Addressing Cognitive Load in Training on Electronic Medical Record Systems

Problems with Health Information Technology (HIT) involve human and technical factors with human factor significantly more likely to harm patients. A human factor contributing to these problems is cognitive load – the load imposed on an individual’s working memory. While the literature explored cognitive load in areas of design and use of HIT, little is discussed about it in the area of training – a prerequisite for competent use of HIT. This study subscribed to Cognitive Load Theory (CLT) and explored cognitive load in training on Electronic Medical Record (EMR) systems as a prevalent form of HIT in intensive care environments. Designers, trainers, and trainees of instructional materials for EMR systems training in a neonatal intensive care unit were interviewed in an interpretive case study. The preliminary results indicated cognitive load as a recognised phenomenon in EMR systems training but pointed to a lack of awareness of CLT techniques for managing cognitive load

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

TURF for Teams: Considering Both the Team and I in the Work-Centered Design of Systems

Teams are an inherent part of many work domains, especially in the healthcare environment. Yet, most systems are often built with only the individual user in mind. How can we better incorporate the team, as a user, into the design of a system? By better understanding the team, through their user, task, representational, and functional needs, we can create more useful and helpful systems that match their work domain. For this research project, we utilize the TURF framework and expanded it further by also considering teams as a user, thus, creating the TURF for Teams framework. In addition, we chose to examine teams in the emergency department environment. We believe that designing a system with the team also fully incorporated and acknowledged in the work domain will be beneficial for supporting necessary team activities. Using TURF for Teams, we first conducted an observational field study in the emergency department to get a better understanding of the users, teams, tasks, workload, and interactions. We then identified the need for team communications to be better supported, especially in the management of interruptions, and further categorized the interruptions by their function in order to design a team tool that could help team members better manage their interruptions by focusing on the necessary, or domain, types of interruptions and more easily disregarding the unnecessary, or overhead, types of interruptions. We then administered some surveys and conducted a card sort and cognitive walkthrough with emergency clinician participants to help us better identify how to design interfaces for the team tool and simulation that would better match the needs of team communication behaviors observed and reported by emergency clinicians. After designing and developing the team tool and simulation, we conducted an evaluation of this system by having emergency medicine, medicine, and informatics graduate student teams go through the system and utilize the team tool and simulation as a team. Though we had a small sample size, we found that emergency medicine teams found the team tool and simulation to be very usable and they reacted favorably to its potential in helping them better understand and manage their team communications. In summary, we were able to utilize the TURF framework for incorporating teams into the design of systems, in this case a team communication tool and microworld simulation for the emergency department. Our findings suggest that TURF for Teams is a viable framework for designing useful and helpful team based systems for all work domains

NEOREG : design and implementation of an online neonatal registration system to access, follow and analyse data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is imortant that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records

Natural-Setting PHR Usability Evaluation using the NASA TLX to Measure Cognitive Load of Patients

While personal health records (PHRs) carry an array of potential benefits such as increased patient engagement, poor usability remains a significant barrier to patients’ adoption of PHRs. In this mixed methods study, we evaluate the usability of one PHR feature, an intake form called the pre-visit summary, from the perspective of cognitive load using real cardiovascular patients in a natural setting. A validated measure for cognitive load, the NASA Task Load Index, was used along with retrospective interviews to identify tasks within the pre-visit summary that increased participants’ cognitive load. We found that the medications, immunizations, active health concerns, and family history pages induced a higher cognitive load because participants struggled to recall personal health information and also due to user interface design issues. This research is significant in that it uses validated measures of cognitive load to study real patients interacting with their PHR in a natural environment