IT tools and methods for healthcare provider subject matter experts to create apps easily and without knowledge transfer to IT staff will:
- enable creation of decision-action rules tables, interactive processes and other real-time/just-in-time digital artifacts
- render knowledge actionable by including static information in the context of actor-/action-specific clinical choreography
- enable accrual/archiving of new knowledge in digital artifacts
- facilitate knowledge peer review, knowledge sharing and collaborative continuous process improvement
“Algorithms that learn from human decisions will also learn human mistakes, such as overtesting and overdiagnosis, failing to notice people who lack access to care, undertesting those who cannot pay, and mirroring race or gender biases. Ignoring these facts will result in automating and even magnifying problems in our current health system. Noticing and undoing these problems requires a deep familiarity with clinical decisions and the data they produce — a reality that highlights the importance of viewing algorithms as thinking partners, rather than replacements, for doctors.
Ultimately, machine learning in medicine will be a team sport, like medicine itself. But the team will need some new players: clinicians trained in statistics and computer science, who can contribute meaningfully to algorithm development and evaluation. Today’s medical education system is ill prepared to meet these needs. Undergraduate premedical requirements are absurdly outdated. Medical education does little to train doctors in the data science, statistics, or behavioral science required to develop, evaluate, and apply algorithms in clinical practice.
The integration of data science and medicine is not as far away as it may seem: cell biology and genetics, once also foreign to medicine, are now at the core of medical research, and medical education has made all doctors into informed consumers of these fields. Similar efforts in data science are urgently needed. If we lay the groundwork today, 21st-century clinicians can have the tools they need to process data, make decisions, and master the complexity of 21st-century patients.”