I recently attended a conference on health care informatics. A number of the speakers discussed the main challenges in health care: cost, quality, integration and including a patient (end-user) voice. These could apply equally to education.
Some presenters spoke about topics I have included in my recent blog entries. For example, in my February post on the curriculum problem, I noted how it is increasingly difficult for the curriculum in most disciplines to keep pace with the accelerated rate of knowledge generation brought about by the internet age. One speaker, a physician, called on members of his profession to stop pretending they can be current with the latest information about their discipline. He talked of the knowledge processing-capacity gap and the need to provide contextual information support at the point of care.
We have the capability to produce checklists, reminders, alerts, warnings and identification of alternatives (automatically updated based on the latest knowledge) for electronic records and treatment protocol forms. However, such technologies are not in widespread use. As long as we are relying solely on knowledge in the heads of medical professionals rather than on just-in-time knowledge embedded in the technology infrastructure, the gap will get wider.
In my March entry on the scientific method, I noted how much educational practice is informed by unsubstantiated belief instead of educational science and effectiveness data. A conference presenter referred to a study of over 1,000 physicians in the UK that revealed only 3 percent successfully reviewed effectiveness data about their practice regularly and 55 percent had never attempted to collect and review data. The majority just have faith that they are making a difference.
There are few virtuous loops in health care that measure patient outcomes, analyze data to determine what is working and instantly share the results with care providers. Large-scale data collection and analysis is required to make this happen and lack of data standards creates a barrier. For example, a project in Massachusetts to create a data interchange is targeting only the 20 electronic health record systems that have substantial market presence, meaning that in one state there are so many incompatible systems in use it is not possible to cover them all.
A whole industry has been created by IT companies that provide translational services between different health related data systems. This problem has been an issue with educational software systems too, although there has been progress in getting many vendors to adopt some common standards for data interchange. Organizations such as the IMS Learning Consortium with its Learning Tools Interoperability standard have been helpful in this regard.
Despite these problems there was a great deal of optimism among conference participants that technology has the potential to radically transform and improve the health care system. In addition to large-scale data analytics to help improve health outcomes, there were a number of presentations on the use of mobile apps and providing greater patent access and involvement through personal health records and virtual visits.
This short video provides an example of the impact of large-scale (big) data analytics in healthcare. It is possible to argue for similar analytics being applied to education.