Accelerating Medical Innovation with Intelligent Solutions

Technology is changing at an ever-increasing pace, opening new opportunities for our future. One area that benefits is medical innovation. New technological solutions are empowering medical professionals to perform new ways of testing, diagnose diseases earlier, and treat and cure illnesses that were previously thought to be incurable.

While these new solutions have helped patients around the world, has the medical innovation sector fully realized the potential offered by today’s intelligent solutions? And are they ready to take advantage of tomorrow’s intelligent solutions?

The Challenges in Medical Innovation

Innovation is the lifeblood of the health and life sciences industry. New innovations help improve patient outcomes, increase the speed to market for treatments and cures, and lower costs while still fulfilling regulatory requirements. These reasons more than justify the billions invested in R&D every year, including the 27% reinvested in innovation by the Belgian biotech sector.

However, there are a number of challenges in the medical innovation sector, including the organization’s strategy, local authorities and their requirements, and the need for quality data. Let’s take a look at these challenges in more detail.

When it comes to effective innovation, organizations require a clear strategy. They need to decide on a potential investment project, which includes carefully selecting the innovation area, deciding a likely path, setting clear and measurable milestones, establishing how to measure ROI, and implementing evaluation procedures.

Some local governments and medical standards authorities require the medical industry to prove the added value of their treatment or cure before it is validated for use by medical professionals. This requires the organization to incorporate this requirement in their innovation process, possibly changing the innovational milestones.

Quality data is essential for innovation. However, it needs to be the right data delivered at the right time. Organizations are still learning how to handle the potentially huge amounts of data that Internet of Things (IoT) could give them access to, as well as work out the advantage that these data sources can give their innovation process.

The Role of AI in Medical Innovation

Artificial intelligence (AI) has an important role to play in accelerating and facilitating the entire innovation process. To give a few examples:

Clinical Data Review

AI can be used to facilitate and accelerate a range of manual processes to review clinical data quickly and efficiently. For example, by analyzing diagnostic and treatment related information from various unstructured medical data sources, detecting missing codes, wrong semantic relationships, or non-causal clinical sequences, and then recommending optimal actions to the reviewer for corrections and augmentations. This accelerates the speed at which the organization can get their treatments and cures onto the market.

Drug Development

The testing required to make a new treatment or cure more effective is extremely intensive. AI can assist in the drug discovery, design, and development, as well as with the regulatory process, e.g., by identifying compounds with desired biochemical properties, recommending optimized patient enrollment criteria for clinical trials, or predicting certain outcomes such as adverse effects or the effectiveness of the drug. This helps accelerate the time to market.

Patient Journeys

Pathways in patient journeys can also be improved by AI. From looking at past and current patient cases, including diagnostic test results, interactions with healthcare professionals (HCP), adherence to treatment plans, and patient outcome information, AI can predict how and how long a new patient is likely to be treated and hospitalized for, respectively, and then recommend improved pathways around care givers and hospitals for an optimal patient outcome.

Streamlining Operations

Daily operations can be streamlined with AI, from R&D and manufacturing to quality controls and regulatory compliance. AI can also assist and improve sales, connecting the right sales approach, e.g., by recommending the next-best actions of a pharmaceutical representative, to specific doctors and medical personnel.

Medical Insights

AI can improve the data mining process, providing new insights on disease indicators and health condition, to assist medical professionals in improving diagnostic tests, assessing onset risk, and predicting disease progression, as well as to design effective treatments or recommend interventions tailored to stratified groups or even single patients.

Putting Data at the Center of Innovation

But how can organizations use data, AI, and other intelligent solutions to achieve their innovation questions? This requires a step-by-step approach.

Barriers to Using Intelligent Solutions

Even though there are distinct advantages to using AI and other intelligent solutions, organizations that want to implement and benefit from these technologies are discovering there are still barriers that they need to overcome. These barriers include:

Data

When it comes to innovation, data is a double-edged sword. On one hand, the right data used in the right way can reduce the time to market, increase treatment effectiveness, and deliver new insights. But on the other hand, there are technical issues concerning data.

Thanks to IoT and other technologies, the velocity that data is expanding is causing organizations to question how to receive, store, and manage the incoming data, especially when this is coupled with the technical depth of the data.

To move forward, organizations will need to investigate and implement ways to harmonize data from different sources. This will include establishing standards, implementing new models, and aligning departments. By bringing data together in this way, innovators across the organization will be able to access the available data.

Evidence Problems

Data might be increasing exponentially, but the available data isn’t always what we need. Innovators often want to explore an idea that looks good from a theoretical viewpoint, but it cannot be fully evaluated as there has been too little data generated via clinical studies, other research, or real-world examples.

Innovators then need to take a step back to identify what data they need, as well as potential sources and possible integration issues. Existing open data sets could also be leveraged to provide the data required. Alternatively, machine learning techniques can evaluate the feasibility of an idea.

Silos

The data within some organizations is hidden in different silos, with no transversal movement, reducing the likelihood that innovators have access to all the data they need when they need it.

There are some cases where these silos have been purposely built, for example, in large hospitals where different medical professionals use different software packages developed explicitly for the needs of their specialization. However, this often prevents one doctor from obtaining a global view of their patient if that patient has passed through several specializations.

These constraints can also be seen in the silos built in other organizations in the health and life sciences sector. A change management program, complete with workshops and enablement sessions, is often required to get silo owners to share their data and benefit the entire organization and their patients.

Privacy Concerns

GDPR and privacy concerns are one reason that organizations maintain separated data silos. However, sharing anonymized data is possible, and often recommended.

To give one example, when it comes to COVID vaccinations, governments around the world are vaccinating at-risk citizens first. However, governments do not have access to their citizen’s medical records so are unable to identify which citizens should be vaccinated first.

In Belgium, the health insurance organizations searched their records to identify the at-risk citizens by looking at the number and frequency of doctor and hospital visits combined with medication requests. This allowed them to provide the Belgian government with a list of at-risk citizens without disclosing any private medical records.

Funding

AI and other intelligent solutions are an investment for any organization. Obtaining the required investment requires trust in the outcome from either the internal budget controller or the external investor.

Often funding is only granted when it is possible to demonstrate that it will generate a clear return on investment. This is usually achieved by producing a small-scale proof of concept supported by relevant data. One method to reduce the overall funding requirement is to share data with partners within the medical sector, including (non-identifiable) patient information.

Looking to the Future of Intelligent Solutions

As we have seen, intelligent solutions can deliver insights, analyze results, and assist the innovation process. However, realizing the full potential of intelligent solutions within an organization requires the right infrastructure, including a comprehensive data platform, established data standardization policies, and clear regulations on data privacy and security.

Is your organization ready to benefit from intelligent solutions? Learn more about how technology can play an important role in staying relevant and seizing business opportunities in the future.