SaMD are software products that are regulated by the FDA (Food and Drug Administration) in the United States and other regulatory bodies around the world. It is intended to be used for one or more medical purposes, such as diagnosis, treatment, or monitoring of a disease or medical condition. Medical Device software that are developed in compliance with SaMD regulations has enormous potential and are attracting a growing number of medical device manufacturers.  Yet it can be challenging to understand this market’s scope or see the value in investing in this technology.

In the US, the FDA has defined Software as a medical device (SaMD) as a computer program, routine or self-contained data intended for an automated information system to carry out a specific function. In Europe, the European Medicines Agency (EMA) has defined “Software as a medical device” (SaMD) as follows:

  • Software that is designed to be operated or used by medical personnel or patients;
  • Software that is designed to automate one or more steps in the clinical process;
  • Software that can be used for clinical decision-making; and
  • Software that is integrated with other equipment used to monitor patient care, such as computerized tomography (CT), magnetic resonance imaging (MRI), nuclear medicine imaging, electrocardiography, ultrasound, and X-ray machines

Although the concept of SaMD (software as a medical device) has been around for some time, it has only lately begun to gain traction in the medical community. This is because there is a wide variety of software available for use in the medical field nowadays.

SaMD is a term that has been bandied about amongst many non-specialists and may have heard from industry insiders; nevertheless, many people have yet to take the time to learn what it means or why it is so important.

This article will provide a high-level overview of SaMD, explain how the software meets the requirements to be classified as a medical device, and address some frequently asked questions.

What is SaMD in terms of developing medical device software?

In terms of developing medical device software, SaMD refers to the process of creating software that is intended to be used for medical purposes and is regulated as a medical device. This can include a wide range of applications such as diagnostic software, treatment planning software, and patient management software.

When developing SaMD, companies and developers must comply with regulatory requirements set by government agencies such as the FDA in the United States and the CE in Europe. This includes submitting premarket clearance or approval applications and demonstrating that the software is safe and effective for its intended use.

Developing SaMD requires a different approach than developing traditional software. It requires a thorough understanding of the regulatory requirements and the ability to design, test and validate the software according to these standards. This includes conducting clinical evaluations and usability studies and maintaining detailed documentation throughout the development process.

In addition, SaMD developers need to have adequate knowledge about the medical field for which the software is being developed, to ensure that the software is providing accurate and reliable results.

Overall, developing SaMD is a complex and highly regulated process, but it has the potential to greatly improve patient care and outcomes in the medical field.

What are the advantages offered by medical device software developed in accordance with SaMD guidelines or regulations?

Among the benefits provided by software for medical devices that follows SaMD standards and regulations are the following:

Safety and effectiveness: The software has been thoroughly evaluated and tested to ensure that it is safe and effective for its intended use, which can help to protect patients from harm and improve their outcomes.

Compliance with regulations: The software has been developed in accordance with the regulatory requirements set by government agencies such as the FDA and CE, which can help to ensure that it is approved for use in medical settings.

Clinical validation: The software has been clinically validated through clinical evaluations and usability studies, which can provide confidence in its accuracy and reliability.

Transparency and traceability: Detailed documentation is maintained throughout the development process, which can provide transparency and traceability of the software’s design, testing and validation, which is required to assure regulatory compliance.

Robustness and scalability: SaMD software is designed to be robust and scalable, which can help to ensure that it can perform well under a wide range of conditions and can be used by a large number of users.

Security and privacy: SaMD software is designed to protect patient data and ensure compliance with data security and privacy regulations, which can help to protect patients’ personal information and sensitive medical data.

Continuous improvement: SaMD software is designed to be continuously improved and updated, which can help to ensure that it remains safe, effective and up-to-date with the latest medical advancements.

Overall, medical device software developed in accordance with SaMD guidelines and regulations can provide a higher level of safety and effectiveness for patients, as well as a higher level of compliance with regulatory requirements.

What are the regulatory requirements for developing medical device software?

The FDA has established regulations for SaMD that include requirements for designing, testing, and labeling medical devices.

The regulations are based on the premise that there should be a gap between designing a new device or modifying an existing one and introducing it into the marketplace. The goal is to ensure that patients have access to safe, effective, and high-quality healthcare products before they are used.

Given the unique features of Software as a Medical Device that extend beyond a traditional medical device or hardware, regulators across the globe recognized the need to converge on a common framework and principles for Software as a Medical Device that enables all stakeholders, including regulators, to promote safe innovation and protect patient safety.

The International Medical Device Regulators Forum (IMDRF)  is a voluntary group of medical device regulators from around the world who have come together to reach harmonization on medical device regulation. IMDRF develops internationally agreed-upon documents related to various topics affecting medical devices. In 2013, IMDRF formed the Software as a Medical Device Working Group (WG) to develop guidance supporting innovation and timely access to safe and effective Software as a Medical Device globally. Chaired by the FDA, the Software as a Medical Device WG agreed upon the key definitions for Software as a Medical Device, a framework for risk categorization for Software as a Medical Device, the Quality Management System for Software as a Medical Device, and the clinical evaluation of Software as a Medical Device.

Reference: www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd

What are some examples of medical device software developed in accordance with SaMD regulations?

Diagnostic software: Software that is used to interpret medical images, such as X-rays, CT scans, and MRI scans, and provide diagnostic information to healthcare professionals.

Treatment planning software: Software that is used to plan and simulate medical procedures, such as radiation therapy and surgery.

Patient management software: Software that is used to manage patient data, such as electronic health records (EHRs) and personal health records (PHRs).

Telemedicine software: Software that is used to conduct virtual medical consultations and remotely monitor patients’ health.

Medical imaging software: Software that is used to enhance, view, and analyze medical images, such as DICOM viewer, 3D reconstruction and CAD software.

Medical monitoring software: Software that is used to monitor vital signs, such as heart rate, blood pressure, and oxygen levels, and provide alerts if there are any changes in the patient’s condition.

Clinical decision support software: Software that helps healthcare professionals make a more accurate diagnosis and treatment decisions by providing real-time data analysis and personalized recommendations.

All of these examples of software are intended for medical purposes, are regulated by government agencies such as the FDA in the United States and the CE in Europe, and have been developed in accordance with SaMD guidelines and regulations to ensure they are safe and effective for their intended use.

What are the critical steps in developing medical device software?

Define the problem and the solution. This includes understanding what your end users want, how they use it, and what they need to achieve. For example, if a doctor needs to assess the effectiveness of a drug, they would be looking at information such as dosage and side effects, but also looking at data such as when patients took their medication and how much time it took for them to feel better.

The next step is to determine the impact of this problem on patients and healthcare providers. This can be done by looking at statistics from other countries or groups of patients with similar conditions. In addition, you should talk with real people who have these issues. Improving patient outcomes should be the primary focus of all efforts.

After determining how much it will cost to solve this problem and how much money will be saved due to its resolution, it’s time to create a business plan for developing and deploying SaMD solutions. It’s vital for all businesses involved in SaMD projects to have clear goals for their organizations and ensure they are measurable to be tracked over time.

Once your board has approved your business plan of directors or shareholders, you’ll need to begin developing your software solutions and finding partners willing.

When it comes to creating a SaMD, you need first to understand the Software in itself. This includes understanding the software development process, the different phases of software development, the use of existing medical devices, and the use of Software in medicine.

Critical processes for creating software for medical devices in line with SaMD rules include:

Regulatory compliance: Conducting a thorough review of the relevant regulations, such as those set by the FDA in the United States or the CE in Europe, and ensuring that the software development process is in compliance with these regulations.

Risk management: Identifying and assessing potential risks associated with the software and implementing measures to mitigate these risks.

Design controls: Establishing a design control process, which includes documentation of the design and development process, testing, and validation.

Clinical evaluation and usability studies: Conducting clinical evaluations and usability studies to demonstrate the software’s safety and effectiveness and to provide evidence of its clinical validity.

Software validation and verification: Verifying and validating the software through testing and documentation of the software’s performance.

Quality system regulation: Implementing a quality management system that complies with the relevant regulations, such as ISO 13485 for medical devices.

Cybersecurity: Ensuring that the software is secure and complies with any relevant cybersecurity regulations to protect patient data.

Premarket clearance or approval: Submitting an application for premarket clearance or approval to the relevant government agency, such as the FDA in the United States or the CE in Europe, which includes demonstrating that the software is safe and effective for its intended use.

Post-market surveillance: Developing a plan for post-market surveillance, which includes monitoring the software’s performance after it has been cleared or approved for use.

It’s important to note that this list is not exhaustive and that depending on the specifics of the software and the regulations, some steps might require more attention than others or there could be additional steps to consider. It’s also important to have a team of experts with knowledge in both software development and medical device regulations to ensure that the software is developed in compliance with the SaMD guidelines.

The medical device software you developed will require updates and fixes even after it has been released, so be sure to factor those into your plans. It is imperative that you maintain a steady cycle of solution updates and adjustments.

Conclusion:

Digital Health companies can develop Medical Device Software and Apps as per SaMD regulations by following a few key steps.

First, they must conduct a risk analysis and determine the classification of their device. This will determine the level of regulatory oversight required.

Next, they should ensure that their software meets the relevant standards and guidelines, such as those set forth by the FDA or EMA. This may include conducting clinical studies or obtaining premarket clearance or approval. Finally, they must implement robust quality management systems to ensure that the software is continually monitored and updated to meet the latest standards and guidelines.

It is also important for Digital Health companies to be aware of the compliance requirements for SaMD. This includes ensuring that their software is secure and protected against hacking or data breaches, as well as complying with data privacy laws such as HIPAA in the US.

Maxis Clinical Sciences can help Digital Health companies in their journey of developing SaMD-compliant software and apps by providing a range of services, such as:

  • SaMD classification and risk analysis
  • Clinical evaluation and study design
  • Regulatory compliance and consulting
  • Quality management system implementation
  • Compliance and security assessments
  • Technical documentation and labeling
  • Software validation and verification

Maxis Clinical Sciences has a team of experts with experience in SaMD regulations and compliance, as well as a track record of successfully navigating the regulatory landscape for Medical Device software and apps. We can provide guidance and support throughout the entire development process, from concept to launch, to help ensure that the software is compliant and meets the needs of both the company and the end users.

In conclusion, Software as a Medical Device (SaMD) is a rapidly growing field with significant potential for improving patient outcomes. Digital Health companies can develop SaMD-compliant software and apps by following the regulatory guidelines and compliance requirements set forth by the FDA and EMA. Maxis Clinical Sciences can provide valuable assistance in this process, with a range of services to help companies navigate the regulatory landscape and develop SaMD-compliant software and apps that are safe and effective for use in medical settings.

By working with a specialized partner like Maxis Clinical Sciences, companies can focus on developing innovative software solutions while also meeting regulatory requirements.

In the last few years, the drug discovery field has seen tremendous advancements in artificial intelligence. Due to its role in RWE analytics and candidate drug identification, artificial intelligence (AI) in drug development is also a contentious area of discussion. Use of AI and ML in drug R&D has been hailed as a new way to cut time, money, and resources. However, the global market for AI in drug development is rising even as experts argue over the technology’s usefulness. As per Transparency Market Research, the global market is projected to cross US$ 10.93 Bn by 2031. If a trend is to be followed, pharma companies must take the lead and proactively adopt the technology.

Although artificial intelligence technology is still in its infancy, it has prompted a paradigm shift in clinical research. Researchers can complete tasks more accurately and quickly by incorporating machine learning (ML), a data-driven branch of AI that automates analytical model development, into clinical processes. This, in turn, allows front-line healthcare personnel to treat patients effectively.

Analytics have long influenced healthcare decision-making.

A priori, mathematical equations were used to describe recurring patterns in data. However, today AI allows scientists to find intricate correlations inside datasets that cannot be seen with traditional, equation-based statistical analysis.

In contrast to human doctors, data-rich computer algorithms with human-like intelligence can see and process enormous amounts of information with pinpoint precision. For instance, analyzing a TB-infected lung, computer vision outperformed the best expert human radiologists in every case, identifying in great detail the sizes of all TB granulomas and the location of any cavities inside each lung. In the future, these algorithms will find correlations where they have not previously been observed, helping researchers make many new discoveries.

Key figures in the healthcare industry are taking notice of the fact that AI has the potential to drastically reduce the financial burden of doing research by improving clinical decision-making and maximizing innovation.

Many organizations are eager to get their hands on AI technology, but it still needs to be put within reach for many smaller companies. Data aggregation, for instance, is still a significant obstacle, and to conform to stringent rules for drug discovery, algorithms also need to be more open and understandable. This essay looks at how much the use of AI speeds up research and sparks new ideas, making it easier to analyze data and find new drugs.

Find out how AI is used in the Drug Discovery and Development Process

Using AI in Clinical Research

Even though the term “artificial intelligence” isn’t clear, most people understand it to mean machine learning that can do tasks that usually require human intelligence, like seeing, hearing, making decisions, and translating languages. Most AI implementations today are narrow in scope, solving only a few problems.

Artificial intelligence (AI) is typically used in the medical research industry for pattern recognition and data analysis in massive datasets. Significantly faster, more accurate, and more cost-effective than standard analytical procedures, this data can be used to cut costs and boost results.

Similarly, AI may be used to combine disparate datasets and sift through mountains of scientific literature to uncover studies with practical use. According to the World Health Organization, cancer is the second leading cause of death worldwide and accounts for nearly 10 million deaths annually. The AI system will be able to apply machine-learning techniques to identify promising new cancer drugs once it has compiled and “translated” this massive amount of data into a common language. Such advances could make a significant impact on cancer treatments and even reduce cancer mortality rates.

As stated by the U.S. Food and Drug Administration, there are five steps to the usual drug discovery approach.

It all starts with research in the lab. As a next step, the potential candidate undergoes a series of tests before entering clinical trials and testing in humans. The next step is a decision by the FDA on whether or not to approve the medicine. Once a drug or medical device is on the market, the FDA keeps a close eye on it to ensure it is safe for use.

Step 1: Discovery and Development
Research for a new drug begins in the laboratory.

Step 2: Preclinical Research
Drugs undergo laboratory and animal testing to answer basic questions about safety.

Step 3: Clinical Research
Drugs are tested on people to make sure they are safe and effective.

Step 4: FDA Review
FDA review teams thoroughly examine all of the submitted data related to the drug or device and make a decision to approve or not to approve it.

Step 5: FDA Post-Market Safety Monitoring
FDA monitors all drug and device safety once products are available for use by the public.

AI can be used in the first three phases of drug development by companies that use it to speed up and cut costs of research and discovery.

This article summarizes the most significant applications of AI across the drug development life cycle.

Possible Drug Discovery Process:

The preliminary phases of a drug discovery project are crucial. Possible applicants are screened at this point. In the early stages of drug discovery, high throughput screening (HTS) is the most common screening method. Rapid testing of large libraries of compounds is made possible by integrating robotics and automation.

How scientists find a chemical that helps patients:

  1. By gathering fresh information regarding disease mechanisms
  2. Molecular testing
  3. Investigating the unintended consequences of current therapies
  4. Using advanced methods of technology to handle genetic engineering

It’s at this point that AI can help out. Scientists may use artificial intelligence to sift through the millions of compounds in their databases far more quickly, efficiently, and accurately than is possible with human scientists alone.

Because of this, a less synthetic effort is required to identify molecules with medicinal potential.

Patient Recruitment

The trial’s overall success depends on recruiting the correct patient population. It is the research team’s responsibility to select participants with the highest probability of benefiting from the treatment being examined. Modern healthcare IT uses artificial intelligence to help find patients and locations for trials.

Mainly, researchers use AI and ML algorithms to identify venues with the most extensive recruitment pool and to propose recruitment techniques. The entire selection procedure is bolstered by artificial intelligence and data analytics, from demographic mapping to patient potential prediction.

Patients are matched with trials with the help of automated technologies that assess medical records and real-world evidence.

Patients Withdraw

Approximately 30% to 40% of participants will withdraw from a study at some point. Contrarily, after five months, 40% of patients are still non-adherent, which results in extra expenses for the pharmaceutical sector. The burdensome nature of patient reporting contributes to high attrition rates. Day-to-day data collection includes recording medication taken, body functioning, and other responses.

Regardless, developments in AI are assisting in enhancing patient retention and medicine compliance. When devices like smartwatches, sensors, fitness trackers or monitors and algorithms are used together, patient data can be automatically read and interpreted.

Clinical trial researchers can then receive immediate results from these deep learning models’ data analyses. Dropout risk prediction is another area where predictive analytics vendors can produce insightful models.

Real World Evidence

The field of RWE analytics examines the usefulness of drugs and medical procedures in actual clinical settings. The pharmaceutical business places a premium on this phenomenon because of its unrealized potential.

This vast trove of information in the medical field serves as a data blanket that can be used for a wide range of analytical purposes. New research paradigms for precision medicine can also be gained using RWE analytics.

Large data sets, including patient outcomes, collected through insurance claims, electronic health records, and other sources are essential for RWE’s cutting-edge analytics.

This information is then utilized to weigh the social benefits of medicine alongside its therapeutic benefits. Data analytics based on real-world evidence is widely recognized as an instrumental component of the drug development process, contributing to the safety and efficacy of treatments while keeping costs down.

Understanding the peculiarities of various patient populations is also a key takeaway from real-world evidence strategy and analytics.

However, tons of computational resources are needed to convert raw data into knowledge to develop real-world evidence. When this occurs, we can rely on artificial intelligence.

Machine learning is a cornerstone of RWE implementation. Intelligent algorithms can be used to generate crucial information to tackle problems that would otherwise take too much time and money to fix.

In particular, machine learning offers novel possibilities for consolidating disparate data sets into a single, authoritative repository. Analytics models can be executed constantly and at scale by tapping into the connected data environments via algorithms.

Trial Data Analysis

Researchers face massive amounts of operational data during study wrap-up. However, the vast majority of insights are hidden by data silos and isolated data architectures. For instance, developing a global trial portfolio takes a lot of work. Dispersed geo-based data is stored in isolated silos on local computers, making it infeasible to put into any analysis rapidly.

Analytics tools with artificial intelligence capabilities can centralize all relevant data and metrics. Algorithms do data cleansing and analysis on trial data and reporting tools display the results graphically to aid decision-making.

The new drug is sent to the FDA for a complete evaluation after undergoing extensive testing and development to ensure its optimal efficacy and safety. The results of clinical trials are reviewed by the FDA, which then decides whether or not to approve the medication.

How can we incorporate real-world data into Drug development process?

RWE’s contributions can be found in every phase of the pharmaceutical industry. Researchers can only explore some feasible therapeutic combo due to a lack of time and money. When paired with automated processing, data analytics applied to evidence from the actual world becomes self-sufficient.

The FDA claims it uses RWE to monitor post-market safety and aid in regulatory decision-making. Healthcare practitioners frequently rely on strategy and analytics based on real-world evidence when making choices about coverage and developing health support tools for use in clinical practice.

Insights from RWE also provide a foundation for clinical trial designs and investigations, which pave the way for novel, data-driven treatment strategies.

Drug development difficulties with artificial intelligence

When compared to more traditional methods, the fact that AI systems can quickly come up with hypotheses for pharmacological targets is a big plus. This makes it possible to look into a broader range of potential treatments and find interesting candidates for more research quickly. But due to several obstacles, AI-developed pharmaceuticals are still in their infancy.

Acquiring Data

Intelligent systems live upon vast datasets to provide a high accuracy rate. Accordingly, the accessibility and quality of data are two pillars for implementing AI in drug research. Healthcare, however, is one of the sectors that has been hesitant to adopt AI. The main barriers are difficulties gaining access to the data, problems with regulations, and conflicts of interest.

In addition, there needs to be more statistical data because there are so few studies on rare disorders. This hinders scientists’ ability to amass enough information to train an AI algorithm adequately.

Data Evaluation

Predictive analytics for businesses often deals with relatively simple data, but analyzing a patient’s genetic makeup provides a significant analytical hurdle. There is a complex interaction between a patient’s biological traits and treatment history. Because of this, reactions to medications are often unpredictable. Analysis and prediction of the outcome are both challenging.

So, ML models can accurately predict the effects of combining medications at the dose level. However, determining the impact of more than two medications used together remains challenging. Because of this, there is still a need to change how ML algorithms are used in the pharmaceutical industry.

Disclosure of algorithms

As the most powerful technology, deep learning is the mysterious engine behind AI. Deep learning predictions are spot on, but they are difficult for a human expert to decipher. The fact that it is not deterministic means that it can be processed in various ways. Instead, the result can be seen by researchers without any additional context being provided.

Many organization developers stress the need for smart systems adhering to the four pillars of trust. As their primary goals, all algorithms should be built with fairness, explainability, robustness, and transparency.

For scientists to rely on algorithms for making essential conclusions, the computer’s reasoning must be concealed.

Data privacy and availability

On the route to a unified data analytics pool, the availability of patient datasets is another obstacle. Even though they gather it, hospitals and clinics do not own patients’ personal information. Instead, they are tasked with working as data trustees who are bound to confidentiality.

In this case, the patient owns all of the information. Only with patient permission may data be shared. As a result, most data sets are confidential and cannot be shared for study.

In need of a trusted innovation partner to assist with AI-driven Drug development? Contact Us!

Conclusion

Making new pharmaceuticals is a lengthy and intricate procedure. From the time a drug is first thought of until it receives FDA approval and then hits the market, it is a lengthy process that typically takes around ten years.

However, with AI’s help, we may cut this time in half while reducing drug discovery expenses and increasing precision.

On the other hand, researchers can avoid making medications with hazardous or off-target effects by using AI technology to forecast how molecules will interact.

Problems still exist, but AI-created pharmaceuticals are making their way onto the market. As more and more information becomes available, we should expect to see AI’s latent capabilities fully realized shortly. However, AI is already changing the pharmaceutical industry, so it makes sense to predict that we will begin seeing AI-created drugs in our lifetime.

Are you falling behind in Drug Discovery because you’re not maximizing AI’s potential?

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Decentralized clinical trials are enhancing efficiency and participation, thanks to advances in clinical research, digitization and technological advances.

Life science industry adopted the new norm of Decentralized clinical trials, which is a crucial component. In the past decade, technological advances and clinical research have created positive momentum. Life sciences companies worldwide have adapted to new technologies on an unprecedented scale.

Biopharmaceutical companies and contract research organizations are now turning to decentralized clinical trials (DCTs) to minimize patient interaction. This is to enhance the overall patient experience and maintain the integrity of ongoing studies. It has already become evident that the virtual approach provided by DCTs is advantageous to life science enterprises. The dynamics of clinical trials have changed, and DCTs are now recognized as a respectable methodology for research projects. There have been many improvements to how clinical trials are done, such as making it easier for patients to take part in the studies and for venues to run them, lowering costs, and making it easier to merge data.

Implementing decentralized clinical trials

Clinical development will take time and effort, no matter what strategy is used. To reduce the overall burden of clinical trials, it is vital to find and use solutions that can be changed and used on a larger scale.

Brick-and-mortar study sites are commonly used in traditional clinical trials, and these locations act as the hub of clinical development within a specific region or geographic location. In most cases, a designated investigator team is responsible for carrying out the testing at the satellite facilities or the direct patient tests with patient-reported outcomes. This methodology calls for in-person observations, examinations, and assessments to be carried out at several research locations.

The most important factors that should guide the design of decentralized clinical trials are the requirements of the studies and the patients’ experiences. In a hybrid clinical trial, for instance, some patients would go to a physical location for an ECG/EKG or a tumor biopsy, while other patients might participate electronically. In this scenario, as well as others like it, patients often use an electronic patient-reported outcome (ePRO) solution, such as a smartphone app or software solutions, in addition to instruments for electronic clinical outcome assessment (eCOA).

Learn more about how Maxis Clinical Sciences can meet your needs for electronic patient-reported outcomes and electronic clinical outcome assessments.

Patients participating in treatment cycles that usually need site visits can now have a patient kit or medicine sent to their homes if they participate in specific research programs. To determine insulin or cortisol levels, for instance, clinical study researchers who need samples of patients’ blood or saliva can send those samples to patients as sampling kits. Patients will then gain access to a digital platform or a virtual healthcare provider who will instruct them on how to carry out the tests remotely using telemedicine. In addition, participants are allowed to communicate their concerns and data with the study coordinators.

Bringing patients’ care closer to their homes has several advantages but presents many challenging obstacles. When conducting a decentralized study, one of the most complex treatment problems that must be solved is the coordination of home-based treatments that call for the expertise of a trained medical practitioner. When a broad population from several different sites is included in the clinical study, it might make overcoming these problems much more difficult.

Focusing on the patient is essential, as it has never been more important than it is right now. Developing scalable, flexible, and intelligent solutions will be vital for long-term success, even though the decentralized clinical trial approach is both distinctive and essential.

Keeping up with the evolving needs of clinical studies with a conformable procedure

The notion of a “decentralized clinical trial” is that researchers can recruit and treat patients at multiple locations to enable a wider geographical distribution of subjects and help detect treatment effects earlier, since the timeliness of treating patients. A robust protocol framework and study structure that uses DCT solutions as an integrated alternative must be designed and implemented for future study designs.

One of the most complex parts of a clinical investigation can be coming up with a strong protocol and study design that can be changed if needed. Many businesses are ready to turn their traditional clinical research methods into fully registered and integrated decentralized clinical trials (DCTs) that use streamlined technology to make the whole trial more effective and easier to manage. Assume sponsors, CROs, and IT firms collaborate. In that case, they can use digital and direct-to-patient solutions that collect data from a distance and reduce or eliminate the need for in-person meetings.

When moving from a traditional research method to a digital or decentralized one, the protocol framework and study structure must be changed to fit the new method. No matter where the study occurs, it helps to have a design that can be changed to fit the problems and changes that clinical trials face.

Additional requirements must be met to carry out a successful decentralized clinical trial. These requirements are based on things usually taken into account in clinical studies. They include schedules for patient visits, methods, and rapid drug formulations or approvals for investigational new drugs. In the case of DCTs, the study’s leaders need to evaluate how sustainable remote or tele-visits are, how to keep track of changing quality and regulatory standards, and how to guarantee that the clinical study will be safe regardless of the location. How clinical study methods are carried out can also differ, which is another factor that can affect clinical results. These variances can bring unpredictability into crucial endpoints of the study, which must ultimately be taken into consideration in the calculations for the sample size and the plans for analysis and monitoring.

To get reliable patient results and consistent patient-reported outcomes, the study must stay on track from when it starts until the last patient takes part. Despite the difficulties, the most important thing to do to make progress is to become more adaptable and efficient, cut administrative burdens, and prioritize patient-centred care without sacrificing the quality of clinical studies or data integrity.

Maintaining patients’ interest within a digital setting

With the help of decentralized clinical trials, patient participation and retention have significantly increased. However, patient engagement and retention remain challenging aspects that must be carefully examined as part of the study design process.

Many things can change the number of patient visits and the quality of the reported data. These problems could become a burden for patients and cause more people to drop out of clinical trials, which can cause results to be delayed or wrong.

Even with the use of direct-to-patient and digital/virtual solutions, DCTs continue to face a significant challenge regarding the retention of their patients. There is no need for direct patient-provider engagement when patients may complete paperwork linked to the protocol on their own time and place, such as online surveys, eFeasibility assessment, eConsent, patient journal forms, and other similar documents. After that, clinicians can use a virtual technology platform to collect the data and check it as it is being collected. When figuring out how much stress a study puts on patients and whether or not it’s possible to collect patient data remotely, it’s essential to look at the study’s overall goal and how complicated it is.

Biopharma companies and large CROs need to work together on digital technology and unified solutions to help patients feel safe and at ease. For instance, a decentralized clinical study might run with participants in different regions. GPS and RFID technologies, which automate reporting through a research app based on location data provided by patient devices like mobile phones, allowed the study’s managers to detect these areas. Therefore, as long as patients are in a region that can support the system’s GPS and RFID technologies, investigators can automatically record the time, date, location, and type of medical encounter that took place with a patient.

In addition, predictive analytics, which is based on earlier studies, can assist in anticipating the actions of patients, which can have a positive effect on enhanced data collecting and patient retention. The ability to monitor patients remotely allows tracking of these trends, which can assist investigators in identifying anomalies before they occur. For instance, it is anticipated that a research site will have a risk rate of adverse events equal to thirty per cent. Although it is currently at 15%, projections show it will eventually reach or even exceed 30%. A sponsor could engage with investigators and patients to avoid problems before they arise by utilizing predictive analytics and artificial intelligence.

Patients’ participation, retention in the study, and receiving quality care are essential success factors. Although decentralized clinical trials frequently improve patient enablement, there are still many factors to consider and obstacles to surmount in terms of patient retention and patient participation. It is very important to consider patient burdens when planning the study, making the protocol, and choosing the equipment for the study.

Establishing digital solutions that are suitable for DCT standards

A direct, patient-centric home healthcare model that efficiently satisfies the interests of sponsors, study participants, and investigators is possible only with the use of technology that is trustworthy and easy to use.

Clinical research is susceptible to being hampered. However, digital health solutions and unified platforms powered by more advanced technology are emerging as potential answers to the difficulties of decentralized clinical trials. Using technologies such as artificial intelligence and advanced analytics, virtual study capabilities, and various interoperability are considered superior alternatives for enterprises conducting clinical research. With these tools, researchers can speed up their work, improve the quality of their data, and get more patients and sites involved.

It is essential to have both a site portal and a patient portal to improve data quality, patient engagement, site engagement, and partnerships with sponsors. This is especially true for completing online surveys and feasibility and eConsent papers. Clinical research start-up management tools can help accelerate site selection and patient recruitment, despite the limitations of these solutions.

There are also options available that allow sponsors and CROs to enhance the patient experience, produce significant evidence during clinical studies, and get more value from the collected data.

Find out what data solutions and technological tools can help your decentralized clinical trials.

Researchers are more interested in employing biosciences technology to enable decentralized clinical trials or improve traditional trials. Sponsors are realizing that a more patient-centric approach increases the efficacy of studies. They are employing technology to deliver better care, be more sensitive to the needs of patients, and lessen the hassles both sites and participants face.

Even before recruiting patients, site teams and investigators can devote more time and attention to patients’ consultation and overall well-being if they use more advanced technology. Recent changes have made it easier for patients to participate in clinical research. For example, patients can now use community websites to find out more about the research before deciding whether to participate.

Patients do not need to travel to provide electronic consent or complete any other documentation because of improvements in technology. Even before the clinical trial has begun, they can raise questions or express concerns to the investigator, which helps develop a relationship between the two parties. Health professionals discovered that remote patients gave more consistent replies, leading to better reporting.

 A new benchmark for the industry

There is nothing unusual about conducting clinical studies in a decentralized manner. Studies that are shown in a format that is either entirely or partially virtual have become the new norm in this field. They make it easier for sponsors, researchers, and patients to do their jobs by improving data quality and helping researchers do their jobs more efficiently. Users can start with a basic digital clinical study solution, being flexible, and adding more advanced features as their clinical operations and goals progress.

In the end, clinical investigations made possible through digital technology give flexibility. They can shift and pivot when the circumstances change and lessen the regulatory documentation and logistical issues connected with such changes. Most crucially, it is possible to achieve all of these objectives while maintaining an unwavering concentration on enhancing the quality of life of patients and minimizing their suffering in general.

Maxis Clinical Sciences’ professionals have extensive experience with the decentralization process of clinical trials. We provide all the necessary tools and technology to ensure the studies go off without a hitch. To ensure that clinical studies get the most benefits possible at each stage, our experts employ strategic collaboration with Sponsors to perform clinical data analysis and trial report preparation and provide a tailor-made solution for clinical trial studies.

Contact us today. We are always available to consult and offer a tailor-made solution for your clinical trial studies.