❤️🩹 Medical Device Discovery
Global healthcare systems are under significant strain to deliver improved, cost-efficient care. The shift away from fee-for-service towards value-based models, which tie reimbursement to quality, has intensified this need.
Certainly the medical device sector needs to go under the knife. With its labor-intensive operations, high costs, and inherent risks, the journey through device development can be arduous.
Below, I have outlined some crucial aspects to contemplate across the device lifecycle. In particular, I am thinking about new medical device products.
Customer Discovery
The early phase of device development involves managing numerous factors like design constraints, experimentation, compliance, and market competitiveness, clinical outcomes and more. Any missteps here can lead to expensive opertational as well as regulatory issues down the line.
I mean, this is why we have ISO 14971 as the risk management standard for medical devices, right?
Now for those working with digital health, frequent software updates provides flexibility.
On the other hand, device hardware developers face stricter regulations and less frequent updates, typically once or twice per year. This makes a well-researched discovery Go-To-Market (GTM) plan critical for longterm success.
Risk management should start early and cover everything from business operations, design requirements to budget. This might seem daunting, but it's key to navigating the complex medical device landscape.
In other words, a risk management file is not something that should be done post-launch. It's an important artefact from the start of the customer discovery phase, all the way to post-launch, as it guides teams toward regulatory approval.
Successful entrepreneurs understand and manage risk. They use risk as a tool to balance technology, cost-effectiveness, and customer needs. By investing time upfront in generating key insights from multiple sources, they avoid unnecessary complications and set devices on the path to success.
Trusted Methods
Failure Modes and Effects Analysis (FMEA) is a valuable tool for identifying potential device failure modes. It translates severity and occurrence metrics into Risk Priority Number (RPN) to determine the relevance of each failure mode.
For example, if a manufacturer is developing an AI-based skin cancer diagnostic tool, "AI Algorithm Accuracy" may arise as a risk during design verification. See below.
| Failure Mode | Effect | Severity | Occurrence | Detection | RPN |
|---|---|---|---|---|---|
| AI Algorithm Accuracy | Misdiagnosis, possibly causing inappropriate treatment | 9 | 3 | 5 | 135 |
Misdiagnosis severity scores high (9) due to potential harm to patients and potential regulatory issues. The occurrence score (3) assumes robust development, but acknowledges the complex nature of AI and variability in data might lead to unexpected failures. Detection is set at 5, admitting potential undetected inaccuracies despite controls. The high RPN of 135 suggests the need for additional safeguards to ensure algorithm accuracy.
Remember, FMEA targets failure modes rather than hazards. Software, for instance, isn't inherently hazardous, but can contribute to failure modes —this distinction is crucial across risk management and can be applied to many other lifecycle phases.
Design Assurance to the Rescue
Design Assurance (DA), the often-underestimated sibling of risk management, plays a crucial role in the medical device industry, especially with the advent of advanced generative design tools that are reshaping its perception.
Entrepreneurs often rush from prototype to market, overlooking the intricacies of design controls pathway throughout the device lifecycle. It's here that DA converges with product and risk management, interweaving with stakeholders from project management, manufacturing, finance, and more.
During the nascent discovery phase, device concepts take shape, with critical decisions about their features, effectiveness, and user benefits. Often, initial design specifications and market research insights are incomplete or exist only in the minds of engineers, R&D staff, or product managers.
Valuable details, like what value we bring to patients, clinicians, or providers, or how to translate these needs into evidence for future regulatory audits, can be lost. Successful DA depends on extracting these details, co-learning, and synthesizing them into a reliable, coherent concept.
An article disclosed top 10 regulatory audit failures in 2021 throughout the US market. Top three most cited clauses remained Design Controls (820.30), CAPA (21 CFR 820.100), and Complaints (820.198), accounting for ~37% of all cited clauses.
This tells a story. Many manufacturers were not establishing procedures to capture thorough research, design requirements, or proving design outputs meet design inputs. The pattern suggests many manufacturers overlook these crucial early-stage design efforts, often till it's too late.
So how to frame DA positvely? DA is the meticulously constructed "rescue parachute" or "jet pack" you might need during a launch or regulatory clearance of a medical device. It demands early attention and care, and should be viewed as a risk tool offering invaluable aid in earning regulatory approval.