What if the future of medicine could be tailored to your unique needs? Traditional treatment methods often follow a one-size-fits-all approach, but what if there was a better way? Enter the world of N-of-1 trials, a groundbreaking method that focuses on individual patients to deliver precise, evidence-based care. This approach is reshaping how we think about treatment, especially for those with rare diseases or complex conditions1.
Unlike conventional randomized controlled trials, N-of-1 trials prioritize the patient’s unique response to treatments. By integrating multiple treatment modalities, this method ensures that decisions are data-driven and personalized. This shift not only improves outcomes but also empowers clinicians to make informed choices1.
Imagine a healthcare system where treatments are designed specifically for you, leveraging real-world data and rigorous experimental principles. This is the promise of N-of-1 trials, a concept that is rapidly gaining traction in modern medicine1.
Key Takeaways
- N-of-1 trials focus on individual patient needs for precise treatment.
- They shift from traditional methods to personalized care.
- This approach benefits patients with rare or complex conditions.
- Real-world data and experimental design improve outcomes.
- Multiple treatment modalities are integrated for better decision-making.
Introduction to Personalized Healthcare Innovations
Healthcare is undergoing a transformation, shifting from broad solutions to tailored treatments. This evolution is driven by the need to address individual patient needs more effectively. Traditional randomized controlled trials (RCTs) often fall short in this regard, as they focus on population averages rather than individual responses2.
Personalized evidence generation, such as N-of-1 trials, is changing the game. By leveraging rich data points and innovative research design, we can create treatments that are precise and effective. This approach bridges the gap between evidence and clinical decision-making, ensuring better outcomes for patients3.
We are committed to patient-centered research that prioritizes individual needs. By combining advanced data analysis with cutting-edge design, we aim to accelerate treatment discovery and implementation. This not only improves care but also empowers clinicians to make informed decisions2.
The integration of real-world data and experimental principles is key to this transformation. As we continue to innovate, the focus remains on delivering the right treatments to the right patients at the right time3.
Overview of Multimodal N-of-1 Trials
Modern medicine is shifting towards solutions that cater to each person’s unique health profile. This approach, known as multimodal N-of-1 trials, focuses on individual responses to treatments rather than generalized outcomes. By combining multiple treatment modalities, these trials provide a comprehensive understanding of what works best for each patient4.
Definition and Key Concepts
Multimodal N-of-1 trials are designed to optimize treatment for individual patients. Unlike traditional methods, they use active comparator crossover designs to test multiple interventions. This allows clinicians to gather data on how a patient responds to different treatments over time4.
These trials are rooted in rigorous experimental methods. They integrate real-world data to ensure that decisions are evidence-based and tailored to the patient’s needs. This approach is particularly effective for conditions with heterogeneous responses, where one-size-fits-all solutions often fall short5.
Historical Context in Personalized Medicine
The concept of N-of-1 trials originated in behavioral research and experimental medicine. Early applications focused on understanding individual responses to specific interventions. Over time, this methodology evolved to address the growing need for personalized treatment plans4.
As interest in patient-centered care increased, so did the adoption of multimodal designs. These trials now play a crucial role in modern medicine, offering a way to tailor treatments based on individual health profiles. This evolution reflects a broader shift towards precision and personalization in healthcare5.
“The integration of multiple modalities ensures that treatments are not only effective but also adaptable to each patient’s unique needs.”
Multimodal N-of-1 Trials: A Novel Personalized Healthcare Design
Individualized care is transforming how we approach medical treatments. The innovative design of multimodal N-of-1 trials focuses on tailoring interventions to each patient’s unique needs. Unlike conventional methods, this approach directly compares treatments within an individual, ensuring precision and effectiveness6.
These trials integrate multiple treatment modalities, allowing clinicians to gather data on individual responses. This method contrasts sharply with traditional trials, which often rely on population averages. By focusing on individual outcomes, we can create more effective and personalized treatment plans7.
The process of designing these trials involves rigorous internal reviews to ensure integrity. Each step is carefully evaluated to maintain high standards of evidence-based methodology. This meticulous approach ensures that the results are reliable and actionable6.
The role of the author in synthesizing research insights is crucial. By reviewing and analyzing data, the author helps promote these advanced methodologies. This synthesis ensures that the findings are accessible and applicable in real-world settings7.
“The integration of multiple modalities ensures that treatments are not only effective but also adaptable to each patient’s unique needs.”
Case examples demonstrate the success of this approach. For instance, participants in a recent study showed significant improvements in outcomes when treatments were tailored to their specific responses. These results highlight the potential of multimodal N-of-1 trials to revolutionize healthcare6.
Advantages of Personalized N-of-1 Trials in Clinical Practice
Tailored treatments are revolutionizing clinical practice by focusing on individual patient responses. Unlike traditional methods, these trials provide precise insights into what works best for each person. This approach reduces uncertainties linked to average treatment effects reported by RCTs8.
Benefits for Individual Patients
One of the key advantages is the ability to generate personalized data for each patient. This ensures that treatments are tailored to their specific needs. Immediate feedback on therapy outcomes allows for quick adjustments, improving overall care9.
For example, patients with rare conditions often face challenges in finding effective treatments. These trials address this issue by closely monitoring symptom fluctuations and treatment responses. This leads to more accurate and effective interventions8.
Optimizing Treatment Outcomes
By integrating multiple modalities, clinicians can optimize treatment outcomes. This method contrasts sharply with traditional trials, which rely on population averages. The focus on individual responses ensures that decisions are evidence-based and precise9.
Practical improvements in clinical decision-making are driven by personalized analysis. This approach not only enhances patient care but also empowers clinicians to make informed choices. The result is a more efficient and effective healthcare system8.
“Personalized trials bridge the gap between evidence and clinical practice, ensuring treatments are both effective and adaptable.”
Comparing N-of-1 Designs with Conventional RCTs
When it comes to clinical research, one size does not fit all. Traditional randomized controlled trials (RCTs) often focus on group averages, potentially overlooking variability in individual conditions. This can lead to treatments that work for some but fail for others10.
In contrast, N-of-1 trials prioritize individual responses. This approach ensures that treatments are tailored to each patient’s unique needs. By focusing on individual data, we can make more precise and effective decisions11.
Systematic review evidence supports the use of personalized strategies. These reviews highlight the benefits of individualized assessment, especially for complex or rare conditions. This shift from group averages to individual focus is reshaping how we approach treatment10.
Methodological differences also play a role. Traditional RCTs often struggle with treatment and carryover effects. N-of-1 trials, however, are designed to handle these challenges more effectively. This approach offers a flexible and responsive pathway to clinical decision-making11.
“By focusing on individual responses, we can create treatments that are both effective and adaptable.”
In summary, N-of-1 trials provide a more precise and patient-centered alternative to conventional RCTs. This method ensures that treatments are tailored to individual needs, improving outcomes and empowering clinicians10.
Real-World Data and Evidence Generation
The power of real-world data is reshaping how we approach clinical research. By leveraging sources like electronic health records, patient-reported outcomes, and wearable devices, we can gather detailed insights into individual responses to treatments12. This systematic collection of data ensures that clinical decisions are both precise and evidence-based.
One of the key benefits of this approach is its ability to improve individual patient care. By analyzing real-world data, clinicians can tailor treatments to specific needs, leading to better outcomes13. At the same time, this data contributes to broader evidence generation, helping researchers identify trends and patterns across diverse populations.
Each individual trial generates a number of precise data points. When aggregated, these points form a robust body of evidence that can inform future clinical decisions12. This process not only enhances the quality of care but also breaks down traditional barriers in evidence-based medicine.
“The integration of real-world data ensures that treatments are both effective and adaptable to individual needs.”
Despite its advantages, real-world data often faces challenges like incompleteness and measurement errors12. However, ongoing efforts to improve data quality and regulatory standards are addressing these issues. By refining our methods, we can ensure that real-world evidence remains a reliable foundation for clinical research.
In summary, the collection and analysis of real-world data are transforming healthcare. This approach not only benefits individual patients but also strengthens the broader evidence base, paving the way for more informed and effective treatments13.
Implementing N-of-1 Trials in Healthcare Settings
Integrating innovative research methods into healthcare systems requires careful planning and collaboration. To ensure success, we must address both infrastructure needs and stakeholder engagement. This approach ensures that trials are seamlessly embedded into existing workflows.
Infrastructure and Operational Strategies
Effective implementation starts with robust infrastructure. Clinical research coordinators play a vital role in managing trial logistics. They ensure data collection systems are in place and operational procedures are followed14.
For example, a recent case study highlighted the importance of structured processes. Institutions that prioritized clear information flow saw higher trial success rates15. This underscores the need for organized systems.
Engaging Multi-Stakeholder Support
Engaging stakeholders is equally critical. Clinicians, patients, and regulatory bodies must collaborate to drive trial success. This multi-stakeholder approach ensures that all perspectives are considered14.
In one case, involving patients in trial design led to better adherence and outcomes. This highlights the value of patient-centered approaches. Clear communication and structured processes are essential for sustainable implementation15.
“Collaboration across stakeholders ensures that trials are not only effective but also adaptable to real-world needs.”
By focusing on infrastructure and stakeholder engagement, we can successfully integrate these trials into healthcare settings. This approach ensures that treatments are tailored to individual needs, improving outcomes for all participants14.
Statistical Approaches in Personalized Trial Analysis
Understanding individual responses to treatments requires advanced statistical methods. These techniques help us interpret data from personalized trials, ensuring that decisions are both precise and evidence-based16. By leveraging robust statistical tools, we can address the unique challenges posed by individual variability and carryover effects.
Time Series Analysis and Autoregression Models
Time series analysis is a key method for tracking sequential treatment effects. It allows us to understand how interventions impact patients over time16. Autoregression models, on the other hand, help account for carryover effects and correlations between successive measurements. These models are essential for interpreting complex data patterns in individual trials17.
For example, autoregressive integrated moving average (ARIMA) models are often used to predict future treatment responses based on past data. This approach ensures that our analysis is both dynamic and accurate16.
Addressing Variability and Carryover Effects
Individual patients often exhibit significant variability in their responses to treatments. This complexity requires robust statistical tools to ensure accurate interpretation17. Carryover effects, where the impact of one treatment influences the next, further complicate analysis. By using advanced methods like mixed-effects models, we can account for these challenges and improve the reliability of our findings16.
“Advanced statistical methods are essential for interpreting the complexities of individual trial data, ensuring that treatments are both effective and adaptable.”
| Statistical Method | Application | Benefit |
|---|---|---|
| Time Series Analysis | Tracking sequential treatment effects | Provides dynamic insights into treatment impacts |
| Autoregression Models | Accounting for carryover effects | Improves accuracy in data interpretation |
| Mixed-Effects Models | Addressing individual variability | Enhances reliability of trial results |
In summary, the use of advanced statistical approaches is crucial for analyzing personalized trial data. These methods not only improve the precision of our findings but also ensure that treatments are tailored to individual needs17.
Applications in Rare Diseases and Comorbid Conditions
Rare diseases and complex comorbid conditions often challenge traditional treatment methods. These cases require tailored approaches that focus on the individual patient’s unique needs. Personalized trials offer a solution where conventional methods fall short18.
For example, patients with rare cancers or orphan diseases are frequently excluded from traditional trials. This exclusion limits their access to effective treatments. Personalized designs, however, allow clinicians to gather precise data on how each patient responds to interventions19.
These trials are particularly valuable in cases with multiple comorbidities. By integrating real-world data, clinicians can identify the most effective treatment pathways. This approach not only improves outcomes but also reduces unnecessary side effects18.
One study highlighted the success of personalized trials in improving survival rates for patients with glioblastoma multiforme. This demonstrates the potential of tailored treatments in challenging clinical scenarios18.
“Personalized trials bridge the gap between evidence and practice, ensuring treatments are both effective and adaptable.”
In summary, these trials are transforming how we approach rare and complex conditions. By focusing on individual responses, we can deliver more precise and effective care, advancing the field of medicine19.
Case Studies Illustrating N-of-1 Trial Success
Case studies reveal how personalized methods improve patient outcomes significantly. These real-world examples demonstrate the flexibility and efficacy of tailored approaches, particularly in complex conditions like ADHD and dose optimization15.
ADHD Trial Examples
ADHD trials have shown how rapid feedback and reliable dose finding can optimize treatment. In one study, participants experienced measurable improvements in symptoms when treatments were adjusted based on their unique responses3.
These trials highlight the importance of monitoring individual patient outcomes. By using real-time data, clinicians can make informed decisions that lead to better results15.
Dose Finding and Treatment Optimization
Dose-finding studies are another area where personalized trials excel. For example, a trial involving inflammatory bowel disease identified specific diets that reduced pain for certain patients15.
This approach ensures that treatments are tailored to each individual’s needs. It also reduces the risk of unnecessary side effects, improving overall care3.
“Personalized trials bridge the gap between evidence and practice, ensuring treatments are both effective and adaptable.”
These case studies reinforce the evidence base for tailored methods. They show how focusing on individual responses can lead to significant improvements in treatment protocols15.
Integration of Patient-Centered Outcomes
Patient-centered outcomes are reshaping how we measure treatment success. By focusing on individual experiences, we ensure that trials reflect real-world effects and patient needs. This approach integrates both subjective and objective measures to enhance reliability20.
We emphasize the importance of frequent symptom tracking. This allows us to assess treatment impacts accurately and make timely adjustments. For example, patient-reported outcomes provide valuable insights into how treatments affect daily life21.
Patient feedback is crucial for tailoring interventions. By listening to their experiences, we can optimize therapy outcomes and improve overall care. This integration strengthens the evidence supporting personalized decisions20.
Combining subjective data with clinical assessments ensures a comprehensive understanding of treatment effects. This dual approach not only improves trial reliability but also empowers patients to take an active role in their care21.
“Patient-centered outcomes bridge the gap between evidence and practice, ensuring treatments are both effective and adaptable.”
| Method | Purpose | Benefit |
|---|---|---|
| Patient-Reported Outcomes | Capture subjective experiences | Provides insights into daily impacts |
| Clinical Assessments | Measure objective treatment effects | Enhances reliability of trial data |
| Frequent Symptom Tracking | Monitor treatment progress | Allows timely adjustments |
In summary, integrating patient-centered outcomes ensures that trials are both precise and meaningful. This approach not only improves care but also strengthens the evidence base for personalized treatments20.
Overcoming Challenges in Personalized Health Research
Personalized health research faces unique hurdles that demand innovative solutions. One of the most significant challenges is recruitment barriers. Finding participants who meet specific criteria for individualized studies can be difficult, especially for rare diseases22.
Ethical concerns also play a critical role. Researchers must ensure that participants fully understand the risks and benefits of personalized therapy. This requires clear communication and robust informed consent processes23.
Methodological limitations, such as carryover effects and blinding issues, further complicate study designs. These factors can introduce biases, making it harder to draw reliable conclusions22. To address this, we recommend using advanced statistical methods to mitigate potential errors.
Real-world obstacles, like limited funding and technological gaps, also hinder progress. Collaboration with stakeholders and leveraging emerging technologies can help overcome these barriers23. For example, integrating multi-omics data can enhance our understanding of complex diseases and improve treatment outcomes. Learn more about this approach here.
“Innovative strategies and stakeholder collaboration are essential to advancing personalized health research.”
Finally, continued support and funding are crucial to drive development in this field. By addressing these challenges, we can ensure that personalized clinical trials deliver precise and effective treatments for all patients22.
Future Directions in Personalized Medicine and N-of-1 Trials
Emerging trends in healthcare are paving the way for more precise and effective treatment strategies. The integration of digital health tools and real-time data collection is transforming how we approach individualized care. These advancements ensure that treatments are tailored to each patient’s unique needs, improving outcomes significantly24.
One key area of focus is the use of advanced data analytics. By leveraging real-world evidence, we can create more accurate treatment plans. This approach not only enhances patient care but also strengthens the evidence base for future research25.
Another critical factor is the need for expanded stakeholder support. Clinicians, researchers, and policymakers must collaborate to drive innovation. This multi-stakeholder approach ensures that trials are seamlessly integrated into healthcare systems24.
Future research priorities include harnessing the power of aggregated data. By analyzing results from multiple trials, we can identify broader trends and improve treatment protocols. This method bridges the gap between individual care and population-level insights25.
“The integration of digital tools and real-world evidence is essential for advancing personalized medicine and ensuring treatments are both effective and adaptable.”
Ongoing development and sustained investment are critical for transformational change. By addressing these priorities, we can ensure that personalized medicine continues to evolve, delivering better care for all patients24.
| Focus Area | Key Strategy | Impact |
|---|---|---|
| Digital Health Tools | Real-time data collection | Enhances treatment precision |
| Stakeholder Collaboration | Multi-stakeholder support | Drives innovation and integration |
| Aggregated Data Analysis | Identifying broader trends | Improves treatment protocols |
In summary, the future of personalized medicine lies in the seamless integration of technology, data, and collaboration. These efforts will ensure that treatments are not only effective but also adaptable to individual needs25.
Ethical Considerations and IRB Processes in N-of-1 Trials
Ethical considerations are at the heart of any research involving human participants, especially in personalized trial designs. We address the ethical dimensions inherent in these trials, emphasizing patient safety and informed consent26. Ensuring that participants fully understand the risks and benefits is critical to maintaining trust and integrity in research27.
Structured IRB processes are essential to the ethical conduct of these trials. For example, the revised US Common Rule mandates that informed consent forms must be publicly available within 60 days after the last study visit26. This transparency ensures accountability and fosters confidence in the research process.
We provide examples of robust ethical frameworks that support method development and safeguard patient interests. The Oviedo Convention, for instance, establishes foundational standards for informed consent in the European Union26. These frameworks ensure that research meets high ethical standards while advancing treatment options.
Transparent processes and peer-reviewed methods help maintain these standards. By adhering to guidelines like the GDPR, we ensure that data use and consent are handled responsibly26. This approach not only protects participants but also strengthens the credibility of the research.
“Ethical diligence is essential to fostering trust and support in both clinical and research communities.”
Ethical considerations extend beyond informed consent to include trial design integrity. We outline the importance of minimizing coercion and ensuring that research aligns with the justice ethos of the Belmont Report26. This commitment to ethical principles is crucial for the development of effective and fair treatments.
| Ethical Framework | Key Focus | Impact |
|---|---|---|
| US Common Rule | Informed Consent Transparency | Ensures accountability and public access |
| Oviedo Convention | Foundational Standards | Sets high ethical benchmarks |
| GDPR | Data Use and Consent | Protects participant privacy |
In summary, ethical considerations and IRB processes are fundamental to the success of personalized trials. By prioritizing patient safety and transparency, we ensure that these trials are both effective and ethically sound27.
Technological Advances Empowering N-of-1 Research
Technological innovations are reshaping how we collect and analyze data in clinical research. These advancements are not only improving the precision of treatments but also making them more accessible to patients. By integrating cutting-edge tools, we can ensure that trial design is both efficient and effective28.
Wearable Devices and Real-Time Data Collection
Wearable devices are transforming the way we monitor patient outcomes. These tools provide real-time data on symptoms and vital signs, allowing for immediate adjustments to treatment plans. For example, devices like smartwatches can track heart rate, sleep patterns, and activity levels, offering a comprehensive view of a patient’s health28.
This continuous monitoring is particularly valuable in trial design, as it reduces the need for frequent clinic visits. Patients can now participate in studies from the comfort of their homes, improving access to clinical trials29.
Digital Health Integration for Seamless Monitoring
Digital health platforms are another game-changer in clinical research. These platforms integrate data from various sources, including wearable devices, electronic health records, and patient-reported outcomes. This seamless integration ensures that evidence is both comprehensive and reliable30.
For instance, AI-powered algorithms can analyze this data to identify patterns and predict treatment responses. This not only enhances trial design but also supports timely decision-making, improving patient outcomes28.
“The integration of wearable devices and digital health platforms ensures that treatments are both effective and adaptable to individual needs.”
These technological tools are also breaking down barriers to access. By reducing travel time and costs, they make it easier for patients to participate in trials. This is especially important for those in underserved areas, who often face challenges in accessing healthcare29.
In summary, technological advances are revolutionizing how we approach clinical research. By leveraging wearable devices and digital health platforms, we can improve trial design, enhance evidence collection, and expand access to innovative treatments28.
Conclusion
The future of medical treatment lies in approaches that adapt to individual needs. By focusing on tailored interventions, we can bridge the gap between research and clinical practice, ensuring treatments are both effective and precise31.
These innovative methods integrate real-world data, allowing for dynamic adjustments based on patient responses. For example, studies have shown that certain diets can reduce pain for specific individuals, highlighting the importance of personalized care31.
We must continue to invest in research and technology to sustain this progress. Collaboration across stakeholders ensures that these advancements are accessible and adaptable to diverse patient needs. This forward-looking vision promises a healthcare system that truly adapts to each individual’s unique profile32.
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Source Links
- Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders – https://pmc.ncbi.nlm.nih.gov/articles/PMC7955843/
- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review – https://pmc.ncbi.nlm.nih.gov/articles/PMC9104788/
- The next generation of evidence-based medicine – Nature Medicine – https://www.nature.com/articles/s41591-022-02160-z
- Healthcare – https://www.mdpi.com/journal/healthcare/special_issues/N_of_1
- Multimodal biomedical AI – Nature Medicine – https://www.nature.com/articles/s41591-022-01981-2
- Multimodal N-of-1 trials: A Novel Personalized Healthcare Design – https://abhishekshar.com/assets/pdf/fu_n_of_1_trials_23.pdf
- N-of-1 Trials in Healthcare – https://www.mdpi.com/2227-9032/9/3/330
- From hype to reality: data science enabling personalized medicine – https://pmc.ncbi.nlm.nih.gov/articles/PMC6109989/
- Healthcare practice strategies for integrating personalized medicine: Management of COVID-19 – https://pmc.ncbi.nlm.nih.gov/articles/PMC8546804/
- Harnessing Unstructured Data and Hospital Interoperability – https://www.appliedclinicaltrialsonline.com/view/harnessing-unstructured-data-and-hospital-interoperability
- The role of machine learning in clinical research: transforming the future of evidence generation – Trials – https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05489-x
- Real-world data: a brief review of the methods, applications, challenges and opportunities – https://pmc.ncbi.nlm.nih.gov/articles/PMC9636688/
- Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union – https://www.mdpi.com/2306-5354/11/8/784
- Frontiers | Enhancing the psychological well-being and sleep quality of healthcare providers with a multimodal psychological support program: a randomized controlled trial – https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1455174/full
- Bayesian Models for N-of-1 Trials – https://hdsr.mitpress.mit.edu/pub/b6efwlql
- Revolutionizing personalized medicine with generative AI: a systematic review – Artificial Intelligence Review – https://link.springer.com/article/10.1007/s10462-024-10768-5
- Deep reinforcement learning for personalized treatment recommendation – https://pmc.ncbi.nlm.nih.gov/articles/PMC9427729/
- The Application of Evidence-Based Medicine in Individualized Medicine – https://pmc.ncbi.nlm.nih.gov/articles/PMC10376974/
- N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine—A Review – https://www.mdpi.com/2072-6694/13/21/5428
- Integrated multimodal artificial intelligence framework for healthcare applications – https://pmc.ncbi.nlm.nih.gov/articles/PMC9489871/
- Case Study of Integrating Electronic Patient-Reported Outcomes as Standard of Care in a Head and Neck Oncology Practice: Obstacles and Opportunities – https://pmc.ncbi.nlm.nih.gov/articles/PMC8375640/
- Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges – https://www.jmir.org/2024/1/e53008/
- Artificial Intelligence and Patient Safety: Promise and Challenges – https://psnet.ahrq.gov/perspective/artificial-intelligence-and-patient-safety-promise-and-challenges
- Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine – https://pmc.ncbi.nlm.nih.gov/articles/PMC11508674/
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments – https://pmc.ncbi.nlm.nih.gov/articles/PMC10961052/
- Ethical Considerations in Clinical Trials for Disorders of Consciousness – https://pmc.ncbi.nlm.nih.gov/articles/PMC8870384/
- Streamlining the institutional review board process in pragmatic randomized clinical trials: challenges and lessons learned from the Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness (ADAPTABLE) trial – Trials – https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05026-w
- Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future—A comprehensive review – https://pmc.ncbi.nlm.nih.gov/articles/PMC11520245/
- Digital health technology in clinical trials – npj Digital Medicine – https://www.nature.com/articles/s41746-023-00841-8
- Artificial intelligence in healthcare: transforming the practice of medicine – https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
- Bayesian Models for N-of-1 Trials – https://pmc.ncbi.nlm.nih.gov/articles/PMC10817775/
- Multimodal system designed to reduce errors in recording and administration of drugs in anaesthesia: prospective randomised clinical evaluation – https://www.bmj.com/content/343/bmj.d5543
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