Nutrition and Dietetic Product Shortages are A Neglected Issue in Alerting Systems and in Registries

Jenzer, Helena; Büsser, ra; Balmer, Franziska Scheidegger; Silva, Marta; Sadeghi, Leila

doi:10.4172/2472-1921.100022

Cited by 4 publications

(9 citation statements)

References 11 publications

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“…As previously stated, there is a paucity of literature describing EN product shortages. In a 2016 review, only one enteral tube feeding product and one therapeutic nutrition drink were identified in an international literature and registry search of nutrition and dietetic product shortages 5 . This minimal documentation may be related to a lack of registered dietitians who are accustomed to reporting EN shortages coupled with less rigid federal oversight of medical foods compared with drugs.…”

Section: Historical Perspectivementioning

confidence: 99%

“…Facilities engaged in manufacturing, processing, packing, or holding medical foods for consumption in the United States must register with the FDA, but the agency does not maintain a comprehensive registry of medical foods 12 . Furthermore, pharmacy‐managed EN products that are affected by shortages are the products that tend to be entered into shortage databases 5 . Another possibility is that there is enough redundancy of EN product types among manufacturers that several options have historically existed for substitution of any EN formula in short supply.…”

Section: Historical Perspectivementioning

confidence: 99%

“…When EN product shortages occur, guidance available to assist the clinician or consumer with ways to manage the shortage is limited. In addition, there is an overall dearth of literature describing the impact of EN shortages on patient outcomes 5 . This article provides a brief history of PN and EN product shortages, lessons learned from previous shortages, and strategies to manage PN and EN product shortages with a focus on patient safety.…”

Section: Introductionmentioning

confidence: 99%

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Managing nutrition support product shortages: What have we learned?

2022

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Product shortages related to the components of parenteral nutrition (PN) therapy have been well described over the past decade. The situation has more recently worsened and expanded globally because of the impact the COVID-19 pandemic has placed on supply chain issues and workforce demand. The impact of enteral nutrition (EN) product shortages is less well documented when compared with PN, and development of management strategies is often left up to the discretion of individual providers. The recent crisis in infant formula supply has heightened the national awareness of how a nutrition support product shortage can significantly impact patient safety. This review provides a historical perspective of PN and EN product shortages to gain insight into the lessons learned and applies this to strategies for managing current and future product shortages. Strategies for managing PN and EN shortages can best succeed if they are tailored to address aspects that are unique to the inpatient and outpatient care setting. In addition, patients who transition between care settings are vulnerable to harm related to product shortages if measures are not in place to communicate and address these shortages. Teamwork and communication within an organization and among key stakeholders are necessary to develop processes that aim to minimize patient harm related to product shortages.

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Section: Historical Perspectivementioning

confidence: 99%

Section: Historical Perspectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Managing nutrition support product shortages: What have we learned?

2022

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“…The study of the authors in ( 25 ) proposed a system that utilizes customized features concerning nutrition that aids users in the change of their attitude toward healthier food choices and diets. The authors of ( 26 ) conducted a review to determine if dieted and nutritional products are issues often left out in shortages research. The authors in ( 27 ) proposed a framework for the recommendation of everyday meals while also managing preference-inclusive and nutritional information.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Mantey

Zhou

Anajemba

et al. 2021

Front. Public Health

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Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

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“…In our approach we have used both user information and product information for a short and long term scenario in making sure patients with diseases are protected. A study was performed by the authors in [15] evaluated several medicines reporting systems which is targeted at reporting medicine shortages. Their system also incorporated different registries for batch recall, and manufacturers homepages.…”

Section: Related Workmentioning

confidence: 99%

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

et al. 2020

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Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician's rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient's data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and F1-measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and 99% F1-measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and F1 Measure score is 80%.

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Nutrition and Dietetic Product Shortages are A Neglected Issue in Alerting Systems and in Registries

Cited by 4 publications

References 11 publications

Managing nutrition support product shortages: What have we learned?

Managing nutrition support product shortages: What have we learned?

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

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