HIMSS Blog: Discussing Medical Device Integration Post Publication of John R. Zaleski’s Book “Connected Medical Devices…”
I was interviewed for the HIMSS Blog by the Editors of HIMSS Books and the interview in total is available for viewing at the following link:
Interview with John R. Zaleski: Medical Device Integration: Growth, Trends & Challenges
Why Medical Device Integration Now?
From my recent HIMSS Blog post:
More than half of U.S. hospitals and health systems are planning to purchase and implement a medical device integration (MDI) solution. This is quite a difference from, say, 5 years ago. There are a number of reasons motivating this. Partially, the maturing deployment of electronic health record systems; partially, the maturing of the complexity of integration that requires higher-frequency, higher accuracy, higher fidelity data, such as clinical decision support methods within electronic health record systems; partially, the motivation of Meaningful Use and needs for improvement in patient safety; partially the PP-ACA. Other specific motivations, such as the recognition that improved patient care management can be achieved through better, more accurate data. Furthermore, MDI is an essential element for achieving better patient safety.
Need for Higher Fidelity Data Drove Medical Device Integration Exposure
As a researcher with more than 20 years working with medical devices and as a product developer and inventor, the following trends and major milestones are, in my opinion, the recognition of the value of MDI, which occurred not too long after electronic health record systems became widespread, perhaps not quite 10 years ago; then, the motivating Federal guidelines surrounding Meaningful Use Stages 1 & 2; the PP-ACA also provided some motivation. But, beyond that, my opinion is that receipt of data into the electronic health record systems motivated new ideas about what to do with those data. This, in my opinion, is leading to “higher-level” use cases other than charting. For example, use of the data to assist in improved patient care management and clinical decision making. When I started in this field, I was a graduate student and needed to collect data on live patients whom I was studying to develop methods for weaning from post-operative mechanical ventilation. I was running a study on patients recovering from coronary artery bypass grafting surgery and was following “my” patients from surgery through to extubation from mechanical ventilation in surgical intensive care and general surgery. When I was conducting this study, it was years before the commercial electronic health record system was widely publicized. Furthermore, none of the medical devices with which I was working had any automated data collection capability that was exploited within the hospital system I was working. Hence, I had to write my own code and perform data collection on my own, right at the patient bedside. My purpose was in using the data to develop models of patient state; to better predict time-based changes in physiologic and respiratory parameters, and guided by patient demographics, intakes and outputs, and other information. Yet, what was really lacking was a way to collect this information using an automated, standardized approach. So, I got into the MDI field as it was a necessary utility to meet my ultimate needs: complete data for better clinical decision making.