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Blog

Effectively Applying AI and Machine Learning in Healthcare

There has been extensive discussion about the potential of AI and machine learning (ML), within the healthcare industry and beyond. We’re starting to see wider implementation of these nascent technologies but at the same time, there’s still some scepticism and hesitancy about their use and the value these technologies bring.

February 18, 2022

In recent years, the healthcare sector has seen many ground-breaking developments such as genomics, stem cell research, cancer therapies and immunotherapy. We’ve also seen significant advances in clinical trials and drug development. AI and ML have undoubtedly played a pivotal role in revolutionising these innovations in healthcare and life sciences, particularly, by accelerating research and keeping costs down.

Furthermore, the pandemic has also spurred on the rapid adoption of new technologies and systems such as AI and ML, to combat the Covid-19 virus, with global cooperation in vaccine research, development, and distribution happening in less than a year – a pace never thought possible before.

This shows that AI and ML technologies are being more widely embraced, but it is important that their benefits are applied and harnessed correctly to ensure their contribution to healthcare continues to advance.


The Role of AI & Machine Learning in Healthcare

AI and ML, when used effectively, have the power to better drive the development of medicines and improve patient outcomes by making the clinical research process more efficient. These new technology models have the capability to collect and analyse huge volumes of anonymised patient data to provide answers to vital clinical research questions to derive valuable insights easier and faster than before. Whilst the pace of digital transformation means there are increasingly more real-world patient sources available (including electronic medical records, patient reported information, personal devices, and health apps) accessing the data can still be a challenge in healthcare settings.

The way that health systems have historically captured patient data means it is often fragmented and unstructured, lacking a common data model and data standardisation. Without the benefit of using AI and ML it can be difficult to curate and draw valuable comparisons and insights from real world data. More widespread adoption of AI and ML, precision data analytics and new research models such as synthetic control arms (SCA) have the potential to improve patient care, enhance the development of new treatment and reduce healthcare costs.

For instance, in the case of using an SCA, the experimental arm is conducted as normal, but the control arm is replaced or supported by an additional data arm, which uses data from the electronic patient records of a comparable group of real-world patients. This means that not only can the research happen quicker, but it can also minimise the patient burden, and potentially avoid the ethical challenges of using patients as a placebo group.

These possibilities can only become a reality for healthcare when AI and Machine Learning are applied in an effective and ethical way. Three key areas should be considered:  


1. Understand the Clinical Context

While an AI-based data analysis tool may be effective, it can’t be used in isolation. How it is being applied to support a wider need must be carefully considered, alongside a solid understanding of the context behind the data analysis.

For example, to accelerate the drug discovery process by using an AI or ML platform to generate insights about a certain patient subset or to predict how a patient will respond to a course of treatment, can help to make an informed decision about someone’s care pathway.  This requires clinicians to share their insights and context about their work with tech experts. The more the teams developing and applying AI technology know about care pathways, standards of care and the experiences of patients during clinical trials, the better the algorithms can be developed to reflect real-world scenarios.

This combined approach of first-hand understanding of clinical pathways, patient care and technology are critical. Without this, clinicians and researchers risk obtaining inaccurate results.


2. Show How Technology Can Support Clinicians and Researchers

Tech experts must effectively demonstrate the benefits of applying technology to enhance and advance the development of medicines, clinical pathways, and patient outcomes – and how these technologies can support the work of clinicians and researchers.

For example, Sensyne’s real-world analytics platform, SENSIGHT, brings together multiple and disparate longitudinal data sources into one common data model. It gives researchers and academics the ability to access and interrogate millions of curated patient records to rapidly address questions, run unlimited trial feasibility assessments and gain invaluable insights that can shape their clinical research and development. There are great advantages for them in using a data analytics platform like this. It can drive significant efficiencies by reducing time spent on manual and time intensive tasks such as stratifying patients, creating patient cohorts in specific research areas, analysing medical image report and minimises clinical research delays and failure rates.


3. Communicate the Benefits

AI and machine learning are still emerging technologies, and they continue to evolve. Importantly, clear demonstrations of the benefits they can bring are now starting to be seen, particularly during the pandemic. These technologies have the potential to revolutionise the way clinical trials are carried out and how treatments are researched and developed to drive better patient outcomes.

However, implementing an AI or ML solution alone is not enough. More than ever, trust and understanding around the applications of these technologies must be earned. We need to ensure that healthcare practitioners and clinicians are part of the journey and are engaged from the beginning of any technology implementation. Communication around the purpose of using new technology and the ways in which it can benefit and complement their work needs to be conveyed. Only then will the sector begin to see significant adoption.


Ray Valencia, Vice President of Clinical Sensyne Health & Martin Gouldstone, Chief Business Officer, Sensyne Health


This article originally appeared in The Journal of mHealth

Blog

Effectively Applying AI and Machine Learning in Healthcare

February 18, 2022
There has been extensive discussion about the potential of AI and machine learning (ML), within the healthcare industry and beyond. We’re starting to see wider implementation of these nascent technologies but at the same time, there’s still some scepticism and hesitancy about their use and the value these technologies bring.

In recent years, the healthcare sector has seen many ground-breaking developments such as genomics, stem cell research, cancer therapies and immunotherapy. We’ve also seen significant advances in clinical trials and drug development. AI and ML have undoubtedly played a pivotal role in revolutionising these innovations in healthcare and life sciences, particularly, by accelerating research and keeping costs down.

Furthermore, the pandemic has also spurred on the rapid adoption of new technologies and systems such as AI and ML, to combat the Covid-19 virus, with global cooperation in vaccine research, development, and distribution happening in less than a year – a pace never thought possible before.

This shows that AI and ML technologies are being more widely embraced, but it is important that their benefits are applied and harnessed correctly to ensure their contribution to healthcare continues to advance.


The Role of AI & Machine Learning in Healthcare

AI and ML, when used effectively, have the power to better drive the development of medicines and improve patient outcomes by making the clinical research process more efficient. These new technology models have the capability to collect and analyse huge volumes of anonymised patient data to provide answers to vital clinical research questions to derive valuable insights easier and faster than before. Whilst the pace of digital transformation means there are increasingly more real-world patient sources available (including electronic medical records, patient reported information, personal devices, and health apps) accessing the data can still be a challenge in healthcare settings.

The way that health systems have historically captured patient data means it is often fragmented and unstructured, lacking a common data model and data standardisation. Without the benefit of using AI and ML it can be difficult to curate and draw valuable comparisons and insights from real world data. More widespread adoption of AI and ML, precision data analytics and new research models such as synthetic control arms (SCA) have the potential to improve patient care, enhance the development of new treatment and reduce healthcare costs.

For instance, in the case of using an SCA, the experimental arm is conducted as normal, but the control arm is replaced or supported by an additional data arm, which uses data from the electronic patient records of a comparable group of real-world patients. This means that not only can the research happen quicker, but it can also minimise the patient burden, and potentially avoid the ethical challenges of using patients as a placebo group.

These possibilities can only become a reality for healthcare when AI and Machine Learning are applied in an effective and ethical way. Three key areas should be considered:  


1. Understand the Clinical Context

While an AI-based data analysis tool may be effective, it can’t be used in isolation. How it is being applied to support a wider need must be carefully considered, alongside a solid understanding of the context behind the data analysis.

For example, to accelerate the drug discovery process by using an AI or ML platform to generate insights about a certain patient subset or to predict how a patient will respond to a course of treatment, can help to make an informed decision about someone’s care pathway.  This requires clinicians to share their insights and context about their work with tech experts. The more the teams developing and applying AI technology know about care pathways, standards of care and the experiences of patients during clinical trials, the better the algorithms can be developed to reflect real-world scenarios.

This combined approach of first-hand understanding of clinical pathways, patient care and technology are critical. Without this, clinicians and researchers risk obtaining inaccurate results.


2. Show How Technology Can Support Clinicians and Researchers

Tech experts must effectively demonstrate the benefits of applying technology to enhance and advance the development of medicines, clinical pathways, and patient outcomes – and how these technologies can support the work of clinicians and researchers.

For example, Sensyne’s real-world analytics platform, SENSIGHT, brings together multiple and disparate longitudinal data sources into one common data model. It gives researchers and academics the ability to access and interrogate millions of curated patient records to rapidly address questions, run unlimited trial feasibility assessments and gain invaluable insights that can shape their clinical research and development. There are great advantages for them in using a data analytics platform like this. It can drive significant efficiencies by reducing time spent on manual and time intensive tasks such as stratifying patients, creating patient cohorts in specific research areas, analysing medical image report and minimises clinical research delays and failure rates.


3. Communicate the Benefits

AI and machine learning are still emerging technologies, and they continue to evolve. Importantly, clear demonstrations of the benefits they can bring are now starting to be seen, particularly during the pandemic. These technologies have the potential to revolutionise the way clinical trials are carried out and how treatments are researched and developed to drive better patient outcomes.

However, implementing an AI or ML solution alone is not enough. More than ever, trust and understanding around the applications of these technologies must be earned. We need to ensure that healthcare practitioners and clinicians are part of the journey and are engaged from the beginning of any technology implementation. Communication around the purpose of using new technology and the ways in which it can benefit and complement their work needs to be conveyed. Only then will the sector begin to see significant adoption.


Ray Valencia, Vice President of Clinical Sensyne Health & Martin Gouldstone, Chief Business Officer, Sensyne Health


This article originally appeared in The Journal of mHealth

Blog

Effectively Applying AI and Machine Learning in Healthcare

Effectively Applying AI and Machine Learning in Healthcare

February 18, 2022
There has been extensive discussion about the potential of AI and machine learning (ML), within the healthcare industry and beyond. We’re starting to see wider implementation of these nascent technologies but at the same time, there’s still some scepticism and hesitancy about their use and the value these technologies bring.

In recent years, the healthcare sector has seen many ground-breaking developments such as genomics, stem cell research, cancer therapies and immunotherapy. We’ve also seen significant advances in clinical trials and drug development. AI and ML have undoubtedly played a pivotal role in revolutionising these innovations in healthcare and life sciences, particularly, by accelerating research and keeping costs down.

Furthermore, the pandemic has also spurred on the rapid adoption of new technologies and systems such as AI and ML, to combat the Covid-19 virus, with global cooperation in vaccine research, development, and distribution happening in less than a year – a pace never thought possible before.

This shows that AI and ML technologies are being more widely embraced, but it is important that their benefits are applied and harnessed correctly to ensure their contribution to healthcare continues to advance.


The Role of AI & Machine Learning in Healthcare

AI and ML, when used effectively, have the power to better drive the development of medicines and improve patient outcomes by making the clinical research process more efficient. These new technology models have the capability to collect and analyse huge volumes of anonymised patient data to provide answers to vital clinical research questions to derive valuable insights easier and faster than before. Whilst the pace of digital transformation means there are increasingly more real-world patient sources available (including electronic medical records, patient reported information, personal devices, and health apps) accessing the data can still be a challenge in healthcare settings.

The way that health systems have historically captured patient data means it is often fragmented and unstructured, lacking a common data model and data standardisation. Without the benefit of using AI and ML it can be difficult to curate and draw valuable comparisons and insights from real world data. More widespread adoption of AI and ML, precision data analytics and new research models such as synthetic control arms (SCA) have the potential to improve patient care, enhance the development of new treatment and reduce healthcare costs.

For instance, in the case of using an SCA, the experimental arm is conducted as normal, but the control arm is replaced or supported by an additional data arm, which uses data from the electronic patient records of a comparable group of real-world patients. This means that not only can the research happen quicker, but it can also minimise the patient burden, and potentially avoid the ethical challenges of using patients as a placebo group.

These possibilities can only become a reality for healthcare when AI and Machine Learning are applied in an effective and ethical way. Three key areas should be considered:  


1. Understand the Clinical Context

While an AI-based data analysis tool may be effective, it can’t be used in isolation. How it is being applied to support a wider need must be carefully considered, alongside a solid understanding of the context behind the data analysis.

For example, to accelerate the drug discovery process by using an AI or ML platform to generate insights about a certain patient subset or to predict how a patient will respond to a course of treatment, can help to make an informed decision about someone’s care pathway.  This requires clinicians to share their insights and context about their work with tech experts. The more the teams developing and applying AI technology know about care pathways, standards of care and the experiences of patients during clinical trials, the better the algorithms can be developed to reflect real-world scenarios.

This combined approach of first-hand understanding of clinical pathways, patient care and technology are critical. Without this, clinicians and researchers risk obtaining inaccurate results.


2. Show How Technology Can Support Clinicians and Researchers

Tech experts must effectively demonstrate the benefits of applying technology to enhance and advance the development of medicines, clinical pathways, and patient outcomes – and how these technologies can support the work of clinicians and researchers.

For example, Sensyne’s real-world analytics platform, SENSIGHT, brings together multiple and disparate longitudinal data sources into one common data model. It gives researchers and academics the ability to access and interrogate millions of curated patient records to rapidly address questions, run unlimited trial feasibility assessments and gain invaluable insights that can shape their clinical research and development. There are great advantages for them in using a data analytics platform like this. It can drive significant efficiencies by reducing time spent on manual and time intensive tasks such as stratifying patients, creating patient cohorts in specific research areas, analysing medical image report and minimises clinical research delays and failure rates.


3. Communicate the Benefits

AI and machine learning are still emerging technologies, and they continue to evolve. Importantly, clear demonstrations of the benefits they can bring are now starting to be seen, particularly during the pandemic. These technologies have the potential to revolutionise the way clinical trials are carried out and how treatments are researched and developed to drive better patient outcomes.

However, implementing an AI or ML solution alone is not enough. More than ever, trust and understanding around the applications of these technologies must be earned. We need to ensure that healthcare practitioners and clinicians are part of the journey and are engaged from the beginning of any technology implementation. Communication around the purpose of using new technology and the ways in which it can benefit and complement their work needs to be conveyed. Only then will the sector begin to see significant adoption.


Ray Valencia, Vice President of Clinical Sensyne Health & Martin Gouldstone, Chief Business Officer, Sensyne Health


This article originally appeared in The Journal of mHealth

Blog

Effectively Applying AI and Machine Learning in Healthcare

There has been extensive discussion about the potential of AI and machine learning (ML), within the healthcare industry and beyond. We’re starting to see wider implementation of these nascent technologies but at the same time, there’s still some scepticism and hesitancy about their use and the value these technologies bring.

In recent years, the healthcare sector has seen many ground-breaking developments such as genomics, stem cell research, cancer therapies and immunotherapy. We’ve also seen significant advances in clinical trials and drug development. AI and ML have undoubtedly played a pivotal role in revolutionising these innovations in healthcare and life sciences, particularly, by accelerating research and keeping costs down.

Furthermore, the pandemic has also spurred on the rapid adoption of new technologies and systems such as AI and ML, to combat the Covid-19 virus, with global cooperation in vaccine research, development, and distribution happening in less than a year – a pace never thought possible before.

This shows that AI and ML technologies are being more widely embraced, but it is important that their benefits are applied and harnessed correctly to ensure their contribution to healthcare continues to advance.


The Role of AI & Machine Learning in Healthcare

AI and ML, when used effectively, have the power to better drive the development of medicines and improve patient outcomes by making the clinical research process more efficient. These new technology models have the capability to collect and analyse huge volumes of anonymised patient data to provide answers to vital clinical research questions to derive valuable insights easier and faster than before. Whilst the pace of digital transformation means there are increasingly more real-world patient sources available (including electronic medical records, patient reported information, personal devices, and health apps) accessing the data can still be a challenge in healthcare settings.

The way that health systems have historically captured patient data means it is often fragmented and unstructured, lacking a common data model and data standardisation. Without the benefit of using AI and ML it can be difficult to curate and draw valuable comparisons and insights from real world data. More widespread adoption of AI and ML, precision data analytics and new research models such as synthetic control arms (SCA) have the potential to improve patient care, enhance the development of new treatment and reduce healthcare costs.

For instance, in the case of using an SCA, the experimental arm is conducted as normal, but the control arm is replaced or supported by an additional data arm, which uses data from the electronic patient records of a comparable group of real-world patients. This means that not only can the research happen quicker, but it can also minimise the patient burden, and potentially avoid the ethical challenges of using patients as a placebo group.

These possibilities can only become a reality for healthcare when AI and Machine Learning are applied in an effective and ethical way. Three key areas should be considered:  


1. Understand the Clinical Context

While an AI-based data analysis tool may be effective, it can’t be used in isolation. How it is being applied to support a wider need must be carefully considered, alongside a solid understanding of the context behind the data analysis.

For example, to accelerate the drug discovery process by using an AI or ML platform to generate insights about a certain patient subset or to predict how a patient will respond to a course of treatment, can help to make an informed decision about someone’s care pathway.  This requires clinicians to share their insights and context about their work with tech experts. The more the teams developing and applying AI technology know about care pathways, standards of care and the experiences of patients during clinical trials, the better the algorithms can be developed to reflect real-world scenarios.

This combined approach of first-hand understanding of clinical pathways, patient care and technology are critical. Without this, clinicians and researchers risk obtaining inaccurate results.


2. Show How Technology Can Support Clinicians and Researchers

Tech experts must effectively demonstrate the benefits of applying technology to enhance and advance the development of medicines, clinical pathways, and patient outcomes – and how these technologies can support the work of clinicians and researchers.

For example, Sensyne’s real-world analytics platform, SENSIGHT, brings together multiple and disparate longitudinal data sources into one common data model. It gives researchers and academics the ability to access and interrogate millions of curated patient records to rapidly address questions, run unlimited trial feasibility assessments and gain invaluable insights that can shape their clinical research and development. There are great advantages for them in using a data analytics platform like this. It can drive significant efficiencies by reducing time spent on manual and time intensive tasks such as stratifying patients, creating patient cohorts in specific research areas, analysing medical image report and minimises clinical research delays and failure rates.


3. Communicate the Benefits

AI and machine learning are still emerging technologies, and they continue to evolve. Importantly, clear demonstrations of the benefits they can bring are now starting to be seen, particularly during the pandemic. These technologies have the potential to revolutionise the way clinical trials are carried out and how treatments are researched and developed to drive better patient outcomes.

However, implementing an AI or ML solution alone is not enough. More than ever, trust and understanding around the applications of these technologies must be earned. We need to ensure that healthcare practitioners and clinicians are part of the journey and are engaged from the beginning of any technology implementation. Communication around the purpose of using new technology and the ways in which it can benefit and complement their work needs to be conveyed. Only then will the sector begin to see significant adoption.


Ray Valencia, Vice President of Clinical Sensyne Health & Martin Gouldstone, Chief Business Officer, Sensyne Health


This article originally appeared in The Journal of mHealth

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Blog

Effectively Applying AI and Machine Learning in Healthcare

February 18, 2022
There has been extensive discussion about the potential of AI and machine learning (ML), within the healthcare industry and beyond. We’re starting to see wider implementation of these nascent technologies but at the same time, there’s still some scepticism and hesitancy about their use and the value these technologies bring.

In recent years, the healthcare sector has seen many ground-breaking developments such as genomics, stem cell research, cancer therapies and immunotherapy. We’ve also seen significant advances in clinical trials and drug development. AI and ML have undoubtedly played a pivotal role in revolutionising these innovations in healthcare and life sciences, particularly, by accelerating research and keeping costs down.

Furthermore, the pandemic has also spurred on the rapid adoption of new technologies and systems such as AI and ML, to combat the Covid-19 virus, with global cooperation in vaccine research, development, and distribution happening in less than a year – a pace never thought possible before.

This shows that AI and ML technologies are being more widely embraced, but it is important that their benefits are applied and harnessed correctly to ensure their contribution to healthcare continues to advance.


The Role of AI & Machine Learning in Healthcare

AI and ML, when used effectively, have the power to better drive the development of medicines and improve patient outcomes by making the clinical research process more efficient. These new technology models have the capability to collect and analyse huge volumes of anonymised patient data to provide answers to vital clinical research questions to derive valuable insights easier and faster than before. Whilst the pace of digital transformation means there are increasingly more real-world patient sources available (including electronic medical records, patient reported information, personal devices, and health apps) accessing the data can still be a challenge in healthcare settings.

The way that health systems have historically captured patient data means it is often fragmented and unstructured, lacking a common data model and data standardisation. Without the benefit of using AI and ML it can be difficult to curate and draw valuable comparisons and insights from real world data. More widespread adoption of AI and ML, precision data analytics and new research models such as synthetic control arms (SCA) have the potential to improve patient care, enhance the development of new treatment and reduce healthcare costs.

For instance, in the case of using an SCA, the experimental arm is conducted as normal, but the control arm is replaced or supported by an additional data arm, which uses data from the electronic patient records of a comparable group of real-world patients. This means that not only can the research happen quicker, but it can also minimise the patient burden, and potentially avoid the ethical challenges of using patients as a placebo group.

These possibilities can only become a reality for healthcare when AI and Machine Learning are applied in an effective and ethical way. Three key areas should be considered:  


1. Understand the Clinical Context

While an AI-based data analysis tool may be effective, it can’t be used in isolation. How it is being applied to support a wider need must be carefully considered, alongside a solid understanding of the context behind the data analysis.

For example, to accelerate the drug discovery process by using an AI or ML platform to generate insights about a certain patient subset or to predict how a patient will respond to a course of treatment, can help to make an informed decision about someone’s care pathway.  This requires clinicians to share their insights and context about their work with tech experts. The more the teams developing and applying AI technology know about care pathways, standards of care and the experiences of patients during clinical trials, the better the algorithms can be developed to reflect real-world scenarios.

This combined approach of first-hand understanding of clinical pathways, patient care and technology are critical. Without this, clinicians and researchers risk obtaining inaccurate results.


2. Show How Technology Can Support Clinicians and Researchers

Tech experts must effectively demonstrate the benefits of applying technology to enhance and advance the development of medicines, clinical pathways, and patient outcomes – and how these technologies can support the work of clinicians and researchers.

For example, Sensyne’s real-world analytics platform, SENSIGHT, brings together multiple and disparate longitudinal data sources into one common data model. It gives researchers and academics the ability to access and interrogate millions of curated patient records to rapidly address questions, run unlimited trial feasibility assessments and gain invaluable insights that can shape their clinical research and development. There are great advantages for them in using a data analytics platform like this. It can drive significant efficiencies by reducing time spent on manual and time intensive tasks such as stratifying patients, creating patient cohorts in specific research areas, analysing medical image report and minimises clinical research delays and failure rates.


3. Communicate the Benefits

AI and machine learning are still emerging technologies, and they continue to evolve. Importantly, clear demonstrations of the benefits they can bring are now starting to be seen, particularly during the pandemic. These technologies have the potential to revolutionise the way clinical trials are carried out and how treatments are researched and developed to drive better patient outcomes.

However, implementing an AI or ML solution alone is not enough. More than ever, trust and understanding around the applications of these technologies must be earned. We need to ensure that healthcare practitioners and clinicians are part of the journey and are engaged from the beginning of any technology implementation. Communication around the purpose of using new technology and the ways in which it can benefit and complement their work needs to be conveyed. Only then will the sector begin to see significant adoption.


Ray Valencia, Vice President of Clinical Sensyne Health & Martin Gouldstone, Chief Business Officer, Sensyne Health


This article originally appeared in The Journal of mHealth