Decompensation is a change in the overall ability to maintain physiological function in the presence of a stressor or disease. In the medical setting, clinicians utilize a wide range of technological tools to aid in their clinical decision making and to identify early warning signals for decompensation. However, many of these technologies have underperformed and are not aligned with the actual role of practitioners, resulting in unintended consequences and adverse events. The primary aim of this study is to explore how different nurses interpret early warning signs in order to anticipate decompensation. The secondary aim is to assess which technologies nurses rely on when anticipating decompensation, and if those technologies are adequately aiding them in their clinical decision making. Two researchers performed semi-structured ethnographic interviews that were recorded and transcribed during the summer of 2017. In total, 43 nurses were interviewed from different medical and surgical floors within the same hospital. Participants were asked questions focused on how they use and respond to alarms and how they anticipate patient decompensation. Constant Comparative Analysis was used to reveal patterns of responses between participants. Based on the qualitative analysis 6 major themes emerged: 1. Anticipating patient decompensation requires creating a complete mental “picture of the patient” by the nurses 2. Nurse-to-nurse communication and expertise is essential to understanding the patient’s history 3. Warning signs for decompensation were largely determined by a patient’s baseline 4. Change over time, or trends, is informative for anticipating decompensation. Numbers (regarding vital signs and labs) alone are not 5. Consistent care of patients improved nurse’s confidence in decision making 6. Anticipating decompensation requires “staying ahead of the machines Our research suggests that there is a gap between the information practitioners need to accurately anticipate patient decompensation, and the information current alarm technologies provide. Alarms are the primary tool provided to nurses to aid them in detecting hazardous events, however, current alarms are not well-suited in supporting signals that anticipate patient decompensation before it happens.
Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.
Traditional event exploration techniques in safety operations such as RCA and FMEA focus on developing explanations of specific events and in so doing, risk producing over-simplified highly linearized models of organizational behavior which are largely divorced from the realities of the work. Techniques such as FRAM, STAMP, and AcciMap, produce highly accurate models of system behavior but are often prohibitively resource intensive. These difficulties have made the types of inquiry which can inform Safety-II-style analysis difficult and expensive. In a review of four recent studies, we discuss implementation of the Systemic Contributors and Adaptations Diagramming (SCAD) framework, demonstrating its value in rapidly identifying useful cases, engaging a wider range of organizational roles, and arriving at useful insights more quickly and with reduced resource burden. Rather than focus on a specific event, SCAD explores the distribution of pressures across the system and the associated adaptive behaviors which employees undertake to address those pressures.
Current models of the clinical surgical process, and as a result, surgical education, present robot-assisted surgery (RAS) 1 as a simple sequential step in the evolution of surgical treatment from laparoscopy. In an ongoing research program on robot-assisted surgery this paper presents data from the first, ethnographic, research phase demonstrating that the requisite skills for success in the robot-assisted environment are altered from those expected within the laparoscopic domain, and as a result, the training paths and noviceto-expert progression trajectories are noticeably dissimilar. BACKGROUND AND BASIC MOTIVATIONS Existing work in adaptive and complex systems has demonstrated the value of studying a system at a point of change and observing how the system adapts around the change as a means to understand how that system functions (Woods and Dekker, 2000). In this case, the rapid introduction of surgical robotics into the operating room provides an ideal setting for the exploration of the surgical process, the nature of surgical expertise and processes of technological change in general.
UNSTRUCTURED Advances in mobile application technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Data collected via mobile device allow for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mHealth but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital application technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile application development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting IT infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.
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