The Healthcare industry is in the midst of a transformation, with the integration of artificial intelligence (AI) playing a major role in driving this change. 

According to research and markets, the healthcare artificial intelligence market is projected to reach USD 44.5 billion by 2026, with a worldwide staggering CAGR of 46.21%. 

The AI in healthcare market growth is driven by: 

• Growth 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 

In this post, we’ll dive into potential healthcare challenges and the ways in which AI is transforming the Patient experience.

What is an AI-enabled patient journey? 

AI-driven patient journey refers to the use of artificial intelligence technology to enhance the patient experience and improve the efficiency of healthcare delivery. This can involve using AI to predict patient needs and preferences, identify potential risks or issues, and provide personalized recommendations for treatment or care. AI in healthcare can automate routine tasks, such as scheduling appointments or following up with patients after treatment, freeing healthcare providers to focus on more complex and critical tasks.

Each step in the process may involve the use of various AI technologies, such as natural language processing (NLP) for patient intake and appointment scheduling, machine learning (ML) algorithms for diagnosis and treatment planning, and predictive analytics for post-treatment monitoring and follow-up care. 

Patient Data Management Challenges
 

• Data Security: Ensuring that Electronic Health Records (EHR) are protected from unauthorized access, hacking, and breaches.
  

AI Solution: Machine learning algorithms can be trained to detect and respond to unusual activity, such as unauthorized access attempts, by alerting the system admin or automatically blocking access to critical data such as EHR. AI in healthcare can also be used to analyze data access logs and identify potential security breaches, such as attempts to access sensitive patient information by unauthorized individuals. Additionally, AI can be used to monitor network activity and detect potential vulnerabilities, such as unpatched software or weak passwords, that could be exploited by attackers.
 

• Data Quality: Ensuring that patient data is accurate, complete, and up-to-date. 
  

AI Solution: AI in healthcare can help ensure the accuracy, completeness, and up-to-date nature of patient data by using techniques such as natural language processing (NLP) to extract information from unstructured data sources, such as clinical notes. This information can then be used to automatically update Electronic Health Records (EHRs) and other databases.  It can automate the process of data entry, reducing the need for manual input and minimizing the risk of errors.

This allows healthcare providers to focus on providing care rather than spending time on data entry. Furthermore, AI can be used to monitor patient data in real-time and alert healthcare providers to any potential issues or changes in a patient’s condition, which can help ensure that the data is always up-to-date. 

• Data Integration: Integrating patient data from multiple sources and systems.
   

AI Solution: AI can help integrate data from multiple sources by using machine learning algorithms to identify patterns and relationships between different data sets. This can help to create a more accurate picture of a patient’s health and medical history.

For example, AI can be used to analyze electronic health records, medical devices, and lab results to identify potential health risks or disease progression. This information can then be used to inform treatment decisions and improve patient outcomes. 

It helps standardize data from multiple sources, making it easier to compare and analyze. This can be especially useful when working with large amounts of data, such as in clinical trials or population health studies.

• Data Governance: Managing patient data in a way that complies with legal and regulatory requirements. 

AI Solution: AI systems can be used to 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. This helps protect patient privacy while still allowing the data to be used for research or other purposes. It can also generate reports and audit logs, that can be used to demonstrate compliance with legal and regulatory requirements. This helps organizations stay compliant and avoid penalties or fines. 

• Data Analytics: Using patient data to generate insights and improve patient care.  

AI Solution: AI can process vast amounts of patient data and generate insights that can improve patient care. By analyzing patient data such as medical history, lab results, and medication prescriptions, AI can identify patterns and trends that may be overlooked by human analysis. This can lead to the identification of potential health risks and the development of personalized treatment plans. 

For example, AI can analyze data from electronic health records to identify patients who are at risk for certain conditions, such as diabetes or heart disease. By identifying these patients early, healthcare providers can take steps to prevent the development of these conditions and improve patient outcomes.

Additionally, AI can be used to analyze data from wearable devices and other remote monitoring tools to identify changes in a patient’s health that may indicate the need for additional care or treatment. 

AI 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 be used to 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 data, such as electronic health records (EHRs), to identify patterns and relationships between patient characteristics and health outcomes. 

For example, a patient who is identified as high-risk for cardiovascular disease based on their age, gender, family history, and lab results may be referred to a cardiologist for further evaluation and treatment. 

Additionally, the patient may be given a personalized care plan that includes regular check-ups, exercise recommendations, and dietary changes to help lower their risk of heart disease. 

Another example of how predictive analytics can be used for patient stratification is using Natural Language Processing (NLP) techniques. NLP can be used to analyze unstructured data, such as notes from clinical encounters, to identify patient characteristics that may not be captured in EHRs. For instance, NLP can be used to identify patients who may have 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 be used to 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: 

• The 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: 

• The 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 

Healthcare organizations have an opportunity to transform the patient experience by embracing technology and collaborating with tech companies like Techspian. Our focus is on streamlining healthcare workflows and improving patient experiences, which will help keep you ahead of the curve.

Let’s work together to revolutionize the healthcare industry and improve the lives of people everywhere!

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