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Transformation in Trials
A podcast about the transformations in clinical trial. As life science companies are pressured to deliver novel drugs faster, data, processes, applications, roles and change itself is changing. We speak to people in the industry that experience these transformations up close and make sense of how the pressure can become a catalyst for transformation.
Transformation in Trials
Decoding Real-World Evidence: Beyond Clinical Trials with Manfred Stapff
What if millions of electronic health records could transform how we understand medicine? In this thought-provoking conversation with Dr. Manfred Stapff, physician and real-world evidence pioneer, we uncover how anonymized patient data is revolutionizing healthcare research.
Dr. Stapff reveals how the 21st Century Cures Act dramatically accelerated electronic medical record adoption in the US, creating unprecedented opportunities for researchers. Today, with 90-95% of American hospitals digitized and similar progress across parts of Europe, we're witnessing a fundamental shift in how medical knowledge is generated. But this transformation comes with crucial questions about data quality, privacy, and interpretation.
Fascinatingly, real-world evidence offers solutions to problems traditional clinical trials can't address. While trials remain essential for developing new treatments, aggregated real-world data can help identify unexpected disease patterns, like rising cancer rates in younger populations. The sheer scale of this information—potentially millions of patient experiences—creates statistical power that individual studies could never achieve.
The conversation takes a particularly compelling turn when Dr. Stapff shares his vision for democratizing medical knowledge. "What did we do 40 years ago when we had a question? We asked our doctor," he explains. "If we had access to electronic medical records... we could have access to the experience of thousands of physicians who treat hundreds of thousands or even millions of patients." This democratization could fundamentally change how patients understand their conditions and treatment options.
Explore the emerging world of federated data networks, statistical literacy challenges, and why Dr. Stapff believes pharmaceutical companies should measure success not just in dollars but in lives improved. Whether you're a healthcare professional, researcher, or simply curious about the future of medicine, this episode offers valuable insights into how data is reshaping healthcare as we know it.
Transformation in Trials is a podcast investigating how we can change life sciences to get treatment to patients faster.
Getting treatment to patients faster requires well-functioning organizations. How do we do that? Ivanna Rosendal has written a book called Maneuvering Monday, about how a group of people try to make their organization better. You are certain to have a good laugh at their expense. And potentially get inspired how you can help make your company better.
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Welcome to another episode of Transformation in Trials. Today, we're going to be focusing on the topic of real-world evidence and its implications for society, and in the studio with me I have Manfred. Manfred, could you tell us a little bit about yourself?
Speaker 2:Thank you very much. It's an honor to be here. Thank you for having me. I'm Manfred Stapf. I'm a physician by training. After a few years of practical medicine internal medicine I changed to the pharmaceutical real-world evidence, which is an alternative to doing clinical trials or more a complementation, I would say and worked quite a lot establishing one of the largest real-world evidence networks. The company is called Trinetics. I worked there building that up as a chief medical officer and somehow this topic didn't let me lose anymore. So it's always exciting to try to get real world data to answer unanswered or still open questions in medicine, and that's what I am doing. Since then Also wrote a book. Perhaps I will show you the title in a few minutes. It's really an exciting topic and that's why we met.
Speaker 1:And I am excited to talk to you because it is an exciting topic and very relevant to most of the work that my listeners do. Manfred, could you tell us more about how real-world evidence found its way into clinical research?
Speaker 2:Yeah, I mentioned already a little bit electronic medical records. I can tell about the situation in the United States. In Europe it is different from country to country. In the United States, there was the so-called First Century Cures Act. Laws in the United States are always huge and big and big and big, and a part of this law was to force by law hospitals and physicians to introduce electronic medical records. No paper anymore, no big big cupboards with files, everything electronic, and this opened basically the world of research. Now, of course, one has to always consider data safety and patients' privacy. Therefore, if you hear electronic medical records, the word big data, no reason to panic. They are, even in the United States, very, very well protected. The data protection laws are much, much stricter than in the normal consumer law, in the normal consumer law, and this allows us to use anonymized and aggregated patient data for answering research questions. But the trigger word is really the 21st Century Cures Act from, I think it was introduced in 2016. That's basically where everything started.
Speaker 1:I actually was not aware that it was. The usage of electronic health records was mandated by law in the US. I'm surprised by this.
Speaker 2:Yeah, we are in the US. We are almost at 90%. 95% in the hospitals, in the veterans affair system also over 90% in the private practices. In the private practices. 10 years ago or 15 years ago these percentages were much lower in the 30s or 40s. There are still some exceptions. Whether these are exceptions by law or other exceptions, I cannot tell. Almost all healthcare providers in the United States have electronic medical records, which is a huge benefit for the patients because they have access to their data. They can exchange the data from provider to provider or between pharmacies and providers, but also for research, obviously, and we will talk about that a little bit more in a few minutes.
Speaker 1:Would you know how the medical records started in Europe? I assume it was not mandatory but more of a gradual implementation. But now I'm not so sure.
Speaker 2:Yeah. So what I know is, for example, the UK, thanks to their NIH system, they are using electronic medical records. Some of the Baltic countries are also very well developed. I think also Denmark has a very good system, especially for infectious diseases and vaccines. Israel seems to be quite well developed. I watch a little bit what's going on in Germany. You hear my German accent so of course I'm a little bit biased, but I also have to say that the Germans try to overdo it. They certainly have challenges to face in the implementation at the healthcare provider side and with the access to the data, but they are also going into that direction.
Speaker 2:Outside of Europe. I have been in Australia, which they were very, very open to. That. I have been in Australia, which they were very, very open to that. I have been in Malaysia Interestingly, they're also very open, and South Korea seems to be very well developed in establishing their electronic medical record system. So it's going on data everywhere, but all have stricter data protection rules than they are for the business or the regular consumer data. The regular consumer data. You know, if you Google something, then you get a few moments later you get already an offer or an advertising because it is individualized. In medical research, you are not interested in individual persons, you are interested in populations, and this allows anonymization and aggregation of the data and allows to follow the much stricter data protection laws which are basically everywhere in the world, much stricter than in the consumer business.
Speaker 1:A question comes to mind that I haven't thought of before. Is all health record data usable as real-world evidence or is there like a? What is the overlap in that Venn diagram?
Speaker 2:Yes. So you open a very important topic. What's about the completeness and the quality of this data? Real world says already it does not come from an experimental situation, from a clinical trial for procedures where a study protocol not only demands the procedures but also the completeness of the data. Source.
Speaker 2:Data verification, data completeness queries these are all trigger words which we know in clinical trials. This does not happen in the real world. So the data may be incomplete, there may be errors, there may be the so-called up-coding no affront to my medical colleagues, but sometimes it's tempting to make the diagnosis a little bit more serious in the coding because it may bring a little bit more financial reward. So all these one has to know. Therefore, the first step is having quality standards in the collection of the data.
Speaker 2:Plausibility checks, just to mention something very, very simple If a patient has thyroid medication in the electronic medical record, we also expect that there are laboratory values for the thyroid T3, t4, and also a respective diagnosis. So plausibility checks are very good for the check of completeness and therefore these data have to go through these checks before they get an entry, so to say, into the database. This reduces, of course, the sample size, because you want to have complete data, especially for the disease or the therapeutic area where your interest is located. But it is still significantly more in terms of sample size than what you can achieve in a clinical trial and therefore the sample size can also compensate a little bit for the variance, for the uncertainty.
Speaker 1:That makes sense. So maybe simplified one could say that all medical records could be real-world evidence if they are controlled for quality for the purpose intended.
Speaker 2:It's a good point. Real-world evidence sooner or later replace clinical trials. I would say no. It will complement clinical trials, especially for drug development. If a drug is not yet on the market, you can give it only in the environment of a clinical trial. There are no real world data for a drug which is in phase two or in phase three. So this is one point. But when you go in general into medical research, what are risk factors for certain cancers, for example? What are the patterns which perhaps allow a prediction of an efficacy or a safety of a drug? There is certainly something where real-world evidence in combination with artificial intelligence will be very successful in the future.
Speaker 1:That makes sense. In the pre-call we discussed that a lot of the real-world evidence we currently have when used for clinical trials it is mainly used to evaluate the clinical trial feasibility. What else can we actually do with this data that we can make use of?
Speaker 2:Yeah, the clinical trial use started because this was a very close collaboration between the data companies and the pharmaceutical companies and it started with protocol feasibility. You know that each study protocol has, let's say, half a dozen inclusion criteria and 20-30 exclusion criteria and then at the end we realized that this does not really represent the population which actually has the disease, not mentioning that there are still about 80% of the sites who do not enroll one single patient in a clinical study, or the respective enrollment problems delay the protocol or delay the conduct of the study or delay the conduct of the study. So these are use cases which, when you use real-world data to test the protocol whether such a population actually exists, and you use this data for site selection meaning identifying locations, geographic or university clinics where such patients actually are seen then the return on investment for the pharmaceutical industry is quite big. If you consider how much is spent for an amendment just to change one inclusion criterion, or if you consider, if you have a two or three months delay of your study results and this delays also the launch date what kind of financial impact this can have. And therefore the starting point was a collaboration between the data companies and the pharmaceutical companies.
Speaker 2:What should come in the future is more access to academia, to practicing physicians, to answer questions which we have still unanswered in medicine, and there are many, many, many, many unanswered questions. Just think about the increasing incidence of cancer in younger generations colon cancer, lung cancer in non-smokers. Using big amounts of data can perhaps identify patterns in the genetic data, in the comorbidities, in the laboratory values, can perhaps identify things or risk factors which no human brain has ever tried to think about. So these are definitely next steps and ideally we would have access as patients, as laypersons, also to information like that. In America you can easily access, for example, employment statistics by the government. You can access by internet the census, the counting numbers. So why not accessing also summaries of electronic medical records where the patient can realize I have diabetes and 60 million other patients like me they also have diabetes and they take this medication and they experience that risk factor. So there are many, many, many questions.
Speaker 2:I sometimes compare that with a situation, let's say, 40 years ago. What did we do 40 years ago when we had a question? We went into a library. We are looking for a magazine or for a journal medical journal or for a book, and it took us most likely a whole day to get a little bit of an answer from these original sources or scientific publications. What do we do now, in the year 2025? We Google and we have the answer, perhaps within a minute. And similar as a patient, what did we do 40 years ago? We asked our doctor. When we were happy enough to get a second doctor, we got a second opinion and then we asked perhaps some uncle or someone in the family or someone a nephew perhaps was just studying medicine? Good idea, okay, let's ask a medical student. So we got perhaps four opinions.
Speaker 2:If we had access to electronic medical records, if we had access to electronic medical records, we could have access to the experience of not only a handful of people. We could have access to the experience of thousands of physicians who treat hundreds of thousands or even millions of patients. You just needed a proper way to demonstrate, to visualize the data, similar like we have to ask Google a correct question or even chat GPD. Also, you have to ask a correct question to get a correct answer. But it should be possible in the future. So it's allowed to dream a little bit.
Speaker 1:Yes, well, today, who has access to this data and how can one get access? I don't assume that the general public has access to the aggregation of health records.
Speaker 2:Not yet. So these are companies. They call themselves or see themselves as data brokers or data aggregators. They have contracts with hospitals or with other places where the sources of data is. Usually it's a hospital or a hospital chain or a healthcare system. It can also be an insurance. Wherever these data are located, these data brokers make a contract and get certain, depending on the contract, certain rights to use these data or to resell this data to data users.
Speaker 2:Let's say the best. In my opinion, the best solution because it's also the safest and it is also ensuring the privacy in the best way is a so-called federated network, which means that a data broker company has a piece of hardware in each healthcare system's basement. Very often these data are not in the basement, but they are somewhere physically or in the cloud, so has access and instead of downloading the data by downloading, taking responsibility for the safety of this data, they do not take the data, they send the question into the system, and that's why they need a piece of hardware in the hospital basement it does not have to be the basement and then the question will be analyzed at the location and the result will then be sent, and this usually are statistical results, not deviations, hablan-meier curves or whatever the question was will be sent to the data broker center and the data broker center collects now all these answers coming from 50 or 100 healthcare organizations and puts that together and spits it out. And this is called federated system. So you do not even buy data. You basically have the right to send a question to the data source and a few moments later you get the answer. That's the best way. But there's also a classical way where you basically get the data.
Speaker 2:I personally would not do it, because if I buy data I take responsibility for the safety of this data. So I personally wouldn't do it. But it's a model which certainly is also possible. And then there's the financial aspect, also possible. And then there's the financial aspect. There are transactional models where you buy a certain defined data set let's say, all diabetic patients in the United States over 65 who don't take insulin, something like that or you make a subscription and then for a certain period, let's say 12 months and within these 12 months you have access and you can send questions to the data center and then it will be answered by a federal network.
Speaker 1:Yeah, very interesting. Would there be any dangers we would face as a society if we suddenly did have access to some of this data? Would it require more from the individual to be able to understand what these results actually mean?
Speaker 2:A little bit of an insight is certainly helpful, but these days patients are very well educated. The whole topic, the overall topic, could be called democratization, democratization of knowledge and information. These days we get so much information by internet, by TV station, by social media, by Google, by Yahoo. It is certainly much, much easier today to get the information than to verify the information. So there is also a lot of garbage in the information and whether this is, let's say, data or publications or policies, it's always the same. User, of course, should be sort of educated, should have some basic knowledge, should be able to understand what the user is asking and what the answers will mean, and also separate the misinformation from disinformation, from fake news. So the more the user is educated, the more helpful this will be, and I see this anyway as new way of democratization, the democratization of information.
Speaker 2:Do not want citizens to be educated and knowledgeable. The tyrants, they want you to stay stupid. So whenever we talk about danger to democracy and in the United States it's, let's say, a saying which is often used, often also misused then I think the bigger danger does not come from above. The biggest danger comes from all of us. If we do not use the information sources and the way how we can educate ourselves. So a long answer to a short question, almost a political answer, but the overall topic democratization, I think, is a good way to look at real world data and that information flow in general.
Speaker 1:I would agree this is probably a departure from our topic a little bit, but how could we educate people to understand especially aggregated data and understanding statistics, and what does seeing a trend in data actually mean and what does it take for us to be able to validate that this is an actual trend and a meaningful one?
Speaker 2:Yeah, how can we learn that? I take this as a trigger to show my book, because everything is written in there. What does it actually mean? What are the statistical pitfalls which you can fall? And these are not too much, not too many. It's relatively simple In the statistic world. You have average and median, you have the mix-up of causality, with coincidence, we have relative versus absolute risk reduction and you have, as a solution for that, the number needed to treat. So all these trigger words, if they tell you something, if you have already a reaction to this word and you say I have heard it, then you are already in a good position. If not, then it's nicely explained in the book and this is basically all what you need.
Speaker 2:Then, interestingly, misinformation and disinformation can be relatively easy identified by a few tricks. These are the statistical knowledge which I just mentioned, but also looking for the sources, looking who is publishing or who is posting this information. What did they post before? What are reactions to their post? What sources are they quoting? If there is a post which has a very frightening headline, but you can read it and read it again and again and you do not find a source where this comes from, then you can basically disregard it. So there are several tricks and methods where you can identify fake news, misinformation, disinformation, statistical errors. It's not that complicated if you have a little bit of a basic interest and a basic knowledge.
Speaker 1:That's a great way to also encourage people to look into your book. I would be curious to hear more about how it was for you to publish a book on this subject, both kind of where we are in life sciences, but also in the current climate in our political cycles, where it might not necessarily be evidence that is appreciated, but more good storytelling, whether true or not. How was your book received?
Speaker 2:yeah, so I mentioned I worked as chief medical officer in one of these real world data companies in the startup and we created together this company in this global network, and I had also a history of being in charge of quality management, so I could use this knowledge also, and I had a background as a physician with many open questions in medicine. This was one reason why I left practicing medicine and went into theoretical research. So all this taken together kind of drove me to taking the time and this time is approximately two years sabbatical to write down and research everything, what's going on in my head and all the memories and all the experiences and all this knowledge and put it 260 pages so that someone who does not have a history like I had can understand it and can benefit from it. It indeed takes a long time. It also takes good editors and a good publishing company, because, you can imagine, a German scientist is not the best English writer, so I really needed people to correct my awful English writing. But I think we came up with a relatively good result.
Speaker 2:It takes forever, very long. It takes always longer than you think. So just in case that one of you thinks I should write a book. Don't plan for six months, plan for two years. And don't plan that it makes you rich. In the contrary, you spend a lot of time and money for research and for writing and for proofreading and for pictures which you may need.
Speaker 2:Nevertheless, it's sort of fun and it enabled me to reorganize all my experiences, memories and whatever is going on in this little piece of brain up there. It was a good time and now obviously it's about bringing it out to the market, inform people, get it marketed and get it sold. But this is not the primary target. Making money by writing a book, especially in the nonfiction area, will not work. It will not work. My name is not Obama. I do not sell automatically. So it is not the financial motivation. It must be a motivation in yourself or in the research, or a kind of a teaching talent or something else that that makes sense and also, I enjoyed reading at least parts of your book.
Speaker 1:Did not get through the whole thing. I would like to finish the rest. It was indeed quite insightful. Well, manfred, as we start rounding off our conversation, I always ask my guests the same question then, and, and that is if I gave you the transformation trial magic wand that can change one thing in the life science industry, what would you wish to change?
Speaker 2:Yeah, the life science industry. What to change? That's a very good question. I think a good idea would be to continue the idea of measuring the right things, what we actually do.
Speaker 2:Already in the clinical trial world, we look for patient-relevant outcomes. We know if this is a surrogate parameter and need to do clinical outcomes or clinical events. We have learned that in the clinical trial world. The industry also, as such, should also learn that. And what I mean with that is if you read some publication or headlines about the 20 most successful pharma companies in the year 2025 or something like this, then they are listed and the big companies are on top.
Speaker 2:I don't know whether it's Pfizer or others. Someone is on top and then the list goes by profit or by turnover, so by dollar figures. Is the success of a pharmaceutical company actually really the dollar figure? Is it really the financial success? Of course they need to have financial success, because if there is no investor money, then all the researchers don't get salaries. So, yes, money is important and keeps the machine turning, but in addition, they should also measure what good things are they doing for patients. Then this is also a success of a pharmaceutical company If a statin reduces cholesterol and, based on the market data, one can say perhaps it avoided 500,000 heart attacks and 200,000 strokes.
Speaker 1:This would be a success.
Speaker 2:Not only because it's money for the healthcare system, not only because it's money for the healthcare system. No, it's also the success of the pharmaceutical company. Or look at Merck's Keytruda. It has 17 or so indications. Keytruda is not only a financial success for Merck, it is also a huge healthcare and treatment success for patients. Unfortunately, you'd never find the numbers somewhere, but is it sure that Keytruda saved thousands of lives or expanded thousands of lives or reduced the cancer-related problems of tens of thousands of patients? This would be a success of the company.
Speaker 2:So if someone makes the next list the most successful pharmaceutical companies of the year 2026, I do not want to see only dollar figures. I want to see outcomes or metrics which really are important for the patients, for their health, for their survival. And the data are available. I mean, they have it. They have the market data, they have the sales data, they have the number of patients, they have the clinical outcome studies. They just need to combine it or use real-world evidence data. That's also okay. You have the prescription data, you have the outcomes data. So make it like that. So show that your company is not only financially successful, but also in terms of healthcare for patients successful.
Speaker 1:That's a great wish. I hope that is something that the industry takes to heart and we start changing in that direction. Manfred, if my listeners have questions to you about real world evidence or want to know where to find your book, how can people reach out?
Speaker 2:So I show it again. It's real-world and it's. What do I have to do with the camera? Real-world evidence unveiled navigating the maze of modern misinformation. So you will find it on Amazon, and on Amazon you can order it, or you can download the e-book version. And if there is any specific questions, then perhaps you can somehow give them my email address, and if it's not too much, then of course I will reply Sure.
Speaker 1:That sounds great. Thank you so much. This was a really interesting conversation.
Speaker 2:Thank you for having me. I enjoyed it and I wish all your listeners and viewers all the best and hope that they got a little bit of a take home message, but then it was really worth it.
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