Digital-first software resources, such as software-as-a-medical device (SaMD), are the need of the hour as patients seek personalized healthcare tailored to their unique needs and preferences. SaMD solutions collect and analyze individual patient data to provide personalized insights to clinicians and patients.
Examples of SaMD solutions include software that displays and processes medical images to detect tumors, software that controls installed medical equipment, insulin dose calculators and controllers for diabetic patients, and other similar solutions. It is included.
Statista predicts that SaMD will become one of the fastest growing categories in the medtech space, with the medical device software market expected to surge from $570 billion today to $719 billion by 2028.
How SaMD improves the care delivery paradigm
As healthcare models evolve to prioritize value-based care, SaMD plays a pivotal role in optimizing outcomes, reducing unnecessary interventions, and enhancing resource allocation. It also accelerates precision medicine by rapidly analyzing extensive datasets to inform targeted treatments. As the healthcare landscape continues to evolve, SaMD stands as a key pillar in providing the personalized care that patients increasingly demand. Here’s how:
- Improve patient care: Customized medical technology software enables real-time patient monitoring, accurate diagnosis, and effective treatment planning, ultimately improving patient outcomes.
- Operational efficiency: Medical software automates tasks and streamlines workflows, freeing up staff time to focus on patient care.
- Ensuring regulatory compliance and safety: Medical software must follow strict regulations, and non-compliance can result in hefty fines. Custom software can be tailored to meet these specific requirements, ensuring device safety without financial impact.
- Facilitating remote patient care: Medical software facilitates remote patient care, enables data-driven decision-making, and provides a platform for telehealth services.
- Advanced features: By leveraging artificial intelligence (AI) and machine learning (ML), health technology software can support features such as predictive analytics and personalized patient care. This is especially important given the challenges of legacy systems.
As the healthcare industry continues to embrace digital innovation, regulatory bodies around the world, including the US FDA, the European Medicines Agency (EMA), and other national authorities, are recognizing the need for SaMD-aligned regulations.
US regulations
Under FDA regulations, SaMDs are classified into three classes based on their intended use and potential risk to patients and users: Class I (low risk), Class II (moderate risk), and Class III (high risk). Masu.
Premarket Submission Contents for Device Software Features (June 2023 – Final): A major change from the 2005 guidance document was the classification of documentation requirements as levels of low, moderate, and serious concern; basic and strengthened level. basic Pre-commercial submissions containing device software features. strengthened Contains features whose failures or defects may result in hazardous conditions.
Marketing Submission Recommendations for Prescribed Change Management Plans (April 2023 – Draft): The manufacturer is Predetermined change management plan Relates to SaMD pre-specifications or ML algorithms without requiring additional submissions for pre-market evaluation.
AI/ML-Based SaMD Action Plan (January 2021 – Draft): This action plan outlines draft guidance issued on predetermined change management plans to better evaluate and improve ML algorithms, promote device transparency, support regulatory science, and Harmonizing ML best practices to drive real-world performance monitoring measures.
EU regulations
The EU Medical Device Regulation (MDR) classifies medical devices into Class I (low risk), Class IIa (moderately low risk), Class IIb (moderately high risk), and Class III based on their intended use and risk level. (high risk). ).
Guidance on clinical evaluation and performance evaluation of medical device software (March 2020): This guidance assesses the benefit-risk ratio, demonstrates clinical relevance and scientific validity, validates technical and analytical performance, and ensures that clinical performance is consistent with patient needs. Outline the evaluation criteria for medical device software (MDSW) by reviewing:
Cybersecurity Guidance (December 2019): This guidance will ensure that devices on the EU market are able to meet new cybersecurity challenges. Manufacturers are required to develop and provide products that take risk management including information security into consideration, and minimum requirements for IT security measures are established to prevent unauthorized access.
GDPR (May 2018): GDPR includes personal data processed by SaMD when used in healthcare services or treatment. Key areas covered include data protection principles, lawful routes for processing personal data, data subject rights, data breach notification, and privacy by design and impact assessment.
These regulations and guidance documents aim to ensure the safety, effectiveness and data protection standards of medical device software. The following section is a route map for navigating these regulatory guidelines.
Clinical evaluation of SaMD
A thorough evaluation of SaMD performance is critical for patient safety and regulatory compliance. This evaluation determines whether the software can consistently provide accurate diagnoses, treatment recommendations, and other valuable medical insights.
Clinical evaluation of SaMD typically includes the following major steps:
- plan: To define how SaMD will be used, it is essential to establish a clear intended use.
- Valid clinical relevance: This step includes validating whether the SaMD’s targeted clinical condition is directly linked to the device’s output and corresponds to a clinically meaningful use case for the targeted patient population. It will be.
- Analytical validity: This ensures that SaMD processes the input data as intended. This stage may include verification and verification activities, adhering to sound software engineering practices, or extracting from previously collected evidentiary data.
- Clinical effectiveness: This phase is intended to ensure that the SaMD output data is accurate and provides the necessary guarantees of safety, performance, and effectiveness in line with the manufacturer’s intended purpose.
The above steps in the clinical evaluation process are important in assessing the SaMD’s credibility and ability to actively contribute to patient care and clinical decision-making.
However, if a manufacturer chooses to utilize clinical experience data, the data report or compilation must contain sufficient information to facilitate a reasonable and objective evaluation. This evaluation should lead to conclusions regarding the significance of the data regarding device safety, clinical performance, and/or efficacy. Information based solely on anecdotal reports or opinions without sufficient data support should not be accepted.
To aid in this assessment, it may be helpful to create a summary table detailing device-related adverse events, with a particular focus on serious adverse events. Additionally, comments should be included regarding whether the observed device-related adverse events can be predicted based on the device’s mode of operation. If a hazard is identified that has not previously been considered in the risk management documentation, steps should be taken to address it. This may include implementing additional mitigation measures, such as design changes or label changes.
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