EFFECTS OF INFORMATION DISPLAY ON THE CONSTRUCTION OF CLINICIAN MENTAL MODELS

Objective: To determine how a clinician’s background knowledge, their tasks, and displays of information interact to affect the clinician’s mental model. Design: Repeated Measure Nested Experimental Design Population, Sample, Setting: Populations were gastrointestinal/internal medicine physicians and nurses within the greater Houston area. A purposeful sample of 24 physicians and 24 nurses were studied in 2003. Methods: Subjects were randomized to two different displays of two different mock medical records; one that contained highlighted patient information and one that contained non-highlighted patient information. They were asked to read and summarize their understanding of the patients aloud. Propositional analysis was used to understand their comprehension of the patients. Findings: Different mental models were found between physicians and nurses given the same display of information. The information they shared was very minor compared to the variance in their mental models. There was additionally more variance within the nursing mental models than the physician mental models given different displays of the same information. Statistically, there was no interaction effect between the display of information and clinician type. Only clinician type could account for the differences in the clinician comprehension and thus their mental models of the cases. Conclusion: The factors that may explain the variance within and between the clinician models are clinician type, and only in the nursing group, the use of highlighting

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Framing the Implementation and Adoption of Innovation in the NHS: An Interactive Multi-User Perspective

The UK National Health Service (NHS) has been slow at adopting seemingly well-evidenced innovation. A great deal of energy and resources have gone into understanding the issues behind the failure to adopt innovation in the NHS. In recent times Accelerated Access Review (AAR) identified new barriers to innovation and put forward solutions at both local and national levels (Department of Health and Department for Business, Energy and Industrial Strategy, 2017). Scholars and policy-makers have investigated the majority of the obstacles to adopting innovation in the NHS and results have appeared in multiple outlets over the last twenty years. Innovation within the NHS have mostly been judged on a least-cost basis or presumed to yield a positive return in the very first year. Some scholars also point to the fact that most perspectives on innovation deem it as a luxury rather than a routine part of the operational management. The failure to successfully adopt innovations is costing taxpayers and despite so many obstacles well documented, the NHS is still struggling to overcome the scale of innovation. To this concern, my research is an attempt to better understand the process of innovation adoption in healthcare. Using a year-long field study at one of the largest UK-based University Hospitals, I have explored the process of adoption of electronic medicine chart (EMEDs) designed to replace traditional paper-based systems. Based on three groups of non-clinical and clinical stakeholders, I have developed a multi-view perspective of the adoption process. Exploring the perspectives of both the clinical users (doctors, nurses, and pharmacists) and technology (clinical and non-clinical) implementer groups, I have developed a multi-view perspective of the adoption process. To address the struggles and complexity of the adoption process underpinning the implementation of innovation in hospitals, my research has advanced a socio-cognitive perspective through examining the groups of technology implementers and technology users. Currently, a body of research exists which has examined the adoption of innovation underpinning implementation in firms. The literature has tended to provide economic-based theories of rational action by focussing on channels through which technical or economic benefits are communicated as a means to propagate its adoption. An alternative to economic-based explanations, the institutional theory perspectives have identified forces triggering adoption, irrespective of the innovation’s technical, work-related, or economic benefits to the adopter. Contributing to the growing interest in socio-cognitive perspective, my research through interactive framing has examined the groups of technology implementers and technology users. This has been done to develop an understanding of how actors ‘make sense’ of the process unfolding through them (the implementers and users). Having used Gioia methodology, I have utilised data collected through rich in-depth interviews of the actors during the process of implementation and adoption. The data collected have been used to build a data structure leading onto an interactive grounded theory model of EMEDs adoption. The model is built on three core dynamic framing activities – intrinsic, frictional and transitioning. Collectively they represent a cognitive transformation of all the actors involved and offer insight into the negotiated state and challenges of adoption process faced by both users and implementers in a complex organisation

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

La Salle University Undergraduate Catalog 2013-2014

https://digitalcommons.lasalle.edu/course_catalogs/1197/thumbnail.jp