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AI for Patient Engagement: Improving Patients' Clinical Trial Experience

Artificial intelligence (AI) is transforming the landscape of patient engagement in clinical trials. Here’s everything you need to know.

Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

No items found.
Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

Artificial intelligence (AI) is transforming the landscape of patient engagement in clinical trials. Here’s everything you need to know.

No items found.
Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

No items found.
Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

No items found.
Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

Artificial intelligence (AI) is transforming the landscape of patient engagement in clinical trials. Here’s everything you need to know.

Download the PDF
No items found.

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
    ‍

The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
    ‍
The future of AI in clinical trials and patient engagement

‍

The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
‍

‍

Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
‍

Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
‍

Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
‍

Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
‍

Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

‍

‍

References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

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Blog

AI for Patient Engagement: Improving Patients' Clinical Trial Experience

Artificial intelligence (AI) is transforming the landscape of patient engagement in clinical trials. Here’s everything you need to know.

Download the PDF

‍

The reach of artificial intelligence (AI) within the healthcare industry has been steadily growing. From the revolutionization of disease diagnosis to the tailoring of treatment plans, AI is omnipresent. Patients themselves are open to the idea of AI playing a crucial role in their healthcare. A recent survey found that an encouraging 42% of patients welcome AI’s assistance in healthcare decisions.1 This signals a monumental shift in patients’ willingness to engage with—and their rising trust in—technology.

This momentum is now crossing over to clinical research. A burgeoning acceptance of AI in clinical trials is taking shape, and researchers are now capitalizing on the ability of AI’s intelligent algorithms to foster greater patient engagement. Imagine a system that understands your health needs as well as your communication preferences and acts as a virtual companion, guiding you through your trial journey. That’s the purpose AI is increasingly serving: making the patient experience more personalized and interactive while also rendering information more accessible.

‍

Use Cases for AI to Engage Patients in Clinical Trials

Keeping patients engaged in clinical trials is a big challenge. The introduction of decentralized clinical trial (DCT) platforms has helped to promote a better patient journey by establishing new methods for data capture and opening up new communication channels to participants. As this DCT technology advances, AI is also becoming a viable option for trial managers and study sponsors to more efficiently engage with patients, thus preserving resources that can be better spent elsewhere. Below are some practical applications for AI-driven patient engagement in clinical trials.
‍

Patient Recruitment

Artificial intelligence can help early on in the clinical trial journey to recruit and enroll patients with relevant profiles. AI can be used to scan electronic health records (EHRs) from connected healthcare systems to identify potential trial candidates based on specific inclusion and exclusion criteria. Not only can this accelerate the recruitment process, but it can also ensure that selected patients are the best fit for the trial, leading to more reliable results.
‍

Patient Motivation and Communication

AI uses machine learning to analyze vast amounts of data on each patient, from their medical history to their daily habits, enabling tailored interventions and interactions. This can enhance patient satisfaction and comfort, as trial participants feel the study is more aligned with their individual needs and circumstances. Here are specific examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation:

  • Notifications: When smartphones are used to capture data, AI algorithms can detect when a participant is lagging and send a gentle nudge through a push notification. It's like having a virtual mentor ensuring you're on track.
ObvioHealth giving examples of how AI can provide personalized interactions to help motivate patients in a clinical trial to maintain their participation.
  • ‍Gamification: Many apps are now gamified; it’s no different for clinical trial applications. AI can help a gamification strategy to have a bigger impact by introducing new rewards or gamified elements at just the right time to retain the participant’s interest.
  • Chatbots: AI-powered virtual assistants can often provide instant responses to simple or frequently asked patient queries, ensuring that patients have greater access to the information they need. This not only increases their confidence in the trial process, but can also improve adherence to trial protocols, as patients have a clearer understanding of expectations and procedures.
  • Study team alerts: In addition to sending notifications directly to patients, artificial intelligence can also send alerts to the study team, such as ObvioHealth’s COACH (Clinical Oversight And Coordination Hub) team. In response to the alert, a real person reaches out the patient. Sometimes a conversation is just what it takes to motivate a patient to stay on track.
  • Trend and predictive analytics: With its ability to process vast datasets, AI can predict which patients might drop out of a trial or not adhere to the protocol. AI can also detect trends—for example, if patients are tending to drop at a specific point in the trial journey. These capabilities enable study teams to intervene proactively.
    ‍

Safety Monitoring

Just as AI can monitor for compliance and send notifications, it can do the same for safety. Here’s how it works: Once data is captured—whether it be from a clinician, a sensor or wearable, or the patient—it flows to a platform where AI can detect a potential adverse event. This triggers an alert to a study team member, who can then intervene immediately if necessary. This proactive approach to safety can more rapidly identify and address issues.
‍

Easier Data Collection

AI-powered tools can help researchers to continuously collect and transmit patient data in real time. Not only can this ensure the data is timely and accurate, but it can also minimize human error. Let’s look at two examples:

  1. Guided capture (augmented ePRO): Mobile phones are often used in clinical trials to capture images of symptoms, such as a skin rash. But, poor-quality images equal inaccurate data. AI can guide the participant step by step, ensuring the collected data is accurate and relevant.
  2. Sensors and wearables: Wearables collect data such as heart rate, sleep patterns, and activity levels more regularly than can be done through punctual reporting. This more granular data can reflect spikes that might otherwise be missed and provide average readings that are more accurate. These devices also have built-in algorithms that are able to detect when data is not properly captured or when it falls outside of standard norms.
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The Permeating Benefits of AI for Patient Engagement in Clinical Trials

The benefits of using AI for patient engagement accrue not only for the participants but also for the study sponsors who see higher completion rates and are able to collect stronger therapeutic and safety evidence for their trials. Below, we’ve summed up three advantages for sponsors.

  • Efficiency and cost-effectiveness: By automating and optimizing processes, AI ensures that clinical trials are conducted more efficiently, saving both time and resources. Instead of tending to every patient in the trial, the technology helps study teams to focus on the ones who need more personalized services. This streamlines work and maximizes success.
  • Higher compliance and retention: Improved patient communication, more readily accessible  information, easier reporting, and the motivational features of AI foster a friendlier environment for patients. This helps to retain patients and keep them compliant.
  • Stronger evidence: Higher compliance and retention translates to fewer missing data points. AI-powered, real-time data monitoring captures adverse events that might otherwise have gone unnoticed. Together, these enhanced functions result in fully powered studies with more complete and accurate data.
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The future of AI in clinical trials and patient engagement

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The Future of AI in Clinical Trials

The capabilities of AI in today’s clinical trials have only scratched the surface of their full potential. As AI algorithms become more sophisticated, their abilities to improve trial outcomes will increase dramatically. During drug discovery, AI will help researchers to understand how different compounds can interact with biological systems. This could streamline the early phases of clinical trials by providing researchers with a clearer idea of which compounds are most likely to be effective.  

In the protocol development phase, AI will facilitate the analysis of genomics data to better target clinical trials to populations precisely matched based on genetic factors. AI will then help to predict which patients will be more prone to adverse events, allowing study teams to anticipate and preempt issues.  

Future intelligent patient engagement algorithms will employ advanced Natural Language Processing (NPL) to analyze unstructured data from forum discussions, patient diaries, and other sources of insight to help make the patient's voice more central to the trial process.
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Essential Considerations for AI's Role in Clinical Trials

The integration of AI into clinical trials presents an array of opportunities, but it also demands serious consideration and management of risks. Understanding these key factors is vital to harnessing AI's full potential in patient engagement.
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Ethical Guidelines

The use of the patient data required to train AI must be governed by clear patient consent processes. A transparent system where patients understand how their data will be used is essential to garner trust.2  

In addition, it’s important to understand that AI can perpetuate bias. If AI algorithms are trained on data that reflects societal biases, AI may inadvertently perpetuate these biases. For example, a women’s health study could be prone to socioeconomic bias by virtue of the datasets that skew towards higher-income women who are more likely to receive routine gynecological care. Active measures to identify and mitigate biases in data and algorithms are paramount.3
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Human Involvement

AI is not a replacement for humans. Patients need support from both technology and humans in a clinical trial; it is essential to strike the right balance between the two. Machines can analyze and compute, but they cannot replace the emotional connection between clinicians and patients. It is very important that clinical trial patients have a way to easily access a study team member for a human-to-human conversation.
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Privacy and Security

The alignment of AI practices with existing healthcare and data privacy regulations is a must. In addition, AI algorithms should use de-identified data to avoid breaches of protected health information.4 Outside of the laws, patients will want to know that their data is secure. Ensuring that AI systems have stringent data protection mechanisms will build trust both with patients and with healthcare organizations, thus encouraging more extensive patient engagement.
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Conclusion

Artificial Intelligence in healthcare is no longer an abstract concept or a distant future possibility; it's an evolving reality which has and will continue to transform the very fabric of patient engagement.  

However, as we chart this new territory, the careful consideration of ethical norms, human collaboration, privacy concerns, and accessibility remains paramount. Striking the right balance between technological advancement and human empathy will be the keystone of harnessing AI's full potential.

The examples and trends illuminated within this article only scratch the surface of what's possible. We stand at the cusp of a new era where properly trained and monitored technology can meaningfully enrich the way we bring therapies to market and improve quality of life in the process.  

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References:

1. Heath S. Patients Ready to Embrace AI, Patient Engagement Technologies. Patient Data Access News. February 19, 2019. Accessed August 12, 2023. https://patientengagementhit.com/news/patients-ready-to-embrace-ai-patient-engagement-technologies  

2. Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121

3. Jarry I. Leading Clinical Trial Recruitment Strategies Center Around Real-World Data. MedCity News. January 3, 2023. Accessed August 13, 2023. https://medcitynews.com/2023/01/leading-clinical-trial-recruitment-strategies-center-around-real-world-data/

4. Malek LA, Jain P, Johnson J. Data privacy and artificial intelligence in health care. Reuters. March 17, 2022. Accessed August 12, 2023. https://www.reuters.com/legal/litigation/data-privacy-artificial-intelligence-health-care-2022-03-17/  

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