHPAEA is in place, health care plans should cover psychiatric services in the same way as they cover other necessary health care services.
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Advanced Search Basic information on implementing MHPAEA can be found on the Centers for Medicaid and Medicare Services (CMS) websites. The premise of the law is quite simple, but several of the required acts can make the implementation of the law a little more complicated.
MHPAEA affects several aspects of health care, including financial requirements such as contributions and deductibles, and treatment restrictions such as number of visits and types of care. If the group health plan includes both MH/SUD and medical/surgical benefits, the MH/SUD benefits ‘shall not be more restrictive than the prevailing economic requirements or treatment restrictions applicable to substantially all medical/surgical benefits’. This simply means that mental health services should be subject to the same financial requirements and treatment restrictions for more than half of the medical services covered.
The following examples can help illustrate MHPAEA in practice:
- Joint Countries: If the total premium for an insurance plan is $ 25 for most of the medical services covered by the plan, the total salary for a mental health or substance abuse service should not exceed $ 25. For example, a person who pays $ 25 for an appointment with a gynecologist or primary care physician can expect to pay $ 25 for a psychiatrist.
- Deductibles: In the case of deductibles, MHPAEA requires MH/SUD services and medical/surgical services to be combined to participate in one deductible plan instead of two separate deductibles. If a person with a $ 500 deductible spends $ 200 on psychotherapy sessions and $ 200 on medical laboratory tests, both should contribute the same $ 500 deductible.
- Types of care: If the plan covers both outpatient and inpatient and surgical services, it should also cover hospital and outpatient care for MH/SUD services. A person who needs hospital treatment for heart surgery should also cover admission to a heart hospital with a hospital drug and alcohol treatment facility . In addition, the maximum number of hospital days covered by most medical/surgical hospital services should also apply to mental health and substance abuse services.
Artificial intelligence Helps Uncover Long-Term Effects of Antidepressants
The truth that has always surrounded depression is its difficult diagnosis. There has always been a fuzzy line that separates depression from what is not. According to the WHO , depression is one of the most common illnesses in the world. In fact, each year about 800,000 depressed people commit suicide and it ranks as the second leading cause of death among young people between 15 and 29 years old.
In order to combat it effectively, and as in other disorders, medical specialists establish patterns with common symptoms. But neither the symptoms nor the treatments work the same for each patient, since, as they say colloquially, each person is different. Faced with this complex situation, Artificial Intelligence appears with the challenge of personalizing both the diagnoses and the treatments of patients. Technological innovation is put at the service of our mental health.
Artificial intelligence can help to know if brain activity influences the effect of antidepressants. The opportunities offered by Artificial Intelligence applied to ML are encouraging even when it comes to predicting the probability of suffering a depression in the future. A study published in Psychiatry Research has shown that it is possible to identify which patients would be depressed through images obtained by magnetic resonance, and their subsequent analysis using machines subject to machine learning. Discovering the differences between the two groups also determined, with 75% accuracy, the severity of depression among patients.
But as we anticipated before, AI is not only capable of predicting whether or not you are going to have depression, but it also detects this disorder even in the most invisible cases. In this context, researchers from the Weill Cornell School of Medicine (USA) used another technique based on machine learning, in this case applied to the collection of data obtained from a brain at rest.. The specialists were able to analyze the data on the state of the brain of the 52 patients who suffered from depression, compared with the accumulated information on 42 healthy patients. In fact, they have reached another level. After the symptom detection phase, scientists have been able to classify depression under four subtypes linked to factors such as anxiety, stress and lack of pleasure. From BlogThinkBig.com we also echo the importance of Big Data with psychiatry. Because depression is a very unstable disease, the collection of data almost to the minute allows knowing the mood of the affected person at all times and thus improving their therapy. The social network Facebook has recently affirmed that it will be able to do something similar with the data of its users to combat depression, although it questions whether its intervention will really be effective.
The first step was already taken. Thanks to technology, even the most hidden depression has been detected. But now it is worth asking, what is the most appropriate and effective treatment for each patient? The Emory University in the US has conducted another study that also uses the analysis of magnetic resonance imaging to establish patterns of brain activity when the patient is in treatment. On the patterning side, New York University published a recent study, which in this case uses vocal patterns among potential depressed patients. And, there is no doubt that ICTs are getting closer to effectively fighting this disorder, not only detecting it but also treating those who suffer from it. Within this area of rehabilitation, research has also been developed so that depression in a patient does not reach the extreme of self-harm or end his life. For this, the University of Florida has built software with Machine Learning technology that analyzes the medical histories of depressive patients and establishes a probability of suicidal thoughts.
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