Pilot Study Implementation

What is a Pilot BE Study? A Pilot BE Study is a preliminary clinical investigation conducted before the pivotal BE study. Its primary role is to assess whether the test formulation performs comparably to the reference product and to guide adjustments in formulation, dosing, or study design before committing to a larger, regulatory-submissible pivotal study. Objectives of Study The pilot BE study serves multiple purposes. It evaluates the feasibility of achieving bioequivalence between the test and reference formulations. It is also used to optimize the formulation or manufacturing process if initial results suggest improvements are necessary. Moreover, the pilot study helps assess intra-subject variability and residual error, which is critical for determining the required sample size for the pivotal study. It also assists in selecting the most appropriate dosage strength, especially when multiple strengths are available. Additionally, it helps estimate the test/reference (T/R) ratio to ensure it is likely to fall within acceptable regulatory limits, and to refine the blood sampling schedule to capture key pharmacokinetic parameters like Cmax, Tmax, and AUC. Key Features of Study Typically, a pilot BE study involves a small number of subjects—usually around 6 to 12. The results are not intended for regulatory submission but instead inform the design of the pivotal study. The study design is most often a 2-way or 3-way crossover, and it is usually conducted as an open-label study. While the pharmacokinetic parameters analyzed are the same as in a pivotal study, the primary purpose is to make a go/no-go decision for progressing to a full-scale BE study. Regulatory Notes Regulatory agencies such as the USFDA do not mandate pilot BE studies, but they are recommended—especially for high-risk, complex, or modified-release formulations.EMA takes a similar position and views pilot studies as useful tools for formulation optimization. In India, the CDSCO also permits and encourages pilot BE studies to support the planning of pivotal BE studies. When Are Pilot Studies Crucial? Pilot studies become particularly important in several scenarios. For new or complex formulations, they help confirm that the in vivo performance aligns with expectations. When high variability is expected in drug absorption, pilot studies help estimate this variability for better planning of the pivotal study. They are also essential for modified-release or narrow therapeutic index drugs, where precise absorption control is critical. Lastly, when it is the first time a generic formulation is being tested in humans, a pilot study provides foundational data to proceed confidently. Limitations Despite their value, pilot BE studies have limitations. They are not acceptable for regulatory filing due to the small sample size and exploratory nature. Furthermore, results from pilot studies may not always predict the outcomes of pivotal studies due to limited statistical power.

Somewhere out there, someone may be asking: ❓How do I start a meaningful paperless trade experiment? ❓Who do I need on board for buy-in and expertise? ❓How will I know it's working and ready to scale? If any of these sounds familiar… we built this resource 𝘧𝘰𝘳 𝘺𝘰𝘶. The Paperless Trade Pilot Playbook is designed to help anyone take that first step from idea to implementation. Not a policy paper. Not a tech guide. Just a practical toolkit you can pick up, flip through, and actually use. The Playbook breaks down six steps - from defining your vision, mapping the process and assembling the right team, to measuring results and learning how to iterate. Because going digital isn’t about “big bang transformation.” It’s about running small, well-scoped experiments that move the needle in real systems. 💡 Whether you’re a policymaker, a banker, a logistics operator, or a technologist - this guide shows you how to start small, start right, and start together. Created by the ICC Digital Standards Initiative, with 💎 insightful gems from across the public and private sectors - people who’ve walked the talk, with blisters and lessons to show for it. As Hui Ling C., Metals and Mining Digitalization Forum (MMDF) member and BHP Vice President, Commercial Global Business Services Outbound Operations, shared: “From our experience across global supply chains, digitalisation takes root when people can test, learn and share what works. The MMDF and its founding members – Anglo American, BHP, Rio Tinto and Vale – have built a wealth of experience from practical pilots. A paperless trade can reduce turnaround times from days into hours. We are sharing those insights to help accelerate new experiments and empower #changemakers driving #digitaltransformation in trade.”  I'd like to thank our content contributors from IMDA (Ren Yuh Kay and Valensia Bingah) as well as industry leaders from the Metals and Mining Digitalization Forum (Maya Sturm, Jasmin Koh, Alex Tan, Cindee Allister, Jennifer Sakaguchi, Pedro Mendes, Vicky Yao, Hui Ling C., Anita Mitter DipCouns MACA and Jacqueline Woo) - without whom this Playbook wouldn’t have come to life. And our interns Javier Lin & Brandon New whose many iterations (graphics included!) made this publication shine ✨ 🧭 Get the Playbook and Press Release here ⬇️ https://lnkd.in/gPJGpiT4 International Chamber of Commerce #ICCDSI Pamela Mar Tianmi Stilphen Wai-Yee WONG

Problem Statement: Within a multinational corporation's finance department, there's a high lead time in month-end financial close processes. This is primarily due to manual reconciliations, multiple hand-offs between teams, and a lack of standardized processes across various regions and business units. The extended lead time leads to delays in financial reporting, impacting strategic decision-making and increasing the potential for errors in the reported figures. Approach as a BA: Stakeholder Identification and Engagement: 1. Identify key stakeholders including team leads, finance managers, and process owners. 2. Engage them to understand their concerns, requirements, and expectations from the process improvement initiative. Process Mapping: Document the current 'as-is' month-end close process. This might involve: 1. Interviews 2. Observing actual processes 3. Reviewing process documentation 4. Identify bottlenecks, hand-offs, and manual interventions. Root Cause Analysis: 1. Conduct workshops and brainstorming sessions to determine root causes for the delays. 2. Use tools like Fishbone Diagrams and the 5 Whys to narrow down specific problem areas. Benchmarking and Best Practices: 1. Research best practices in financial close processes within the industry. 2. Benchmark the current process against industry standards or similar sized companies. Solution Design: 1. Propose standardized processes that can be adopted across all regions and business units. 2. Recommend tools or software that can automate certain aspects of the reconciliation process. 3. Introduce checkpoints or controls to ensure quality and accuracy. Pilot Testing: 1. Before a full-scale rollout, test the proposed changes in one business unit or region to validate the improvements. 2. Analyze results, gather feedback, and adjust as necessary. Implementation and Change Management: 1. Develop a detailed implementation plan, considering the sequencing of changes. 2. Engage with change management teams to ensure smooth transition and adoption of new processes. 3. Provide training sessions and documentation to help teams understand and adapt to the new process. Performance Metrics and Monitoring: Establish KPIs (Key Performance Indicators) to monitor the effectiveness of the new processes, such as: 1. Lead time for financial close 2. Accuracy of reports 3. Number of manual interventions Set up regular review meetings to monitor these KPIs and gather feedback. Continuous Improvement: 1. After the initial rollout, continue to engage with teams and gather feedback. 2. Look for opportunities to further refine and optimize the process. 3. Stay updated with industry trends and incorporate relevant best practices. Feedback and Iteration: 1. Periodically revisit the process to ensure it's still aligned with the business objectives. 2. Take feedback from users and make iterative improvements. BA Helpline #businessanalysis #businessanalyst #businessanalysts #ba #finance

Your healthtech pilot just saved lives. So why won't the hospital sign the contract? I've watched too many healthtech founders nail their clinical outcomes, then lose the deal in procurement limbo. The problem isn't your technology. It's your discovery. Before deploying ANY healthtech pilot, lock down: WHO makes the buying decision? (Clinical champion ≠ procurement authority. You need both.) WHAT'S the realistic budget? Healthcare budgets are planned 12-18 months out. WHEN is their implementation timeline? (Q4 budget cycles, compliance reviews, IT freezes—timing is everything) HOW are they managing this today? If it's "paper and prayers," the pain is real. If it's "we get by," walk away. WHICH clinical + operational metrics matter? (Patient outcomes AND cost savings) WHAT ROI justifies their CFO signing off? (Revenue cycle improvement? FTE reduction? Risk mitigation?) The healthtech founder trap: Thinking great clinical results automatically convert to contracts. Healthcare systems move slow. Include procurement timelines, compliance requirements, and budget cycles in your pilot planning from week one. Your pilot isn't proving your tech works—it's proving it works within their system, their workflow, their reality. Clinical validation without commercial planning is just expensive research.

After 12+ years in the space, this is the No. 1 mistake I see Health Systems make when implementing Digital Health: 𝗡𝗼𝘁 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘂𝗽 𝗳𝗼𝗿 𝘀𝗰𝗮𝗹𝗲 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝟭. It doesn’t mean you have to be committed to scale. You shouldn’t… because the pilot COULD flop. BUT even if the pilot is “successful”, the overall initiative could still be set up to fail. How? By MISSING any of these critical steps: → Having an executive sponsor co-design the success criteria that would justify funding the tool at scale across the system → Implementing the tool with the right clinical workflow that would actually be utilized long term at scale → Strategically selecting a pilot use case that would provide meaningful data relatively quickly → Cultivating a stable of willing champions ready to take the innovation to the next set of clinical areas or business units → Aiming to achieve the success criteria instead of running the initiative like a “let’s see what happens” research study where the thing that matters most is a journal publication → Continuously iterating the implementation and workflows throughout the pilot instead of treating it like a rigid research study where you can’t iterate the intervention → Leaning on the Vendor Partner for their guidance on how to set the initiative up for success - partially because they should have the expertise and partially because you want to test their fit for a long-term partnership. If you don’t feel like they know how to scale this even better than you… that’s a bad sign. Nail all of these and you’ve done everything you can to maximize your innovation’s chance for success. But miss a single one? You can do everything else right… and still ultimately end up with a “successful Pilot” and a nice conference presentation… that ends up collecting dust on the shelf a year later. Sound like a pretty big commitment? Yes, it is. But when the transformation potential is enormous and you’re asking staff and patients to take a chance on your vision… Well then you gotta commit to doing it right. And design for scale from Day 1.

Sample Size Guidance - How do you calculate a sample size for a pilot or a feasibility study? First, some definitions: - Pilot Study: A version of a main (Pivotal) study run in miniature to test whether the components of the study can all work together. - Feasibility Study: Research done before a main study to answer the question "Can this study be done". The difficulty of calculating a sample size for a pilot or feasibility study is that there is (typically) no hypothesis test. Without a hypothesis to test, there is no type 1 or type 2 error, and without the type 2 error, there is no sample size calculation. Recall that we calculate sample sizes to estimate the statistical power we can achieve and recall that statistical power is our ability to avoid a type 2 error - be able to reject the null hypothesis when it is warranted to do so. So what to do? What should we use to inform our sample size in our pilot or feasibility study? Let's start by answering the question - who is the study for? These early studies are 100% FOR THE SPONSOR. They are meant to provide the sponsor with the information they need to make a go/no-go decision on later studies or to help inform them with evaluating the current performance/development status of their product. With this in mind, the sample size should be driven by what the sponsor needs to achieve their goal (product development/improvement, go/no-go decisions, etc). So should there be no math involved? Not necessarily - you, as the statistician can still provide the sponsor with direction and insight by doing the following: - You can calculate a sample size as if there was a hypothesis to test so the sponsor gets an idea of what will be needed in the pivotal study - You can produce example confidence intervals based on the sample size the sponsor is thinking and a standard alpha value of 0.05 (so create a 95% CI) - You can help identify reference studies that have been completed previously to give a good semblance of what is used to achieve similar objectives In general, I typically see pilot and feasibility studies at or around 50 patients per arm but this is not a general rule - this is just my observations over the years. I wish you all the best with your pilot and feasibility studies and if you need any assistance with them, please don't hesitate to reach out. Happy Monday

Pilots, trials, proof-of-concepts. Are they worth doing? It might extend your sales cycle and require more resources However 75% of companies that engage in a well-structured pilot progress to full implementation. Pilots aren't just a test run. They can be the most effective way to convert conversations when run properly. Here's how to manage them effectively: 🎯 Set clear objectives Know what success looks like for both parties. Without clear goals, you're navigating without a map. 📆 Agree on timelines Keep everyone focused and accountable. Also, this prevents the pilot from dragging on indefinitely. 📈 Agreed measurables Establish KPIs and metrics from the start. This ensures you're measuring progress and success effectively. 👥 Involve key stakeholders Ensure decision-makers are involved and engaged throughout the pilot. Their buy-in is crucial for moving forward. ✅ Regular check-ins Schedule periodic updates to discuss progress, address challenges, and adjust strategies with the relevant stakeholders as needed. 🗣️ Feedback Loop Encourage honest feedback. It's gold dust for improving your product and you can set up a Slack Connect / Teams channel to manage this. 🏆 Post-pilot review Book it in from the start to analyse results against your agreed measurables. Use this data to refine your business case and highlight ROI. ➡️ Next steps Clearly define the transition from pilot to full-scale implementation - ideally, this is communicated as early on as possible, even pre-pilot. Make it easy for the customer to say yes. Remember, a successful pilot isn't just about proving your product's worth It's about building a relationship and setting the stage for a long-term partnership. How are you successfully running pilots?

When I started tarka, I had 0 customers and 100 problems. Today, we have a waitlist of 50 qualified customers and a "path" to product-market fit. Here's my 3-step strategy that made it possible: (This could well be your task list for the next 3+ months) Step #1 → Validate the value proposition Don't assume you know what customers want. Test it. Create at least 3 different value propositions for your idea. Then, reach out to potential customers and ask them to rate each one on a scale of 0-10. Go deeper: Ask them to rank the propositions together and explain their thinking. This gives you quantitative and qualitative data to work with. The highest-rated proposition becomes your focus. Step #2 → Create a pilot offer Forget about building a fully-fledged product. Start with a pilot. When we tested Tarka's concept, we created three different pilot versions: small, medium, and large. This allowed us to test price sensitivity and feature preferences. Pro tip: Include a 50% "pilot discount" for your first round. It incentivizes early adopters and gives you room to increase prices later, with the same users. You could also just grandfather them in. Step #3 → Convert them to a pilot When a potential customer shows interest, don't just say yes to everything. Dig deeper. Ask questions like: - "Why is that a requirement?" - "What about that is absolutely necessary?" - "Can we deliver that faster or slower?" These conversations help you design a pilot that truly takes care of a burning problem. Don't rush to create a perfect product. Learn as much as possible WHILE delivering real value. With these strategies, we turned Tarka from an idea into a waitlist of 50 qualified customers in just a few months. Your turn: Implement these steps. I promise you'll uncover insights you have never considered before. P.S. If you're struggling with identifying customer problems, check out my previous post on turning prospective customers into solvable problems.

Our pilot conversion rate is high and it’s the result of a very deliberate approach. Most pilots in the market today fail for predictable reasons. For example, implementation takes too long. Support isn't there when the customer needs it. Or the pilot ends before anyone can see real outcomes. We've designed our process to avoid all three of those problems. Here's how it works. 1/ Speed of implementation. The goal is to get students in the product as soon as humanly possible, because the faster students are using the product, the faster teachers and administrators can see whether it works. And in a three-month pilot, every day matters. 2/ White-glove customer success. We have a world-class CS team that takes care of every step of the way. They make sure any potential blockers are addressed immediately. They have a team that's as invested in the pilot's success as they are. 3/ Outcomes. Students need to complete courses. Teachers need to see data. Administrators need to understand that students learned something meaningful. We build that feedback loop into the pilot from the start and in a three-month window, we need to deliver proof that this works. So by the end of the pilot, districts can look at completion rates, student feedback, and learning outcomes and say: yes, this is valuable. We want to keep using this. It works because it removes the three biggest reasons pilots fail and that's what our conversion rate reflects.

Generative AI is revolutionizing the way businesses operate, innovate, and compete. However, adopting a technology as disruptive as AI can be intimidating. For organizations unsure about making a full commitment to AI deployment, a Pilot Proof of Concept (PoC) is often the ideal first step. What is a Pilot Proof of Concept? A Pilot Proof of Concept is a small-scale, time-limited project designed to prove the viability of a concept or technology. In the context of generative AI, a Pilot PoC is a mini-project that utilizes AI to solve a specific business problem or capitalize on an opportunity. The aim is to validate the technology's utility and ROI before going all-in on implementation.   Why Choose a Pilot PoC? Reduced Risk Adopting new technology is always a risk—especially something as cutting-edge as generative AI. A Pilot PoC allows you to test the waters without the substantial financial and operational commitments involved in full-scale deployment. It provides an opportunity to assess not just the technology's capabilities but also your organization's ability to integrate it into existing workflows. Skill Development Even a small PoC project requires cross-functional collaboration involving data scientists, IT professionals, and business stakeholders. This experience can help your team develop the vital skills needed for larger AI projects, fostering an organizational culture that understands and appreciates what AI can offer. Immediate Value While the scale may be small, the insights and benefits gained from a Pilot PoC can be immediate and significant. For example, a PoC focused on automating customer service responses could lead to quicker ticket resolution, thereby improving customer satisfaction levels. These quick wins can generate enthusiasm and buy-in for future projects. Future-Proofing The data and insights gathered during a PoC don't just prove value; they also inform future implementations. They can be used to fine-tune models and provide valuable input for scaling the project. The learnings from a PoC act as a roadmap, helping to ensure that future rollouts are smoother and more effective.   Steps for a Successful Pilot PoC Define Objectives: Be clear about what you aim to achieve with the PoC, whether it's improving a specific business process, enhancing customer engagement, or testing the feasibility of a new product idea. Assemble a Team: Form a dedicated PoC team with members from various departments who can contribute diverse skills and perspectives. Data Preparation: Generative AI models require data. Ensure you have access to quality data that the model can learn from. Implementation: Develop and deploy the AI model, keeping track of metrics that will help you assess its success or failure. Review: Once the PoC is complete, analyze the data to evaluate performance against your set objectives. This is the time to identify what worked, what didn’t, and why. #poc #pilot #llm #datascience #knowledgebase