Hello, and welcome to our course on Clinical Decision Support. I am Paul Nagy, I am a faculty member here at Johns Hopkins University, and I have the pleasure to serve as the Deputy Director for the Johns Hopkins Medicine Technology Innovation Center. That is a really fun team where we have software developers, and designers, and data analysts that are sitting here right within a health system partnering with care providers to create new digital health solutions. Now, clinical decision support can range from many areas. From assisting care providers in selecting the most appropriate imaging tests for their patients, as well as, directly for patients themselves trying to make a decision about what to do with a cancer diagnosis. I designed this module for three audiences in particular, whether you are a software developer or designer trying to create a new clinical decision support solution, or if you're an IT professional looking to learn how to implement those decisions support solutions into clinical care, as well as informaticist ones who are trying to look for ways to study those solution to see how they impact the process of care and its delivery. A seminal book in our field is written by Dr. Bob Wachter in 2015 called the Digital Doctor. In there, he described the transition to electronic medical records at the University of California, San Francisco a large, academic medical center. He really described the square that we're at the dawn of the computer age in health care, and how today we're still seeing care providers having to spend too much of their clinical visit facing a computer, instead of the patient, and having to record all their observations around the patient. How in many ways it's slowing down their productivity, and interfering with the care that they want to deliver. Over the past ten years, we've seen this enormous adoption of this first generation of liturgical medical records go within the US between 10 percent adoption in 2008 to over 90 percent adoption in 2018. So, we've seen this large transition of adoption of medical records in the US, and so we're really at that first generation of being able to record medical records, and be able to order tests, and be able to register patients through our system. In a recent paper last year, Dr. Walker wrote about this productivity paradox of health IT. About that, yes the first generation of information technology in most industries, like manufacturing and retail, actually leads to a drop in productivity. The goal of course is for information technology to be a productivity aid for care providers. But this usually takes time for us to be able to do, and it requires continued technology advancements as well as re-imagining the work itself. So, today we have automated all of our paper processes for ordering tests, for registering patients, for documenting our clinical visits, but we still need to look at applying this technology to re-imagine our work. What is re-imagining work mean for health care? This could mean several things that we can now do. It could be re-inventing the clinic visit to use telemedicine for virtual patient visits, where patients don't need to come in to see someone face to face for consultation. It could mean using voice enabled technology to assist in clinical documentation. So, our care providers don't have to be transcriptionists in documenting everything that they see. It could involve using wearables on patients to collect diagnostic data on patients at home, and in the hospital to help with and monitor clinical diseases. It could be helping providers collaborate on coordinating care. So, we're really seeing now all of our our paper-based processes have been transformed into digital ones, and we're seeing this great opportunity over the next decade or two to be able to really transform medical care by using these new technologies. This means a lot of this data is being generated and needs to be synthesized into creating effective clinical decision support tools for providers and patients at work, we're creating enormous amounts of data from our wearables, from our diagnostic tests, and in our latrine amount or systems, and we want to be able to help provide it into clinical pathways to make sure that patients are getting best care they possibly can.
