This article is sponsored by Altus Assessments, data insights for health care education.
As diversity, equity, and inclusion (DE&I) initiatives in medical education work towards improving the heterogeneity of learners, the weaknesses of an educational system with uniform instruction will become more apparent. Uniform instruction does not take into account the complex mechanisms underlying each individual’s learning goals or needs.
Movement from novice to master in medicine is a journey that is unique to every health care practitioner. In an era of rapid change and focus on increasing diversity, medical schools must be prepared to accommodate different learning needs, speeds, and techniques. The interventions and remediation steps appropriate for one learner are not necessarily what will drive best results with another. Interventions need to be appropriately tailored to produce benefit for individual students.
Luckily, the foundations for personalized, precise medical education are in place. The digitization of vast amounts of medical education data – from admissions through residencies – and recent work to normalize and structure that data have created the requisite foundation for data science and real-time insights. Advancements in educational research overlaid on those data insights is guiding a new era in medical education.
Education is following new norms established elsewhere in health care
Medical education is following in the footsteps of other health care disciplines that are more established in leveraging the power of big data combined with human ingenuity and expertise to advance the field of practice. In health care, initiatives aimed at personalizing an individual’s patient care are called “precision medicine.” As opposed to a one-size-fits-all approach, precision medicine takes into account individual variability in genes, environment, and lifestyle for each person – leading to disease treatment and prevention strategies that consider the differences between individuals.
Other fields like pharmacogenetics and multiomics are also combining data, data science, and specialist expertise to personalize treatment as a means to improve efficacy.
Education, enabled by technology, is also speeding towards mass personalization. As a reflection of the precision medicine work being done to evolve and advance health care practice, we’ve chosen to use the term Precision Medical Education for the data-driven insights and practices emerging across health care education. A working definition of Precision Medical Education, or PME, is:
Precision Medical Education is an ethical, equitable, science-based approach to optimizing and individualizing the learning environment, training, and career outcomes of medical professionals based on multiple data points, data science, and research.
Just as the patient must be at the center of personalized (precision) medicine, the learner must be at the heart of Precision Medical Education. To varying degrees, PME will also impact medical schools, institutions, health care providers, industry associations, and other stakeholders.
The foundation for Precision Medical Education includes:
- Appropriate data
- People with sufficient knowledge of medical education AND data science that can responsibly bring positive and effective change
Why PME is developing now
1. Medical education data is primed for algorithmic use
Medical education has a long history with standards: accreditation standards, standard competency sets, work standards, curriculum standards. Medical education data tied to standards has largely been captured digitally into internal and external data instruments over the last 20 years. Due to the accreditation process, it’s mostly available for on-demand reporting right now.
Machine learning algorithms need structured data sets to train on in order to generate intelligent insights. Medical education has been building comprehensive, structured digital datasets for the past 20 years. As more medical schools are compelled to make better use of their data, their operational datasets will become increasingly suitable for advanced analytics and precisely modeled recommendations.
2. Research and researchers are emerging that will inform PME
You need health data (mostly genomics) and physicians working alongside data scientists to do precision health. Instead of health data, PME requires medical education data. Instead of physicians working with data scientists, PME requires medical education researchers and practitioners working with data scientists to support learners and their programs.
As medical education research goes, so too do the technology-infused tools and approaches to problems in the med-ed industry. Just like the research of Olle ten Cate and others on Entrustable Professional Activities (EPAs) is leading to a wholescale reorientation towards more competency-based medical education (CBME), medical education data science research will lead to an embrace of the educational methods required for PME.
A few examples of recent papers highlight a growing trend:
A Call to Investigate the Relationship Between Education and Health Outcomes Using Big Data. Published in 2018, this paper (and three related papers) call for more investigation into the link between education and health outcomes using advanced analytics. The papers highlight the growing volumes of health care data that can be tied back to individual clinicians.
Signatures of medical student applicants and academic success Published in 2020, this paper uses machine learning approaches to cluster students into distinct groups with unique characteristics. From the abstract (emphasis mine): “The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.”
Schools are starting to recognize the importance and opportunity of PME-related work. They are increasingly looking at their data as an important, structured asset. They are collaborating with (or training) people with advanced data capabilities.
Until now, this PME approach has been limited to a small number of schools with enough resources to try and manage the data and who were fortunate enough to have researchers to dive in to find the needles in the haystack.
But today, we’re at a moment where the people and the data sets necessary to achieve something approaching Precision Medical Education are coming together across medical education. Products like Altus Analytics are enabling schools to tackle PME-related projects without the previous need for a large-scale team of technologists and data scientists.
Over the last year, Altus (who recently acquired One45) customers have:
- used Altus Analytics to revise long-held grading policies to fit with the clearer picture integrated data provides
- repeatedly chosen to open up previously closed data silos to more and more varied stakeholders — creating the foundations of a data democracy within some medical schools.
- begun using longitudinal data sets for machine-learning around student cohort segmentation.
- kicked off conversations and research around data benchmarking and sharing
The opportunity ahead
As these developments continue, medical schools will collectively be in a position to start tackling some of the biggest questions in medical education — with data. Questions like:
- How do I ensure 100% matching of my learners into best-aligned residency programs?
- How do I select the applicants that will stay in my community and choose to serve our mission?
- How do I identify struggling students and create a custom, personalized set of interventions to help them?
- Which aspects of my program are truly adding value for learners and our broader mission?
Enabling Precision Medical Education is at the core of our mission at Altus. As we continue to build upon our solutions, we won’t just provide data to programs but contextualize that data so programs and learners know what to take action on. We won’t be aiming to increase the cost of medical school for learners but helping schools understand where to truly deploy their resources — and where they’re not necessary. We won’t be trying to hide our research and algorithms, rather we will be building a large-scale research community that is fair, transparent, and evidence-based.
Tomorrow’s health care professionals will require resilience, empathy, communication, collaboration, and digital aptitude. The pandemic has only underlined how important this is. As educators, we must remember that our students are not like weeds, capable of growth in any soil. Most require the right conditions to become their best selves, and that is what PME will enable for aspiring professionals.
Image credit: Altus Assessments