3,510 research outputs found

    RFID in Healthcare: A Six Sigma DMAIC and Simulation Case Study

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    Develop a business model to generate quantitative evidence of the benefits of implementing Radio Frequency Identification (RFID) technology limiting the scope to outpatient surgical processes in hospitals. Analysis showed significant estimated annual cost and time savings in carrying out patients’ surgical procedures with RFID technology implementation for the outpatient surgery processes in a hospital. This is largely due to elimination of both the non-value added activities of locating supplies and equipment and also the elimination of the “return” loop created by preventable post-operative infections. Several poka-yokes developed using RFID technology were identified to eliminate those two issues, as well as, for improving the safety of the patient and cost effectiveness of the operation to ensure the success of the outpatient surgical process. Several poka-yokes developed using RFID technology were identified for improving the safety of the patient and cost effectiveness of the operation to ensure the success of the outpatient surgical process

    Using discrete event simulation to improve the patient care process in the emergency department of a rural Kentucky hospital.

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    The patient care process of a rural Kentucky hospital is a complex process that must be flexible in order to deal with a large variety of patient needs and a fluctuating patient volume where all patients are unscheduled. A simulation model of an average month in the emergency department was built using the Arena Simulation package. Methods for creating a simulation using Arena are included in this work. Statistics were generated from a number of different sources to create an accurate representation of the model. The Hospital reporting shows a need to improve on two quality measures being tracked, the length of time a patient is in the emergency department from entry to completion of care, and the number of patients who leave without being seen by the physician (most often due to the length of their waiting room time prior to the initiation of care). Due to the complex nature of the emergency department and its impact by other departments of the Hospital as well as outside factors such as patient demand, the ability to quantify an expected gain from a change to the facility or to a process can be difficult to establish. A simulation model will allow for experiments on the system to be created and observed, thus enabling the Hospital to identify the best opportunities for improvement. Experiments included in this work show changes to the emergency department facility by adding an additional patient treatment bed, and changing a policy regarding transfer of a patient from the emergency department to inpatient care in the Hospital. Both experiments show improvement in quality measures, with reduced waiting room times, fewer patients who leave without being seen by the physician, and an overall reduction in the length of stay from entry to completion of care in the ED. In the creation of the simulation model, an objective was to develop a model that could be used to guide decision through its flexibility and statistical reliability. The model can be used to test a variety of physical or procedural changes to the emergency department, as well as to test to the impacts of increased patient volume

    Identifying Risk Factors for Anchoring Bias during Emergency Department Transitions of Care

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    Transitions of care have been associated with breakdowns in communication and medical errors. In emergency departments (ED) these handoffs are typically known as sign outs. Sign outs provide continuity of care for ED patients whose diagnosis and care fall across shift changes. They are short interactions where pertinent information and responsibility for the patient is transferred to the physician assuming care for them. However, these exchanges may also be an opportunity for cognitive biases to be transferred or introduced, leading to erroneous decision making. Anchoring bias is known to have a significant impact on clinical decision making. Yet, little is known of the factors that increase the risk of anchoring bias during patient diagnoses that involve sign outs. This exploratory research aims to understand how the communication of patient information during sign out influences the clinician’s use the information and develop the patient’s diagnosis and thus identify the factors that contribute to anchoring bias in clinical decision making in the ED. A mixed method approach was used to identify and evaluate potential risk factors for anchoring. Initially a review of a dataset from a medical incident reporting system was conducted to identify potential contributing factors from known cases of medical error. This was followed by an interview study with emergency medicine (EM) physicians to gain their perspectives on peer influence and communication factors between outgoing and oncoming clinicians that might affect sign outs and thus potentially impact decision making. The findings were used to design an experimental evaluation study to assess the impact of potential risk factors identified on diagnostic and treatment planning of EM clinicians. The study was conducted using patient case vignettes as control cases and stimuli cases, which contained these risk factors as test conditions to assess their effect on clinical decision making. The cases were presented in a format simulating sign out communications and the volume of information presented at sign out. Volume of information was represented by the two test conditions of explicitness of the sign out information and the stage in the diagnostic process the case was in at the time of sign out. The study was conducted at two academic hospital ED sites with a total 69 participants. The results indicated that the explicitness of the sign out information had no significant influence on the diagnostic accuracy in stimuli cases or on the confidence of the clinician participants in their diagnosis for the case. However, the stage in the diagnostic process of the case at the point of sign out, did significantly influence both clinicians’ diagnostic accuracy and their confidence in the diagnosis. The earlier stage stimuli cases were associated with lower diagnostic accuracy and lower confidence in the diagnosis. The test condition of explicitness did not have a significant effect on a number of outcome measures whereas the test condition of stage of the case did not. These findings suggest that additional support may be required for during sign out for cases that are in an earlier stage in the diagnostic process at the time of sign out to as they are at higher risk for diagnostic error and for the influence of anchoring bias

    Guidelines for the user interface design of electronic medical records in optometry

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    With the prevalence of digitalisation in the medical industry, e-health systems have largely replaced the traditional paper-based recording methods. At the centre of these e-health systems are Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), whose benefits significantly improve physician workflows. However, provision for user interface designs (UIDs) of these systems have been so poor that they have severely hindered physician usability, disrupted their workflows and risked patient safety. UID and usability guidelines have been provided, but have been very high level and general, mostly suitable for EHRs (which are used in general practices and hospitals). These guidelines have thus been ineffective in applicability for EMRs, which are typically used in niche medical environments. Within the niche field of Optometry, physicians experience disrupted workflows as a result of poor EMR UID and usability, of which EMR guidelines to improve these challenges are scarce. Hence, the need for this research arose, aiming to create UID guidelines for EMRs in Optometry, which will help improve the usability of the optometrists’ EMR. The main research question was successfully answered to produce the set of UID Guidelines for EMRs in Optometry, which includes guidelines built upon from literature and made contextually relevant, as well as some new additions, which are more patient focused. Design Science Research (DSR) was chosen as a suitable approach, and the phased Design Science Research Process Model (DSRPM) was used to guide this research. A literature review was conducted, including EHR and EMR, usability, UIDs, Optometry, related fields, and studies previously conducted to provide guidelines, frameworks and models. The review also included studying usability problems reported on the systems and the methods to overcome them. Task Analysis (TA) was used to observe and understand the optometrists’ workflows and their interactions with their EMRs during patient appointments, also identifying EMR problem areas. To address these problems, Focus Groups (FGs) were used to brainstorm solutions in the form of EMR UID features that optometrists’ required to improve their usability. From the literature review, TAs and FGs, proposed guidelines were created. The created guidelines informed the UID of an EMR prototype, which was successfully demonstrated to optometrists during Usability Testing sessions for the evaluation. Surveys were also used for the evaluation. The results proved the guidelines were successful, and were usable, effective, efficient and of good quality. A revised, final set of guidelines was then presented. Future researchers and designers may benefit from the contributions made from this research, which are both theoretical and practical

    UTRGV School of Medicine Course Catalog 2018-2019

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    https://scholarworks.utrgv.edu/utrgvcatalogs/1009/thumbnail.jp

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

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    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

    Diagnosis (Berl)

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    Objectives:Clinical laboratory testing provides essential data for making medical diagnoses. Generating accurate and timely test results clearly communicated to the treating clinician, and ultimately the patient, is a critical component that supports diagnostic excellence. On the other hand, failure to achieve this can lead to diagnostic errors that manifest in missed, delayed and wrong diagnoses.Content:Innovations that support diagnostic excellence address: 1) test utilization, 2) leveraging clinical and laboratory data, 3) promoting the use of credible information resources, 4) enhancing communication among laboratory professionals, health care providers and the patient, and 5) advancing the use of diagnostic management teams. Integrating evidence-based laboratory and patient-care quality management approaches may provide a strategy to support diagnostic excellence. Professional societies, government agencies, and healthcare systems are actively engaged in efforts to advance diagnostic excellence. Leveraging clinical laboratory capabilities within a healthcare system can measurably improve the diagnostic process and reduce diagnostic errors.Summary:An expanded quality management approach that builds on existing processes and measures can promote diagnostic excellence and provide a pathway to transition innovative concepts to practice.Outlook:There are increasing opportunities for clinical laboratory professionals and organizations to be part of a strategy to improve diagnoses.CC999999/ImCDC/Intramural CDC HHSUnited States/2021-01-26T00:00:00Z33554526PMC82553201023

    Low Medicaid Spending Growth Amid Rebounding State Revenues: Results From a 50-State Medicaid Budget Survey State Fiscal Years 2006 and 2007

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    Examines the implementation of the new Medicare prescription drug benefit and the rate of Medicaid spending growth and enrollment in 2006. Identifies possible state level changes in eligibility requirements, program expansion, and enrollment processes
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