Sarma Gopal P, Levey Allan, Faundez Victor
Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA.
F1000Res. 2019 Dec 19;8:2123. doi: 10.12688/f1000research.21448.1. eCollection 2019.
The training of physician-scientists lies at the heart of future medical research. In this commentary, we apply Narayanamurti and Odumosu's framework of the "discovery-invention cycle" to analyze the structure and outcomes of the integrated MD/PhD program. We argue that the linear model of "bench-to-bedside" research, which is also reflected in the present training of MD/PhDs, merits continual re-evaluation to capitalize on the richness of opportunities arising in clinical medicine. In addition to measuring objective career outcomes, as existing research has done, we suggest that detailed characterization of researchers' efforts using both qualitative and quantitative techniques is necessary to understand if dual-degree training is being utilized. As an example, we propose that the application of machine learning and data science to corpora of biomedical literature and anonymized clinical data might allow us to see if there are objective "signatures" of research uniquely enabled by MD/PhD training. We close by proposing several hypotheses for shaping physician-scientist training, the relative merits of which could be assessed using the techniques proposed above. Our overarching message is the importance of deeply understanding individual career trajectories as well as characterizing organizational details and cultural nuances to drive new policy which shapes the future of the physician-scientist workforce.
医学科学家的培养是未来医学研究的核心。在这篇评论中，我们运用纳拉亚纳穆尔蒂和奥杜莫苏的“发现—发明循环”框架来分析医学博士/哲学博士联合培养项目的结构和成果。我们认为，“从实验室到临床”的线性研究模式（这在当前医学博士/哲学博士的培养中也有所体现）值得持续重新评估，以便利用临床医学中出现的丰富机会。除了像现有研究那样衡量客观的职业成果外，我们建议，有必要运用定性和定量技术对研究人员的努力进行详细描述，以了解双学位培养是否得到了有效利用。例如，我们提议将机器学习和数据科学应用于生物医学文献语料库和匿名临床数据，这可能使我们能够看出医学博士/哲学博士培养是否独特地催生了某些客观的研究“特征 ”。最后，我们提出了几个关于塑造医学科学家培养模式的假设，其相对优点可以使用上述技术进行评估。我们的总体观点是，深入了解个人职业轨迹以及描述组织细节和文化细微差别对于推动制定塑造医学科学家队伍未来的新政策至关重要。
