How actual data supports tag extensions and safer usage of new therapies

The rise in direct access treatments through online compound pharmacies and other direct access to the patient’s sales model increases the likelihood of patients using drugs with other therapeutic agents and a variety of complex comorbidities.

With the growth of GLP-1 drugs and the growth of associated direct prescription drugs, this category of drugs has a good location to leverage real-world data to identify and demonstrate benefits for patients in other treatment areas – beyond the current recognition of obesity and diabetes. While GLP-1 drugs are at the forefront of new opportunities for real-world research and surveillance, each new drug and drug category can benefit from ongoing post-market surveillance and observational studies to identify opportunities for label ransomware and new patient similarities that were not initially studied in clinical trials.

With the increasing real-world data, most of the new drug applications of the U.S. Food and Drug Administration (FDA) now contain real-world data. This is attributed in part to the 21st century treatment bill, which requires the FDA to evaluate real-world data to support new drug indications and tag extensions.

Policy makers, regulatory leaders and clinical development supervisors should consider healthcare stakeholders can gain insight into new approaches to new drugs to identify risks and efficacy when used in the real world. Participating in data sharing, observation cohort studies, and deeper analysis of available data for further insights can drive improved outcomes for patients, which can be faster and more powerful.

Data sharing among providers is more reliable and will ensure that all prescribers have the latest information on the patient’s health.

The different nature of patient data sharing in the U.S. healthcare system poses a significant challenge for prescribers who often lack other medications used by patients within line of sight. If a prescription is prescribed by another doctor, or if the patient does not explicitly disclose his full prescription history and lineup, the prescription will not be able to access the entire situation. Especially in online composite models with direct access to patients, it may not be necessary for patients to provide medical records to prescribers who interact with patients in online transactions.

The capabilities of healthcare providers, such as medical reconciliation and health information exchange, can increase transparency of data between prescribers. This will promote real-world insight into the use of therapeutic agents, the use of any adverse events, and the evolving safety and efficacy profile of specific drugs. These features are supported by AI and other innovative technologies to reduce the risk of contraindications in patients and generate new evidence to support labels and accomplice expansion. Although these tools are effective with extensive security profiles and risk information, they have not yet adapted to the large-scale and real-time data sharing requirements of direct prescription scenarios.

Access channels directly to patients should utilize these data sharing technologies to access more health information about patients using them. Another option is to work with electronic health records companies or healthcare provider organizations to gain a more comprehensive understanding of the patient’s health status and prescription history.

This year, the Drug Enforcement Agency (DEA) issued recommended rules that advise telemedicine providers to attend a special registry that tracks regulations for prescription controlled substances. “The rise of DTC online telehealth platforms in recent years has further changed the delivery of healthcare, but due to the distant nature of care delivery, it has also introduced new challenges and increased transfer risks,” the DEA noted in the proposed rules, indicating the need for improved data sharing and immobility across the industry.

Observational studies can enable new elucidation on the risks of drug interactions and the benefits that are not understood in the real world.

Observational studies of co-treatment medication use can shed light on potential risks of use that may be used under conditions that randomized grouping is immoral. For example, it is unethical to randomize the interventional trial to test the safety of GLP-1 drugs for maternal and fetal health outcomes. Alternatively, retrospective self-report cohort studies can be used to better understand the results associated with GLP-1 during pregnancy.

Observational study design involves researchers collecting data on their behavior or exposure to the study participants. While causality cannot be demonstrated with observational research methods, statistical methods can be used to normalize data and determine the association between behavior and outcomes, resulting in insights that provide evidence for clinical recommendations and guidelines.

Additionally, compared to another treatment, the January 2025 publication in Natural Medicine used a group of diabetic patients (n = 215,970) generated by the U.S. Department of Veterans Affairs database (n = 215,970) to understand the health effects of GLP-1 drugs compared to another treatment approach. Observational studies found that the health risks associated with 175 health outcomes evaluated were reduced and increased, and the authors concluded that: “The results provide insights into the benefits and risks of GLP-1. [drugs] and may help inform clinical care and guide the research agenda. “If it is not an observational study, these insights on these scales will take longer.

Large amounts of real-life data provide new opportunities for clinical development supervisors and regulators to conduct more in-depth novel analysis

As consumers and patient-reported data platforms reproduce, such as Whoop and Oura, these tools can be used to encourage consumers to report their health behaviors and digitally track their health. For example, Hoop has published several observational studies based on data collected by its wearable devices. These patient or user-reported datasets can complement healthcare records and laboratory data to facilitate domovariant analysis to generate further insights and stricter cohort stratification.

In addition to expanding access to effective and safe drugs to treat novel indications in new patient populations, real-world data can also improve diagnostic approaches to ensure that patients are treated with appropriate conditions, as demonstrated by their healthcare data. For example, actual data can facilitate early detection and diagnosis by analyzing large data sets from electronic health records, wearable devices, and other sources to identify patterns and abnormalities that may indicate early stages of the disease.

Real-world data can also facilitate personalized medicine by developing customized diagnostic tools that collect patient health history, genetic information and lifestyle data. These insights are collected through patient reporting channels, electronic medical record data and other databases. Combining machine learning and artificial intelligence, diagnostic algorithms can be refined to support patients and healthcare providers to make earlier and more accurate diagnosis, generating basic data to extend approved therapeutic agents to new patient likes or health indicators.

Clinical and observational studies may be valuable tools to demonstrate the risks and benefits of using prescriptions in different patient populations and to determine the risks and benefits of tag expansion opportunities. By implementing strong data sharing, healthcare stakeholders can better understand the use of emerging products in the real world. Improving patients’ access to therapeutic agents through direct access to patients will increase the availability of treatments and may provide new opportunities to collect data on the realistic use of novel therapeutic agents. With the expansion of GLP-1 use, real-world data can support the expansion of tags to cardiovascular disease and other indications, including neurodegenerative diseases, PCOS, chronic kidney disease, and more. With more data, drug tissue can improve diagnostic protocols, enhance personalization of treatment plans, and better understand potential risks and safety indicators. Strict observational studies and data sharing among healthcare stakeholders can support the safe and effective use of all new categories of medications.

Editor's Note: The author has no relationship with any company/product mentioned.

Photo: Yuichiro Chino, Getty Images

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