Clinical Trials

16 Sep

Posted at 16:34h in Clinical Trials

Clinical trial analytics helps research teams turn fragmented healthcare data into governed insights for recruitment, monitoring, and evidence generation. Clinical trials already produce and depend on large amounts of data: EHR records, lab results, claims, registries, EDC systems, patient-reported outcomes, and post-market data. The issue is not that data does not exist. The issue is that it is often fragmented, inconsistently coded, and difficult to use when trial teams need answers. That is where analytics in clinical trials becomes strategic — helping organizations move from scattered information to governed, research-ready intelligence that can support patient recruitment, trial monitoring, safety analysis, and evidence generation. According to the FDA (U.S. Food and Drug Administration), real-world evidence derived from EHRs, claims, registries, and other clinical data sources is increasingly recognized as a critical complement to traditional clinical trial data for regulatory decision-making — making the ability to access and analyze that data a strategic priority for research organizations.

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  • Clinical trial data analytics supports feasibility, recruitment, monitoring, safety, and evidence generation.
  • Recruitment delays are often not only outreach problems — they are data usability problems.
  • FHIR® and OMOP can help connect clinical care data with research analytics.
  • AI can support faster analysis, but only when it works with governed data and approved definitions.
  • Kodjin Analytics by Edenlab helps turn fragmented healthcare data into queryable, decision-ready insights and dashboards for every role in an organization.

13 Aug

Posted at 13:54h in Clinical Trials

Clinical trials take a long time. This is treated as a structural feature of the field rather than a management problem. Development timelines measured in years have become a baseline assumption for sponsors, CROs, and site staff alike. What gets less attention is how much of that time is consumed by work that is not the trial itself.

The Tufts Center for the Study of Drug Development has tracked protocol complexity over two decades and found consistent increases in endpoints, procedures, and eligibility criteria per trial. Between 2001 and 2020, the average number of endpoints per protocol increased by 86%, and the number of procedures per protocol more than doubled. Each addition creates downstream administrative and operational work that falls on site staff, CRO monitors, and sponsor teams.

The labor implications are substantial. A 2020 analysis published in Therapeutic Innovation and Regulatory Science found that staff time is the largest single cost driver in clinical trial operations, accounting for a majority of site costs in Phase II and Phase III studies. Most organizations have limited visibility into how that time is actually distributed across trial activities — which makes it difficult to identify where it accumulates unnecessarily, and harder to model staffing requirements accurately for future studies.

[caption id="attachment_75553" align="aligncenter" width="500"]clinical-research-studies-pexels.jpg Photo by Polina Tankilevitch[/caption]

30 Jul

Posted at 16:04h in Clinical Trials

When people think about clinical studies, they often focus on the testing phase. Images of laboratory equipment, data analysis, and scientific discoveries typically come to mind. However, many researchers know that some of the most significant challenges in a study can arise long before any testing takes place. In fact, the success of a clinical study is often determined during the earliest stages of sample collection, handling, and preparation. If mistakes occur at this point, even the most sophisticated testing methods may struggle to produce reliable results.

For research organisations seeking accurate, reproducible data, getting the foundations right is just as important as the analysis itself. For guidance on how to read and assess the quality of a clinical study once results are published, see this primer on how to evaluate a clinical trial.

[caption id="attachment_75340" align="aligncenter" width="500"]First Mistake in a Clinical Study Photo by Jonathan Borba[/caption]

10 Jul

Posted at 10:33h in Clinical Trials

Most people think finding a research study is mainly a matching problem. They search by condition, age, location, and payment, then look for a study that seems to fit. Those details matter, but they are not the whole problem. For many patients, the harder issue is timing. A study can look perfect and still be unavailable because it has not opened yet, has already filled, or has moved into follow-up without accepting new participants.

That is why Hipa.ai treats study search as a regularly updated discovery problem, not a static list. The platform helps people browse clinical trials across the United States, with recruiting status visible on each listing and source data drawn from ClinicalTrials.gov and AACT, then rebuilt into its own index on a weekly cadence. Hipa.ai does not ask patients to guess whether an old page is still useful. It keeps the practical question in front of them — is this study open now, and what should I do next?

11 Jul

Posted at 12:58h in Clinical Trials, Technology
[caption id="attachment_69487" align="aligncenter" width="500"]remote-digital monitoring Source[/caption] Behavioral health research has traditionally relied on patient self-reporting, clinical interviews, and psychometric scales to study mood, cognition, and mental wellness. While these methods remain foundational, they often fail to capture the dynamic, real-time shifts in human behavior that define mental health conditions. Enter digital phenotyping—a cutting-edge approach that uses data from smartphones, wearables, and other digital devices to passively and actively measure behavioral and physiological markers. As behavioral health becomes more deeply intertwined with digital health technology, digital phenotyping is emerging as one of the most promising tools for personalized, data-driven mental health care and research. By continuously collecting and analyzing signals such as movement, sleep, speech, social interaction, and phone usage patterns, researchers are uncovering new ways to understand, predict, and manage mental health conditions like depression, anxiety, schizophrenia, and bipolar disorder. This data-rich approach is reshaping how mental health is assessed and offers immense potential in both clinical research and everyday practice.

11 Jul

Posted at 10:00h in Clinical Trials, Electronic Records
[caption id="attachment_69467" align="aligncenter" width="500"]importance-data-management-clinical-trials Photo by Christina Morillo[/caption] Every clinical trial produces mountains of data. From patient enrollment logs and lab results to adverse event reports and protocol deviations, clinical data is the backbone of every decision made during drug or device development. Yet, collecting data is only the beginning — it’s how that data is managed, validated, and interpreted that determines a study’s success. In the age of decentralized trials, real-time analytics, and global regulatory oversight, the importance of reliable clinical data management can’t be overstated. High-quality data doesn’t just support regulatory submissions — it protects patient safety, ensures compliance, and strengthens confidence in results.

Why Is Clinical Data Management No Longer Just a Technical Task?

Gone are the days when data management was treated as an afterthought or a purely technical role. Today, it’s central to trial strategy. From the very beginning of a study, data management professionals are involved in shaping case report forms (CRFs), planning how endpoints will be measured, and ensuring systems are in place to capture data accurately and securely. This shift in thinking is due to the increasing complexity of trial protocols, the rise in remote data capture tools, and the growing pressure from regulators for traceable, auditable datasets. Sponsors and CROs alike are realizing that data management is no longer an isolated function — it’s the foundation of trial integrity.

What Does a Modern Clinical Trial Data Management Service Include?

A robust clinical trial data management service goes far beyond database design. It encompasses an ecosystem of systems, people, and processes designed to ensure that every data point collected is clean, consistent, and ready for analysis. Typical services include:
  • CRF design tailored to protocol endpoints
  • Electronic Data Capture (EDC) system configuration
  • Real-time data monitoring and discrepancy resolution
  • Medical coding using standard dictionaries (e.g., MedDRA, WHO Drug)
  • Query management and investigator communication
  • Data cleaning, validation, and database lock support
The goal is simple: to transform complex, multi-source data into a reliable and statistically sound dataset that regulators can trust — and that sponsors can use to make decisions.