Impact of workflow interruptions on baseline activities of the doctors working in the emergency department

Mobeen, Asyia; Shafiq, Muhammad; Aziz, Muhammad Haris; Mohsin, Muhammad

doi:10.1136/bmjoq-2022-001813

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The model can be used for providing an enhanced, more accurate framework that would lead to a better human disease prediction model. Reference [1] Reference [2] Reference [3] Reference [4] Reference [5] Reference [6] Proposed Method…”

Section: Future Scopementioning

confidence: 99%

“…In the current digital age, innovation in the medical industry can be achieved through the digitalization of medical *Corresponding Author Email: mmkasar@bvucoep.edu.in (M. Kasar) processes [5]. One of the most pressing issues in the medical industry is the workload on the doctors [6] and the unaffordable consultation cost [7]. This issue is highlighted mainly in the disease prediction with the symptoms of the patients as input.…”

Section: Introductionmentioning

confidence: 99%

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Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Gaurav,

Kumar,

Singh

et al. 2023

IJE

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Disease prediction of a human means predicting the probability of a patient's disease after examining the combinations of the patient's symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher's models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.

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Section: Future Scopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Gaurav,

Kumar,

Singh

et al. 2023

IJE

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“…Thus, the healthcare industry thrives on innovation to make logistics efficient [1]. The current general paradigm is mostly dependent on the existence of symptoms and interpersonal cummunication [2].The rapid expansion of medical data necessitates the development of effective analytic methods in order to glean insightful information and promote early disease identification. In this regard, the ultimate objective of our research is to create a comprehensive illness prediction platform in order to overcome the difficulties related to late disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

PrognosisAI: An AI-Enabled Disease Prediction System for Early Detection and Prevention

2024

IRJMETS

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This study aims to assist a person in predicting the sickness they are experiencing based on their symptoms and accurate measurements of their vital signs. Sometimes, people choose to ignore health conditions because the cost of care is so high. In the ever-changing world of today, where human existence is always changing, population health becomes a critical issue. When health conditions are diagnosed too late, there might be serious repercussions, such as higher death rates. Taking note of this urgent problem, we suggest a unique machine learning-based method for disease prediction. Our research intends to enable early disease identification through vital sign evaluations and symptom analysis, empowering individuals to take proactive measures for their health.

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“…Implementing a new process into any medical setting is difficult, but implementation into the fast paced, high acuity, continuously active ED setting is especially challenging. ED patient care activities are continuous and concurrent, so any new process initially requires a higher cognitive load for staff, can increase interruptions, and detracts from time spent on other types of patient care 5–7 . Additionally, patients in the ED are the most heterogenous of any medical setting, so new processes often are not applicable to all patients.…”

Section: Introductionmentioning

confidence: 99%

“…ED patient care activities are continuous and concurrent, so any new process initially requires a higher cognitive load for staff, can increase interruptions, and detracts from time spent on other types of patient care. [5][6][7] Additionally, patients in the ED are the most heterogenous of any medical setting, so new processes often are not applicable to all patients. For instance, screening all older adults for fall risk may not apply to patients in nursing homes who already have fall risk precautions in place or to patients who are non-ambulatory.…”

Section: Introductionmentioning

confidence: 99%

Implementation of geriatric screening in the emergency department using the Consolidated Framework for Implementation Research

Southerland,

Gulker,

Van Fossen

et al. 2023

Academic Emergency Medicine

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ObjectiveImplementation of evidence‐based care processes (EBP) into the emergency department (ED) is challenging and there are only a few studies of real‐world use of theory‐based implementation frameworks. We report final implementation results and sustainability of an EBP geriatric screening program in the ED using the Consolidated Framework for Implementation Research (CFIR).MethodsThe EBP involved nurses screening older patients for delirium (Delirium Triage Screen), fall risk (4‐Stage Balance Test), and vulnerability (Identification of Seniors at Risk score) with subsequent appropriate referrals to physicians, therapy specialists, or social workers. The proportions of screened adults ≥65 years old were tracked monthly. Outcomes are reported January 2021–December 2022. Barriers encountered were classified according to CFIR. Implementation strategies were classified according to the CFIR‐Expert Recommendations for Implementing Change (ERIC).ResultsImplementation strategies increased geriatric screening from 5% to 68%. This did not meet our prespecified goal of 80%. Change was sustained through several COVID‐19 waves. Inner setting barriers included culture and implementation climate. Initially, the ED was treated as a single inner setting, but we found different cultures and uptake between ED units, including night versus day shifts. Characteristics of individuals barriers included high levels of staff turnover in both clinical and administrative roles and very low self‐efficacy from stress and staff turnover. Initial attempts with individualized audit and feedback were not successful in improving self‐efficacy and may have caused moral injury. Adjusting feedback to a team/unit level approach with unitwide stretch goals worked better. Identifying early adopters and conducting on‐shift education increased uptake. Lessons learned regarding ED culture, implementation in interconnected health systems, and rapid cycle process improvement are reported.ConclusionsThe pandemic exacerbated barriers to implementation in the ED. Cognizance of a large ED as a sum of smaller units and using the CFIR model resulted in improvements.

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Impact of workflow interruptions on baseline activities of the doctors working in the emergency department

Cited by 6 publications

References 19 publications

Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

PrognosisAI: An AI-Enabled Disease Prediction System for Early Detection and Prevention

Implementation of geriatric screening in the emergency department using the Consolidated Framework for Implementation Research

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