Building an academic portfolio during medical training: Part 1 – research outside the box

As a medical trainee in the US, whether you are pursuing an academic career or applying for a fellowship or advanced fellowship, your academic profile is one of the most important currencies you rely on for this endeavor. Academia as a general term refers to 2 main areas: research and education. Many trainees, like myself, start their residency with no or very minimal research experience. It then becomes essential to create a reasonable research portfolio during medical training, which is often not an easy task, especially in clinically demanding specialties. In this series of blogs, I will try to share some ideas and tips that can help you build a competitive research résumé during residency and fellowship. These ideas also apply to medical students, inside or outside the US, who are trying to match their dream US residency program.

The first idea that I would like to talk about is one that I thought was particularly a game changer for me when it comes to research. I like to call this one “research outside the box”, and by the box here, in addition to the abstract meaning of doing things in unorthodox ways, I’m also referring to the literal box that is the walls of your training institution. Residents and fellows are rarely involved in multicenter clinical trials or prospective studies. In fact, the vast majority of research done during medical training is retrospective observational studies. One of the main reasons trainees rely on retrospective studies is the time factor. Prospective studies often take longer to execute, and it becomes difficult to get a tangible product, a conference abstract, or a published manuscript on time for your next fellowship or job application. Therefore, retrospective studies become the more realistic option, and traditionally, these are carried out using institutional databases (i.e. clinical data from patients treated at your own training hospital), which is and will remain one of the most valuable research resources. Then comes the fundamental question – why should I consider doing research in a non-traditional way, or “outside the box”? – For many reasons:

  • Many training hospitals do not have large clinical databases that can produce impactful research projects.
  • You may not find a good research mentor in your training institution.
  • Even with available databases and good research mentors, some retrospective studies may still take long to come to fruition, sometimes longer than you can afford without a back-up plan.
  • Diversifying the ways you do research by pursuing both traditional and non-traditional means, can lead to a marked increase in productivity.
  • Most importantly, collaborating with motivated medical students, residents, and fellows around the country (and sometimes even around the globe), not only enhances your research output but is in itself a great learning and networking opportunity.

The next logical question would be – as a student or a trainee, what type of research can I do outside my institution?

For the same practical reasons that I previously mentioned, I am still referring to retrospective observational research rather than multicenter trials or prospective studies. In that case, to be able to easily collaborate with researchers across different institutions the data has to be publicly available and not protected by privacy laws. There are different types of publicly available data, some are mostly free, such as already published literature, some can be purchased for a fee, such as national and state administrative databases, and others require a research proposal that goes through a grant-like process, such as societal databases. The latter typically requires a higher degree of research expertise and are restricted by application cycles, so I would not recommend them as the first go-to option if you are still taking your very first steps in medical research. Here are some examples of observational research work that can be done collaboratively using these publicly available data sources, without being limited by institutional boundaries:

  • Published medical literature can be used for meta-analyses and systematic reviews. These types of studies commonly address hot topics in medicine or topics with controversy or equipoise. A common scenario where topics are considered “hot” is immediately after the publication of a large clinical trial, particularly if the results are not in line with prior trials on the topic. Meta-analyses are also ideal for examining uncommon side effects or complications of medications or medical procedures.
  • National administrative databases can be used to perform retrospective observational studies, e.g. the National Inpatient Sample (NIS) and the Nationwide Readmissions Database (NRD), which are commonly used in cardiovascular research. They are particularly helpful in researching rare conditions or special populations where getting a large sample size using single-center data is challenging, or to examine trends in diseases or therapies over time. Most of these databases are available for purchase per calendar year (e.g. 2010, 2011, 2012 etc.), meaning that you can buy one or more year worth of data, depending on your budget and your research question.
  • Societal databases can also be used for original outcomes and quality improvement research, e.g. the American Heart Association (AHA) Get With The Guidelines and the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) Although these do not cost money, yet, they mostly require more work including submission of a proposal during an annual or bi-annual application cycle, which is a very competitive process.

These are just examples of what can be done and some common resources that can be used to start with, but in reality, the possibilities and the available resources are endless. Now that we talked about “why” and “what”, the next question is “how” – how to reach potential collaborators? how to build a successful multi-institutional team of young researchers? And what are the challenges to this approach? This will be the topic of my next monthly AHA Early Career Voice blog. So stay tuned..

The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.


Moving to a New Era of Clinical Trials

Frequently on rounds,  my colleagues argue that we should not do something to a patient since “there is no evidence that it works.”   This phenomenon of avoiding practice that has insufficient clinical trial evidence is often more common among young trainees in academic settings.    The practice of evidence-based medicine inherently involves integrating doctor’s experience, patient preferences and best available research. In an ideal world, every single question would have been tested in a clinical trial; but in reality this is not possible.   In fact, even the majority of recommendations in practice guidelines in cardiovascular disease are not supported by clinical trials.

Even for questions where there is a dire need for clinical trial evidence, such as adding new therapies to current standard of care or expanding or narrowing indications of existing therapies, multiple barriers remain.   Clinical trials are very expensive. In a recent analysis, the median cost of a clinical trial was estimated at 19 million U.S. dollars.   For pivotal cardiovascular disease trials, the numbers are much higher (north of 150 million USD) since those trials have to be larger and for longer duration to detect clinically meaningful outcomes (e.g. heart attack) and to also compare new interventions to current standards of care.   While cost is the biggest barrier, it is not the only one. Finding patients for trials has been a challenge that often leads to long periods of completion or even worse, aborting the trial due to inability to meet enrollment targets.  Even when patient enroll, they can easily lose interest and eventually dropout. Regulatory hurdles around accessing trial data add to the complexity. Even after successful trial completion, extensive inclusion and exclusion criteria_which often enable the trial to prove a positive outcome_ limit our ability to generalize the findings to many patients who do not fit those criteria.

A lot has changed in the world since we started doing randomized clinical trials in the mid 20th century, whether in science, health, technology, media, or even people’s behavior. Yet, we still do our trials the same.  It’s about time to move into a new era of clinical trials by thinking outside the box.  Smaller, smarter trials are possible. Analysis of big data from the real world could help drive hypotheses that we can test in clinical trials. For example in genomics a technique known as  Mendelian randomization uses genetic variation present as birth as a natural experiment to identify a causal relationship between an exposure and disease. Insights from big data could also help identify clusters of patients that are most likely to benefit from a treatment, have higher chance of having the outcome, or higher chance of having the side effect of the treatment, all of which could inform inclusion and exclusion criteria and increase the chance of having a smaller but more informative trial.

Another potential for innovating in design is by recruiting through direct-to-patient approaches. The Apple Heart Study recruited more than 400,000 people in a very short time and showed that this approach is possible. That should also be a coupled with  new approaches in statistical design as well as re-envisioning how we  ascertain outcomes, by capitalizing on the use of technology and patient engagement though ownership of their data. Changes in regulation are necessary to enable those innovations. Finally, despite the fact that clinical trials are so expensive, the value (yield divided by cost) has been low because we traditionally focused only on strong effects on the primary outcome. With appropriate data sharing of patient level data including those for negative trials, so much more could be learned.

As medical and scientific knowledge continues to increase, the cost of an incremental yield to health outcomes from new interventions will exponentially increase. Healthcare providers should be conscious of their practice of evidence-based medicine by always remembering that not all interventions necessarily require the highest level of evidence.  At the same time, we should re-envision our approaches to clinical research using the tools around us in today’s world to generate better evidence at lower cost.