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3 Health Tech Trends Shaping The Future Of Clinical Operations
“3 Health Tech Trends Shaping The Future Of Clinical Operations” was originally published on Clinical Leader. Click here to read the original article.
At the Clinical Operations Retreat for Executives (CORE) hosted by Halloran Consulting Group this past October, we got a glimpse of the innovations that are changing the way we conduct clinical research. While all this change is exciting, it can be overwhelming to keep up with the rapid pace of health technology advancement; in many ways, it feels like we’re just along for the ride. Let’s fast forward and take a look into three key areas of innovation that are forcing us to face and embrace our changing industry landscape.
1. Interoperability
Interoperability will certainly play a significant role in advancing healthcare and getting the most out of our data. Knowing this, a global consortium of experts (known as HL7 international healthcare standards organization) came together to build Fast Healthcare Interoperability Resources (FHIR), a set of technical standards designed to be applied to electronic health data that make it possible to share data across various systems, application programming interface (APIs), and platforms quickly and seamlessly. Using FHIR, data transfer from electronic medical records (EMRs) into third-party apps can be as quick as 90 minutes. FHIR is making it possible to democratize health data and to empower patients to hold the information they need to make informed decisions about their care. FHIR allows patients to shift their focus from how to obtain their health data to how to use it and enables them to share and receive this data on their own accord, no strings attached.
A good example of the power of a universal standard like FHIR is Blue Button 2.0., which the Centers for Medicare and Medicaid Services (CMS) developed to be a “developer-friendly, standards-based API that allows Medicare beneficiaries to connect their claims data to the apps, services, and research programs they trust.”1 These apps feed back into larger platforms that can process the data, making it possible to expand reach and aggregate data. It’s exciting to think about interoperability as a way to increase the avenues by which we collect and share data.
How do innovations in standardizing data affect our practice as clinical operations professionals? Experts predict we’ll begin to see real change over the next 18 to 24 months in areas such as risk management, protocol training, and patient experience. Innovative clinical development teams are already pushing to find novel ways to access all this connected real-world health data and link it to clinical trial data to provide a more complete view of the patient’s health. Such interoperability will soon allow groups like clinical operations, who are typically used to working in more traditional clinical trial settings, to work more easily across multiple different sources of data. Clinical operations will therefore have to make a big change in how they think about data and their processes and tools for receiving, managing, and reporting on data outside of the traditional study development activities. Understandably, the rate-limiting factors for interoperability uptake will be establishing trust in data systems and repositories and regulating how data is used. Regardless, we should certainly expect more requests to pursue data collaborations. Furthermore, as the reimbursement model shifts from fee-for-service to value-based, physicians will want more access to clinical data to know how to manage their patients and optimize outcomes.
2. Big players making their entrance into healthcare
With data becoming more portable and available, nontraditional healthcare entities, such as Walmart and Verizon, will use their access to patient data to enter the healthcare space.
Walmart is piloting in-store clinics, which will offer full-service, discounted healthcare. The company’s assumption is that if conventional care is situated in a high-traffic area, such as a Walmart Supercenter, healthcare services will be utilized by people unlikely to visit a traditional healthcare setting due to inconvenience and cost. By doing this, Walmart is betting that patrons are likely to change their behavior if medical care can be offered in a setting that’s more accessible and recognized.
What does this have to do with clinical operations? Well, Walmart isn’t the first or last retail company that will open a nontraditional clinic, and with those new access points, it is crucial that those new care settings partner with a trustworthy carrier to transport the data they generate to its next destination, which could be clinical research trials, research institutions, or off-site servers. This is where Verizon comes in. With more clinics popping up, there is an increase in patient touch points, as well as more opportunities to expose an increased volume of patients to trials for which they may be eligible. With only 3 to 5 percent of the U.S. population participating in clinical trials, Verizon has identified a huge opportunity in transporting data from various clinics to a range of different recipients, with the hope of identifying and maximizing medical progress. Verizon aims to be a trustworthy and reliable courier that can facilitate information exchange and make data accessibility scalable.
Beyond Verizon and Walmart, Amazon and Best Buy are disruptors not to be overlooked. The former is offering near-instant healthcare services with an in-app chat function and drug-delivery capabilities, and the latter is working to build out its inventory in the fitness equipment and tracking space.2 Regardless of which organizations are making the biggest moves, healthcare access is evolving rapidly, and clinical operations personnel need to stay abreast of the changes as the number of patients with direct connections to healthcare providers and clinical trials continues to grow.
3. Expanded capabilities in artificial intelligence (AI) and machine learning (ML)
As the applicability of AI and ML capabilities becomes more widely understood and recognized, the opportunity to unlock new avenues for clinical research with transformative technology is massive.
It’s important that we take advantage of today’s increased data connectivity and use it to inform the design, implementation, and gathering of clinical data. With new AI and ML capabilities surfacing so rapidly, we now have a host of tools to leverage to glean critical insights from this data. Presently, AI’s chief application is in the early drug discovery and development stage, where we can use AI to tell us things like which patients may respond better to a particular treatment, strengths of interactions between a multitude of variables, and which patient factors lead to greater risk of disease progression.
Now, use of AI and ML is taking a stronger foothold in clinical operations and will be key in mitigating many of the current pain points in clinical trials, such as low trial participation, retention issues, high study costs, and slow decision-making. AI will enable us to write protocols with honed inclusion/exclusion criteria, as well as procedures that are more effective. ML will facilitate better recruitment by making it easier to identify high-risk patients and forecast poor prognosis and predict risks and data-driven mitigation strategies – where in the past, we had to rely on mainly a collection of subjective inputs. In the ever-growing world of clinical analytics and aggregator technologies, ML-enabled functionality will allow us to use chat features to ask questions about our clinical data and get instant answers, all via our handheld devices. Moreover, innovative ML-enabling strategies, such as embedding the technology into tools used by novice clinical trialists, have shown to enhance data analytics capabilities and empower these trialists to run analyses independently. Eventually, real-world data powered by AI and ML and other digital technologies will supplement clinical trial data on a more regular basis. The goal is to make this common practice, which will allow us to reduce the number of subjects needed, keep recruitment costs down, and accelerate development.
Nonetheless, it will be essential to get the FDA and other regulatory agencies to join the effort and commit to supporting use of these digital tools, as they are doing with real-world data to answer tough research questions in our industry and fill the data gaps that currently only exist due to the limitations of running traditional clinical trials. AI and ML are now becoming skills our kids are exposed to in schools, which will further accelerate innovation within clinical research. The tides of innovation in health tech are high, so it’s in our best interest to be prepared to recognize what’s going on around us, understand how to evolve our thinking, and ride the wave.
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