During the product development pipeline, a lot has to go smoothly. Everything from the planning, design, tweaking, and especially implementation hinges on the quality and safety of your product. Whether your industry includes health science, app development, or even space, nuclear, or automotive sectors, understanding how quality control versus quality assurance works, and how they are different, is crucial. Quality planning is part of quality management. Organizations should set quality objectives and then specify the necessary requirements for operational processes, including related resources to achieve those quality objectives.

An organization focused on quality promotes a culture that results in the behavior, attitudes, activities, and processes that deliver value through fulfilling the needs and expectations of customers and other

relevant interested parties. Delivering a quality product refers to a product’s ability to meet consumer demand, expectations, and requirements while satisfying certain criteria like safety and consistent functionality. These standards not only ensure that your product or services satisfy consumers, but that it remains consistent throughout development and launch. Assurance and control are how quality is determined and kept. The quality of products and services includes not only their intended function and performance but also their perceived value and benefit to the customer. 

Related: What Is The Difference Between Clinical Evaluation and Clinical Investigation?

What is Quality Control?

Quality control, sometimes abbreviated as QC, is a process by which manufacturers identify and correct defects in a product. Its focus is on the product or service itself. Quality control focuses on fulfilling quality requirements. The quality control pipeline ensures that the manner in which a product or service is developed, maintained, and implemented are followed closely and accurately. Quality control is also an important step for verifying that deliverables meet standards set by your company. 

Quality control is a mainly reactive process since it is done in response to a known failure or defect. You are, in essence, controlling for the mistake or defect in order to prevent it from happening again. This is crucial since no product can survive development and launch if it is riddled with problems that remain unsolved. 

Consult with an expert to learn strategies to identify and solve problems to get your product launched.

What is Quality Assurance?

Quality assurance, also known as QA, is the cousin of quality control, in that both are needed in order to make doubly sure a product works as promised. The main goal of quality assurance is to prevent a problem or a defect before it even happens. Its focus is on processes. It works alongside quality control procedures in order to correct and plan ahead for issues that could lead to problems with the product. Quality assurance protocols monitor the activities, procedures, and processes that create the product are being followed appropriately. Quality assurance focuses on providing confidence that quality requirements will be fulfilled. Quality assurance departments are required to manage the compliance of government regulations and maintain production costs to ensure quality and patient safety. QA professionals oversee operations, so products and services meet current Good Manufacturing Practices (cGMP), for example, and internal company standards. They are also responsible for training, audits, documentation, and communication to leadership.

Quality assurance, due to planning for defects rather than reacting to them as quality control does, is a proactive manner of correcting mistakes. It prevents the defects from happening, recognizing potential failures that could arise in the pipeline, and may include contingency instructions for personnel involved.

Related: NEW Podcast Episode: 6 ways to collect Clinical Data with David Rutledge

Quality Assurance vs. Quality Control

Now that we have reviewed what quality control and quality assurance are in principle, it’s time to go over how they differ exactly between each other. Both have the same end goal of correcting problems and creating a better product. However, how and when quality control or quality assurance is implemented is what sets them both apart. 

Learn how our quality management service can play a significant role in helping you get your products approved.

Proactive and Reactive

Quality assurance is what is known as a proactive process, meaning that it aims to prevent problems before they happen through the design and construction of a product. Quality control is a reactive process, in that it responds to and corrects problems that have arisen already. Both are needed for quality to be maintained effectively. 

Quality assurance includes the design of documentation procedures like standard operating procedure (SOP) that all personnel must follow. These are preventative measures that keep things running smoothly. Quality control usually includes consistent and constant testing of products in order to not only find faults and areas for improvement but to confirm the product is performing as intended. The general order of things is that a quality control test sparks a quality assurance inquiry that corrects the documentation to include the results of the testing. 

Process and Product

The other most obvious difference when explaining quality assurance vs. quality control is that quality assurance procedures are focused more so on the processes that lead to a product’s quality, while quality control is concerned with the issues of the product itself. In other words, quality assurance concerns itself with the process, while quality control cares more about the product. 

Quality assurance can include detailed documentation, auditing, training, and implementations of engineering of administrative controls. Quality control involves a lot of product and laboratory testing in order to elucidate common problems that may occur. 

System and Parts

Quality assurance is concerned with the systems and procedures in place that establish quality standards. This process then tailors each pipeline towards creating better, more unified products. Quality control protocols are more focused on the individual parts that may fail within a final product; for instance, during testing of medical device systems that contain many components to support access, delivery, deployment, and then withdrawal.  Finding areas of a component or part failure is an element of quality control. QA procedures will usually lead to these instances of QC by modifying the system to include different parts.

Creation and Verification

Quality assurance activities spark changes in the pipeline for creating better-quality products. Quality control verifies that this created system works as intended. It is used to build better products in the future. QC is done post-manufacture for verification, while QA occurs early on prior to manufacture during the planning process.

Team and Personnel

Quality assurance protocols usually involve the efforts of the entire team; every member of the group works together to develop and adhere to QA documentation and SOPs. Quality control is usually the responsibility of individual personnel by testing products and following the SOPs for product verification. QA’s training across the entire workforce is part of this machinery.

Related: Mythical Medical and the Unbuilt Team: A hiring process that works even when regulatory requirements change, or global health pandemics occur

What is The Role of an eQMS?

An effective Quality Management System marries QA and QC together to ensure that they are followed appropriately. An enterprise quality management system (eQMS) allows leaders to oversee and document the process by way of cloud-based oversight. This system will ideally integrate all aspects of QA and QC to track changes.

If you do choose to use electronic systems, which I support regardless of the size of the company, make sure you use them consistently. Do not make exceptions for certain individuals, e.g., selected healthcare employees or senior-level management. It creates new levels of inefficiency and risks. This requires documents to be signed electronically or manually, scanned, and attached to records. Emails are required to document notification times or escalation of issues. It is inefficient and consumes the use of additional personnel resources. It sets a very poor example for employees that are being informed by management about the importance of quality.

Take-Home Message

Quality planning is part of quality management which focuses on setting quality objectives and specifying necessary operational processes, and related resources to achieve the quality objectives. Quality control  and quality assurance are essential parts of unifying the process for product creation and streamlining testing for better products. Both are critical for implementing new and exciting products for consumers.

Contact us today to learn how you can get your products approved with the proper quality assurance and quality control techniques.

Will artificial intelligence (AI) and machine learning (ML) be the next big thing in medical devices? Will it be that new adventure for you?

These technologies have the potential to transform health care and medical devices and are at the center of a new revolution in this field. The ability of AI and ML software to learn from real-world feedback (or training) and improve its performance makes these technologies remarkably positioned to further the development of software as a medical device.

AI is a general name for ways that computers and computer programs build models of the world from data and examples. These models can then be used to answer questions, find patterns, predict futures, etc. It is broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs.

ML is an AI method that can be used to design and train software algorithms to learn from and act on data. Software data analytic engineers can use ML to create an algorithm that is “locked” so that its function does not change, or “adaptive” so its behavior can adjust over time based on new data inputs. It is this unlocked, active, real-world learning that is so very exciting.

AI and ML technologies differ from other software in that they have the potential to adapt and optimize device performance in real-time to continuously improve health care for patients. Many clinical, regulatory and quality personnel in the medical device industry are familiar with “inputs and outputs” within a quality management system. Conceptually, there are similarities here. However, the regulatory framework to support this will have to mature.

Demystifying Artificial Intelligence (AI) and Machine Learning (ML)

The figure below is commonly used in the field to introduce AI and ML. In simplistic terms, it is like a set of Russian dolls nested within each other. AI is an umbrella term for any computer program that does something smart. AI is not ML, per se. If there is something you can do in a few minutes or hours, AI, with the right rules, or “if-then” statements, in place should then be able to do that in a second or less. AI is the overarching discipline that covers anything related to making machines or devices smart. Once AI begins learning and starts making suggestions, then ML has kicked in. AI, and specifically ML, are techniques used to design and train software algorithms to learn from and act on data.

If you use the typical cruise control within your vehicle, then you are using the results of AI. Cruise control is not making decisions for you but using inputs and outputs to set the velocity you have chosen. Commonly used cruise control is not changing your speed based on driving conditions, so it’s not technically ML. If you have email spam filters or priority mail filters on your computer, then you are using AI. If you have a smartphone that uses location technology, maps can be used to show you where you are. Vast amounts of additional data can be fed into software programs to assist with finding fuel, food, hotels or resorts. All ML is AI, but not all AI is ML. As helpful as these AI software products are, they typically lack the ability to learn independently. They cannot think outside their code. Machine learning thus is a branch of AI that aims to give machines the ability to learn a task without pre-existing code.

However, if your smart phone begins to incorporate user-reported traffic incidents like construction and accidents, and learns from that, then it can suggest the fastest routes. If that program is learning from your previous choices, then it can suggest the types of fuel, food, or resorts that are best for you. These would be examples of ML. Other examples would be voice to text and typing auto correction.

AI and ML are good for large data sets where patterns are difficult to find using human intelligence alone. AI is good at understanding interactions. The challenge is to ask a specific, relevant question.

Nisha Talagala is the CEO of Pyxeda AI here in Silicon Valley and she provides her perspective on AI’s future potential.

“AI has tremendous potential for medical devices, with applications ranging from diagnostic assistance to personalized health, preventative care, and holistic wellness. The AI industry is maturing and moving from a research-oriented phase to broader commercial impact, driven by innovations in cloud tools and automation.”

-AI Club

Programs like http://aiclub.world, enabling individuals of varied domains and expertise levels to learn and apply AI to their fields, accelerate the broad production value of this transformational technology.” 

Demystifying Software as a Medical Device Under the EU MDR

With much of the recent emphasis of device manufacturers centering on meeting the requirements of the upcoming European Union (EU) Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR), pausing to briefly reflect on how this field might affect your work now, and in the future, is timely. It’s already affecting you at home anyway.

The EU MDR states that it is necessary to clarify that software in its own right, when specifically intended by the manufacturer to be used for one or more of the medical purposes set out in the definition of a medical device, qualifies as a medical device. See EU MDR Whereas statement (19) on page 4. However, software for general purposes, even when used in a healthcare setting, or software intended for lifestyle and well-being purposes is not a medical device. The qualification of software, either as a device or an accessory, is independent of the software’s location or the type of interconnection between the software and a device.

The EU MDR (Chapter 1, Article 2, page 15) provides the following definition of a “medical device” to better understand how software can be a medical device too. A medical device means any instrument, apparatus, appliance, software, implant, reagent, material or other article intended by the manufacturer to be used, alone or in combination, for human beings for one or more of the following specific medical purposes:

— diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of disease,

 — diagnosis, monitoring, treatment, alleviation of, or compensation for, an injury or disability,

 — investigation, replacement or modification of the anatomy or of a physiological or pathological process or state,

— providing information by means of in vitro examination of specimens derived from the human body, including organ, blood and tissue donations, and which does not achieve its principal intended action by pharmacological, immunological or metabolic means, in or on the human body, but which may be assisted in its function by such means.

On October 2019, the Medical Device Coordination Group (MDCG) released a helpful guidance document 2019-11, “Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR.” It gives clear direction that medical device software that is intended to be used, alone or in combination, for a purpose as specified in the definition of a “medical device” (above) in the EU MDR/IVDR will be strictly regulated.

The use of software as a medical device can have its own intended purpose and also drive or influence the use of a device or hardware for a medical purpose. Software then is classified on its own, based on its intended medical purpose. As you work your way through the risk classification algorithm, it cannot be classified in a risk class lower than the risk class of the medical device or hardware it is influencing. Not all software used in healthcare settings qualifies as a medical device; software used for staff planning, invoicing and simple searches would not qualify as a medical device.

Demystifying Software as a Medical Device within the Global Regulatory Environment

Let’s put this discussion into a broader, global regulatory context. The International Medical Device Regulators Forum (IMDRF) defines software as a medical device as software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device. The USA Food and Drug Administration (FDA) under the Federal Food, Drug, and Cosmetic Act (FD&C Act) considers medical purpose as those purposes that are intended to treat, diagnose, cure, mitigate, or prevent disease or other conditions.

The FDA recently released a discussion paper on AI and ML software and its potential implications to the medical device industry (April 2019 https://www.fda.gov/media/122535/download). 

FDA has defined AI as, “A device or a product that can imitate intelligent behavior or mimics human learning and reasoning. Artificial Intelligence includes machine learning, neural networks, and natural language processing. Some terms used to describe artificial intelligence include computer-aided detection/diagnosis, statistical learning, deep learning, or smart algorithms.”

Demystifying How AI and ML can Transform Software as a Medical Device as We Know it Today

Intriguing applications could include more accurate diagnosis, earlier disease detection, identification of patterns or clinical data relationships previously undetected, and development of personalized diagnostics and/or therapeutics. One of the greatest benefits of AI and ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance.

In 2018, the FDA authorized a type of clinical decision support software designed to analyze computed tomography (CT) results that may notify providers of a potential stroke in their patients. The agency also authorized an artificial intelligence-based screening device for detecting diabetic retinopathy, an eye disease that can cause vision loss in some diabetic patients.

The medical device industry is encouraged to commit to broader educational programs on the application of AI and ML. We need to transform from relatively unfamiliar engineers, clinical/regulatory/quality scientists, and healthcare practitioners to more informed, critical and key developers and users of this technology.

The intended use of AI and ML-based software as a medical device brings new possibilities but also requires a solid understanding of current regulations guiding software in this industry today and then having open discussions on how regulations and education might need to evolve.

Now to be a bit provocative. What’s good enough in AI and ML when applied to medical devices? Should we be solving 80%, 90% or 100% of the problem? There could be value in solving 80% of the problem, but do we know in what areas? How do we build and apply AI and ML that patients, healthcare practitioners, and regulators can trust? Finally, should we think more critically about testing these applications that learn, modify and change within a randomized clinical investigation setting? There are other provocative areas, such as:

1. What is the potential for bias as the software is learning?

2. Should lower risk devices be allowed to use “unlocked” software first?

3. Should manufacturers be allowed to let each device learn and evolve separately versus all together (or both)?

Using software as a medical device and then expanding and combining this to develop and apply additional algorithms create new, amazing, and wonderful possibilities. The hope is to deliver safe and effective device functionality that improves the quality of care that patients receive. There is a need to befriend AI and ML to both continue medical device innovation and remain competitive globally. In fact, there is a lot of room for innovation.

I started out by asking, “Will artificial intelligence (AI) and machine learning (ML) be the next big thing in medical devices?” What do you think? Will a new friendship with AI help you to choose new ideas, new experiences, and new adventures in the medical device field? I hope so because this is going to fun. Let’s go!

David R Rutledge, Pharm.D., FCCP, FAHA 
President & CEO
Global Strategic Solutions, LLC
david.rutledge@globalstrategicsolutions.com
+1 (630) 846-0350 cell
www.globalstrategicsolutions.com