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 or apply it.
There is probably no more important time to understand these landmines of data literacy than during a pandemic.
The COVID-19 pandemic has brought innovation in healthcare to the forefront through the industry’s effort to rapidly expand telehealth services, reminding many health systems of a key lesson: information technology (IT) has a catalytic role to play, but not necessarily as the protagonist in the play. Instead, IT should be a key facilitator bringing clinical and operational leadership together to identify the right “business” problems that need to be solved and the various alternatives to solving them.
More than one-third of younger professionals in health care report being overwhelmed by digital patient data, survey finds Health IT Analytics (3/9, reports 35% “of younger health care professionals are overwhelmed by digital patient data or are unsure about how to use patient data and analytics to inform care, according to a global survey from Philips.” However, “despite these concerns, younger providers reported that their organizations are open to adopting new digital tools: 78% said their hospital or practice is completely or somewhat willing to embrace new technology, which would lead to even higher volumes of data in the future.” The “findings point to gaps in education and training that could significantly impact clinical efficiency.”
Healthcare executives have been ramping up their organizations’ adoption of artificial intelligence during the past year, according to new survey from Optum. In addition, most employees are not being trained quickly enough to keep up with the growth of AI—because as much as 50 percent of new roles will require experience working with AI.
The survey of 500 executives shows an 88 percent increase in the number of organizations who said they have implemented an AI strategy, compared with an earlier Optum survey in 2018.
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, inluding 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.
There are several reasons why data literacy is a necessary physician skill:
- Patient care: The ability to correctly interpret and react to data
- Education: Teaching medical students, residents and fellows the importance of data literacy and how to interpret the medical literature as it applies to digital health
- Research: Designing and executing clinical trials using data and helping to validate algorithms and models.
- Non clinical career development; Having the necessary data knowledge, skills, attitudes and competencies to lend value to a digital health, care delivery, medtech or biopharma company
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
- On the subject of hiring and developing data analysts, A. Charles Thomas, who is General Motors’ first-ever chief data and analytics officer (CDAO), said that academic programs that train data scientists should include a focus on the whole person, not just the science. Otherwise the result is data scientists who can get too wrapped up in the breadth of data they’re handling and lose focus on actionable insights.
- Clinical data analysts (CDAs) should be part of the care team. Think Moneyball for patient care.
But, data literacy is just the first step to digital transformation. The missing ingredient of most digital transformation initiatives is a sustained and successful focus on improving employees’ and leaders’ digital dexterity: the ambition and ability to use technology for better business outcomes. If people aren’t able to use the technology, then the investments will be wasted and in fact can heighten employee change fatigue.
But a look at the data tells us that most companies are still struggling to build data literacy. Ninety percent of business leaders cite data literacy as key to company success, but only 25% of workers feel confident in their data skills. Here are some more ways to make your students, trainees, employee and leadership teams data literate.
Here is a nice summary of available AI education resources for medical professionals.
What do medical students need to know about artificial intelligence? Here are some emerging programs offering data science and artificial intelligence education and training to medical students, healthcare professionals and trainees:
AI in medicine certificate program
Harvard External Education Program
University of Colorado Anschutz Medical Campus
Oregon Health Sciences University
Here is a list of other data analytics courses from Johns Hopkins
American Board of Artificial Intelligence in Medicine (ABAIM)
With over 200 Osteopathic and Allopathic Medical schools in the United States, Medical Artificial Intelligence (A.I). education is neither a mandatory course offering, nor part of the School’s Curriculum. The Universities that do offer a semblance of AI education is either through a dual program or through an elective (if available).
1. Medical A.I education is offered at Texas A&M University through a dual program in Engineering and Medicine. a. Texas A&M University’s new Engineering Medicine program receives a new, unique space | Building Design + Construction (bdcnetwork.com)
2. Keck School of Medicine of USC is currently touching on AI in some elective courses on digital health with the hopes of offering a mandatory series to USC medical Students in the future.
3. The University of Texas at Galveston is not offering AI education to their medical school students. However, student interest groups are free to do doing their own research on the topic of Medical A.I under the supervision of faculty.
4. The University of Illinois Urbana-Champaign has launched a new self-paced online AI in Medicine Certificate Program. This program is a partnership between the department of Bioengineering at the Grainger College of Engineering, the Carle Illinois College of Medicine, and the University of Illinois College of Veterinary Medicine. a. University of Illinois Urbana-Champaign Launches First-of-Its-Kind AI in Medicine Certificate Program for Healthcare Professionals | National News | kpvi.com
More data collecting is underway to more accurately quantify the number of Medical Schools in the United States that are offering A.I education to their Medical Students. Healthcare’s increase in innovation, data literacy is necessary to provide a comprehensive care model. The question is not “should” Medical Schools offer Medical AI education to Medical Students? The question is “why” isn’t it?
While it’s also important to have a robust portion of the workforce skilled in more complex technologies, it’s time for domain experts to be self-sufficient with most of their data needs. In 10 years’ time, every knowledge worker will, to some extent, have to be a “data scientist.” As the labor market analytics firm Burning Glass puts it, a job candidate without skills in the “humbler world of everyday software,” including spreadsheets, word processing and billing programs, “won’t even get in the door.”
At the same time, data scientists need to understand medical problems to help decide whether using AI or other data science techniques are the right solutions to the problem. Some confuse technology with a product with a business.
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.
আর্লেন মেয়ার্স, এমডি, এমবিএ এর প্রেসিডেন্ট এবং সিইও চিকিত্সক উদ্যোক্তাদের সোসাইটি on Twitter@SOPEOfficial and Co-editor of Digital Health Entrepreneurship and Elsie Foli at MI10
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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