Keywords
Digital Health - Stroke
Palavras-chave
Saúde Digital - Acidente Vascular Cerebral
INTRODUCTION
Digital health has emerged as a transformative force in managing stroke, a leading
cause of morbidity and mortality worldwide—especially when we consider remote and
low- and middle-income areas.[1] By leveraging advances in mobile and wireless technologies, big data analytics,
and artificial intelligence (AI), digital health can potentially improve stroke prevention,
diagnosis, treatment, and rehabilitation.[1]
[2] Over the past decade, several landmark trials have demonstrated the efficacy and
safety of digital health interventions in stroke care.[1]
[2]
Digital health has the potential to revolutionize stroke care and improve patient
outcomes. One of the most promising digital health applications in stroke care is
telemedicine, which enables remote consultation and diagnosis by stroke specialists.[3] Telestroke programs have been shown to reduce treatment delays, improve clinical
outcomes, and increase access to expert stroke care, especially in underserved areas.[3]
A major challenge in the adoption of these digital health solutions is the lack of
formal clinical validation for many of these applications. Despite the rapid development
and deployment of digital health tools in the stroke field, there is a significant
gap in the rigorous assessment of their efficacy and safety. This shortfall raises
concerns about the reliability and effectiveness of these technologies in real-world
clinical settings, where the stakes are high. The absence of robust validation processes
means that the impact of these digital tools on critical clinical outcomes such as
survival rates, functional independence, and quality of life for stroke patients remains
unclear.
This lack of clarity regarding the clinical outcomes associated with digital health
applications in stroke care underscores the necessity for a more structured and evidence-based
approach to their development and implementation. It is essential to bridge the gap
between technological innovation and clinical validation to ensure that these digital
solutions can truly benefit patients and healthcare providers. The role of digital
literacy becomes paramount in this context, as it empowers both clinicians and patients
to navigate, evaluate, and effectively integrate digital health technologies into
routine stroke care practices.
Our main objective is to provide a comprehensive narrative review of the current digital
health applications available in the field of vascular neurology. By systematically
evaluating the existing digital tools, their development processes, clinical validations,
and real-world applications, we aim to identify the gaps in current practices and
discuss the integration of digital health technologies in stroke care.
METHODS
In this narrative review, we meticulously explored the role of clinically validated
digital health applications in stroke patients, focusing on the integration and impact
of these technologies in clinical settings. Our methodology was structured to encompass
a comprehensive search and analysis of both review articles and original research
that discuss digital health solutions and stroke.
Search strategy
To identify relevant literature, we employed a combination of keywords and medical
subject headings (MeSH) terms tailored to capture the breadth of digital health applications
pertinent to stroke management. Our search descriptors included digital applications, automated algorithms, clinical decision support platforms, web-based platforms, artificial intelligence, and mobile applications, paired consistently with the fixed term stroke. These terms were used to search databases for articles published in English from
January 2015 to January 2024, ensuring the inclusion of the most recent and pertinent
studies in the field.
Study selection
The inclusion criteria were set to select peer-reviewed original research articles
and comprehensive review papers that specifically addressed the development, validation,
implementation, and impact of digital health technologies in the context of stroke.
Exclusion criteria were applied to omit studies that did not focus on clinically validated
tools, were not available in full text, or were outside the specified publication
date range.
Quality assessment
To ensure the credibility and relevance of the included studies, a rigorous quality
assessment was conducted. This evaluation was based on predefined criteria that considered
the study design, methodology, sample size, bias, and the strength of findings. Each
study was independently reviewed by two members of the research team, with discrepancies
resolved through discussion or consultation with a third reviewer.
Data synthesis
Data extracted from the selected studies were synthesized to highlight key findings,
technological innovations, clinical outcomes, and the overall impact of digital health
applications on stroke management and patient care. A narrative synthesis approach
was adopted to accommodate the diverse nature of the studies.
Visualization tools
To effectively summarize and present the vast array of digital applications identified
in our review, we developed a detailed figure and a mind map. These visual tools were
designed to categorize the applications based on their functionality, target user
(healthcare professionals versus patients), and the aspect of stroke care they address
(prevention, acute management, rehabilitation).
Ethical considerations
Throughout the review process, ethical considerations were meticulously observed.
The study was conducted in accordance with the ethical standards of the Declaration
of Helsinki and was exempt from institutional review board approval due to its nature
as a secondary analysis of publicly available data. Care was taken to ensure the confidentiality
and anonymity of the data extracted from the studies included.
In our endeavor to enhance the efficiency and accuracy of our work, we have adopted
the ChatGPT 4.0 generative AI tool (OpenAI, San Francisco, CA, USA), leveraging its
capabilities for tasks such as translating from Portuguese to English, reviewing grammar
mistakes, making textual adaptations, and generating images for the creation of mind
maps through the ChatMind tool (Xmind Ltd., Hong Kong, China). This decision was made
with careful consideration of the ethical implications associated with the use of
advanced AI technologies. We ensure that all data processed through ChatGPT 4.0 and
ChatMind are handled with strict confidentiality and in compliance with applicable
data protection laws. We do not use these AI tools to process sensitive or personal
information without appropriate consent and safeguards.
DIGITAL HEALTH APPLICATIONS
DIGITAL HEALTH APPLICATIONS
Mobile health technologies such as smartphone applications, wearables, and sensors
offer exciting opportunities for remote monitoring of patient's vital signs, medication
adherence, and rehabilitation progress, enabling personalized and proactive management
of stroke patients.[4] These applications have been developed to enhance diagnosis, treatment, and management
of stroke patients, leading to better patient outcomes. Studies have shown that the
use of mobile applications in stroke care has led to improved clinical outcomes, reduced
treatment time, and improved patient satisfaction.[4] Some of these apps use AI algorithms to analyze symptoms and medical records to
identify patients who are at high risk of stroke—and even differentiate those who
may benefit from specific reperfusion therapies. These applications help healthcare
providers quickly assess the patient's condition and initiate treatment, improving
the their chances of a successful outcome.[1]
[4]
[5]
Machine learning algorithms can help analyze large datasets of medical images, patient
records, and clinical trials, leading to more accurate diagnoses, personalized treatment
plans, and more efficient drug discovery, incorporated to complex platforms and even
medical devices (such as monitor vital signs, tomography machines and others).[4]
[5] Still, it is critical to ensure these technologies are validated, safe, and accessible
to all stroke patients, regardless of their socioeconomic status or geographical location.[1]
In the field of vascular neurology, digital health modalities have diversified, offering
a broad spectrum of technologies aimed at enhancing stroke care and rehabilitation.
Telestroke services have become a cornerstone, enabling remote consultations and assessments
through video conferencing, which is pivotal in acute stroke management, in which
time is of the essence. This modality allows for rapid decision-making, often facilitating
timely interventions such as thrombolysis or thrombectomy. Furthermore, mobile health
applications and wearable devices are increasingly utilized for patient monitoring
and management, offering features such as medication reminders, lifestyle modification
tips, and real-time monitoring of vital signs. These tools support continuous patient
engagement and self-management, which are crucial for secondary stroke prevention
and rehabilitation.
Beyond telemedicine and mobile health, advanced digital platforms incorporating AI
are emerging within the vascular neurology landscape. Artificial intelligence-driven
algorithms are being developed to enhance diagnostic accuracy by interpreting imaging
studies, predicting stroke risk, and personalizing rehabilitation protocols based
on patient-specific data. Virtual reality (VR) and augmented reality (AR) technologies
are also being explored for their potential in stroke rehabilitation, providing immersive,
adaptive environments for patients to engage in therapeutic exercises. These modalities
aim to improve motor skills, cognitive function, and overall recovery outcomes by
simulating real-life activities and feedback in a controlled setting. Collectively,
these digital health modalities are reshaping the approach to stroke care, offering
innovative solutions to traditional challenges in diagnosis, treatment, and rehabilitation
in vascular neurology.
In [Figure 1], we summarize many of these modalities of technologies in digital health. This figure
illustrates the diverse range of technological modalities utilized in the digital
health landscape. It categorizes these technologies based on their application areas,
including telehealth, mobile health apps, wearable devices, electronic health records,
and AI in healthcare. Each category is represented with examples and icons that reflect
their use in clinical settings, patient monitoring, data management, and decision
support systems.
Abbreviation: AI, artificial intelligence.
Figure 1 Modalities of technologies in digital health.
CURRENT EXAMPLES OF DIGITAL HEALTH INTERVENTION AND IMPLEMENTATION IN STROKE
CURRENT EXAMPLES OF DIGITAL HEALTH INTERVENTION AND IMPLEMENTATION IN STROKE
Digital health solutions are available to support both healthcare professionals and
patients throughout all stages of stroke. These solutions have been designed to provide
access to necessary treatments and therapies, such as intravenous thrombolysis and
thrombectomy in acute settings and rehabilitation in poststroke care.[2] Additionally, various applications are available to monitor vital signs and encourage
patients to adhere to their prescribed treatments.
In the field of secondary prevention, for example, a very interesting initiative for
improving the adherence to oral anticoagulants has been published in the literature.[6] According to the authors, the app/intervention adopts reminders, educational material,
and monitoring features to help patients manage their medication regimen and reduce
the risk of stroke and other complications associated with atrial fibrillation. The
app sends reminders to patients to take their medication, provides educational material
about the importance of medication adherence, and allows patients to track their international
normalized ratio (INR) levels, providing feedback and guidance to improve adherence.
The study concludes that 72% of the patients in the low adherence group moved to either
the medium or high adherence group.[6] The use of a smartphone app can significantly improve oral anticoagulation adherence
in patients with atrial fibrillation and reduce the risk of stroke and other complications.
The results suggest that mobile health technologies could play a valuable role in
improving medication adherence and disease management in patients with chronic conditions.
Telemedicine enables patients to connect with healthcare professionals remotely, providing
timely access to medical consultations and enabling doctors to assess stroke patients'
conditions quickly.[1]
[7]
[8] The Stroke Telemedicine Outcomes Project (STOP) showed that telemedicine consultations
could effectively reduce the time to treatment with thrombolytic therapy in patients
with acute ischemic stroke.[9] The study involved 12 hospitals across California and demonstrated that telestroke
consultations were associated with significantly higher rates of thrombolytic therapy
administration within the recommended time frame compared with standard care.[9] This and other trials paved the way for the widespread adoption of telestroke programs,
which have improved access to expert stroke care, reduced treatment delays, and improved
clinical outcomes.[1]
[7]
[8]
[9]
Telemedicine also allowed the incorporation of mobile stroke units (MSUs) in our clinical
practice. Even restricted to a small number of areas, these vehicles are equipped
with diagnostic and treatment equipment for stroke patients. These units have become
increasingly important in improving clinical outcomes by providing faster and more
efficient stroke care.[10] The MSUs can be deployed to remote or underserved areas, where there may be limited
access to stroke specialists or stroke centers, thereby reducing the time to treatment
and improving patient outcomes.[10] The use of MSUs enables patients to receive timely diagnosis and treatment, which
may be crucial in the stroke care. With the availability of computed tomography (CT)
scanners, laboratory testing equipment and telemedicine with an experienced neurologist
onboard, MSUs can provide immediate diagnostic tests, and stroke treatment can be
initiated in the prehospital setting, even before patients arrive at the hospital.
This rapid response time reduces the time to treatment and leads to better clinical
outcomes, including reduced disability and mortality rates.[11] In addition, MSUs can facilitate the transfer of patients to stroke centers, where
advanced stroke care can be provided, ensuring that patients receive the best possible
care.
Wearable technology has been used to monitor patients' vital signs and physical activity,
enabling healthcare providers to quickly detect and respond to potential strokes.
Remote monitoring allows healthcare providers to monitor patients' vital signs, such
as blood pressure, heart rate, and oxygen saturation, from a distance. Mobile applications
provide patients with access to rehabilitation exercises, medication reminders, and
self-monitoring tools to track their progress. In a large review published in 2022
in The Lancet Digital Health, telemedicine consultations and remote monitoring using
wearable technology improved medication adherence, blood pressure control, and health-related
quality of life in cardiovascular patients.[12] Similar findings have been published in the literature.[13] In 2018, Apple launched a study to investigate whether the heart rate sensor on
its Apple Watch (Apple Inc., Cupertino, CA, USA) could detect atrial fibrillation
(AFib). The study involved over 400,000 participants and used an algorithm to analyze
heart rate data for irregularities suggestive of AFib.[14] The results showed that the Apple Watch had a high degree of accuracy in detecting
AFib, with a sensitivity of 71% and a specificity of 84%.[14] The study demonstrated the potential of wearable technology, such as the Apple Watch,
to help screen for AFib and provide early detection of the condition, leading to earlier
intervention and improved patient outcomes.[14] As a result of the study, Apple received U.S. Food and Drug Administration (FDA)
clearance for its electrocardiogram (ECG) app, which allows users to take an ECG directly
from their Apple Watch and detect AFib. In a comparison of many wearables, including
some from famous brands such as Apple and Samsung (Samsung Group, Swon-si, South Korea),
the authors stated a high accuracy of wearables in identifying suspected rhythms of
AFib.[15]
Automated neuroimaging interpretation has emerged as a promising tool for diagnosing
and treating acute stroke. Neuroimaging, such as CT and magnetic resonance imaging
(MRI), is critical to stroke diagnosis and management, providing detailed images of
the brain and blood vessels. However, the interpretation of these images can be time-consuming
and require specialized expertise. Automated neuroimaging interpretation tools use
AI algorithms to analyze imaging data and identify potential stroke lesions, improving
the accuracy and speed of diagnosis. On the other hand, this kind of technology is
still expensive. Several studies have evaluated the effectiveness of automated neuroimaging
interpretation in acute stroke, with promising results.[16] A study conducted by Straka et al.[16] showed that an automated software algorithm had a sensitivity of 92% and a specificity
of 99% in detecting large vessel occlusion on CT angiography scans. The use of automated
neuroimaging interpretation led to a significant reduction in time from symptom onset
to treatment, resulting in improved outcomes for patients with acute stroke. A study[17] published in 2023 introduces an end-to-end learning approach for the automatic determination
of collateral scores from CT angiography (CTA) images, which are crucial for treatment
decision-making in acute stroke patients. The method involves preprocessing the CTA
image to align it with an atlas and dividing it into affected and healthy hemispheres,
followed by feature extraction using a VoxResNet-based convolutional neural network
within a Siamese model framework. The findings suggest that end-to-end learning for
collateral scoring is feasible, performs comparably to traditional methods, and can
be integrated into existing functional outcome prediction models, indicating its potential
utility in clinical settings.[17]
Automated neuroimaging interpretation can potentially improve stroke diagnosis and
treatment, enabling earlier intervention and better patient outcomes.[18] As automated algorithms continue to develop and then may become more accurate, their
use in acute stroke care is likely to become more widespread, allowing for even faster
diagnoses and treatments, and ultimately improving the lives of stroke patients.[18]
[19] Implementing these resources has many challenges, including the need for large datasets
and the potential for bias in algorithm development.[19] Collaborations between radiologists, stroke specialists, and computer scientists
can help overcome these challenges and accelerate the development and implementation
of AI in acute stroke care.[19]
Mobile applications and virtual reality-based telerehabilitation programs have been
used to deliver personalized rehabilitation programs to stroke patients, improving
their recovery outcomes. A randomized controlled trial published[20] in JAMA Neurology, in 2019, found that a virtual reality-based telerehabilitation program significantly
improved upper limb motor function in stroke patients.[20] A study by Maier et al.[21] explored the effectiveness of a mobile app-based telerehabilitation program for
poststroke patients. The results showed significant improvements in motor function,
balance, and quality of life compared with traditional in-person therapy.[21]
Virtual reality technology has gained importance in telerehabilitation as it provides
an immersive and interactive experience for patients.[22] A virtual reality-based telerehabilitation utilizes headsets or motion-sensing devices
to create virtual environments that simulate real-world scenarios, engaging patients
in therapeutic exercises.[22] Research conducted by Perez-Marcos et al.[22] demonstrated the potential of this rehabilitation modality in stroke patients. The
study reported significant improvements in upper limb motor function and cognitive
performance among stroke patients who underwent virtual reality-based telerehabilitation.
By simulating functional tasks and challenging environments, virtual reality promotes
motor learning and neuroplasticity, enhancing rehabilitation outcomes.[22]
Electronic health records (EHR) and data analytics have been used to collect and analyze
stroke patient data, enabling healthcare providers to identify patterns and trends
and improve patient outcomes.[23] The study by Yang et al.[24] developed an automated method to extract NIHSS (National Institutes of Health Stroke
Scale) scores from EHR to aid in stroke-related clinical investigations. Utilizing
a two-step pipeline approach and the MIMIC-III database, the method showed high accuracy,
outperforming traditional rule-based methods. This approach facilitated the retrieval
of structured scale data for clinical research in real-world settings.[24] Another study by Fonarow et al. (2010)[25] found that EHR adoption was associated with lower in-hospital mortality rates for
stroke patients. The authors[25] describe a large-scale analysis from the Get With The Guidelines (GWTG)-Stroke program,
which covered 1,000,000 patient admissions for various types of stroke and transient
ischemic attack (TIA) from 1,392 U.S. hospitals between 2003 and 2009. The study[25] found notable variations in in-hospital mortality rates across different types of
cerebrovascular events, with the highest mortality associated with intracerebral hemorrhage
(25.0%) and subarachnoid hemorrhage (20.4%), followed by ischemic lowest stroke (5.5%),
and the in TIA patients (0.3%). The study[25] also noted improvements in the length of hospital stays and a reduction in risk-adjusted
in-hospital mortality for ischemic stroke and TIA patients. The clinical impact of
these findings is significant, demonstrating that systematic quality improvement programs
like GWTG-Stroke can lead to substantial enhancements in the care and outcomes of
stroke and TIA patients.
Usually incorporated into EHR resources, the data analytics is a powerful tool in
stroke care that allows for the analysis of large datasets to identify patterns and
trends that can improve stroke care outcomes. Data analytics can be used to predict
stroke risk, facilitate early diagnosis, and monitor treatment response. For instance,
a study by Amarasingham et al.[26] showed that the use of data analytics helped reduce emergency department boarding
times for stroke patients by 19%. Data analytics also provide insights into stroke
care quality metrics, enabling healthcare providers to identify areas for improvement
and implement evidence-based interventions to enhance care delivery.[26]
[27]
In [Figure 2], we summarize digital health applications and their adoption in stroke field.
Note: Image generated with ChatMind.
Figure 2 Mental map summarizing the role of digital health in vascular neurology.
CURRENT CHALLENGES PREVENTING DIGITAL HEALTH INTERVENTION IN STROKE
CURRENT CHALLENGES PREVENTING DIGITAL HEALTH INTERVENTION IN STROKE
While there is great potential for digital health interventions in stroke medicine,
several challenges must be addressed. One of the biggest challenges is limited access
to technology, particularly among underserved and rural populations.[28] Digital health interventions require access to devices such as smartphones, computers,
wearables, and internet connectivity. However, many stroke patients, particularly
those from low-income and rural communities, may not have access to these resources,
limiting the reach of digital health interventions.[28]
[29] These facts are related to the concept of “digital exclusion.” Digital exclusion
in health refers to the disparity experienced by certain individuals or groups in
accessing and benefiting from digital health technologies and services.[28]
[29] This phenomenon is often driven by factors such as socioeconomic status, age, literacy,
geographic location, and disability, leading to a significant portion of the population
being inadvertently marginalized in the rapidly evolving digital healthcare landscape.
The implications of digital exclusion are profound, as it not only widens the health
equity gap but also undermines the potential of digital health innovations to deliver
universal and personalized care. Without concerted efforts to address digital exclusion,
advancements in healthcare technology risk reinforcing existing disparities, thereby
compromising the overarching goal of improving public health outcomes and ensuring
that the benefits of digital health are accessible to all, irrespective of their background
or circumstances.[29]
Another challenge is ensuring the privacy and security of patient data. Digital health
interventions rely on collecting and sharing patient data, including personal health
information. However, ensuring the privacy and security of this data are critical
to maintaining patient trust and preventing data breaches. Robust security measures,
such as encryption and two-factor authentication, must be in place to protect patient
data and comply with data protection regulations. In Brazil, for example, many EHR
platforms were obliged to incorporate security features for dealing with, storing,
and sharing sensitive data after the approval of a national law on the protection
of personal data.[30] Moreover, challenges about the adoption of AI include issues around data privacy
and security, ethical concerns around the use of this resource in medical decision-making,
and the need for regulatory frameworks to ensure the safe and effective use of AI
in healthcare. As AI technology continues to advance, it will be important to address
these challenges to ensure that AI can be effectively integrated into stroke care
and improve patient outcomes.[31]
A third challenge is integrating digital health interventions with clinical workflows.
Digital health interventions must be seamlessly integrated into existing clinical
workflows to be effective.[32] However, this can be challenging due to the complexity of healthcare systems and
the need for interoperability between different technologies. There is a need for
greater collaboration between healthcare providers, technology companies, and policymakers
to establish standards for interoperability and promote the seamless integration of
digital health interventions into clinical practice.[32]
Addressing these challenges will be critical to achieve the full potential of digital
health interventions in stroke medicine. Efforts to increase access to technology,
ensure data privacy and security, and promote integration with clinical workflows
will be essential to expanding the reach and impact of digital health interventions
and improving outcomes for stroke patients.
FUTURE DIRECTIONS WITH AN EMPHASIS ON HOW DIGITAL HEALTH INTERVENTION CAN HELP SOLVE
SPECIFIC PROBLEMS IN STROKE CARE
FUTURE DIRECTIONS WITH AN EMPHASIS ON HOW DIGITAL HEALTH INTERVENTION CAN HELP SOLVE
SPECIFIC PROBLEMS IN STROKE CARE
Digital health interventions can potentially address several specific problems in
stroke care, leading to improved patient outcomes. Early detection and intervention
are critical in stroke care, as timely treatment can reduce disability and improve
outcomes. Digital health interventions, such as wearable devices and mobile applications,
can help detect and monitor stroke risk factors, such as high blood pressure and irregular
heartbeat. These interventions can also provide real-time feedback to patients and
healthcare providers, enabling earlier intervention and preventing stroke.
We strongly believe the AI has the potential to revolutionize stroke care by improving
efficiency, accuracy, and outcomes. In the future, as we said, AI could be used to
develop predictive models that identify patients at high risk of stroke and personalize
their treatment plans. Artificial intelligence could also be used to interpret complex
neuroimaging data and provide real-time diagnostic support to stroke care teams.[33]
Stroke can cause long-term disability and impairments, requiring extensive rehabilitation
and support. Digital health interventions, such as virtual reality and telerehabilitation,
can provide innovative stroke rehabilitation and recovery solutions. Virtual reality
can simulate real-life scenarios to help patients practice and regain motor skills.
At the same time, telerehabilitation can provide remote access to rehabilitation services,
increasing access to care for underserved populations.[33]
Patient education and empowerment are critical in stroke prevention and management.
Digital health interventions can provide patients with easy access to educational
materials, such as videos and interactive tools, to help them understand their condition
and take an active role in their care. These interventions can also provide real-time
feedback to patients, enabling them to track their progress and make informed decisions
about their health.[34]
Regarding the adoption of complex algorithms for assessing diagnoses, making predictions
or prognostications,[24]
[25] or performing neuroimaging diagnoses,[16]
[17]
[18]
[19] the realm of AI applications demands meticulous selection and preprocessing of variables
to ensure the robustness and reliability of the resulting algorithms.[24] This process typically involves a series of methodologically rigorous steps designed
to enhance the predictive accuracy and generalizability of AI models.[27]
[31] The initial phase in the development of a healthcare AI application involves the
careful selection of relevant variables. This selection is driven by the specific
clinical question or healthcare problem being addressed. Variables are chosen based
on their proven or hypothesized relevance to the outcomes of interest, informed by
existing clinical knowledge and prior research findings.[31] This step is crucial, as the inclusion of irrelevant or redundant variables can
lead to model overfitting and decreased generalizability. After the AI model is developed,
it is imperative to validate its performance using an independent cohort that was
not involved in the model training phase. This validation is critical for assessing
the model's ability to generalize to new, unseen data, which is a hallmark of a robust
AI application. Validation involves comparing the model's predictions against actual
outcomes using performance metrics such as accuracy, sensitivity, specificity, and
area under the receiver operating characteristic curve (AUC-ROC). The independent
cohort should ideally be diverse and representative of the population where the AI
application is intended to be used. This approach not only affirms the efficacy of
the model across different subgroups but also identifies potential limitations or
biases in the model.[31]
Future directions in digital health interventions for stroke care are poised to address
some of the most pressing challenges in the field, including the need for timely diagnosis,
personalized rehabilitation programs, and ongoing patient support outside the clinical
setting.[33]
[34]
[35] Advancements in AI and data analytics hold the potential to revolutionize stroke
diagnosis by enabling the rapid interpretation of imaging studies, which is crucial
for initiating time-sensitive treatments such as thrombolysis. Moreover, digital platforms
can facilitate tailored rehabilitation programs by leveraging virtual reality and
gamification, thereby enhancing patient engagement and adherence to treatment plans.[36] These interventions can also support continuous monitoring and adjustment of treatment
regimens based on real-time patient data, potentially leading to improved outcomes
in terms of recovery speed and quality of life poststroke.
However, the integration of digital health technologies in stroke care is not without
its challenges, particularly in the realms of regulation and ethics.[35]
[36]
[37] Ensuring the privacy and security of patient data are paramount, as these technologies
often involve the collection and analysis of sensitive health information. Regulatory
frameworks must evolve to keep pace with technological advancements, ensuring that
digital health interventions are not only effective but also safe and compliant with
data protection laws.[38]
[39]
[40]
[41] Additionally, ethical considerations related to patient autonomy, informed consent,
and equity in access to these technologies must be addressed to prevent disparities
in care. Addressing these regulatory and ethical challenges is essential for building
trust in digital health solutions and ensuring their sustainable integration into
stroke care practices.[42]
[43]
[44]
[45]
In conclusion, digital health is set to transform stroke care by enhancing prevention,
diagnosis, treatment, and rehabilitation. Telestroke programs are reducing treatment
times, boosting clinical outcomes, and broadening access to specialized care. Meanwhile,
wearable tech and apps offer tailored rehab programs and remote monitoring, enhancing
recovery and adherence to treatment. Innovations in automated neuroimaging tools are
also speeding up and refining stroke diagnoses, leading to earlier interventions.
Ensuring these technologies are validated, safe, and universally accessible is crucial
for benefiting all stroke patients, irrespective of their background or location.
The future of digital health interventions in stroke care is incredibly promising,
with innovations aimed at enhancing diagnosis, personalized rehabilitation, and patient
support. Technologies like machine learning are set to transform the speed and accuracy
of stroke diagnosis, crucial for timely treatments. Digital platforms, through virtual
reality and gamification, are expected to make rehabilitation programs more engaging,
improving adherence and potentially speeding up recovery. However, the successful
integration of these technologies must navigate regulatory and ethical hurdles, ensuring
data privacy, security, and equitable access to prevent care disparities.
Bibliographical Record
Gisele Sampaio Silva, João Brainer Clares de Andrade. Digital health in stroke: a
narrative review. Arq Neuropsiquiatr 2024; 82: s00441789201.
DOI: 10.1055/s-0044-1789201
