The adoption of telehealth services has exploded over the past few years, fueled by the COVID-19 pandemic and advancements in technology. With telehealth, patients can conveniently consult with doctors through video chat, phone calls, and messaging without having to visit the clinic or hospital.
According to research from McKinsey, the total annual US spending on telehealth is expected to grow to more than $250 billion after the pandemic, which is a massive increase from $3 billion in 2019.
While telehealth improves accessibility for patients, it also presents new challenges for healthcare providers. Doctors now need to diagnose and treat patients remotely, often without access to vital signs or test results that would be available during an in-person visit. This is where artificial intelligence (AI) and real-time analytics come in.
The Potential of AI in Telehealth
AI has the potential to augment and enhance nearly every aspect of telehealth:
- Symptom checking - AI chatbots can interview patients to collect information about their symptoms before their telehealth visit. The chatbot can then analyze the symptoms and give recommendations to the patient and highlight potential issues to the doctor ahead of the visit.
- Remote monitoring - Wearables and sensors that connect to smartphones can track key vitals signs and health data like heart rate, blood pressure, blood oxygen levels, etc. AI algorithms can monitor this real-time data to flag any anomalies to the doctors.
- Medical imaging diagnostics - Algorithms can analyze medical images like X-rays, MRIs, and CT scans to detect abnormalities and highlight areas of interest to support doctors in making faster and more accurate diagnoses.
- Documentation and administration - Voice recognition AI can automatically transcribe doctor-patient conversations into medical notes and records. Chatbots and virtual assistants can help with scheduling appointments, prescription refills, billing, and other administrative tasks.
- Triage and referrals - Based on symptoms and medical history, AI can help identify patients in need of urgent care and recommend specialists for certain conditions when a referral is required. This allows doctors to focus on the patients who need them most.
- Precision medicine - Analyzing diverse health data from sensors, medical records, genetics, and more can help AI models personalize diagnosis and treatment for each patient. This is the future of data-driven, personalized medicine.
The benefits here are two-fold. AI augments the capabilities of doctors to improve patient outcomes. It also increases efficiency and allows doctors to handle more patients safely.
Real-Time Data is Key for Effective AI
To enable the AI applications above, healthcare organizations need access to real-time patient data. Data is the fuel that powers AI algorithms. Without quality data, even the most advanced AI won't reach its full potential.
Here are some of the key real-time data streams that are essential for AI-driven telehealth:
- Video - Recording video interactions creates data that AI can analyze for visual diagnostics and documentation.
- Audio - Transcribing conversations into text documents using speech recognition.
- Vital signs - Temperature, blood pressure, pulse rate, respiratory rate, etc. measured by connected devices.
- Lab tests - Results of blood tests, cultures, biopsies, etc. from external labs.
- Medical imagery - X-rays, MRIs, CT scans, ultrasounds images and associated reports.
- Health history - Electronic health records with medical, family, and prescription history.
- Genomics - Gene sequencing results to enable personalized medicine.
- Behavioral data - Activity tracking and sleep monitoring through wearables.
- Contextual data - Weather, location, traffic, and other real-world information.
- Pharmacy info - Medication adherence and refill data from pharmacies.
The diversity and volume of data required makes integrating all these streams very challenging. Just collecting the data is not enough either. It needs to be normalized, structured, and contextualized so it's useful for downstream analytics and machine learning models.
This requires a real-time data platform capable of ingesting, processing, and analyzing disparate data streams in real-time.
Challenges of Real-Time Analytics in Healthcare
Healthcare organizations have traditionally relied on batch processing and data warehouses for analytics. While these work for billing, regulatory reporting, and hindsight analysis, they fall short in supporting real-time clinical and operational use cases.
Here are some of the challenges faced:
- Data latency - Batch processing introduces delays between data generation and availability for analysis. This hinders real-time use cases.
- Poor analytics performance - Large databases with complex queries lead to slow response times. Unacceptable for real-time monitoring.
- Inflexible costs - Data warehouses require provisioning for peak capacity. The fixed costs are unable to scale up and down with variable workloads.
- Siloed data - Data from IoT devices, EHR systems, labs, pharmacies, etc. is isolated in disparate systems. Provides an incomplete view.
- Skill gaps - Specialized skills needed for data warehousing and traditional analytics restrict developer productivity.
- Compliance risks - Meeting stringent healthcare data compliance requirements like HIPAA is difficult with fragmented analytics systems.
The pieces required to build a real-time analytics solution end up becoming an expensive patchwork of cloud services, databases, and custom integration code. Managing and monitoring all these complex pieces introduces operational burdens for healthcare IT teams.
Real-Time Data Platform Requirements
To overcome these challenges, healthcare organizations need a real-time data platform purpose-built to simplify collecting, processing, analyzing, and acting on data in real-time.
Here are the key capabilities such a platform needs:
- Flexible data ingestion - Should support standard interfaces like REST APIs, database streams, log files, and messaging systems to integrate with existing data sources.
- Real-time processing - Data needs to be processed as soon as it is received with sub-second latency for real-time use.
- Scalable storage - Store and query large volumes of streaming data cost efficiently. Auto-scale capacity up and down.
- Unified analytics - Analyze streaming, historical, and context data together to derive real-time insights.
- Dashboards and alerts - Visual analytics and alerts to monitor healthcare operations and patient health in real-time.
- Portability - Runs equally well on cloud and on-prem. Makes transitioning between environments or adopting hybrid cloud straightforward.
- Security - Fine-grained access controls, encryption, data masking that adhere to healthcare security and compliance regulations.
- Management - Out-of-the-box monitoring, logging, and troubleshooting capabilities to operate the system reliably.
With a purpose-built real-time data platform, healthcare organizations can make the best use of data to increase the quality and efficiency of telehealth services powered by AI.
Real-Time Analytics in Action
Let's look at a few examples of real-time analytics applications powered by a modern real-time data platform:
Remote Patient Monitoring Dashboard
Challenge: Doctors need real-time visibility into critical vital signs to detect emerging health issues and prevent avoidable ER visits for high-risk patients.
- Vitals data like heart rate, blood pressure, temperature, etc. is streamed in real-time from wearables and devices.
- Data is processed to detect thresholds crossed or anomalies.
- Doctors monitor patients through real-time dashboards and receive alerts on critical events.
- If needed, proactively schedule a telehealth consult or in-person appointment.
AI Diagnostics for Medical Imaging
Challenge: Radiologists are overwhelmed managing growing imaging workload. Need AI assistance to improve speed and accuracy of anomaly detection.
- Medical imagery studies and reports are streamed from hospitals' PACS systems.
- AI model analyzes each image for anomalies in real-time upon receipt.
- Doctors review flagged images first. Model improves over time with feedback.
- Doctors are alerted about urgent cases requiring immediate attention.
Capacity Management and Forecasting
Challenge: Telehealth providers need to scale doctor capacity up and down dynamically based on real-time demand.
- Current patient waiting times and queues are monitored in real-time.
- Historical demand patterns are analyzed to forecast short term future demand.
- Provider capacity is adjusted dynamically based on forecasts to meet service level objectives.
- Doctors are proactively notified about expected surge in demand.
Build With a Real-Time Data Platform
The examples above highlight how a real-time data platform can drive data-centric AI innovations in telehealth. From personalized care to improved operations, the benefits are realized when data and AI come together.
Traditional analytics architectures make this challenging. Purpose-built real-time data platforms radically simplify building streaming data pipelines, analyzing data in motion, and driving action.
With telehealth adoption poised for massive growth, now is the time for healthcare organizations to invest in a modern real-time analytics foundation to reap the benefits of emerging technologies like AI and shape the future of healthcare.
What are some common use cases for AI in telehealth?
Some of the most common use cases include:
- Symptom checking chatbots that can interview patients to collect information before their visit. This data primes the doctor and highlights potential issues early.
- Medical image diagnostics using computer vision techniques to process X-rays, MRI, CT scans to detect anomalies and abnormalities. This acts as a second set of expert eyes for doctors.
- Documentation assistance using voice recognition to automatically transcribe doctor-patient conversations into medical notes. This automates tedious documentation.
- Patient triage and referrals by analyzing symptoms and history to identify patients who need urgent care or specialists. This allows efficient patient routing.
- Precision medicine by making personalized care decisions based on genetics, family history, behaviors, and other historical data analysis.
What types of real-time data are needed to enable these AI applications?
The key real-time data streams used by AI algorithms in telehealth include:
- Video and audio streams from telehealth consultations.
- Vital signs like heart rate and blood pressure measured by connected devices.
- Lab test results and medical imaging feeds from hospital systems.
- Health records and prescription data from EHR and pharmacy systems.
- Wearable data like activity tracking and sleep monitoring.
- Contextual data such as weather, location, and traffic.
This diverse data provides comprehensive input for AI models to operate effectively.
How is real-time data analytics different from traditional batch analytics?
Real-time analytics focuses on processing and analyzing data streams in milliseconds or seconds to drive immediate actions. Batch analytics operates on larger data sets but with delays ranging from hours to days.
Real-time analytics enables use cases like monitoring, alerting, and automation that require low latency. Batch is better suited for hindsight analysis and reporting. Healthcare organizations need both real-time and batch analytics capabilities.
What are some key challenges of real-time analytics in healthcare?
Some key challenges include:
- Integrating siloed data across various hospital systems, labs, pharmacies, and devices.
- Meeting stringent compliance requirements regarding patient data security and privacy.
- Achieving millisecond latency for analysis when data volumes are large.
- Correlating real-time and historical data to derive context-rich insights.
- Managing the complexity of real-time data pipelines and infrastructure.
What capabilities should a real-time data platform provide?
The key capabilities include:
- Flexibly ingesting streaming data from many sources and interfaces.
- Storing and processing very large volumes of time-series data.
- Enriching real-time data with historical context.
- Analyzing both streaming and historical data using SQL.
- Building operational apps for monitoring, alerting, and automation.
- Ensuring regulatory compliance, security, and privacy.
- Simplified management and monitoring built-in.
How can healthcare organizations get started with real-time analytics and AI?
Some best practices include:
- Starting small with pilot projects that address focused pain points.
- Engaging cross-functional teams of clinicians, IT, and data scientists.
- Investing in real-time data platforms that can accelerate development.
- Instrumenting applications to measure tangible ROI and outcomes.
- Scaling real-time capabilities out with governance to additional use cases.
- Making real-time data and analytics a core part of your technology roadmap.
What risks should be considered when implementing real-time analytics?
Some risks to consider:
- Patient health data security and privacy implications. These must be addressed up front.
- Accuracy and explainability of AI models. Rigorously validate models before clinical use.
- Change management with clinicians. Get user buy-in before widespread adoption.
- Technology risks of implementing new data architectures. Take an iterative approach.
- Organization cultural shift. Build trust by proving value with smaller projects first.
How can healthcare IT teams develop the skills needed for real-time analytics?
Some recommendations include:
- Focused training for data engineers and analysts on streaming systems and data science.
- Hackathons to foster hands-on experimentation with new technologies.
- Job rotations between development and operations to build end-to-end ownership.
- Partnerships with external experts who can mentor internal teams.
- Multi-disciplinary teams combining clinical, engineering, and data talent.
What compliance considerations are there for patient data used in analytics?
Real-time analytics must address regulations including:
- HIPAA for protecting patient privacy by de-identifying data used for analytics.
- Securing systems handling patient data and limiting access.
- Providing comprehensive audit trails of data access and application changes.
- Following chain of custody principles for third-party data sharing.
- Building applications to allow patient choice in data usage.
- Following local regulations governing medical AI systems.
What financial models work well for funding real-time analytics?
Some effective financial approaches include:
- Investing in real-time data platforms and infrastructure as shared services.
- Allocating dedicated budget for pilots and experiments.
- Tying projects to clear cost savings or revenue goals once deployed.
- Using service-based pricing models for analytics rather than fixed hardware costs.
- Considering risk-sharing partnerships with external analytics and AI experts.
The key is to start with small steps focused on driving tangible value before making large investments to scale.
Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.