Real-World-Data-and-Real-World-Evidence: diferenças entre revisões

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= Real World Data (RWD) and Real World Evidence (RWE) =
= Real World Data (RWD) and Real World Evidence (RWE) =
== 1. Introduction ==
== Definition of RWD and RWE ==
=== 1.1 Definition of RWD and RWE ===
According to the [https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence FDA], '''Real World Data (RWD)''' refers to “data relating to patient health status and/or the delivery of health care routinely” (Commissioner O of the. FDA, 2024 <ref name="fda">Commissioner O of the. FDA. FDA; 2024 [cited 2024 Dec 10]. Real-World Evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence </ref>), derived from various sources such as electronic health records (EHRs), claims and billing data, patient registries, patient-reported data and wearable devices.
According to the [https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence FDA], '''Real World Data (RWD)''' refers to “data relating to patient health status and/or the delivery of health care routinely” (Commissioner O of the. FDA, 2024 <ref name="fda">Commissioner O of the. FDA. FDA; 2024 [cited 2024 Dec 10]. Real-World Evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence </ref>), derived from various sources such as electronic health records (EHRs), claims and billing data, patient registries, patient-reported data and wearable devices.


=== 1.2 Sources of RWD ===
== Sources of RWD ==
RWD is derived from diverse and expansive data sources:
RWD is derived from diverse and expansive data sources:


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=== 1.3 Definition of RWE ===
== Definition of RWE ==
'''Real World Evidence (RWE)''' refers to evidence derived from the analysis of RWD to generate insights about the usage, safety, and effectiveness of medical products and interventions in real-world settings. It encompasses clinical, operational, and patient-reported outcomes, providing a holistic view of healthcare delivery and its impact on diverse populations. RWE complements data from randomized controlled trials (RCTs) by reflecting the complexities of everyday clinical practice, supporting decision-making for regulators, clinical teams, policymakers, and stakeholders in the healthcare ecosystem. Recent advancements in digital health and big data analytics underscore their transformative potential in contemporary medicine (Magalhães et al., 2022 <ref name="rwe">Magalhães T, Dinis-Oliveira RJ, Taveira-Gomes T. Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers. Int J Environ Res Public Health. 2022 Jul 8;19(14).</ref>).
'''Real World Evidence (RWE)''' refers to evidence derived from the analysis of RWD to generate insights about the usage, safety, and effectiveness of medical products and interventions in real-world settings. It encompasses clinical, operational, and patient-reported outcomes, providing a holistic view of healthcare delivery and its impact on diverse populations. RWE complements data from randomized controlled trials (RCTs) by reflecting the complexities of everyday clinical practice, supporting decision-making for regulators, clinical teams, policymakers, and stakeholders in the healthcare ecosystem. Recent advancements in digital health and big data analytics underscore their transformative potential in contemporary medicine (Magalhães et al., 2022 <ref name="rwe">Magalhães T, Dinis-Oliveira RJ, Taveira-Gomes T. Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers. Int J Environ Res Public Health. 2022 Jul 8;19(14).</ref>).


== 2. Methods for Generating RWE ==
= Methods for Generating RWE =
Generating RWE requires robust methodologies to ensure that insights are valid, reliable, and actionable. Techniques such as propensity score matching, regression analysis, and machine learning models are commonly used to mitigate biases and confounding in observational data.
Generating RWE requires robust methodologies to ensure that insights are valid, reliable, and actionable. Techniques such as propensity score matching, regression analysis, and machine learning models are commonly used to mitigate biases and confounding in observational data.


Integrating data from diverse sources to design pragmatic trials and observational studies presents both challenges and opportunities, making it a pivotal topic in the field. Advanced data analytics empower researchers to assess treatment effectiveness, evaluate safety outcomes, and identify trends in real-world clinical practice (Klonoff, 2020 <ref name="rwe-trials">Klonoff DC. The Expanding Role of Real-World Evidence Trials in Health Care Decision Making. J Diabetes Sci Technol. 2020 Jan;14(1):174–9.</ref>).
Integrating data from diverse sources to design pragmatic trials and observational studies presents both challenges and opportunities, making it a pivotal topic in the field. Advanced data analytics empower researchers to assess treatment effectiveness, evaluate safety outcomes, and identify trends in real-world clinical practice (Klonoff, 2020 <ref name="rwe-trials">Klonoff DC. The Expanding Role of Real-World Evidence Trials in Health Care Decision Making. J Diabetes Sci Technol. 2020 Jan;14(1):174–9.</ref>).


== 3. Applications and Use Cases of RWD and RWE ==
= Applications and Use Cases of RWD and RWE =
=== 3.1 Use Case in Orthopedics ===
== Use Case in Orthopedics ==
RWD and RWE have been applied in orthopedics to analyze outcomes from surgical interventions and rehabilitation. Recent created methods provide insight into the effectiveness of implants and treatment protocols in broader patient populations, offering evidence to optimize care (Hak et al., 2021 <ref name="ortho">Hak DJ, Mackowiak JI, Irwin DE, Aldridge ML, Mack CD. Real-World Evidence: A Review of Real-World Data Sources Used in Orthopaedic Research. J Orthop Trauma. 2021 Mar 1;35(Suppl 1):S6–12.</ref>).
RWD and RWE have been applied in orthopedics to analyze outcomes from surgical interventions and rehabilitation. Recent created methods provide insight into the effectiveness of implants and treatment protocols in broader patient populations, offering evidence to optimize care (Hak et al., 2021 <ref name="ortho">Hak DJ, Mackowiak JI, Irwin DE, Aldridge ML, Mack CD. Real-World Evidence: A Review of Real-World Data Sources Used in Orthopaedic Research. J Orthop Trauma. 2021 Mar 1;35(Suppl 1):S6–12.</ref>).


=== 3.2 Use Case in Oncology ===
== Use Case in Oncology ==
In oncology, RWE has become pivotal for understanding long-term outcomes and treatment efficacy in real-world settings. For example:
In oncology, RWE has become pivotal for understanding long-term outcomes and treatment efficacy in real-world settings. For example:
* Molecular profiling combined with RWD to enhance the precision of treatment recommendations (Saesen et al., 2021 <ref name="onco1">Saesen R, Van Hemelrijck M, Bogaerts J, Booth CM, Cornelissen JJ, Dekker A, et al. Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer. Eur J Cancer. 2023 Jun;186:52–61.</ref>).
* Molecular profiling combined with RWD to enhance the precision of treatment recommendations (Saesen et al., 2021 <ref name="onco1">Saesen R, Van Hemelrijck M, Bogaerts J, Booth CM, Cornelissen JJ, Dekker A, et al. Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer. Eur J Cancer. 2023 Jun;186:52–61.</ref>).
* Use of large-scale patient registries to evaluate the effectiveness of immunotherapies across diverse populations, particularly for rare or advanced cancers (Verkerk et al., 2024 <ref name="onco2">Verkerk K, Voest EE. Generating and using real-world data: A worthwhile uphill battle. Cell. 2024 Mar 28;187(7):1636–50.</ref>).
* Use of large-scale patient registries to evaluate the effectiveness of immunotherapies across diverse populations, particularly for rare or advanced cancers (Verkerk et al., 2024 <ref name="onco2">Verkerk K, Voest EE. Generating and using real-world data: A worthwhile uphill battle. Cell. 2024 Mar 28;187(7):1636–50.</ref>).


== 4. Challenges and Limitations of RWD and RWE ==
= Challenges and Limitations of RWD and RWE =
Despite its promise, the use of RWD and RWE presents significant challenges:
Despite its promise, the use of RWD and RWE presents significant challenges:
* '''Data Quality and Consistency:''' RWD sources often lack standardization, introducing variability that complicates analysis, which leads to data harmonization as a critical need (Grimberg et al., 2021 <ref name="challenges">Grimberg F, Asprion PM, Schneider B, Miho E, Babrak L, Habbabeh A. The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data. Digit Biomark. 2021 Aug;5(2):148–57.</ref>).
* '''Data Quality and Consistency:''' RWD sources often lack standardization, introducing variability that complicates analysis, which leads to data harmonization as a critical need (Grimberg et al., 2021 <ref name="challenges">Grimberg F, Asprion PM, Schneider B, Miho E, Babrak L, Habbabeh A. The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data. Digit Biomark. 2021 Aug;5(2):148–57.</ref>).
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* '''Ethical and Privacy Concerns:''' Safeguarding patient confidentiality, particularly with wearable devices and mobile apps, is vital to maintain public trust and comply with existing regulations (e.g. GDPR, HIPAA).
* '''Ethical and Privacy Concerns:''' Safeguarding patient confidentiality, particularly with wearable devices and mobile apps, is vital to maintain public trust and comply with existing regulations (e.g. GDPR, HIPAA).


== 5. Future Directions and Opportunities ==
= Future Directions and Opportunities =
The future of RWD and RWE is marked by opportunities to expand their role in personalized medicine, regulatory decision-making, health delivery efficiency and healthcare innovation:
The future of RWD and RWE is marked by opportunities to expand their role in personalized medicine, regulatory decision-making, health delivery efficiency and healthcare innovation:
* '''Data Integration and Standardization:''' The adoption of frameworks like the OMOP Common Data Model (OMOP-CDM) is expected to harmonize RWD across different sources, enabling broader and more meaningful analyses.
* '''Data Integration and Standardization:''' The adoption of frameworks like the OMOP Common Data Model (OMOP-CDM) is expected to harmonize RWD across different sources, enabling broader and more meaningful analyses.
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* '''Optimizing Drug Development:''' pharmaceutical companies are, nowadays, investing in utilizing RWD to streamline drug approval processes and post-market surveillance (Berger et al., 2015 <ref name="leverage-red">Berger ML, Lipset C, Gutteridge A, Axelsen K, Subedi P, Madigan D. Optimizing the Leveraging of Real-World Data to Improve the Development and Use of Medicines. Value in Health. 2015;18(1):127–30.</ref>).
* '''Optimizing Drug Development:''' pharmaceutical companies are, nowadays, investing in utilizing RWD to streamline drug approval processes and post-market surveillance (Berger et al., 2015 <ref name="leverage-red">Berger ML, Lipset C, Gutteridge A, Axelsen K, Subedi P, Madigan D. Optimizing the Leveraging of Real-World Data to Improve the Development and Use of Medicines. Value in Health. 2015;18(1):127–30.</ref>).


== 6. Conclusion ==
= Conclusion =
RWD and RWE are revolutionizing healthcare by bridging the gap between controlled clinical trials and real-world clinical practice. By overcoming current challenges in data quality, interoperability, and ethics, they hold the potential to shape the future of medicine, benefiting patients, healthcare professionals, and policymakers.
RWD and RWE are revolutionizing healthcare by bridging the gap between controlled clinical trials and real-world clinical practice. By overcoming current challenges in data quality, interoperability, and ethics, they hold the potential to shape the future of medicine, benefiting patients, healthcare professionals, and policymakers.


= References =
= References =
<references />
<references />

Edição atual desde as 14h36min de 26 de dezembro de 2024

Authors (HEADS-INFORM 2025):

  • Henrique Pereira
  • Olívia Oliveira

Real World Data (RWD) and Real World Evidence (RWE)

Definition of RWD and RWE

According to the FDA, Real World Data (RWD) refers to “data relating to patient health status and/or the delivery of health care routinely” (Commissioner O of the. FDA, 2024 [1]), derived from various sources such as electronic health records (EHRs), claims and billing data, patient registries, patient-reported data and wearable devices.

Sources of RWD

RWD is derived from diverse and expansive data sources:

Source Type Description
Electronic Health Records (EHRs) - Provide longitudinal patient data, including diagnoses, medications, and outcomes.
Claims and Biling Data - Reflect administrative and financial aspects of healthcare utilization.
Patient Registries - Disease-specific datasets used for long-term monitoring, such as oncology or cardiovascular registries.
Wearable Devices and Mobile Health Apps - Capture real-time, patient-generated health data, offering continuous insights into lifestyle, activity levels, and biometric trends.
Patient-Reported Outcomes (PROMs) - Direct feedback from patients on their health status, symptoms, and quality of life. PROMs provide valuable insights into the patient perspective, enabling the assessment of treatment effectiveness and satisfaction in real-world contexts.

Definition of RWE

Real World Evidence (RWE) refers to evidence derived from the analysis of RWD to generate insights about the usage, safety, and effectiveness of medical products and interventions in real-world settings. It encompasses clinical, operational, and patient-reported outcomes, providing a holistic view of healthcare delivery and its impact on diverse populations. RWE complements data from randomized controlled trials (RCTs) by reflecting the complexities of everyday clinical practice, supporting decision-making for regulators, clinical teams, policymakers, and stakeholders in the healthcare ecosystem. Recent advancements in digital health and big data analytics underscore their transformative potential in contemporary medicine (Magalhães et al., 2022 [2]).

Methods for Generating RWE

Generating RWE requires robust methodologies to ensure that insights are valid, reliable, and actionable. Techniques such as propensity score matching, regression analysis, and machine learning models are commonly used to mitigate biases and confounding in observational data.

Integrating data from diverse sources to design pragmatic trials and observational studies presents both challenges and opportunities, making it a pivotal topic in the field. Advanced data analytics empower researchers to assess treatment effectiveness, evaluate safety outcomes, and identify trends in real-world clinical practice (Klonoff, 2020 [3]).

Applications and Use Cases of RWD and RWE

Use Case in Orthopedics

RWD and RWE have been applied in orthopedics to analyze outcomes from surgical interventions and rehabilitation. Recent created methods provide insight into the effectiveness of implants and treatment protocols in broader patient populations, offering evidence to optimize care (Hak et al., 2021 [4]).

Use Case in Oncology

In oncology, RWE has become pivotal for understanding long-term outcomes and treatment efficacy in real-world settings. For example:

  • Molecular profiling combined with RWD to enhance the precision of treatment recommendations (Saesen et al., 2021 [5]).
  • Use of large-scale patient registries to evaluate the effectiveness of immunotherapies across diverse populations, particularly for rare or advanced cancers (Verkerk et al., 2024 [6]).

Challenges and Limitations of RWD and RWE

Despite its promise, the use of RWD and RWE presents significant challenges:

  • Data Quality and Consistency: RWD sources often lack standardization, introducing variability that complicates analysis, which leads to data harmonization as a critical need (Grimberg et al., 2021 [7]).
  • Bias and Confounding: Observational data can suffer from diverse biases, namely selection bias or incomplete reporting. Rigorous statistical methods and data validation frameworks are essential to mitigate these risks.
  • Ethical and Privacy Concerns: Safeguarding patient confidentiality, particularly with wearable devices and mobile apps, is vital to maintain public trust and comply with existing regulations (e.g. GDPR, HIPAA).

Future Directions and Opportunities

The future of RWD and RWE is marked by opportunities to expand their role in personalized medicine, regulatory decision-making, health delivery efficiency and healthcare innovation:

  • Data Integration and Standardization: The adoption of frameworks like the OMOP Common Data Model (OMOP-CDM) is expected to harmonize RWD across different sources, enabling broader and more meaningful analyses.
  • Leveraging Emerging Data Sources: for example, the integration of genomic data with RWD can drive advances in precision medicine (O’Leary et al., 2020 [8]).
  • Optimizing Drug Development: pharmaceutical companies are, nowadays, investing in utilizing RWD to streamline drug approval processes and post-market surveillance (Berger et al., 2015 [9]).

Conclusion

RWD and RWE are revolutionizing healthcare by bridging the gap between controlled clinical trials and real-world clinical practice. By overcoming current challenges in data quality, interoperability, and ethics, they hold the potential to shape the future of medicine, benefiting patients, healthcare professionals, and policymakers.

References

  1. ↑ Commissioner O of the. FDA. FDA; 2024 [cited 2024 Dec 10]. Real-World Evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
  2. ↑ Magalhães T, Dinis-Oliveira RJ, Taveira-Gomes T. Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers. Int J Environ Res Public Health. 2022 Jul 8;19(14).
  3. ↑ Klonoff DC. The Expanding Role of Real-World Evidence Trials in Health Care Decision Making. J Diabetes Sci Technol. 2020 Jan;14(1):174–9.
  4. ↑ Hak DJ, Mackowiak JI, Irwin DE, Aldridge ML, Mack CD. Real-World Evidence: A Review of Real-World Data Sources Used in Orthopaedic Research. J Orthop Trauma. 2021 Mar 1;35(Suppl 1):S6–12.
  5. ↑ Saesen R, Van Hemelrijck M, Bogaerts J, Booth CM, Cornelissen JJ, Dekker A, et al. Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer. Eur J Cancer. 2023 Jun;186:52–61.
  6. ↑ Verkerk K, Voest EE. Generating and using real-world data: A worthwhile uphill battle. Cell. 2024 Mar 28;187(7):1636–50.
  7. ↑ Grimberg F, Asprion PM, Schneider B, Miho E, Babrak L, Habbabeh A. The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data. Digit Biomark. 2021 Aug;5(2):148–57.
  8. ↑ O’Leary CP, Cavender MA. Emerging opportunities to harness real world data: An introduction to data sources, concepts, and applications. Diabetes Obes Metab. 2020 Apr;22 Suppl 3:3–12.
  9. ↑ Berger ML, Lipset C, Gutteridge A, Axelsen K, Subedi P, Madigan D. Optimizing the Leveraging of Real-World Data to Improve the Development and Use of Medicines. Value in Health. 2015;18(1):127–30.
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