Telemedicine capabilities expanded through artificial intelligence

This post is a quick overview of an Abto Software’s blog article.

Although the COVID-19 pandemic and the coronavirus-related restrictions have influenced healthcare delivery, telemedicine services have gained their popularity even before social distancing and lockdowns.

Given recent healthcare challenges, most organizations and individuals moved towards adopting digitalization. Telemedicine implementation became broader, more user-friendly and engaging, and, what’s most important, more accurate and secure – a dream made reality by applying artificial intelligence.

Market opportunities

Having transformed healthcare perception and accessibility during the coronavirus outburst and quarantine, telehealth and telemedicine technologies became essential to future-proof healthcare-related businesses.

Healthcare facilities and patients now enjoy greater convenience, transparency, confidentiality, and more. Improved communication and collaboration, enhanced coordination, and straightforward data management are just some benefits that come with adopting advanced technology.

Telemedicine explained

Telemedicine transforms common approaches to administration and coordination among many other aspects. Computational technology enables messaging, convenient audio- and videoconferencing, as well as integration with personal medical devices and sensors.

Telemedicine helps healthcare professionals to provide patient-centric services without overload and burnout. These include condition assessment, precise diagnosis and treatment, progress tracking, timely adjustments, and other healthcare-related services.

Back to the topic, how can artificial intelligence expand conventional healthcare capabilities?

Let’s dive deeper into computer intelligence and the additional opportunities it provides.

Artificial intelligence to complement telemedicine platforms

Remote patient monitoring (RPM)

AI-based systems can help to track health indicators – heart rate, blood pressure, oxygen saturation, and more. This way, healthcare providers can analyze and interpret patient information to perform timely interventions, thus improving customer satisfaction and loyalty.

Remote patient monitoring enables:

• Real-time data collection
• Automated data analysis
• Remote tracking
• Personalized recommendations and programs, and more

Natural language processing (NLP)

AI-driven solutions can transcribe patient information – health-related symptoms, and other relevant details. This way, healthcare professionals can maintain consistent documentation and eliminate potential errors, simultaneously enjoying informed decision-making.

Natural language processing enabled:

• Record summarization to review relevant information
• Sentiment analysis to assess patient satisfaction
• Inquiry classification
• Code identification

Medical imaging

Deep learning can be successfully implemented to detect and classify abnormal patterns, including tumors, fractures, lesions, and other common conditions, which cannot be recognized by the naked eye.

Medical imaging, if empowered by trained DL algorithms, can accelerate:

• Image analysis and interpretation
• Automated triage and prioritization
• Image enhancement

Decision support

Machine learning can generate insight-driven recommendations to facilitate greater accuracy and efficiency throughout typically manual processes.

Decision support, if complemented by advanced ML algorithms, can streamline:

• Treatment planning by analyzing medical records, scientific literature, and guides
• Custom alerts by analyzing potential interactions and allergies and providing custom alerts
• Clinical rules by assisting in applying clinical rules to facilitate accurate planning

Virtual assistants and chatbots

AI empowered virtual assistants and chatbots are another modern-day innovation used by healthcare facilities. These systems are integrated into existing telemedicine platforms to improve customer experience and service.

This accelerates:

• Patient triage – modern tools can assess patient concerns and conditions
• Appointment management – those tools can handle session scheduling, rescheduling, cancellations, and reminders to minimize administrative burden
• Information retrieval – quickly processing medical databases and cross-checking knowledge bases, virtual assistants and chatbots can provide appropriate answers to received patient queries

Pose detection

AI supported pose detection (motion recognition and analysis) is another new innovation becoming popular. This solution accurately assesses body posture, positioning, orientation, and other relevant details to enhance physical therapy and rehabilitation, as well as other related segments.

This streamlines:

• Exercise guidance – pose detection might provide motion analysis, real-time feedback and guidance
• Performance analytics – pose estimation might provide objective measurements and assessments
• Progress assessment – those algorithms can track body movements to enhance progress monitoring, problem identification, program adjustment, and engagement

Why use artificial intelligence to expand telemedicine platforms?

By utilizing artificial intelligence, healthcare providers might leverage some additional business benefits:

• Reduced time and cost – AI systems can optimize resource allocation by eliminating human mistakes and minimizing manual workflows
• Increased performance – AI solutions can simplify administrative processes (data entry and processing, session scheduling, rescheduling, cancellations, and more)
• Accurate diagnostics and treatment – smart algorithms can improve patient diagnosis and treatment, thus accelerating patient outcomes
• Data-driven decision-making – advanced algorithms can process patient information and progress, eventually streamlining personalized care

And provided high-quality services, in particular personalized programs and interventions, patients enjoy:

• Healthcare accessibility
• Personalized programs
• Timely interventions
• Faster recovery

Main challenges worth mentioning

Security vulnerability

Telemedicine software, which utilizes artificial intelligence, is gathering and processing sensitive information. This means application integration might present additional challenges, in particular security vulnerability – data breaches, unauthorized access, and more.

To resolve this challenge, business leaders must implement security measures, for example data encryption, access controls, security audits, and similar.

Ethical concerns

It’s crucial to balance advanced capabilities and continuous human oversight to maintain ethical standards:

• Firstly, responsible data utilization to ensure patient privacy and consent, as well as protection
• Secondly, the involvement extent to which the technology should affect identified processes

Handling integration without disruptions

During integration, business leaders should focus on the following considerations:

• Data collection and management
• Data quality and preprocessing
• Model development and validation
• Model integration with already existing systems
• Software interoperability
• Continuous evaluation and improvement

What’s more, responsible executors should prioritize meeting acknowledged, domain-specific standards:

• Data privacy and protection
• Data sharing and interoperability
• Healthcare regulations (HIPAA, GDPR, and others)
• Liability and accountability frameworks
• Consent policies
• Security policies and protocols

Summing up

Abto Software has the required expertise to deliver value-added products that transform healthcare services. Our company applies technology – artificial intelligence along with data analytics, computer vision, and more – to empower business leaders moving towards digital transformation.

Our expertise:

• AI based pose estimation for remote physical therapy – movement analysis for telerehabilitation
• CV enabled jump recognition and analysis – sensorless human motion detection
• CV supported self-diagnosis application – markerless human pose detection
• CV-based application for precise blood recognition and analysis
• Computer vision to drive medical imaging
• Computer vision to empower fall detection for video analytics platform