Greenlight Guru Launches Halo for Change Management, World’s First AI & ML Recommendation Engine for Medical Device Quality

Greenlight Guru Launches Halo for Change Management, World’s First AI & ML Recommendation Engine for Medical Device Quality

New predictive recommendation engine transforms the way medical device companies are able to discover, assess, and manage change

Greenlight Guru, the leading medical device quality management software (MDQMS) platform, announced the launch of Halo for Change Management, the world’s first AI and machine learning recommendation engine for medical device quality. This revolutionary product provides quality and product teams with recommendations of items impacted by a change order and the real-time visibility needed to discover, assess and manage the impact of a change. With the launch of Halo℠ for Change Management, medical device companies can stop reacting and start predicting.

Today, identifying every document affected by a proposed change order and understanding their impact is a tedious, error-prone process. Medical device organizations are left flying blind in their complex quality environment while managing the pressures to balance speed, quality and costs. Without full visibility and strategic insights, many organizations find themselves reacting to change because of siloed information, labor-intensive searches, and increased risk from missing important assets.

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For example, when executing a change order the first step is to trace and identify all items affected by the change, which is very manual and leaves room for human error. Documents and quality artifacts are siloed-away in different departments, leading to things being missed during the first pass and subsequently requiring a second or even third change order to be conducted. This not only presents extreme inefficiencies to the change management process, but also exposes companies to major compliance risk, or worse, potential patient safety concerns.

“Our recent 2021 State of Medical Device Quality Report revealed that, currently, 60% of organizations rely on traceability for conducting change, while 28% of medical device professionals say it still takes a full day or more to run a change impact analysis. This tells us that medical device professionals are working from a reactive state that is tedious and error-prone, limiting organizations from gathering the quality insights that are needed to stay ahead of change,” said David DeRam, CEO at Greenlight Guru. “Halo℠ for Change Management is the first AI feature that allows you to predict the impact of your change and provides you with recommendations for what is impacted by a change, giving you real-time visibility, reduced risk, and overall improved quality.”

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By leveraging AI and machine learning capabilities, Halo℠ for Change Management goes beyond the human capacity to predict downstream impacts and gather actionable recommendations to transform your change management process. No longer do organizations have to put compliance at risk by changing or referencing multiple change orders during an audit. Now teams can spend more time designing quality products and less time doing paperwork. Demonstrating traceability and understanding the impact of a change is made seamless through a solution that replaces reactivity with predictability.

“I am very excited about the application and potential of AI and machine learning capabilities making their way into Greenlight Guru’s platform. Halo℠ for Change Management introduces a new way to evaluate critical information regarding your change management process that will be a game-changer in the scope of medical device compliance,” says Kyle Rose, President at Rook Quality Systems. “Halo for Change Management automatically gathers all of the quality data and documents that may be impacted by a change and presents it to the user to clearly identify the scope of the change. This is one of the hardest compliance issues to manage manually and will greatly improve efficiency for all Greenlight Guru users.”

Features of Halo℠ for Change Management include:

  • AI and ML capabilities to identify and recommend documents, product development artifacts, and quality events impacted by a change

  • A recommendation engine that enables proactive feedback and provides data to support the reasoning for a change

  • Visualization of all relationships within the QMS to understand the connectivity and context of the system recommendations

  • Access to cross-functional information to provide seamless visibility and closed-loop traceability throughout the change management process

Halo℠ for Change Management introduces new efficiencies to the change management process that allows medical device organizations to move away from siloed, reactive decision-making and adopt a predictive approach to quality management that enables the best possible outcomes for both the patient and your business.

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