Latest news with #KamyaElawadhi
Forbes
10-04-2025
- Business
- Forbes
Synergy In Innovation: Exploring The Collaborative Potential Of AI And Human Creativity
Kamya Elawadhi is Chief Client Officer at Doceree. The dawn of artificial intelligence (AI) has had a major impact on the healthcare industry. Apart from improving both diagnosis and delivery of healthcare, AI has also affected functions such as patient and healthcare professional (HCP) outreach—30% of outbound marketing messages will be synthetically generated by 2025. Taking advantage of this opportunity, modern-day marketers are using innovative solutions backed by advanced AI algorithms to improve their interactions with HCPs—and, ultimately, the overall effectiveness of their marketing strategies. To drive meaningful change, marketing experts are realizing the importance of backing AI with human intelligence. This is especially true for point of care (POC) marketing, which is undergoing a transformation thanks to the utilization of AI-based solutions married to human expertise. In the months ahead, the convergence of the two is expected to significantly impact how marketers deliver hyper-contextualized and relevant messaging to HCPs at critical junctures of the healthcare delivery cycle, thus enhancing patient outcomes and driving more personalized, impactful strategies. At its core, POC marketing in healthcare involves sharing targeted, relevant messaging with HCPs and sometimes patients at precise moments of the healthcare delivery cycle. Traditionally, POC marketing relied heavily on sharing marketing information through physical means such as brochures and posters. While somewhat effective, this one-size-fits-all approach falls short of addressing the nuanced needs of individual patients and healthcare providers. In today's digital-first world, where HCPs spend the majority of their clinical hours switching between electronic health records (EHRs), telemedicine and eRx platforms, the delivery of marketing messaging is best received when the HCP is found within the HIPAA-protected walled garden of trusted digital touchpoints. The goal for the marketer here is to provide relevant and actionable information in a timely manner that supports decision-making, fosters trust between stakeholders and helps the healthcare system provide value-based care to patients. This is where AI—combined with human ingenuity—comes into play, offering unprecedented potential for personalization and engagement. One of the biggest advantages of AI and machine learning (ML) models is their ability to process vast amounts of data—in this case, of patients and HCPs involved—in an ethical manner. They can extract insights from EHRs and other connected platforms, as well as devices, to uncover patient needs and preferences. Fed with the correct instructions, these tools can identify patterns that may otherwise be too time-consuming for humans. AI tools can also share hyper-personalized messaging at scale using natural language processing (NLP) models, all in real time. A study suggested a 50% increase in engagement when marketers adopt AI-powered personalization strategies. Using an advanced AI-backed solution, marketers can also push identity-agnostic, condition-based triggers to improve their messaging outcomes. For example, trigger-based AI solutions can reach an HCP at critical moments of patient care and healthcare delivery with information about a drug that a patient may need. Apart from this, AI can also offer access to predictive analytics by forecasting health trends and patient behaviors, allowing marketers to anticipate the needs of patients and HCPs and serve them better in a timely manner. This not only enhances patient outcomes but also maximizes the efficiency of marketing efforts. AI's ability to stick to its core programming and follow set rules can also ensure marketing materials adhere to regulatory standards and ethical guidelines. Automated systems can scan content for compliance with HIPAA and other regulations, reducing the risk of costly errors. While AI offers impressive capabilities, the human element remains indispensable in POC marketing. For example, in the case of marketing messaging, it must resonate on a deeper level with HCPs and patients. This is where a human marketer's ability to bring empathy and cultural sensitivity to the table cannot be underestimated. Similarly, while AI can generate data-driven insights, human ingenuity can help craft creative strategies that align with brand identity and patient expectations. Human intelligence will remain essential in overseeing AI-generated strategies and content to ensure accuracy, relevance and ethical alignment. By applying behavioral sciences and determining when to use smarter, more subtle marketing tactics that nudge rather than sludge, humans will continue to enhance AI-driven marketing efforts. Additionally, humans will be vital in identifying biases in AI algorithms and correcting any deficiencies. In an industry where not only money but millions of lives are at stake each day, the human touch will be indispensable. Suffice it to say, AI-human collaboration in POC marketing will enable marketers to craft strategies that offer better results for their pharma and healthcare partners. Instead of sludging, nudging HCPs with real-time, hyper-personalized and contextualized messages will remain the core objectives of marketing strategies. Marketers are also likely to adopt newer ways of reaching their goals. As such, augmented reality (AR) experiences are likely to become part of the POC ecosystem, along with solutions that enable humans and AI to collaborate in delivering marketing messages through AR. AI and human collaboration should also strengthen feedback loop systems. Both HCP and patient feedback will likely be captured and analyzed in real time, allowing marketers to quickly adjust their strategies. By combining the analytical power of AI with the creativity and intelligence of human marketers, POC marketing will become more personalized, efficient and impactful. This collaborative effort will enhance patient engagement and drive meaningful improvements in healthcare delivery. Data privacy and algorithmic biases will remain challenges, but with human ingenuity and intelligence at their side, AI-based solutions will be able to help work toward a more patient-centric approach to marketing. 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Forbes
26-03-2025
- Business
- Forbes
How Harnessing AI Can Transform Clinical Trials
Kamya Elawadhi is Chief Client Officer at Doceree . getty Clinical trials are an essential part of medical breakthroughs that help patients, but getting them off the ground and running them is often fraught with challenges. Specifically, qualification, activation, compliance and data monitoring and analysis can be complex, resource-intensive, time-consuming and expensive for pharmaceutical researchers. Consider this: Many clinical trials aren't able to even move past the recruitment and enrollment stage. According to Deloitte , a 'study on the benefits of virtual randomized clinical trials shows that more than 80% of in-person studies are delayed because of insufficient patient recruitment, while 80% of research sites fail to meet enrollment goals.' Once past the initial stage, other problems can arise. As noted in Nature Reviews Drug Discovery , one report assessed 7,455 drug development programs that went 'through the clinic between 2006 and 2015.' The researchers 'found that the probability of success was 63% in Phase I trials, 31% in Phase II trials, 58% in Phase III trials and 85% during the regulatory review process, for an overall success rate of 9.6%.' However, pharmaceutical companies can leverage AI to overcome challenges and streamline the clinical trial landscape. There are several key ways AI can help pharmaceutical companies address common challenges associated with clinical trials. For one, AI can be used to improve patient recruitment. When patients have doctor's visits, physicians capture critical data, such as concerns discussed and any medications that were prescribed, via electronic health records (EHRs). Currently, pharmaceutical researchers depend on physicians to pinpoint who might qualify for a clinical trial. In the hectic day-to-day of providing patient care, physicians may not remember which patients qualify and which don't. That's where AI can step in. AI can sift through structured and unstructured EHR data, such as diagnoses, lab results, prescriptions and physicians' notes, to match patients with trial criteria more efficiently than physicians having to rely on memory alone. From there, AI can be used to activate patient engagement. Through an app or an EHR system, physicians can receive notes that particular patients qualify or are strong candidates for given clinical trials—and can also get guidance on the details they need to share with patients, such as the purpose of the trial and next steps for getting involved (if the patient expresses interest in doing so). Once patients enroll, researchers can use AI to monitor compliance and analyze feedback during trials. For instance, if patients have to take a medication twice a day, they can get push notifications on their phones, reminding them when it's time to take their medication and instructing them to indicate when they've finished taking the medication. AI can keep track of which patients have completed the task and which haven't—and then message them appropriately. Additionally, patients can input their feedback, such as their adverse side effects, into an app or system. AI can then analyze that feedback, uncovering common threads and outliers to present to researchers. Researchers won't have to spend hours combing through the data themselves. Instead, they can gain a quick understanding of the risks and benefits of their drugs and adjust accordingly. The Risks Of Using AI In Clinical Trials AI can streamline clinical trials, but it's not without risks. AI should be used as an assistive tool for clinical trials, not a replacement for human expertise and judgment. While AI tools offer value, they aren't infallible. Human oversight is crucial. Researchers and physicians should proactively review the recommendations that AI tools make. For example, an AI tool might indicate that a patient is a good fit for a clinical trial, but a physician might decide that, say, based on a contraindication, they shouldn't participate in the trial. Additionally, compliance with HIPAA and other privacy regulations is imperative. Pharmaceutical companies must thoroughly evaluate prospective AI vendors and review their data policies to ensure compliance The Benefits Of A Crawl, Walk, Run Approach In AI Implementation Leaders of pharmaceutical companies who are considering implementing AI for clinical trials should consider a crawl, walk, run approach—starting with specific use cases in their clinical trials, rather than fully overhauling their approaches at once. Rolling out AI for clinical trials is a major organizational change. And given the stakes in clinical trials, pharmaceutical leaders should be extra cautious. By implementing AI in stages, pharmaceutical leaders can pace themselves and their teams, get a stronger sense of what is effective for patients and adjust as needed. By streamlining clinical trials, particularly the clinical matching process, pharmaceutical companies can save time and money—and allocate that time and money to research and development. Ultimately, using AI in clinical trials can help drugs get launched sooner, helping more patients. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
