There’s a huge load on both pharmaceutical industry and academia in terms of data and information that is circulated through documents such as study protocols, reports, investigator brochures, manuscripts, and several other regulatory and marketing publications. Both writers and reviewers of these documents are overwhelmed with data and information overload, more so while adhering to guidelines/recommendations, which inadvertently increases the volume of the documents. Given the limited writing/reviewing resources with required expertise at regulatory agencies, research organizations, and scientific journals, it’s important to reduce this excess load, particularly the volumes. It would further reduce the turnaround time for these documents, accelerate research and knowledge dispersal, and would also benefit the end users of this information. This idea of shredding the volumes brings with it the concept called “lean and mean writing”, which would increase the efficiency, utility, and practicality, while retaining the absolute essentials, which is the call of the time.
However, this idea of ‘lean and mean’ isn’t new. Lean, almost a century-old practice, works by eliminating the futile processes or components in any setup, thus bringing innovation and improvement at the micro-level and then call it the best practice before it becomes a routine practice. Various industries have evolved and benefited over the years using the lean and mean concepts. The concept of lean is now percolating fast into clinical research with significant impact on all activities associated with it, including medical writing, that is, right from automating protocol authoring, digital patient recruitment, electronic data capture, reporting to publishing the study in public domain, use of e-CTD rather than paper CTD, and so on. This means that lean encompasses data interpretation as well, which is more clear and precise making it to the point or “mean.”
The “lean” approach includes developing problem-solving skills, refining processes, and driving for excellence, and one needs to work on both the process and the content of the documents. The concept of lean has now found a place in health and pharmaceutical industry, and organizations are working toward leaning several age-old set processes. Clinics and hospitals work on improving the outcome where the customer is patient while they aim to reduce the waiting time for reports, or say appointments. Furthermore, the ‘mean’ concept is equally important and fully complements the concept of lean. Large data sets, and an equally elaborate analysis, encompassing all the aspects of possible analyses, and the multiple publications of data including repetition of study methods pay a toll on authors, reviewers, publishers, and the readers. Thus, it will be good if the key, exploratory, and post hoc analysis is clearly demarcated and overlapping publications are discouraged. Statistical analysis that is not preplanned should be scrutinized and labeled clearly. A complete abolishing of such analysis is not a good practice as this not only precludes the subgroup efficacy or selectivity, but would also hamper the decision on inclusion and exclusion criteria. Therefore, there is a need to lean toward mean, but with a pinch of salt.
Lean and mean writing can have a double benefit, viz. a reduced load on the reviewers, and lesser challenges while managing, accessing, and archiving these data. This clearly underlines the need of lean CSRs and dossiers with lots of external hyperlinking so as to avoid repetition or duplication of information and data. The trend continues to grow in academics too. Right from writing a thesis to writing the manuscripts, ‘lean writing’ is everywhere. The theses have gone “slimmer” so that not only it takes less time to write, but also to review and archival. The scientific papers have a limitation, and publishing additional pages often incur an additional cost. Furthermore, additional tables or supporting data can be published as “online” only. These trends are being globally appreciated, and are taken positively, with several pharmaceutical companies and journals putting up initiatives in place for the same.
The road ahead should be clear and uncluttered, cutting off the redundant data such as repeating the information in CSR that is already available in protocol, data tables, listings, figures, etc. Similarly, the manuscripts can be condensed, and a pattern such as hypothesis, key studies in the past 5 years, results, and summary and conclusion can be followed. Going forward, when the practice of data transparency is followed globally, it would be only enough to publish the results of clinical trials, and possibly the key highlights of study for patients and medical professionals, eliminating the need for research articles.