Three recommendations for improving clinical assessment
Multiple logins, one patient
Recently, a sponsor's Chief Medical Monitor remarked to me that it requires six different logins for them to pull together a coherent view of patients in their studies. Unfortunately, this is only the tip of the iceberg.
With the rise of outsourcing and proliferation of data sources, it takes data management even longer to aggregate, clean, and validate data so it provides a useful picture of each study. Once this data is aggregated, it is often outdated. Even when it is accurate, it can be tough to sift through it to find specific sites or metrics that require additional attention from the medical team. When the team finally identifies patients that require additional investigation, the medical team typically runs into this six-logins problem. And once this patient data is compiled, it is often tough to create visualizations that help the medical team perform clinical assessment.
This problem is widespread. Over 85% of medical and data teams recently identified disparate data in multiple systems as the most significant barrier to medical review and clinical assessment.
How can we improve this medical review process?
Three recommendations for improving the clinical assessment process
We have worked with dozens of medical and data management teams to address this particular challenge. As a byproduct of these partnerships, we have identified three key recommendations for teams to overcome these challenges - and, in turn, create a more efficient and effective process for medical review and clinical assessment.
1. Automate data aggregation
Increased outsourcing and digitization of clinical trials has created a wealth of data - and also a wealth of data sources. Although a huge opportunity, it has now become a significant challenge that typically requires a data manager to spend hours manually compiling data from this disparate sources across multiple vendors into a single, usable dataset. By the time manual aggregation is complete, the data is typically outdated. Automating this process will not only improve productivity, but provide real-time, up-to-date insights on each study.
Though perhaps the most obvious and impactful recommendation, it has yet to be widely adopted in the industry. In a survey performed earlier this year, over 90% of clinical operations teams report that they still have not automated this process, and the same respondents identified real-time data aggregation as the single most significant barrier to meeting milestones and keeping studies on track.
2. Standardize and monitor the most critical KPIs
As MCC and Tufts CSDD recently reported, industry dynamics have placed a greater emphasis on standardizing and tracking key performance indicators (KPIs) across clinical research teams to identify best practices and diagnose areas for improvement. In fact, two-thirds of sponsors now use a standard set of KPIs to gauge study progress.
Standardizing KPIs is a critical step, but is only important to the extent that accurate data can provide insights on these KPIs and analytics can direct the team to focus on operational metrics that matter most for each study, site, or patient. These requirements suggest that data aggregation is necessary but insufficient. Once aggregated, it is critical to organize and analyze it so the research team can make decisions fully informed on progress against their standard KPIs.
In a world where research team members face multiple priorities competing for their attention, advanced algorithms that monitor this data and notify the team when there are outliers or unexpected outcomes are even more critical to focusing on only the most important issues.
3. Administer medical review with graphical patient profiles
After implementing the first two recommendations, teams will have access to data across their sources, analytics on KPIs that show insights to adverse events and protocol compliance for each study, and monitors that notify specific members when a KPI requires their attention.
But when medical monitors need to perform a clinical assessment, they will still be forced to log into multiple systems to cobble together a clear picture of each patient. And once this data on each patient is aggregated, it is often tough to discern which are the most important data points for the particular patient in question.
To overcome this hurdle, teams should invest in graphical patient profiles that provide a clear picture of labs, adverse events, concomitant medications, and other critical inputs to clinical assessment for any given study. Visualizing this data will rapidly accelerate the process of performing clinical assessment and reduce the time it takes to begin drafting the clinical narrative.
The opportunity to benefit research teams and patients alike
Though implementing these three recommendations may seem like a chore, it is within reach for any clinical research team, regardless the size of their company. A new class of cloud software called Clinical Intelligence empowers teams to adopt each of these three recommendations in a matter of weeks.
Teams who follow these recommendations are experiencing productivity gains, cost savings, and, as a result, reduced cycle time for their studies. And, critically, the research teams are not the only ones to benefit. Patients who are participating in these trials are also finding that their issues are more quickly and readily addressed, resulting in a better patient experience.
By investing in Clinical Intelligence, clinical research teams automate data aggregation, standardize analysis of KPIs, and visualize patient profiles, thereby improving the clinical assessment process and patient experience at the same time.
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