After WHO already made a prognosis in 2017 that depression would become the first world-wide reason for work disability by 2030, the pandemic happened, causing economic changes and a sense of instability in many people’s realities. That probably contributed to a surge in numbers of many psychiatric diagnoses and due to pandemic's rules and restrictions many realized that mental health care became increasingly hard to access, as waiting lists increased. This change in dynamics called out for innovative approaches to augment the efficacy of clinicians and help patients to facilitate easy access to care. To enable this, many contributions coming from technical sciences were aimed at objective-based detection of biomarkers extracted from electrophysiological signals such as EEG and ECG. With the higher acceptance rates for many Telehealth & IoT solutions, we would expect that already demonstrated methods of detection and possible remote patient monitoring (RPM) solutions would be introduced and translated to everyday clinical practice. On top of that many AI applications were shown to be helpful in psychiatry, yielding computational psychiatry solutions. As telehealth has shown in this crisis, in many cases it is not inevitable for patients to physically visit their dedicated specialist; naturally, we expected that solution to be adopted in psychiatry also.
But contrary to many existing and validated telehealth solutions we see zero acceptance rate in psychiatry. So why is that?
Of course, we need to discuss and better understand the reason for this such a low acceptance. We are aware of many problems like privacy and protection of the data, but electrophysiological recordings are anonymized and are GDPR compliant. There is a lot of published research showing that clinicians can better inform their future decisions on treatment management of depression and increasingly recognize comorbidities more easily, as well as prevent some adverse developments. For example, one such risk factor, known to be frequently present among patients diagnosed with depression, is the risk of cardiovascular diseases, that could be screened early and not only prolong the lifespan of a patient but also help in better navigation of the overall therapy cycle. Some researchers showed that not only EEG analysis can help improve accurate diagnostics but also detect the responders to certain kinds of therapy (such as ECG, rTMS, or tDCS). Cardio-vagal control is so well understood and explored (based on an impressive amount of published literature) that it is fascinating that clinicians are not using it in their practice to take better care of their patients. Part of that technical development is due to enormous rise of various AI methods, that are already in use in many other areas, but in medicine the average acceptance time for any innovation is 17 years. It is understandable that any innovation in medicine should be rigorously examined before accepted, but in electrophysiology, which is basically the oldest among imaging methods (and a low-cost non-invasive methodology), this would be just a re-use of already heavily examined practices adding just another layer of analytics proven to be accurate.
With this Research Topic, we would like to ignite a discussion about the conditions needed, concerns raised and reasons for both acceptance and refusal of innovative approaches in clinical treatments of depression and other psychiatric illnesses (mood disorders).
Some of the areas of interest for this Research Topic are the Internet of Things, Mobile Health, Telemedicine, Artificial Intelligence, Wearable devices, Health Informatics, Acceptance of innovations translation, Augmentation of clinical practice in psychiatry.
We invite researchers/contributors from different fields to send papers on their recent innovative approaches to augment the clinical diagnostic, treatment and monitoring of patients with mental health issues, including use cases, decision support solutions, experiences (both positive and negative) with current innovation projects or prior experience of unsuccessful practices considered to be innovative.
Submissions are welcome for the following article types: original research, review, mini-reviews, innovative research protocol/method, opinion and hypothesis.
We particularly welcome contributions that include, but are not limited to, the following topics:
1. Advanced algorithms for non-invasive electrophysiological assessment in psychiatry
2. Novel sensor system development
3. Simulation and modeling strategies for cognitive assessment based on electrophysiology
4. Novel biomarkers used for depression detection
5. Telehealth & IoT solutions
6. Remote patient monitoring in psychiatry
After WHO already made a prognosis in 2017 that depression would become the first world-wide reason for work disability by 2030, the pandemic happened, causing economic changes and a sense of instability in many people’s realities. That probably contributed to a surge in numbers of many psychiatric diagnoses and due to pandemic's rules and restrictions many realized that mental health care became increasingly hard to access, as waiting lists increased. This change in dynamics called out for innovative approaches to augment the efficacy of clinicians and help patients to facilitate easy access to care. To enable this, many contributions coming from technical sciences were aimed at objective-based detection of biomarkers extracted from electrophysiological signals such as EEG and ECG. With the higher acceptance rates for many Telehealth & IoT solutions, we would expect that already demonstrated methods of detection and possible remote patient monitoring (RPM) solutions would be introduced and translated to everyday clinical practice. On top of that many AI applications were shown to be helpful in psychiatry, yielding computational psychiatry solutions. As telehealth has shown in this crisis, in many cases it is not inevitable for patients to physically visit their dedicated specialist; naturally, we expected that solution to be adopted in psychiatry also.
But contrary to many existing and validated telehealth solutions we see zero acceptance rate in psychiatry. So why is that?
Of course, we need to discuss and better understand the reason for this such a low acceptance. We are aware of many problems like privacy and protection of the data, but electrophysiological recordings are anonymized and are GDPR compliant. There is a lot of published research showing that clinicians can better inform their future decisions on treatment management of depression and increasingly recognize comorbidities more easily, as well as prevent some adverse developments. For example, one such risk factor, known to be frequently present among patients diagnosed with depression, is the risk of cardiovascular diseases, that could be screened early and not only prolong the lifespan of a patient but also help in better navigation of the overall therapy cycle. Some researchers showed that not only EEG analysis can help improve accurate diagnostics but also detect the responders to certain kinds of therapy (such as ECG, rTMS, or tDCS). Cardio-vagal control is so well understood and explored (based on an impressive amount of published literature) that it is fascinating that clinicians are not using it in their practice to take better care of their patients. Part of that technical development is due to enormous rise of various AI methods, that are already in use in many other areas, but in medicine the average acceptance time for any innovation is 17 years. It is understandable that any innovation in medicine should be rigorously examined before accepted, but in electrophysiology, which is basically the oldest among imaging methods (and a low-cost non-invasive methodology), this would be just a re-use of already heavily examined practices adding just another layer of analytics proven to be accurate.
With this Research Topic, we would like to ignite a discussion about the conditions needed, concerns raised and reasons for both acceptance and refusal of innovative approaches in clinical treatments of depression and other psychiatric illnesses (mood disorders).
Some of the areas of interest for this Research Topic are the Internet of Things, Mobile Health, Telemedicine, Artificial Intelligence, Wearable devices, Health Informatics, Acceptance of innovations translation, Augmentation of clinical practice in psychiatry.
We invite researchers/contributors from different fields to send papers on their recent innovative approaches to augment the clinical diagnostic, treatment and monitoring of patients with mental health issues, including use cases, decision support solutions, experiences (both positive and negative) with current innovation projects or prior experience of unsuccessful practices considered to be innovative.
Submissions are welcome for the following article types: original research, review, mini-reviews, innovative research protocol/method, opinion and hypothesis.
We particularly welcome contributions that include, but are not limited to, the following topics:
1. Advanced algorithms for non-invasive electrophysiological assessment in psychiatry
2. Novel sensor system development
3. Simulation and modeling strategies for cognitive assessment based on electrophysiology
4. Novel biomarkers used for depression detection
5. Telehealth & IoT solutions
6. Remote patient monitoring in psychiatry
