Creating learning health systems and the emerging role of biomedical informatics

Abstract

Introduction:The nature of information used in medicine has changed. In the past, we were limited to routine clinical data and published clinical trials. Today, we deal with massive, multiple data streams and easy access to new tests, ideas, and capabilities to process them. Whereas in the past getting information for decision-making was a challenge, now, it is how to analyze, evaluate and prioritize all that is readily available through the multitude of data-collecting devices. Clinicians must become adept with the tools needed to deal with the era of big data, requiring a major change in how we learn to make decisions. Major change is often met with resistance and questions about value. A Learning Health System is an enabler to encourage the development of such tools and demonstrate value in improved decision-making. Methods: We describe how we are developing a Biomedical Informatics program to help our medical institution's evolution as an academic Learning Health System, including strategy, training for house staff and examples of the role of informatics from operations to research. Results:We described an array of learning health system implementations and educational programs to improve healthcare and prepare a cadre of physicians with basic information technology skills. The programs have been well accepted with, for example, increasing interest and enrollment in the educational programs. Conclusions:We are now in an era when large volumes of a wide variety of data are readily available. The challenge is not so much in the acquisition of data, but in assessing the quality, relevance and value of the data. The data we can get may not be the data we need. In the past, sources of data were limited, and trial results published in journals were the major source of evidence for decision making. The advent of powerful analytics systems has changed the concept of evidence. Clinicians will have to develop the skills necessary to work in the era of big data. It is not reasonable to expect that all clinicians will also be data scientists. However, understanding the role of AI and predictive analytics, and how to apply them, will become progressively more important. Programs such as the one being implemented at Wake Forest fill that need.

Informatics education for translational research teams: An unrealized opportunity to strengthen the national research infrastructure

“…Kohn et al recently suggested that the approach to informatics training constitutes in and of itself a learning health system. 6 They posited three distinct elements of the institutional biomedical informatics LHS:…”

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

“…Although major challenges related to data governance/ management, 135,[175][176][177][178][179][180][181][182][183] ethical/legal, [184][185][186][187] and environmental 188,189 considerations would need to be addressed, these "dynamic multimodal ML models" may become cost-effective in different populations 169,[190][191][192][193] if conceived as the integrative tools needed to provide precision health 22,164,165,168,[194][195][196] and support some of the iterative cycles of knowledge generation and continuous improvement of "learning health systems". 11,[197][198][199][200]…”

Engaging Housestaff as Informatics Collaborators: Educational and Operational Opportunities

“…8,13 As frontline clinicians working in both ambulatory and inpatient settings across multiple teams, housestaff have first-hand knowledge of complex and varied workflows and have a vested interest in identifying, implementing, and improving patient care and system usability. 8,[14][15][16] Despite these opportunities, housestaff are an underutilized resource, often relegated to being recipients of system changes, rather than primary drivers and champions.…”