Sick care has turned into a data industry that happens to take care of patients. There is data, data everywhere. More students are interested in writing the book. Unfortunately, few know how to read it.
Medical data illiteracy is growing. How to read the medical literature has taken a back seat to promoting statistical thinking and interest in quantitative data analysis, and the gap extends from k-12 through graduate and professional school. Data literacy has become one of the tools the workforce of the future, including doctors ,will need to win the 4th industrial revolution.
For example, The Duke Margolis Center for Health Policy partnered with AI and healthcare experts to identify the top three issues slowing the development, adoption and use of AI-enabled CDS (clinical decision support) software.
- Not enough evidentiary support: Developers and researchers should provide more evidence on how AI-enabled CDS systems may affect patient outcomes, care quality, costs of care and clinician workflow. More available evidence would help ensure the effectiveness and trustworthiness of the technology.
- Patient risk assessments: Developers should provide more information about how the technology was made and trained, which would allow regulators and clinicians to assess the technology’s risk to patients.
- Bias: It should be ensured that the software was developed with data-driven AI methods that don’t perpetuate existing clinical biases. Researchers also suggested assessing the technology’s scalability and ability to protect patient privacy.
Patient-collected data is becoming much more prevalent in recent years, and as a result it has begun to play a substantial role in many recent organizational innovations and informatics projects. When patients bring this data to preventive and follow-up visits, they expect it to help care providers to inform their decision-making and improve their care. However, physicians and care providers often struggle to find value in and utilize this data due to gaps in training, confidence in security and privacy, as well as general lack of capabilities to use this data. As such, many organizations are calling for increased data literacy training so as to strategically drive the message of the overall capabilities of data.
So, how, where and what should we teach medical professionals to be data literate?
- The learning objectives , curriculum, KSAs and competencies should be market driven.
- Learning management systems should conform to the realities of time available, adult learning theory and availability of mobile devices, recognizing that there is a sick care digital divide throughout the US and other countries.
- Content should be presented to health professionals the way they learn, typically mentored case based instruction in medical school and residency and throughout continuous medical education.
- Competencies should be measured, recognized and rewarded.
- The idea is not to train every health professional to be a computer scientist. The goal is to help them interpret data so that it translates into stakeholder defined value.
- We should collaborate with technology partners and organizations to help create a cohort of instructors and train the trainers.
- Data literacy should be a component of a mandatory course in digital health.
- Existing biostatistics courses and programs should be updated to expand student data literacy capabilities.
- Professional associations and medical societies should offer their members instruction in data literacy as it pertains to their clinical needs.
- Data literacy should be taught in CMO school.
Data scientists, computer technologists and healthcare professionals, increasingly, are becoming more and more separated by a common language. Patients are the ones who get lost in the translation.
Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs.
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