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The healthcare industry is in the midst of a transformation, and artificial intelligence (AI) is playing a crucial role in driving this change.
According to research and markets, AI in the healthcare market will reach USD 44.5 billion by 2026, with a worldwide staggering CAGR of 46.21%.
The AI in healthcare market growth is driven by:
- Rise in patient volume and complexity of data driving demand for AI in healthcare
- Shrinking workforce facilities leading to increased adoption of AI in healthcare
- Advancements and innovations in AI technology influencing market growth
- Rising investment in advanced drug discovery and development processes driving the adoption of AI
What is an AI-enabled patient journey?
AI-driven patient journey refers to artificial intelligence technology enhancing the patient experience and improving the efficiency of healthcare delivery.
It involves AI predicting patient needs and preferences, identifying potential risks or issues, and providing personalized recommendations for treatment or care.
AI in healthcare can automate routine tasks, like scheduling appointments or following up with patients after treatment.
Artificial Intelligence frees healthcare staff to focus on more complex and critical tasks.
Each step in the AI-enabled patient journey process involves technologies like Natural Language Processing (NLP), Machine Learning (ML) and predictive analytics.
These technologies help in patient intake, appointment scheduling, diagnosis and treatment planning, post-treatment monitoring and follow-up care.
AI in Healthcare: Patient Data Management Challenges
1. Data Security
The primary challenge is protecting Electronic Health Records (EHR) from unauthorized access, hacking, and breaches.
AI Solution
Machine learning algorithms can detect and respond to unusual activity.
During unauthorized access attempts, AI can alert the system admin or automatically block access to critical data such as EHR.
AI in healthcare can analyze data access logs and identify potential security breaches.
AI can monitor network activity and detect vulnerabilities like unpatched software or weak passwords.
2. Data Quality
A challenge ensuring that patient data is accurate, complete, and up-to-date.
AI Solution
AI in healthcare can ensure patient data is accurate, complete, and up-to-date.
With Natural Language Processing (NLP), AI extracts information from unstructured data sources, like clinical notes.
AI then provide a structure to the data and automatically update Electronic Health Records (EHRs) and other databases.
It automates the data entry process, reducing the need for manual input and minimizing the risk of errors.
It allows healthcare providers to focus on providing care rather than spending time on data entry.
Furthermore, AI can monitor live patient data and alert hospital authorities to potential issues. AI can help ensure that the data is always up-to-date.
3. Data Integration
The challenge integrating patient data from multiple sources and systems.
AI Solution
AI can help integrate data from multiple sources using machine learning algorithms to identify patterns and relationships between various data sets.
It can help to create a more accurate picture of a patient’s health and medical history.
For example, AI can analyze electronic health records, medical devices, and lab results to identify potential health risks or disease progression.
This information is vital for treatment decisions and improving patient outcomes.
It helps standardize data from multiple sources, making it easier to compare and analyze.
It simplifies working with large amounts of data, such as in clinical trials or population health studies.
4. Data Governance
An issue in managing patient data in a way that complies with legal and regulatory requirements.
AI Solution
AI systems can monitor compliance with legal and regulatory requirements, such as HIPAA regulations.
It automatically encrypts patient data to protect it from unauthorized access or breaches.
It de-identifies patient data by removing or masking personal identifiers.
AI in healthcare helps protect patient privacy.
It can also generate reports and audit logs to demonstrate compliance with legal and regulatory requirements.
It helps organizations stay compliant and avoid penalties or fines.
AI in Healthcare Use Cases Driving Patient Experience
Self-service portal using AI
The AI-powered Patient Self-Service Portal is a platform that allows patients to access and manage their medical information and appointments through a user-friendly interface.
The portal uses artificial intelligence (AI) to provide personalized recommendations and information to patients based on their medical history and current health needs.
Predictive analytics for patient stratification
Predictive analytics can group patients according to similar characteristics, such as demographics, medical history, and current health status.
This process, known as patient stratification, can help healthcare providers identify high-risk patients and target them for early interventions or preventative care.
Machine learning algorithms can analyze large amounts of electronic health records (EHRs) and identify patterns and relationships between patient characteristics and health outcomes.
For example, a patient with high-risk cardiovascular disease, based on age, gender, family history, and lab results, may be referred to a cardiologist for further evaluation and treatment.
Additionally, the patient will get a personalized care plan. It might include regular check-ups, exercise recommendations, and dietary changes to help lower their risk of heart disease.
Another example of how predictive analytics can help in patient stratification is with Natural Language Processing (NLP) techniques.
NLP analyze unstructured data, like notes from clinical encounters, to identify patient characteristics.
For instance, NLP can identify patients with social determinants of health, such as poverty or lack of access to transportation, that could impact their health outcomes.
AI-enabled Virtual Healthcare for Remote Patient Monitoring
Virtual healthcare can help patients manage chronic conditions and improve their overall health.
For example, virtual health assistants, such as IBM Watson’s “Oncology Expert Advisor,” can provide personalized information and support to patients with cancer.
These AI systems can help answer questions, provide education, and offer resources to help patients better understand their condition and treatment options.
Preconditions
- A patient has a device with internet connectivity and a camera, such as a smartphone or a tablet.
- The patient has registered with the virtual healthcare provider and has agreed to remote monitoring.
- The virtual healthcare provider has access to the patient’s medical history and has assigned a primary healthcare provider to the patient.
Postconditions
- A patient’s symptoms are effectively managed and treated.
- The patient’s primary healthcare provider can monitor the patient’s health remotely, reducing the need for in-person visits.
- The patient can receive healthcare services remotely, providing them with more convenient and accessible healthcare.
Finally
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Healthcare organizations have an opportunity to transform the patient experience by embracing technology and collaborating with tech companies like Techspian.
We help streamline healthcare workflows and improve patient experiences.
Let’s work together to revolutionize the healthcare industry!
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FAQs
AI automates routine tasks and personalizes care for better patient journeys.
AI enhances data accuracy, integration, and security across healthcare systems.
Rising patient volumes and workforce shortages are driving AI adoption.
It handles repetitive tasks, freeing up time for critical patient care.
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