Oncology care means handling large amounts of patient information that comes from many places. This data is often spread out across different systems, such as electronic medical records (EMRs), genomic reports, clinical notes, and lab test results. Because the data is scattered, it has been hard for healthcare workers to get a full and correct view of a patient’s medical history. For administrators and IT managers, gathering and combining these records by hand takes a lot of time and causes problems.
Data stored in different formats makes it even harder to bring all the information together for easy use. This splitting up of data affects not just patient care but also slows research and the process of finding patients for clinical trials. This delay increases costs and slows down improvements in cancer treatment.
To solve these problems, healthcare groups, tech companies, and consultants have worked together to build cloud-based platforms that use AI. One example is a partnership with PwC, which made an AI-driven oncology data platform running on Google Cloud. This platform gathers oncology data from many different sources into one safe and standard place. It changes the data to the FHIR (Fast Healthcare Interoperability Resources) format, which is a U.S. health data standard.
The platform processes data from more than 70 million patient records. It pulls out and standardizes unorganized information like clinical notes and genomic reports. This single system lets providers quickly see full patient histories, which cuts down on time spent finding data and lowers mistakes often made when entering data by hand.
The platform follows strict rules to keep data safe and meets HIPAA and other law requirements. People still oversee important decisions to make sure AI is used responsibly in healthcare.
For medical administrators and IT managers, using cloud-based AI platforms means better data quality and smoother operations. This reduces the usual work involved in managing data.
AI and cloud technology also help with clinical trial matching for cancer patients. Clinical trials test new cancer treatments. Finding the right patients for these trials has often been slow and tricky.
Massive Bio, a company focusing on AI in oncology, has improved this matching through a partnership with Datavant, a company that connects healthcare data. Together, they use AI analytics combined with Datavant’s system that links over 60 million medical records from more than 80,000 hospitals and clinics across the U.S.
This teamwork allows faster access to full clinical histories, including hard-to-get data like pathology reports and detailed clinical notes. AI looks at all this data to find patients who qualify for specific trials. This helps enroll patients faster and more accurately.
Massive Bio and Datavant also make sure patient data stays private. They use methods like tokenization and privacy-preserving links to share data without showing personal information. This meets HIPAA rules and keeps patient information safe.
For cancer clinics in the U.S., these changes mean patients join clinical trials more quickly. This helps get new treatments to patients sooner.
Personalized cancer treatment tries to give therapy that fits each patient’s unique medical and molecular information. This can make treatment work better and lower side effects. AI platforms play a key role in this approach.
Tempus is a company that shows how this works. It keeps one of the largest collections of clinical and molecular data in the U.S. Their AI platform combines data from DNA sequencing, medical records, and real-life health facts to suggest targeted treatments and clinical trials.
Tempus’ xT Platform uses both molecular data and clinical records to better find treatments linked to tumor genetics. It does better than the usual tests that only check tumor DNA because it uses more detailed sequencing methods. This gives clearer information about possible treatments.
Tempus also tests how patient tumors react to drugs by growing small tumor models called organoids in the lab. These tests predict how a tumor might respond to different drugs. This helps doctors make better treatment choices.
Besides tissue tests, liquid biopsies can detect tumor DNA floating in the blood. Tempus’ tests find changes that older tests might miss. This gives a fuller picture of the tumor’s makeup.
For oncology managers in the U.S., using AI-powered precision medicine platforms helps doctors make good decisions and gives them useful suggestions. This may lead to better patient care while fitting with value-based healthcare goals.
AI doesn’t just help analyze oncology data. It also automates repeated tasks and improves the way work is done in cancer clinics. AI automation helps with front-office tasks like scheduling appointments, sending reminders to patients, and managing phone calls. This lets staff spend more time caring for patients.
For example, AI phone systems can understand patient calls, book appointments, and answer questions without a person. This cuts wait times, improves communication, and uses staff resources better.
AI built into electronic health records (EHR) can also pull key patient information automatically. It can flag high-risk patients, remind about screenings, and alert staff when follow-ups are needed.
AI tools also help doctors decide on treatments. These tools look at lab tests, imaging, genetic data, and patient history in real time. They suggest treatments that follow medical guidelines and new research.
One example is Tempus’ AI assistant called “David.” It works inside healthcare IT systems and helps doctors by giving focused advice and quick access to research and trials while they are using the EHR.
Using AI automation in clinics needs strong rules to keep data safe, protect privacy, and make sure AI is used fairly. Platforms such as PwC’s agent OS run many AI agents across healthcare networks while keeping control and following laws to keep patients safe.
Healthcare managers must set up ways to supervise AI tools and keep checking how well they work. This makes sure AI is used responsibly and fits with care goals.
These technologies have a clear effect on cancer centers across the U.S. Projects using the PwC AI platform have helped over 200,000 patients. Tempus has analyzed more than 40 million anonymous clinical records. Data and AI use are growing fast.
Hospitals and cancer centers linked to universities and local clinics now have better AI tools to guide treatment decisions and make it easier for patients to join trials. This helps reach groups who often have less access to care.
For administrators and IT leaders, investing in cloud AI platforms improves how clinics run. It cuts down manual data work, saves time and money, and keeps or improves patient care quality.
Clinical trial groups get help with recruiting patients faster and collecting more data. This speeds up research and gets new treatments to patients sooner.
As AI and cloud tools grow, healthcare groups must keep investing in their technology, train their staff, and work well with tech partners. They should check if new solutions work well with current systems and can grow with future needs.
Privacy and following rules like HIPAA and the 21st Century Cures Act remain important. Administrators should make sure AI tools protect data, are used ethically, and avoid bias.
To get the most out of AI in oncology, administrators and IT managers should:
Using cloud technology and AI analytics in managing oncology data is a useful step toward more organized, efficient, and patient-focused cancer care in the United States. Medical practices that use these tools can improve care results, simplify workflows, and help cancer research by finding patients for clinical trials and personalizing treatments better.
Healthcare data is often scattered across multiple silos and stored in disparate formats, making consolidation difficult. The unified platform addresses this by ingesting diverse data sources, converting them into a standard FHIR format, and enabling a single, centralized, and accessible data source while ensuring compliance with privacy regulations like HIPAA.
AI agents automate the extraction and standardization of unstructured data from sources like electronic medical records, genomic reports, and clinical notes. They streamline data integration processes, reduce administrative burdens, and enable tasks that were previously impractical due to resource constraints.
The platform leverages Google Cloud, Vertex AI, and Google’s Healthcare Data Engine 2.0 to ingest, process, and standardize oncology data. Google BigQuery and Looker support advanced analytics, while Vertex AI enables AI-powered natural language processing and generative AI capabilities.
The platform incorporates strict data governance measures to comply with regulations like HIPAA and CDISC. It uses a responsible AI approach including human oversight for high-risk decisions, ensuring both legal compliance and ethical standards are maintained.
It enables personalized care through improved data access and analytics, faster identification of treatment trends, better clinical trial matching, and enhanced population health management, ultimately leading to improved patient outcomes and more efficient value-based care delivery.
NLP is applied to perform deep searches across millions of records extracting meaningful insights, while generative AI identifies patient trends, treatment patterns, and potential candidates for clinical trials, significantly enhancing research and clinical decision-making capabilities.
PwC’s agent OS is a scalable, flexible platform that accelerates AI agent deployment across multiple providers and domains. It supports collaboration between clinical, operational, and research agents with built-in governance to ensure safety, compliance, and quality, enabling broad AI adoption in healthcare workflows.
Close collaboration among healthcare providers, PwC, and Google Cloud ensures alignment with clinical, technical, and executive stakeholders. This trust-based, high-touch engagement supports buy-in, smooth implementation, and ongoing innovation, which is critical to realizing the platform’s full potential.
The platform’s rich, standardized data and AI capabilities enable more accurate and efficient clinical trial matching, speeding up patient recruitment and enabling faster access to relevant studies, which accelerates research and improves trial outcomes.
Beyond oncology, the platform can be expanded for population health management, supply chain optimization, and other healthcare domains. Its scalable architecture ensures organizations can continue modernizing operations, enhancing care delivery, and supporting business performance through intelligent automation and data-driven insights.
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