Why Big Data is the Key to Co-Producing Medical Devices

A medical device may have perfect calibration, tight engineering tolerances, and even great trial results. However, the same device can encounter unexpected problems in the field, leaving patients frustrated and forcing clinicians to work around design shortcomings.

Devices can be particularly troublesome when required to work with real-world variables such as the unique health profiles of different patients and differing hospital systems and processes.

This lack of compatibility, or at least inflexibility when faced with real-world data, is a significant obstacle for device users and developers.  

Big data analytics in medical devices is changing this. Below, we discuss big data’s potential in medical device development, with insights from Prof. Yang Wei, Professor of Wearable Technology, Lead of the Smart Wearable Research Group (SWRG), and MTIF Fellow in Smart Medical Textiles and Wearable Technologies at Nottingham Trent University.

Big data builds a complete picture of how devices are used. It does this by drawing on sources such as Electronic Health Records (EHRs), connected wearables, imaging systems, and patient-reported outcomes.

This level of insight informs both initial design decisions and ongoing adjustments once the device is in use, thereby improving its functionality. Through remote software updates, companies can continually refine devices using big data analytics, including device performance data after they’re in the field, so that they stay aligned with the realities of care and the needs of those using them.

Now, instead of relying only on data from clinical studies with a few hundred participants, big data draws upon millions of data points across varied patient populations, and it can do this continuously to deliver up-to-date insights.

For example, we can learn how long a medical device, such as a prosthetic joint, lasts under certain activity patterns by incorporating usage data from relevant connected devices and sensor readings. Using this alongside patient-reported outcomes helps provide clear insights into how medical devices can be improved.

Due to the smaller sample sizes and controlled environments required for testing and approval, these aren’t insights you typically get from a lab. Big data analytics in medical devices is uniquely placed to uncover these patterns.

Combining large-scale analytics with input from the people who use and maintain the devices allows manufacturers to make design decisions with greater certainty and precision.

This is part of the reason why co-production helps to drive development. Co-production encourages patients, clinicians, engineers, and other stakeholders to work together to provide insights that improve medical device functionality. 

Each group is given a role in shaping requirements, providing feedback, and influencing design priorities, even if they contribute at different stages.

This is important because medical devices form an integral part of people’s daily lives. They exist in homes, clinics, and emergency wards. They’re handled by different medical staff and worn by people with different lifestyles.

When patients and clinicians are embedded in the design process, the benefits of co-production in healthcare are hard to ignore. 

Perhaps a device feels different in larger or smaller hands. This can make a difference as to whether or not a device fits into a patient’s daily routine without disruption. With this insight, manufacturers can validate designs against lived experience and produce a device that better reflects the realities of day-to-day use. In this way, co-production can be seen as bridging the gap between a device’s intended functionality and practical usability.

Another benefit is the opportunity to improve equity in healthcare. Traditional design processes have sometimes overlooked the needs of underrepresented patient groups, whether from a standpoint of geography, culture, physical ability, or socioeconomic status. Co-production actively brings those voices in, leading to fairer, more inclusive outcomes. 

Data provides the “what”, the objective evidence of problems and patterns. Co-production explains the “why”, the human context behind the numbers. Together, they form a feedback loop that’s far more effective than traditional development cycles. 

For example, noteworthy patient-reported outcomes from the PMC highlight reduced disease activity in rheumatoid arthritis and both greater quality of life and higher survival rates with adult cancer patients.

Within co-production projects, data provides engineers, clinicians, and patients with the same evidence base. It acts as a single source of truth and eliminates assumptions. 

Data can also identify mismatches between intended and actual use. For example, a surgical tool that has been designed for a certain grip, but usage logs show clinicians hold it differently. This allows the design team to make adjustments before the device is widely deployed, and patient care is potentially affected.

Data supports continuous improvement, too. In traditional development, patient involvement usually ends at product launch. However, with connected devices, feedback can keep flowing for years. Firmware updates, interface tweaks, and even hardware adjustments can be made and measured against performance data.

In regulated industries such as healthcare, data is a priceless asset. Large datasets can be used to validate documented improvements. They strengthen regulatory submissions and prove that changes are evidence-based.

In co-production, data keeps manufacturers informed and focused on what works in the real world. Access to a medical device database can even streamline post-market surveillance, ensuring that safety monitoring is ongoing and transparent.

Big data is the accelerator of co-production. It makes the process faster and more adaptable through the vast and varied insights it can provide.

Aggregated datasets can reveal hidden needs that likely wouldn’t arise in a small sample. They can help to determine how a specific age group interacts with a device differently, or how environmental factors influence durability. 

Big data can even be used for automated sentiment analysis. This process involves scanning app reviews, patient forums, and customer service logs to surface recurring issues.

Predictive modelling, another branch of big data analysis, goes further by using historical and real-time data to forecast potential issues before they occur. With predictive modelling, manufacturers can identify trends in how devices are used, how they wear over time, and the environments in which they operate.

Transparency is crucial when using big data in medical device development. Big data systems are valued for their robust audit trails. They make it far easier to demonstrate that the choices made were grounded in evidence, particularly when it comes from the subjective standpoint of lived experience.

For this reason, the co-production process needs to be traceable, with device makers able to share results with regulators, partners, and patients, if required. 

That same principle extends beyond development. Transparency also means monitoring how a device performs once it’s in service and sharing those results where appropriate. To do this effectively, manufacturers need widespread, reliable, and secure connectivity.

By combining robust audit trails with advancing Industrial IoT technologies, the possibilities for collecting real-time usage information from devices already in the field are endless.

When big data analytics in medical devices is paired with co-production, the benefits are clear for patients. For providers, the commercial case is as strong as the clinical one.

First, there’s the development speed. With data-backed co-production, the path from concept to market is shorter because the number of redesign loops is reduced.

Second, quality improves. Devices shaped by both data and user input tend to have fewer recalls, lower rates of adverse events, and stronger post-launch performance.

Third, efficiency increases across operations. Predictive analytics can guide supply planning, maintenance scheduling, and compliance processes. For example, by tracking when a device’s power supply is nearing the end of its service life, replacements can be planned to prevent any disruption to device availability.

Lastly, a company’s reputation can get a boost. Showing dedication to patient-focused, data-driven innovation increases brand value and provides protection in legal challenges. It also offers a strong advantage in value-based care models, where reimbursement depends on proven patient outcomes.

The future of medical device design belongs to organisations that can merge the scale and precision of big data with the accuracy of lived patient experience. This represents a mindset shift for the entire industry.

Patient insights, alongside other important data sources, will need to influence decisions at every stage, from defining early design needs and refining prototypes to guiding improvements after-market launch. For manufacturers, this approach leads to quicker market entry, enhanced brand reputation, and greater success in value-based care systems.

From a patient perspective, by combining big data analytics in medical devices with co-production, it’s possible to create devices that work better, last longer, and serve a broader range of people.

Connecting the science of data with the art of understanding, through advanced analytics platforms, medical device databases, connected devices, and proven co-production methodologies, the industry can bring a new generation of electronic healthcare devices to life.