Research To Read, Edition 2.
For the second edition of Research To Read (R2R), let’s dive into more topics and background reading related to evidence-based medicine, big data in healthcare, clinical prediction models and risk prediction tools- some hot topics many recent research studies revolve around.
What is the R2R newsletter all about?
To recap, this newsletter is my 2025 resolution in action: to make reading medical research a consistent habit. Just like any muscle that needs regular exercise, the only way to strengthen this practice is by doing it—repeatedly.
That’s the motivation behind Research to Read. It's a way for me to share the research I’m diving into, while also expanding my own knowledge. And I’m inviting my fellow medicos to join me on this journey. We all stand to benefit from staying informed and sharpening our understanding of the latest studies in our field.
So, let’s get into the research, together!
Before you dive in:
This article, How I Read Science Research Papers and Journal Articles without Dying of Confusion by Halimat Chisom is a great guide “to stay afloat in the sea of jargon, information, and technicalities” for people who want to learn more about the topics they’re curious about and keep up with trends in the world of science. Do give this a read :))
Here’s my list of research papers to read:
The illusion of evidence based medicine
Jureidini J, McHenry L B. The illusion of evidence based medicine BMJ 2022; 376 :o702 doi:10.1136/bmj.o702
This article offers the authors’ view on the commercial interests of the pharmaceutical industry that is permeating into research, medical universities, institutions and regulatory bodies. The article outlines how scientific data is being manipulated and moulded to serve the interest of hierarchical powers in the healthcare industry, the neo-liberalization of research funding in universities, the compromise of academic leadership and how current regulatory practices allow big pharma to “mark their own homework”.
The authors of this article, McHenry and Jureidini, are also joint authors of the book The Illusion of Evidence-Based Medicine: Exposing the Crisis of Credibility in Clinical Research.
Read it here: https://www.bmj.com/content/376/bmj.o702
2. The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice
Muharremi, G.; Meçani, R.; Muka, T. The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice. J. Pers. Med. 2024, 14, 53. https://doi.org/10.3390/jpm14010053
This research study dives into the nascent concept of precision medicine, which aims to integrate omics and environmental data of individuals to more precisely diagnose, prevent and treat diseases. It shows a view on topics of research interest among research articles related to PM, possible advantages and disadvantages of a shift towards personalised medicine, the role of evidence-based medicine and crucial factors to be considered for it’s possible implementation.
Read it here: https://www.mdpi.com/2075-4426/14/1/53
3. Overview of clinical prediction models
Chen L. Overview of clinical prediction models. Ann Transl Med. 2020 Feb;8(4):71. doi: 10.21037/atm.2019.11.121.
From my viewpoint, the use of clinical prediction models seems like the equivalent of precision medicine for the masses (and a more accessible reality) because of its potential to use patient data to improve outcomes for people beyond the individual whose data was originally utilized. This study describes how risk prediction models come to life along with potential applications, limitations and recent advances.
Read it here: https://pmc.ncbi.nlm.nih.gov/articles/PMC7049012/
4. The use and misuse of risk prediction tools for clinical decision making
van Maaren MC, Hueting TA, Völkel V, van Hezewijk M, Strobbe LJ, Siesling S. The use and misuse of risk prediction tools for clinical decision-making. Breast. 2023 Jun;69:428-430. doi: 10.1016/j.breast.2023.01.006.
As with every medical advancement, it’s important to view it with a candid and critical lens to truly understand the consequences of implementing such tools and innovations. This article is a good starting point for understanding how to do that for risk prediction tools and is a great accompaniment to the previous article I’ve mentioned.
Read it here: https://pmc.ncbi.nlm.nih.gov/articles/PMC10300578/
5. Big data in healthcare
Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis and future prospects. J Big Data 6, 54 (2019). https://doi.org/10.1186/s40537-019-0217-0
Painting a picture of the massive “digital universe” and the birth of the field of data science, this article shows the workflow of big data analytics with data from different sources in the world of healthcare. It’s important to learn more about such topics because the healthcare industry is lagging in adapting to the big data movement compared to other industries, but is well on its way to catching up.
Despite the technical topics discussed in the articles, I appreciate how it was masterfully broken down for the readers to understand along with diagrams and flowcharts that made it all easier to understand.
Read it here: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0
…and that brings us to the end of the second edition of Research to Read!!
I hope you enjoyed it and look forward to reading more research along with me, where I’ll be diving into research rooted in different medical specialties :)
Check out Research to Read, Edition 1 here
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