Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors.

Objectives: This research aims to present and assess the Primary Functions of autoinjectors introduced in ISO 11608-1:2022. Investigate the risks in current autoinjector technology, identify and assess risks and benefits associated with Artificial Intelligence (AI) powered autoinjectors, and propose a framework for mitigating these risks. ISO 11608-1:2022 is a standard that specifies requirements and test methods for needle-based injection systems intended to deliver drugs, focusing on design and function to ensure patient safety and product effectiveness. 'KZH' is an FDA product code used to classify autoinjectors, for regulatory purposes, ensuring they meet defined safety and efficacy standards before being marketed. Method: A comprehensive analysis of autoinjectors problems is conducted using data from the United States Food and Drug Administration (FDA) database. This database records medical device reporting events, including those related to autoinjectors, reported by various sources. The analysis focuses on events associated with the product code KZH, covering data from January 1, 2008, to September 30, 2023. This research employs statistical frequency analysis and incorporates pertinent the FDA, United Kingdom, European Commission regulations, and ISO standards. Results: 500 medical device reporting events are assessed for autoinjectors under the KZH code. Ultimately, 188 of these events are confirmed to be associated with autoinjectors, all 500 medical devices were seen to lack AI capabilities. An analysis of these events for traditional mechanical autoinjectors revealed a predominant occurrence of malfunctions (72%) and injuries (26%) among event types. Device problems, such as breakage, defects, jams, and others, accounted for 45% of incidents, while 10% are attributed to patient problems, particularly missed and underdoses. Conclusion: Traditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements. AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework that includes standards, tools, training, and ongoing monitoring. The integration of AI promises to improve functionality, enable real-time monitoring, and facilitate remote clinical trials, timely interventions, and tailored medical treatments.

Framework for creating a qualified medical device development tool of autoinjectors.

Objectives: Autoinjectors are pivotal for precise self-administration of medications across a wide range of medical conditions. Nevertheless, the absence of a dedicated Medical Device Development Tool (MDDT) for autoinjectors represents a gap that may result in variations in the quality and regulatory compliance of autoinjectors as components of combination products. This research aim is to utilize the recently introduced Primary Functions outlined in ISO 11608-1:2022 with the title "Needle-based injection systems for medical use. Requirements and test methods. Part 1: Needle-based injection systems" to create a comprehensive MDDT framework tailored specifically for autoinjectors. Methods: To support the creation of the framework, the analysis of the FDA MDDTs that were already approved, FDA's design controls regulations, FDA's guidance related to autoinjectors, and the Primary functions outlined in ISO 11608-1:2022 were utilized. Results: The research identifies the Primary Functions in autoinjector to be Holding Force, Cap Removal Force, Activation Force, Extended Needle Length, Injection Time, Dose Accuracy and Needle Guard Lockout. Leveraging these Primary Functions and the FDA's MDDT approach, the research aims to bridge the gap by proposing a structured framework for the development of a specific MDDT tailored to autoinjectors. Conclusion: This study presents a MDDT framework tailored to the development of autoinjectors for drug delivery. This framework provides a structured methodology to support predictability and effectiveness of the autoinjector development and support regulatory review process, thereby expediting FDA approval for autoinjectors as part of combination product.

An Overview About Connected Medical Devices and Their Risks.

Connected medical devices may send and receive orders from other devices or networks, such as the internet. A connected medical device is often equipped with wireless connection, allowing it to interface with other devices or computers. Connected medical devices are becoming more popular in healthcare settings because they provide a variety of advantages, such as quicker patient monitoring and more efficient healthcare delivery. Connected medical devices may help doctors make educated treatment decisions, enhance patient outcomes, and lower costs. The usage of connected medical devices is especially beneficial for patients who reside in rural or distant locations, have mobility limitations that make traveling to a healthcare center difficult, or during the COVID-19 epidemic. Monitoring devices, infusion pumps, implanted devices, autoinjectors, and diagnostic devices are among the connected medical devices. Smartwatches or fitness trackers that monitor heart rate and activity levels, blood glucose meters that can upload data to a patient's electronic medical record, and implanted devices that can be monitored remotely by healthcare practitioners are examples of connected medical devices too. Yet, connected medical devices also carry risks that might jeopardize patient privacy and the integrity of medical records.

Patients and Organizations eHealth Services Model.

The current Finnish public hospitals' eHealth services are organization-oriented rather than patient-oriented. Patients remain passive and use eHealth services that are designed by private or governmental organizations. Patients had little or no involvement in that process. This paper introduces a novel eHealth Services Model that can be used as a tool to capture the needs of both organizations and patients. Two eHealth services were used to verify and support the validity of the presented model. The first eHealth service features came from an existing Finnish eHealth service that was designed by a Finnish organization. The second eHealth service features have been derived from the collected patients' feedback. A survey was carried out to compare the selected two eHealth services features. The finding of this paper suggests that an eHealth service with features that capture patients' needs is favorable against the eHealth service features that came from a Finnish organization. The presented model is a long-term solution that can be utilized in designing and providing the right technologies and services that will continuously satisfy the needs of patients and organizations.

Impact of Class I Software as Medical Devices (SaMDs) on Public Health.

There exist numerous low-risk class I SaMDs with CE marking under European Medical Device Directives (MDD). However, if the manufacturers will make any significant change to these class I SaMDs, the manufacturers shall comply with Medical Device Regulation (MDR) 2017/745 classifications. Class I SaMDs are self-declared without the need for notified body involvement. It is unclear how these devices are monitored if they will undergo any significant changes. Significant change may shift existing low-risk class I SaMDs to higher risk classification. In another hand, it is not clear if all class I SaMDs that are certified under MDD are registered with relevant EU Competent Authorities. Class I SaMDs may have an impact on public health if they are not known and monitored by European competent authorities.

Development Towards Patient-Centered eHealth Services in Finland.

The paper analyses the development of public eHealth services from 2014 to 2021 from the patients' point of view. The merits and missing features of the eHealth services were identified with patient interviews in 2014-2015. The list of missing features was again checked against the eHealth services in 2021. The main finding was that all the features wanted by the patients had still not been implemented. The finding of this paper suggests that current Finnish public eHealth services are organizations oriented rather than patient oriented.