Introduction

In an era of unprecedented scientific discovery, the pace of innovation in oncology is staggering. A critical paradox remains: while our understanding of cancer biology deepens, the operational framework for testing new therapies—the clinical trial—often remains a bottleneck. While there has been 22% growth in oncology clinical trials over last 5 years¹, 85% of clinical trials fail to meet enrollment goals.² Health systems and research sites, from large academic medical centers to community practices, face a common set of hurdles: identifying and enrolling eligible patients can be slow and inefficient, research teams are often stretched thin, and fragmented workflows can create administrative burdens that hinder research operations.

At Tempus, we believe that overcoming these challenges requires more than just incremental improvements. It demands a fundamental shift from a siloed, manual approach to an integrated, technology-enabled ecosystem. Smart site networks that harness artificial intelligence (AI), multimodal data, and collaboration can help create a more efficient and sustainable model for clinical research. Tempus is working to make this a practical reality for research sites today.

The persistent challenges of modern clinical trials

Clinical trial operations are growing more complex, yet many sites are forced to do more with less. This tension manifests in several key areas that hinder the ability to bring novel treatments to patients efficiently.

• The patient identification bottleneck: The success of any trial hinges on enrolling the right patients, yet this remains one of the most significant challenges. Manual chart review is labor-intensive and prone to error. Critical patient data, including biomarker results, is often buried in unstructured formats like clinical notes or pathology reports or not completed at the right time in the patient’s journey, making it difficult to surface eligible candidates in a timely manner. This inefficiency can delay trial timelines and may result in missed opportunities for some patients to participate in research or gain access to novel treatments.
• Resource scarcity and staff burnout: Research sites consistently report that limited staffing and high workloads are major barriers to trial execution. Clinical research coordinators (CRCs) and nurses are the operational backbone of trials, but they are often overwhelmed by administrative tasks, from managing documents to screening growing and dynamic patient records. This operational strain limits a site’s capacity to take on new trials and contributes to high staff turnover, further draining institutional knowledge and resources.
• Operational silos and manual workflows: Research operations are frequently hampered by a patchwork of disconnected technology systems and manual, paper-based processes. This fragmentation creates inefficiencies at every step, from study start-up and document management to patient tracking and data collection. The lack of a unified, automated system increases the administrative burden and makes cross-functional and multi-site collaboration difficult.
• The financial sustainability dilemma: For a research program to thrive, it must be financially sustainable. Sites must balance the high overhead costs of running trials with the need to select studies that are not only scientifically promising but also financially viable. The lengthy timelines and high costs associated with trial activation and patient recruitment can strain budgets, making it difficult to maintain a robust and diverse trial portfolio.

A new paradigm: The AI-powered smart site clinical research network

Accelerate patient identification with multimodal data and AI 

To find the right patients faster, it’s important to see the full picture. Our network leverages advanced AI, including natural language processing (NLP) and large language models (LLMs), to analyze both structured and unstructured data from the electronic medical record (EMR), medical imaging reports, lab results and genomic information to support rapid and accurate patient identification.

• Unlock unstructured data: The platform can extract information such as biomarker status and disease characteristics from clinical notes, labs, and genomic reports, supporting accurate patient identification regardless of next-generation sequencing vendor.
• Identify care gaps: Our care pathway intelligence platform, Tempus Next, can surface patients who may have fallen off guideline-directed care pathways. Upstream alerts for biomarker testing can expand the pool of identifiable patients for precision oncology trials.
• Ensure quality with human oversight: Technology is powerful, but clinical nuance is essential. All AI-identified matches are reviewed by nurses to help ensure quality and validity before being presented to the site, supporting research staff efficiency.

Empower research teams through intelligent automation

Our goal is to augment, not replace, the expertise of on-site research teams. By providing intuitive, automated tools, we help reduce manual workload and support staff in focusing on higher-value research activities.

• Streamline workflows: The Tempus Link platform provides an intuitive interface for patient tracking and data integration, reducing the need for manual data entry and reconciliation.
• Provide hands-on support: We offer dedicated customer success teams for training, dedicated liaisons to help each site, and additional physician support through access to steering committees and scientific forums.

Unify operations from startup to completion

We help break down operational silos by providing a unified platform and standardized processes that accelerate trial timelines.

• Reduce startup timelines: Through our Just-in-Time (JIT) activation model, sites can open a trial in as little as 10 days once an eligible patient is identified. This model can help reduce the upfront cost and effort of opening trials that may not accrue patients.
• Centralize management: Our platform provides a central repository for clinical trial documents and creates efficiencies through the use of a standardized clinical trial agreement, pre-negotiated rate card, and centralized IRB that has proven to consistently open sites within days.

Practical implications for research sites

By joining an AI-powered network, research sites can leverage these technological capabilities to support their core strategic goals.

For Principal Investigators (PIs): Gain access to a broad portfolio of innovative, industry-sponsored trials. Spend less time on administrative hurdles and more time focused on patient care and advancing science.

For Research Administrators: Activate trials faster³ and more efficiently with the JIT model. Use recruitment and performance dashboards to make data-driven decisions, optimize resource allocation, and demonstrate the value of your research program to leadership.

For Hospital Executives: Improve the financial sustainability of your research program by optimizing trial selection and improving cost recovery. Support quality metrics by helping to close gaps in care and supporting access to clinical trials.

Looking to the future

The future of clinical research depends on our ability to build a more connected, intelligent, and efficient ecosystem. The challenges of patient recruitment, resource constraints, and operational fragmentation are too significant to be solved by any single institution alone.

By creating a collaborative network powered by Tempus innovation, data, and technology, we can accelerate clinical development for our pharmaceutical partners while promoting greater efficiency and sustainability for research sites. Together, we can help streamline the path from discovery to treatment, supporting the goal of bringing precision medicine to more patients.

To learn more about joining the Tempus smart site network, contact us today.

1. IQVIA Institute Global Oncology Trends 2024

2. Nouvini, R., Parker, P.A., Malling, C.D., Godwin, K. and Costas-Muñiz, R. (2002), Interventions to increase racial and ethnic minority accrual into cancer clinical trials: A systematic review. Cancer, 128: 3860-3869.
https://doi.org/10.1002/cncr.34454

3. James F. Maher et al. TriHealth Cancer Institute’s collaboration with the Tempus AI TIME program impact on clinical trial operations and enrollment. JCO 42, 1553-1553(2024).
https://ascopubs.org/doi/10.1200/JCO.2024.42.16_suppl.1553