stuart.mccully, Author at rwr-regs

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?
Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?

2023, RWE 101

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?

RWE 101 – Is ICH GCP Applicable to Non-Interventional Studies?

No, the International Council for Harmonisation (ICH) Good Clinical Practice (GCP) guidelines are not applicable to non-interventional studies (NIS) in the context of real-world evidence (RWE).

ICH GCP guidelines are designed to ensure the protection of human subjects and the quality and integrity of data generated in clinical trials of investigational medicinal products (IMPs). In contrast, NIS are observational studies that do not involve the administration of IMPs, and instead rely on the collection of data from routine clinical practice or other non-experimental settings.

However, there are other guidelines and frameworks that apply to non-interventional studies in the context of real-world evidence, such as the International Society for Pharmacoepidemiology’s (ISPE) “Guidelines for Good Pharmacoepidemiology Practices” (GPP).

It is important to note that while NIS may not be subject to the same regulatory requirements as clinical trials, they still need to adhere to the applicable local regulations, ethical and scientific standards, and to ensure the protection of patient privacy and confidentiality.

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?stuart.mccully2023-08-07T22:57:28+00:00

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence
Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

2023, RWE 101

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

RWE 101 – Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

Medical AI applications hold great promise for improving healthcare outcomes, but they also raise ethical concerns related to patient privacy, algorithmic bias, and the reliability of the underlying data. When deploying medical AI in the context of real-world evidence, there are several ethical principles and safeguards that should be considered:

Transparency: Medical AI algorithms should be transparent about how they make decisions, what data they use, and the potential limitations of their predictions. This allows patients and clinicians to better understand the reasoning behind the AI’s recommendations and assess its accuracy.

Data privacy: Medical AI algorithms should comply with data privacy regulations, such as HIPAA in the United States, and should ensure that patient data is protected from unauthorized access, use, or disclosure.

Informed consent: Patients should be informed about how their data will be used by medical AI algorithms and should provide explicit consent for its use. They should also have the right to withdraw their consent at any time.

Fairness and bias: Medical AI algorithms should be designed to minimize bias and ensure that their predictions are fair across different patient populations. This requires careful attention to the selection of training data and the use of appropriate validation methods.

Human oversight: Medical AI algorithms should be designed to augment, not replace, human decision-making. Clinicians should have the ability to review and modify the AI’s recommendations, and patients should have access to human experts to address any concerns or questions they may have.

Accountability: Developers and providers of medical AI applications should be accountable for the accuracy and reliability of their algorithms, and should be transparent about any limitations or uncertainties associated with their predictions.

By following these ethical principles and safeguards, medical AI can be deployed in a responsible and effective manner, enabling healthcare providers to make better-informed decisions and improve patient outcomes.

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidencestuart.mccully2023-08-07T22:56:13+00:00

Real World Evidence (RWE) 101 – The Declaration of Helsinki
Real World Evidence (RWE) 101 – The Declaration of Helsinki

2023, RWE 101

Real World Evidence (RWE) 101 – The Declaration of Helsinki

RWE 101 – The Declaration of Helsinki

The Declaration of Helsinki is a set of ethical principles that govern the conduct of medical research involving human subjects. It was first adopted in 1964 and has been revised several times since then, most recently in 2013.

In the context of real-world evidence (RWE), the Declaration of Helsinki is particularly relevant because RWE often involves the collection and analysis of data from sources that were not originally intended for research purposes, such as electronic health records or claims data. This raises important ethical considerations, such as privacy and confidentiality concerns, and the need to obtain informed consent from study participants.

The Declaration of Helsinki provides guidance on these and other ethical issues related to medical research involving human subjects. For example, it states that research involving human subjects must be conducted in accordance with ethical principles, including respect for persons, beneficence, and justice. It also emphasizes the importance of obtaining informed consent from study participants, protecting their privacy and confidentiality, ensuring that the risks and benefits of the research are appropriately balanced, and ensuring research transparency by publishing the results (both positive and negative).

In the context of RWE, these ethical principles can help guide the development and implementation of research studies that use real-world data. For example, researchers can use the Declaration of Helsinki as a framework for designing studies that protect the privacy and confidentiality of study participants, obtain informed consent, and balance the risks and benefits of the research.

Overall, the Declaration of Helsinki provides important ethical guidance for medical research involving human subjects, including research that uses real-world data. Adhering to these principles can help ensure that RWE studies are conducted in an ethical and responsible manner.

Real World Evidence (RWE) 101 – The Declaration of Helsinkistuart.mccully2023-08-07T22:55:06+00:00

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research
Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

2023, RWE 101

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

RWE 101 – Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

Real-world evidence (RWE) research, which is the study of data from real-world settings, is founded on a number of ethical principles, including:

Respect for autonomy: This principle recognizes the importance of individuals’ ability to make their own decisions regarding their healthcare. In RWE research, this means obtaining informed consent from individuals before using their data.

Beneficence: This principle requires that researchers seek to maximize the benefits of their research while minimizing any potential harm. In RWE research, this means ensuring that the research is designed to answer important questions that will improve health outcomes for individuals.

Non-maleficence: This principle requires that researchers avoid causing harm to study participants. In RWE research, this means minimizing any risks associated with data collection and use, such as breaches of confidentiality.

Justice: This principle requires that researchers treat individuals fairly and equitably, ensuring that the benefits and burdens of the research are distributed fairly. In RWE research, this means ensuring that the use of data is transparent and that individuals are not unfairly excluded from research opportunities.

In addition to these principles, RWE research is also guided by ethical standards established by international bodies, such as the World Medical Association’s Declaration of Helsinki. These standards emphasize the importance of obtaining informed consent, protecting privacy and confidentiality, and ensuring that research is conducted in a manner that respects the dignity and rights of study participants.

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Researchstuart.mccully2023-08-07T22:52:57+00:00

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research
Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

2023, RWE 101

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

RWE 101 – Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

The General Data Protection Regulation (GDPR) is a regulation in EU law on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA). Its implementation in May 2018 has had a significant impact on research, particularly in the context of real-world evidence (RWE).

RWE refers to data collected outside of the traditional clinical trial setting, such as electronic health records (EHRs), claims data, and patient-generated data. RWE is increasingly being used to support regulatory decisions and to inform clinical practice. However, the use of RWE must comply with GDPR, which has implications for the collection, processing, and use of personal data in research.

Under GDPR, personal data must be collected and processed lawfully, fairly, and transparently, and individuals have the right to be informed about how their data is being used. This means that researchers must obtain explicit and informed consent from individuals to use their personal data for research purposes. In addition, the data must be pseudonymized or anonymized to protect individuals’ privacy.

GDPR has also increased the administrative burden for researchers, who must ensure that their data management practices are compliant with GDPR. This includes developing and implementing policies and procedures for data protection, privacy, and security, as well as appointing a Data Protection Officer to oversee data management activities.

Overall, GDPR has had a positive impact on research by increasing transparency and protecting the privacy of individuals whose data is used in research. However, compliance with GDPR can be challenging, particularly in the context of RWE, where large volumes of data are collected from multiple sources. It is essential for researchers to work closely with data protection and privacy experts to ensure that their research practices are compliant with GDPR.

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Researchstuart.mccully2023-08-07T22:51:18+00:00

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation
Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

2023, RWE 101

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

RWE 101 – Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

De-identification and pseudo-anonymization are two commonly used techniques for protecting personal information in real world evidence (RWE) studies.

De-identification involves removing or obscuring any personal identifiers, such as names, addresses, and social security numbers, from a dataset. The goal is to make it impossible to identify individuals in the dataset. However, de-identified data can still potentially be re-identified if combined with other data sources or through statistical analysis.

Pseudo-anonymization involves replacing personal identifiers with a unique identifier, or code, that cannot be traced back to the individual without access to a separate database. This technique provides an additional layer of protection as the original personal identifiers are not included in the dataset. However, there is still a risk that individuals can be re-identified if the codes are compromised or if the separate database is breached.

In the context of RWE, both de-identification and pseudo-anonymization can be effective in protecting personal information. The choice of technique will depend on the level of risk associated with re-identification and the specific requirements of the study. For example, if the dataset contains sensitive information or the risk of re-identification is high, pseudo-anonymization may be preferred. If the risk of re-identification is low and the dataset does not contain sensitive information, de-identification may be sufficient.

It is important to note that neither de-identification nor pseudo-anonymization can guarantee complete protection of personal information. Additional measures, such as access controls and data use agreements, may be necessary to further reduce the risk of re-identification and protect the privacy of individuals in RWE studies.

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisationstuart.mccully2023-08-07T22:50:09+00:00

Real World Evidence (RWE) 101 – Data Tokenization
Real World Evidence (RWE) 101 – Data Tokenization

2023, RWE 101

Real World Evidence (RWE) 101 – Data Tokenization

RWE 101 – 4 Potential Uses for Improving Drug Development

Real world evidence (RWE) refers to data collected outside the context of traditional clinical trials, such as observational studies, registries, and electronic health records. RWE can provide valuable insights into the safety, effectiveness, and real-world use of drugs, and has the potential to transform drug development.

Some of the opportunities of real world evidence in drug development include:

1. Improved patient recruitment: RWE can help identify patient populations that are typically underrepresented in clinical trials, such as elderly patients, patients with multiple comorbidities, and those with rare diseases. This can help improve patient recruitment and enable more representative clinical trials.
2. Enhance clinical trial design: RWE can help inform the design of clinical trials, for example, by identifying appropriate endpoints, understanding patient demographics, and identifying potential confounding factors that need to be accounted for.
Identify safety concerns: RWE can help identify safety concerns that may not have been detected in clinical trials, especially those related to long-term use or rare adverse events. This can help improve post-marketing surveillance and ensure that drugs are used safely in the real world.
3. Better understanding of effectiveness: RWE can provide insights into the effectiveness of drugs in the real world, including how drugs are used in combination with other treatments, and how patient outcomes vary across different subpopulations.
4. Accelerate drug development: By leveraging RWE, drug development timelines can be accelerated as fewer resources are required for clinical trials, making it easier to conduct larger and more complex studies. Additionally, RWE can help optimize the design of clinical trials, reducing the likelihood of failed trials and resulting in faster regulatory approvals.

In summary, real world evidence has the potential to improve drug development in a number of ways, including patient recruitment, clinical trial design, safety monitoring, and accelerating drug development timelines. By leveraging RWE, drug developers can gain a better understanding of how drugs work in the real world, which can ultimately improve patient outcomes.

Real World Evidence (RWE) 101 – Data Tokenizationstuart.mccully2023-08-07T22:48:59+00:00

Real World Evidence (RWE) 101 – Primary Data versus Secondary Data
Real World Evidence (RWE) 101 – Primary Data versus Secondary Data

2023, RWE 101

Real World Evidence (RWE) 101 – Primary Data versus Secondary Data

RWE 101 – Primary Data versus Secondary Data

Primary data and secondary data are two types of data used in research. The main difference between the two is that primary data is collected directly from the source, while secondary data is collected from sources that have already collected the data.

Primary data is original data that is collected for a specific research project. This type of data can be collected through various methods, including surveys, interviews, observations, and experiments. Primary data is collected with a specific research objective in mind, and the data is usually more focused and targeted than secondary data.

On the other hand, secondary data is data that has already been collected by someone else for a different purpose. This type of data can be collected from a wide variety of sources, including healthcare organisations, government agencies, academic institutions, and commercial organizations. Secondary data can be used to supplement primary data or to answer research questions that are not directly related to the original research objective.

There are advantages and disadvantages to both types of data. Primary data is more likely to be accurate and relevant to the specific research question being studied, but it can also be more time-consuming and expensive to collect. Secondary data is generally less expensive and easier to access, but it may not be as accurate or relevant to the specific research question being studied.

In general, researchers will use a combination of primary and secondary data to address their research questions and achieve their research objectives

Real World Evidence (RWE) 101 – Primary Data versus Secondary Datastuart.mccully2023-08-07T22:47:38+00:00

RWE 101 – Secondary Use of Existing Data
RWE 101 – Secondary Use of Existing Data

RWE 101, Secondary Use of Existing Data

RWE 101 – Secondary Use of Existing Data

Secondary use of existing data refers to the practice of analyzing data that was collected for a different purpose than the current research question. This approach is becoming increasingly popular in real-world research because of the large amounts of data that are available through various sources, such as electronic health records, administrative databases, and social media.

In many cases, secondary data analysis can provide valuable insights and answer research questions that would otherwise be difficult or impossible to answer with primary data collection. For example, researchers can use existing data to study disease trends, evaluate the effectiveness of health interventions, and identify risk factors for various health outcomes.

Secondary data analysis can also be more cost-effective than primary data collection, as the data has already been collected and often comes at a lower cost than conducting a new study. Additionally, secondary data analysis can allow researchers to study topics that may not have been feasible to study with primary data collection due to ethical or practical limitations.

However, there are also potential limitations to secondary data analysis, such as the lack of control over the quality and accuracy of the data, and the potential for biases and confounding factors that were not accounted for in the original data collection. Therefore, researchers must carefully evaluate the suitability of existing data for their research question and take steps to address any limitations or potential biases in the data.

RWE 101 – Secondary Use of Existing Datastuart.mccully2023-08-07T22:46:24+00:00

RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?
RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?

RWE 101

RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?

The use of Artificial Intelligence (AI) in the context of Real World Evidence (RWE) is generating excitement because it has the potential to transform the way healthcare is delivered and improve patient outcomes. RWE refers to data collected outside of the traditional clinical trial setting, such as electronic health records, claims data, and patient-generated data. This data provides valuable insights into how drugs and medical devices perform in real-world settings and how they impact patient health.

AI has the ability to rapidly analyze large volumes of complex data from multiple sources and identify patterns and insights that can help healthcare providers make better treatment decisions. For example, AI can help identify patient populations who may benefit most from a particular treatment, or it can help identify adverse events associated with a medication or medical device that may not have been detected in clinical trials.

The use of AI in RWE can also lead to cost savings by identifying more efficient and effective treatment options, reducing the need for trial-and-error treatments, and avoiding unnecessary procedures and tests.

Overall, the excitement surrounding the use of AI in RWE is due to its potential to improve patient outcomes, enhance healthcare delivery, and reduce costs.

RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?stuart.mccully2023-08-07T22:45:06+00:00

About stuart.mccully

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?

RWE 101 – Is ICH GCP Applicable to Non-Interventional Studies?

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

RWE 101 – Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

Real World Evidence (RWE) 101 – The Declaration of Helsinki

RWE 101 – The Declaration of Helsinki

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

RWE 101 – Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

RWE 101 – Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

RWE 101 – Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

Real World Evidence (RWE) 101 – Data Tokenization

RWE 101 – 4 Potential Uses for Improving Drug Development

Real World Evidence (RWE) 101 – Primary Data versus Secondary Data

RWE 101 – Primary Data versus Secondary Data

RWE 101 – Secondary Use of Existing Data

RWE 101 – Secondary Use of Existing Data

RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?

RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?

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RWE 101 – Is ICH GCP Applicable to Non-Interventional Studies?

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RWE 101 – Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

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RWE 101 – The Declaration of Helsinki

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RWE 101 – Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

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RWE 101 – Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

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RWE 101 – Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

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RWE 101 – 4 Potential Uses for Improving Drug Development

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RWE 101 – Primary Data versus Secondary Data

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RWE 101 – Secondary Use of Existing Data

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RWE 101 – Why is there so much excitement about the use of AI in the context of real world evidence?

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