Improving Clinician Decisions and Communication in Critical Care Using Novel Information Technology

Academic Electronic Health Record in Mental Health Clinical: A Quality Review

Developing competency in the use of EHRs is essential for entry-level professional nurses. Although nursing education has been encouraged to integrate this technology into nursing curriculum, many students still graduate feeling unprepared in this area. As a result, nursing graduates lack the skills necessary to effectively use EHRs, which may have negative consequences for safe patient care. Use of academic EMRs provides students the opportunity to integrate informatics education, develop critical thinking, and incorporate problem-solving skills in the clinical area. An academic EMR was introduced to students in the second semester of a baccalaureate degree nursing program. Students completed documentation on one patient from the mental health clinical rotation. A retrospective chart review was conducted, using a rubric to determine charting efficacy. Data analysis indicated that students struggled with documentation of the mental health assessment, care plan development, and nursing notes. Student documentation was strongest in vital signs and basic information. Students need practice documenting on the critical aspects of nursing care. Utilization of an academic EMR for clinical charting provides an opportunity for students to practice documentation and develop necessary skills for clinical practice.

Healthcare commissioners' experience with antibiotic resistance during the COVID-19 pandemic in Saudi Arabia: a qualitative study

Introduction

The occurrence of antibiotic resistance (AR) has become a critical issue during the Novel coronavirus disease 2019 (COVID-19) pandemic. This study explores the experiences of healthcare commissioners with AR during the COVID-19 pandemic, identifies challenges, and provides recommendations for combating AR during pandemics.

Methods

This qualitative study was multi-centered and used a phenomenological approach. Semi-structured interviews were conducted between December 2022 and January 2023 among 11 health commissioners using video calls.

Results

Seven themes emerged from the data, including knowledge of AR and its consequences, the antibiotic prescription system, the future of AR and potential contributory factors, the impact of COVID-19 on AR and their relationship, the experience of AR during the COVID-19 pandemic in healthcare facilities, barriers that prevent the misuse of antibiotics during pandemics, and recommendations regarding antibiotic resistance during pandemics.

Conclusion

The findings of this study could be used to inform policy and practice for government healthcare workers (HCWs) and the public. Furthermore, this study identified the main challenges of AR during the pandemic, and the recommendations of health commissioners were provided accordingly. Such recommendations could be beneficial on a national and international scale to reduce the impact of future pandemics on AR.

Abbreviations

COVID-19: Novel coronavirus disease 2019; AR: Antibiotic Resistance; IPC: Infection prevention and control; MDRO: multi-drug resistant organism; ASP: Antimicrobial Stewardship Program; HCW: Healthcare worker; KSA: Kingdom of Saudi Arabia; WHO: World Health Organization; MOH: Ministry of Health; MOEWA: Ministry of Environment, Water, and Agriculture; AMR: Antimicrobial Resistance; PHCC: Primary Healthcare Center.

Developing a Comparative Effective Methodology for Technology Usability During a Simulated Casualty Event

INTRODUCTION

Future combat environments will be complex, making effective care for multi-domain battlefield injuries more challenging. Technology and resources are essential to reduce provider burden enabling more accurate assessments, decision-making support, expanded treatment, and outcome improvements. Experimentation exercises to evaluate concepts and technologies to incorporate into the Army's future force ensure rapid and continuous integration across air, land, sea, space, and cyberspace domains to overmatch adversaries. A medical lane was first integrated on the communications networks for experimentation in 2022. We describe a project to develop a method for empirically comparing devices intended to support combat casualty care through high-fidelity simulation in preparation for an Army experimentation exercise.

METHODS

Six medics participated in a series of high-fidelity simulation medical casualty injury scenarios with and without technology devices. The participants provided usability information about their care delivery experiences using the System Usability Scale and Adapted Telehealth Usability Questionnaire-Telemedicine and Advanced Technology Research Command and qualitative feedback.

RESULTS

A comparative effectiveness design compared the devices regarding their usability, size, weight, and power with the addition of cost, connectivity, and cyber security, and the qualitative feedback this methodology holistically assessed the technologies as they were applied in the combat casualty care scenario.

CONCLUSIONS

Results were used by decision makers to determine technology inclusion in experimentation exercise, develop proof of concept methodology to scale for the exercise, and provide technology developers feedback for iterative updates of their devices before participation in experimentation exercise. This project supports the body of simulation studies conducted to understand combat casualty care. It is one of few empirical medical technology assessments with medical personnel end user input that has been reported. The methodology incorporates a user-centered design for rapid technology improvements before fielding.

Prolonged, High-Fidelity Simulation for Study of Patient Care in Resource-Limited Medical Contexts and for Technology Comparative Effectiveness Testing

Most high-fidelity medical simulation is of limited duration, used for education and training, and rarely intended to study medical technology. U.S. caregivers working in prehospital, resource-limited settings may need to manage patients for extended periods (hours to days). This "prolonged casualty care" occurs during military, wilderness, humanitarian, disaster, and space medicine. We sought to develop a standardized simulation model that accurately reflects prolonged casualty care in order to study caregiver decision-making and performance, training requirements, and technology use in prolonged casualty care.

DESIGN

Model development.

SETTING

High-fidelity simulation laboratory.

SUBJECTS

None.

INTERVENTIONS

We interviewed subject matter experts to identify relevant prolonged casualty care medical challenges and selected two casualty types to further develop our model: a large thermal burn model and a severe hypoxia model. We met with a multidisciplinary group of experts in prolonged casualty care, nursing, and critical care to describe how these problems could evolve over time and how to contextualize the problems with a background story and clinical environment with expected resource availability. Following initial scenario drafting, we tested the models with expert clinicians. After multiple tests, we selected the hypoxia model for refinement and testing with inexperienced providers. We tested and refined this model until two research teams could proctor the scenario consistently despite subject performance variability.

MEASUREMENTS AND MAIN RESULTS

We developed a 6-8-hour simulation model that represented a 14-hour scenario. This model of pneumonia evolved from presentation to severe hypoxia necessitating advanced interventions including airway, breathing, and shock management. The model included: context description, caregiver orientation scripts, hourly progressive physiology tracks corresponding to caregiver interventions, intervention/procedure-specific physiology tracks, intervention checklists, equipment lists, prestudy checklists, photographs of setups, procedure, telementor, and role player scripts, business rules, and data collection methods.

CONCLUSIONS

This is the first standardized, high-fidelity simulation model of prolonged casualty care described in the literature. It may be used to assess caregiver performance and patient outcomes resulting from that performance during a complex, 14-hour prolonged casualty care scenario. Because it is standardized, the model may be used to compare differences in the impact of new technologies upon caregiver performance and simulated patient outcomes..

Methods used to examine technology in relation to the quality of nursing work in acute care: A systematic integrative review

AIMS AND OBJECTIVES

To systematically locate, assess and synthesise research to describe methods used to examine technology in relation to the quality of nursing work in acute care. Specific objectives were to (a) describe the types of nursing work examined; (b) describe methods used to examine technology in nursing work; (c) identify outcomes used to evaluate technology in relation to the quality of nursing work; and (d) make recommendations for future research.

BACKGROUND

New technologies can offer numerous benefits to nurses; however, it is challenging to evaluate health information technologies in relation to the quality of nurses' complex day-to-day work.

DESIGN

A systematic integrative review using a five-step process.

METHODS

Five databases were searched using search terms "nurs*," "workload," "task," "time." Data screening, extraction and interpretation were conducted independently by at least two authors and agreement verified by discussion. Data extraction followed PRISMA guidelines.

RESULTS

Of the 41 studies included, most (87.8%, n = 36) examined physical dimensions of nursing work; 31.7% (n = 13) organisational dimensions; 17.1% (n = 8) cognitive dimensions; and only 12.2% (n = 5) emotional dimensions. More than half (58.5%, n = 24) examined only one dimension; one captured all four dimensions. Most frequently examined technologies were electronic medical/health records (36.5%) and electronic medication management (19.5%). Direct observation (58.8%, n = 28) and multiple methods (19.5%, n = 8) were the most common methods; nurse tasks, frequency, duration and time distribution were variables most often measured.

CONCLUSIONS

Examinations of technology in nursing work often failed to capture the multiple dimensions of this work nor did they recognise the complexity of day-to-day nursing work in acute care. There is a paucity of literature to inform how and what technology should be measured in relation to the quality of nursing care.

RELEVANCE TO CLINICAL PRACTICE

The outcomes inform useful research methods to comprehensively examine technology to enhance the quality of complex nursing work.

Using machine learning to selectively highlight patient information

BACKGROUND

Electronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician's attention to the right data, at the right time. We developed a Learning EMR (LEMR) system that learns statistical models of clinician information-seeking behavior and applies those models to direct the display of data in future patients. We evaluated the performance of the system in identifying relevant patient data in intensive care unit (ICU) patient cases.

METHODS

To capture information-seeking behavior, we enlisted critical care medicine physicians who reviewed a set of patient cases and selected data items relevant to the task of presenting at morning rounds. Using patient EMR data as predictors, we built machine learning models to predict their relevancy. We prospectively evaluated the predictions of a set of high performing models.

RESULTS

On an independent evaluation data set, 25 models achieved precision of 0.52, 95% CI [0.49, 0.54] and recall of 0.77, 95% CI [0.75, 0.80] in identifying relevant patient data items. For data items missed by the system, the reviewers rated the effect of not seeing those data from no impact to minor impact on patient care in about 82% of the cases.

CONCLUSION

Data-driven approaches for adaptively displaying data in EMR systems, like the LEMR system, show promise in using information-seeking behavior of clinicians to identify and highlight relevant patient data.