Digital databases for clinical trial searches have already changed healthcare accessibility, bringing critical information about clinical trials to the patients who need it – and to the physicians who care for them. But these databases are also enormous – www.clinicaltrials.gov alone has details of close to 500,000 studies. As a result, information about clinical trials can often be too complex and inaccessible – even when relevant – for people to navigate.
Fortunately, new and ongoing technological advances have enabled more efficient, intuitive tools for searching and understanding clinical trials. We’ve witnessed similar tech-driven improvements in other industries: take e-commerce – when people are looking to buy a TV online, they can input a range of specifications, from processing power to graphics capabilities to brand, and quickly receive results tailored to their viewing preferences for them to consider. It’s time for the search for clinical trials to tap into the computer-processing evolution that’s now standard practice in e-commerce.
Why shouldn’t finding potentially recruiting and suitable clinical trial results be just as accurate as finding the perfect TV viewing set? Indeed, there is already technology available with the power to narrow and refine the search for clinical trials. Now, it’s just a matter of adaptation, adoption, and integration.
Challenges with clinical trial databases
While digital databases have successfully consolidated clinical trial resources, their effectiveness in generating useful search results is tempered by a lack of standardization, labeling, ease of use and filtering across databases. For instance, the particular wording of any disease related search – i.e. “Squamous Cell Carcinoma of the head and neck” versus “head and neck Squamous Cell Carcinoma” – might yield different results. Such divergent parameters make it extremely difficult for the average user to discover the most relevant information and to cross-reference the options amongst different platforms. As a result, patients and physicians may miss out on relevant trials even when searching with great specificity.
Eligibility criteria as shared by the trial sites are not uniform due to the clinical research objectives, thus complicating searches even further. Patient details such as prior medications, genomic sequencing, or numbers of lines of therapy are typically input as unstructured text, making them even more difficult to index. As such, patients and physicians are not always able to decipher whether a patient meets all the necessary entry criteria to apply for any given trial. This challenge is compounded by the fact that search results are not prioritized based on the personal characteristics of the patient – any result deemed applicable to the query will be shown, with no filtering for what is or is not relevant. The bottom line: across these databases, the results of very specific searches can still yield hundreds of untargeted options. Even if a searcher has the time to narrow down the overwhelming number of results – indeed, given the volume, sifting through trial pre-qualification assessment options can be a full-time job – the average patient typically lacks the expertise to know which trials may turn out to be the most applicable and worth the time and energy pursuing an application for. Even trained physicians tend to struggle with the deluge of complex information in these databases, especially given their typically hectic workloads.
Finally, the data offered up by these digital resources may be outdated. It can be devastating for both the physician and the patient to wade through endless results and finally reach a suitable option, only to find that a possible trial option is no longer open to applicants.
Enabling effective e-commerce-like searches
So how can the healthcare industry mitigate these challenges? By adapting tech solutions trailblazed by other industries such as e-commerce. Here are some features, commonplace on most e-commerce platforms, that can help transform clinical trial search databases:
Like a standard online TV-set purchase, clinical trial searches must employ detailed product descriptions – i.e., proper keywords, titles, and meta description tags. It is critical for clinical trial databases to collect and standardize their metadata similarly to ensure patients and caregivers are served the most accurate results.
Most e-commerce platforms benefit from hyper-specific filters, offering users the option to sort by category, such as item type, power, price, brand, and more throughout the entire customer journey. Advanced e-commerce platforms also excel in result prioritization, making sure to offer the items that most closely match users’ profiles and preferences.
In a clinical trial database, similar filters and prioritized results would let patients and physicians tailor their search results throughout the process, eliminating the need to start from scratch with every new query.
E-commerce is adept at autofill features, prompting suggestions to help users enter queries aligned to the product database. Once someone has previously searched for a specific clinical trial topic, the system can remind them of other results that may be relevant in the future.
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- AI and natural language processing
AI-powered query tracking utilizes market research that can significantly improve an e-commerce site. By leveraging data on the specific terms people use to search for any given offering, keywords or relabel products can be prioritized accordingly. This data, when paired with natural language processing tools such as AI-fueled search engines and chatbots, can allow patients and physicians to search effectively even if their query doesn’t exactly match the keywords listed on the trial page.
Fewer tribulations for clinical trials
Patients in search of clinical trials often desperately need to understand their options and yet they are still faced with a level of complexity and obfuscation even their physicians and other experienced healthcare professionals find difficult to navigate. Similarly, clinical trial databases are in dire need of an upgrade as some hospitals and trial site staff struggle under the weight of legacy practices and equipment. It is no longer enough for trial databases to just be “digital” – they must leverage and learn from patient data, patient and physician search time, and bolster personalization through filters and standardization, to streamline the search process and prioritize relevant information. Applying an e-commerce approach – honed by years of practice and copious amounts of user queries – to clinical trial databases stands to greatly improve the experience of searching for clinical trials, producing faster and more accurate search results for those seeking up-to-date information on possible treatments.
Photo: FotografiaBasica, Getty Images, 503105612
