Cognitive Computing Applications for Medical Education ProgramsCognitive computing applications are being developed to help medical education programs improve clinical education outcomes
What is Cognitive Computing?
The term cognitive computing is typically used to describe artificial intelligence (AI) systems that aim to simulate human thought. Human cognition involves real-time analysis of environment, context and intent, among many other variables that inform a person's ability to solve problems.
Cognitive computing differs slightly in application and purpose from that of AI.
Where AI's purpose is to provide an answer to a question, cognitive computing is used to assist humans in their decision-making process.
Where AI relies on algorithms to solve a problem or to identify patterns hidden in data, cognitive computing systems have the loftier goal of creating algorithms that mimic the human brain's reasoning process to solve an array of problems as the data and the problems change.
Cognitive Computing Applications in Healthcare
Like any other industry, health care is being disrupted and transformed by an exponential growth in data. More healthcare data are produced today than at any other time in history.
It is estimated that by 2020, the footprint of medical data will double every 73 days - and an estimated 80% of this data will be unstructured.
Unstructured data is data that is scattershot or "messy" but full of valuable medical facts - information about medications, diseases, lab results, patient histories, family histories, clinical studies and more.
While medical professionals have access to an abundance of data, the volume is simply too significant for them to consume, analyze, and apply in ways that are meaningful for patients. In addition, the growth and pace of medical research, clinical trials, and treatment options are staggering. By some estimates, it would take a doctor 150 hours each week to read every piece of content published in his or her field of interest.
Some examples of cognitive computing applications include supporting medical doctors in their treatment of disease. IBM Watson for Oncology, to use a better known example, has been used at Memorial Sloan Kettering Cancer Center to provide oncologists with evidence-based treatment options for cancer patients. When medical staff input questions, Watson generates a list of hypotheses and offers treatment options for doctors to consider.
There are other applications as well:
- Cognitive computing enables researchers to uncover new insights in relationships among genes, proteins, pathways, phenotypes, and diseases.
- Cognitive computing helps identify the most critical attributes of a patient case and can provide easy-to-consume summaries for both patients and healthcare providers.
- Cognitive capabilities have been used in clinical trial matching to optimize patient selection and recruitment.
- Cognitive computing can assist in creating individualized treatment plans and thereby enhance the patient and physician experience.
Every corner of medicine will work with unstructured data in new ways.
- Computer systems can help attention-fatigued radiologists quickly find the anomalies of interest in images. There is less risk of missing something or of wasting time searching for an anomaly that does not exist.
- Patients will be able to send updates to their doctors through speech or text, anytime and from anywhere. These messages can be analyzed to find signs of progressing or deteriorating health, and a doctor will be notified if any patterns suggest intervention.
- Research has already shown that analyzing patients’ descriptions of symptoms in the context of their demographics can help diagnose cardiovascular disease. In the case of illnesses that affect the mind, patients’ communications can provide clues about what is wrong or show improving or declining cognitive functions over time. The more sources of information computers understand, the more they can help us make better decisions.
Where AI's purpose is to provide an answer to a question, cognitive computing is used to assist humans in their decision-making process in a sea of unstructured and changing data.
Cognitive Computing Applications for Medical Education Programs
Ultimately, cognitive computing can aid any field where new insights and decisions are sought amidst a sea of accumulating data.
Medical education programs are beginning to look at cognitive computing for insights into better education outcomes and specialized, personalized lifelong education quality.
Competency-based education (CBE) is now the norm for formal medical education programs and thus there is an enormous growth in the data points generated by institutions and their students each year. Residencies, nursing programs and healthcare organizations are increasingly reliant on CBE for clinical knowledge assessment and high-stakes exam preparation.
For example, Challenger Corporation, makers of Med-Challenger, is a long-standing CBE platform servicing medical education programs worldwide. It now has over 5 billion adaptive knowledge assessment scores over modular exam-based clinical curricula spanning each of the major medical specialties. The score data, student assessment histories, question-specific metrics, and other data points are allowing better decisions by program administrators.
Challenger Corporation CEO, Robert Anderson says, "We've long provided the type of education controls and predictive insights institutions have desired since computer-based certification testing became widespread. What's new is cognitive computing is allowing us to develop ways for the education program itself to see how a certain curricula is best utilized, where concepts are being missed in specific cohorts or by authors or at a particular time of day, and understand where curriculum content and assessment is effective and not so effective."
Formal education programs can know more about what the program might consider forward going in some very specific ways. Of course, these insights come from the data and to get data you have to implement CBE and apply it to students over time.
"We can see how some programs outperform in various areas - and the insights produced give program administrators more actionable options for improvement," says Anderson.
Cognitive computing in medical education has the potential to provide new perspectives for improved outcomes and efficiencies that weren't possible before. Beyond a test score, beyond the test itself, cognitive computing is proving to be as helpful in clinical education as it is clinical care.