Minimum data set development for a drug poisoning registry system

Hajesmaeel-Gohari, Sadrieh; Bahaadinbeigy, Kambiz; Tajoddini, Shahrad; Kalhori, Sharareh R. Niakan

doi:10.1177/2055207619897155

Cited by 8 publications

(11 citation statements)

References 18 publications

(29 reference statements)

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“…These centers provide specialized information to the public and medical staff. These toxicology information centers offer counseling for individuals who have been poisoned and provide education and research services for the prevention and treatment of poisoning [ 33 ]. However, based on the searches performed in scientific databases and the review of the systems used by the Ministries of Health of different countries, no coherent information management system was found that specifically covered corrosive ingestion and most of the systems included all poisonings [ 38 , 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…Registry systems typically use MDS to enable accurate data analysis, decision-making, and appropriate management of poisoning cases [ 32 ]. Using an MDS in a clinical registry system is critical to achieve consistent and comparable data on a specific disease or condition [ 33 ]. The MDS of corrosive ingestion is required for the ongoing collection and maintenance of the data and is the main requirement for implementing a registry and information system.…”

Section: Introductionmentioning

confidence: 99%

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Developing the minimum data set of the corrosive ingestion registry system in Iran

Mahmoudvand

Yazdani

Shafiee

et al. 2022

BMC Health Serv Res

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Background Corrosive ingestion is still a major health problem, and its outcomes are often unpredicted. The implementation of a registry system for poisoning with corrosive substances may improve the quality of patient care and might be useful to manage this type of poisoning and its complications. Therefore, our study aimed to establish a minimum data set (MDS) for corrosive ingestion. Methods This was an applied study performed in 2022. First, a literature review was conducted to identify the potential data items to be included in the corrosive ingestion MDS. Then, a two-round Delphi survey was performed to attain an agreement among experts regarding the MDS content, and an additional Delphi step was used for confirming the final MDS by calculating the individual item content validity index (CVI) and content validity ratio (CVR) and by using other statistical tests. Results After the literature review, 285 data items were collected and sent to a two-round Delphi survey in the form of a questionnaire. In total, 75 experts participated in the Delphi stage, CVI, kappa, and CVR calculation. Finally, the MDS of the corrosive ingestion registry system was identified in two administrative and clinical sections with 21 and 152 data items, respectively. Conclusions The development of an MDS, as the first and most important step towards developing the corrosive ingestion registry, can become a standard basis for data collection, reporting, and analysis of corrosive ingestion. We hope this MDS will facilitate epidemiological surveys and assist policymakers by providing higher quality data capture to guide clinical practice and improve patient-centered outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing the minimum data set of the corrosive ingestion registry system in Iran

Mahmoudvand

Yazdani

Shafiee

et al. 2022

BMC Health Serv Res

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“…Disease registries are designed to collect and manage information about the approaches and outcomes of a population of patients to evaluate and improve care quality and safety, patient monitoring and follow-up, and facilitate new research (29). As an information manage-ment tool, the poisoning registry is an important source of information about poisoning patterns, decision-making, and healthcare provision (30). In registries, a minimum dataset (MDS) is usually utilized to facilitate the precise data analysis, decision-making, and management of disease cases (31).…”

Section: Introductionmentioning

confidence: 99%

Data Infrastructure for a Poisoning Registry with Designing Data Elements and a Minimum Data Set

et al. 2022

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Background: The prevalence of poisoning is on the rise in Iran. A poisoning registry is a key source of information about poisoning patterns used for decision-making and healthcare provision, and a minimum dataset (MDS) is a prerequisite for developing a registry. Objectives: This study aimed to design a MDS for a poisoning registry. Methods: This applied study was conducted in 2021. A poisoning MDS was developed with a four-stage process: (1) conducting a systematic review of the Web of Science, Scopus, PubMed, and EMBASE, (2) examining poisoning-related websites and online forms, (3) classification of data elements in separate meetings with three toxicology specialists, and (4) validating data elements using the two-stage Delphi technique. A researcher-made checklist was employed for this purpose. The content validity of the checklist was examined based on the opinions of five health information management and medical informatics experts with respect to the topic of the study. Its test-retest reliability was also confirmed with the recruitment of 25 experts (r = 0.8). Results: Overall, 368 data elements were identified from the articles and forms, of which 358 were confirmed via the two-stage Delphi technique and classified into administrative (n = 88) and clinical data elements (n = 270). Conclusions: The creation of a poisoning registry requires identifying the information needs of healthcare centers, and an integrated and comprehensive framework should be developed to meet these needs. To this end, a MDS contains the essential data elements that form a framework for integrated and standard data collection.

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“…Disease registration systems provide an important tool for public health surveillance (8). Electronic record of patient health information, while providing a dynamic and flexible structure for reporting (9,10), Allows the discovery of hidden knowledge from stored data. With the expansion of electronic data registration in the health care, there is a large and complex amount of data that cannot be analyzed by traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Approach to Analyze COVID-19 Clinical Dataset

Yazdani¹,

Ravangard²,

Sharifian³

2021

PJMHS

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The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

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Minimum data set development for a drug poisoning registry system

Cited by 8 publications

References 18 publications

Developing the minimum data set of the corrosive ingestion registry system in Iran

Developing the minimum data set of the corrosive ingestion registry system in Iran

Data Infrastructure for a Poisoning Registry with Designing Data Elements and a Minimum Data Set

Data Mining Approach to Analyze COVID-19 Clinical Dataset

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