scholarly journals Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters’ Views

2021 ◽  
Vol 18 (17) ◽  
pp. 9206
Author(s):  
Marja Härkänen ◽  
Kaisa Haatainen ◽  
Katri Vehviläinen-Julkunen ◽  
Merja Miettinen
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
University Hospital ◽  
Free Text ◽  
Medication Information ◽  
Information And Communication ◽  
Drug Doses ◽  
Incident Reports ◽  
Medication Incidents ◽  
Record Review

The purpose of this study was to describe incident reporters’ views identified by artificial intelligence concerning the prevention of medication incidents that were assessed, causing serious or moderate harm to patients. The information identified the most important risk management areas in these medication incidents. This was a retrospective record review using medication-related incident reports from one university hospital in Finland between January 2017 and December 2019 (n = 3496). Of these, incidents that caused serious or moderate harm to patients (n = 137) were analysed using artificial intelligence. Artificial intelligence classified reporters’ views on preventing incidents under the following main categories: (1) treatment, (2) working, (3) practices, and (4) setting and multiple sub-categories. The following risk management areas were identified: (1) verification, documentation and up-to-date drug doses, drug lists and other medication information, (2) carefulness and accuracy in managing medications, (3) ensuring the flow of information and communication regarding medication information and safeguarding continuity of patient care, (4) availability, update and compliance with instructions and guidelines, (5) multi-professional cooperation, and (6) adequate human resources, competence and suitable workload. Artificial intelligence was found to be useful and effective to classifying text-based data, such as the free text of incident reports.

scholarly journals CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Imjin Ahn ◽  
Wonjun Na ◽  
Osung Kwon ◽  
Dong Hyun Yang ◽  
Gyung-Min Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
Language Processing ◽  
Medical Center ◽  
Unstructured Data ◽  
University Hospital ◽  
Common Data Model ◽  
Free Text ◽  
Medical Examinations ◽  
Center Research

Abstract Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

ICD10Net: An Artificial Intelligence Algorithm with Medical Background Conducts ICD-10-CM Coding Task with Outstanding Performance (Preprint)

2019 ◽  
Author(s):  
Chin Lin ◽  
Yu-Sheng Lou ◽  
Chia-Cheng Lee ◽  
Chia-Jung Hsu ◽  
Ding-Chung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
General Hospital ◽  
Pearson Correlation ◽  
Model Performance ◽  
International Classification Of Diseases ◽  
Free Text ◽  
Daily Work ◽  
Medical Background ◽  
Icd 10 ◽  
F Measure

BACKGROUND An artificial intelligence-based algorithm has shown a powerful ability for coding the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in discharge notes. However, its performance still requires improvement compared with human experts. The major disadvantage of the previous algorithm is its lack of understanding medical terminologies. OBJECTIVE We propose some methods based on human-learning process and conduct a series of experiments to validate their improvements. METHODS We compared two data sources for training the word-embedding model: English Wikipedia and PubMed journal abstracts. Moreover, the fixed, changeable, and double-channel embedding tables were used to test their performance. Some additional tricks were also applied to improve accuracy. We used these methods to identify the three-chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. Subsequently, 94,483-labeled discharge notes from June 1, 2015 to June 30, 2017 were used from the Tri-Service General Hospital in Taipei, Taiwan. To evaluate performance, 24,762 discharge notes from July 1, 2017 to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from other seven hospitals were also tested. The F-measure is the major global measure of effectiveness. RESULTS In understanding medical terminologies, the PubMed-embedding model (Pearson correlation = 0.60/0.57) shows a better performance compared with the Wikipedia-embedding model (Pearson correlation = 0.35/0.31). In the accuracy of ICD-10-CM coding, the changeable model both used the PubMed- and Wikipedia-embedding model has the highest testing mean F-measure (0.7311 and 0.6639 in Tri-Service General Hospital and other seven hospitals, respectively). Moreover, a proposed method called a hybrid sampling method, an augmentation trick to avoid algorithms identifying negative terms, was found to additionally improve the model performance. CONCLUSIONS The proposed model architecture and training method is named as ICD10Net, which is the first expert level model practically applied to daily work. This model can also be applied in unstructured information extraction from free-text medical writing. We have developed a web app to demonstrate our work (https://linchin.ndmctsgh.edu.tw/app/ICD10/).

scholarly journals P005 Rheumatology virtual clinics during COVID-19: are our patients satisfied?

Rheumatology ◽  
2021 ◽  
Vol 60 (Supplement_1) ◽  
Author(s):  
Fajer A Altamimi ◽  
Una Martin
Keyword(s):  
Medical Information ◽  
Worker Safety ◽  
University Hospital ◽  
Free Text ◽  
Risk Of Infection ◽  
Further Training ◽  
Return Envelope ◽  
The Right ◽  
Reduced Risk

Abstract Background/Aims  Telemedicine can be broadly defined as the use of telecommunication technologies to provide medical information and services. It can be audio, visual, or text. Its use has increased dramatically during the COVID-19 pandemic to ensure patient and healthcare worker safety. Any healthcare professional can engage with it. It carries benefits like reduced stress and expense of traveling, maintenance of social distancing, and reduced risk of infection. There are some potential drawbacks such as lack of physical examination, liability and technological issues. Methods  A questionnaire was sent to 200 patients, selected from different virtual clinics (new and review, doctor and ANP led) run between March and May 2020 in the rheumatology department of University Hospital Waterford. We formulated 14 questions to cover the following aspects: demography, the purpose of the consult, punctuality, feedback, medico-legal concerns, and free text for comments. A self-addressed return envelope was included. Results  83 responses were received. 2 were excluded. The ratio of females to male respondents was 59: 41, with the majority over 60 years old. The main appointment type was review 67 (83%). 80% of patients were called either before or at the time of their scheduled appointment. The vast majority (98.8%) of our patients had confidence in our data protection and trusted our system to maintain their confidentiality. 95% stated that they felt comfortable, were given enough time to explain their health problem and felt free from stress. The respondents who preferred attending the clinic in person (17 in total) compared to the virtual were mostly follow up patients- 12 vs. 5 new. Conclusion  Patient satisfaction among those surveyed was high, despite having to introduce the service abruptly during the COVID-19 pandemic. There are many improvements we can adopt to improve our service and even maintain after the pandemic as a way of communicating with our stable patients. As we are covering a large geographical catchment, we can continue to implement the virtual clinic for some appointments. We should prioritize our efforts on identifying the right patient and the type of service we can offer, further training of staff, and increasing awareness of the patients as to how to get the most out of a virtual appointment. Disclosure  F.A. Altamimi: None. U. Martin: None. C. Sheehy: None.

scholarly journals Use of Artificial Intelligence to understand adults’ thoughts and behaviours relating to COVID-19

2021 ◽  
pp. 175791392097933
Author(s):  
SW Flint ◽  
A Piotrkowicz ◽  
K Watts
Keyword(s):  
Artificial Intelligence ◽  
Online Survey ◽  
Severe Illness ◽  
Free Text ◽  
Community Recovery ◽  
Substantial Impact ◽  
Lifestyle Behaviours ◽  
Artificial Intelligence Methods ◽  
High Concern ◽  
The Impact

Aims: The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people’s movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults’ thoughts and behaviours in response to the outbreak and resulting lockdown measures. Methods: Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. Results: Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. Conclusions: This time-sensitive study provides important insights into adults’ perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people’s responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.

scholarly journals The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Daniele Giansanti
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Mobile Health ◽  
Digital Pathology ◽  
Digital Radiology ◽  
Cellular Diagnostics ◽  
Information And Communication ◽  
Electronic Health ◽  
New Applications

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

scholarly journals Benchmarking experience to improve paediatric healthcare: listening to the voices of families from two European Children’s University Hospitals

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ilaria Corazza ◽  
Kendall Jamieson Gilmore ◽  
Francesca Menegazzo ◽  
Valts Abols
Keyword(s):  
Children And Adolescents ◽  
Quality Care ◽  
University Hospital ◽  
Free Text ◽  
University Hospitals ◽  
Post Discharge ◽  
Important Indicator ◽  
Published Evidence ◽  
Patient Reported ◽  
Electronic Administration

Abstract Background Patient Reported Experience Measures (PREMs) are recognized as an important indicator of high quality care and person-centeredness. PREMs are increasingly adopted for paediatric care, but there is little published evidence on how to administer, collect, and report paediatric PREMs at scale. Methods This paper describes the development of a PREMs questionnaire and administration system for the Meyer Children’s University Hospital in Florence (Meyer) and the Children’s Clinical University Hospital in Riga (CCUH). The system continuously recruits participants into the electronic administration model, with surveys completed by caregivers or adolescents at their convenience, post-discharge. We analyse 1661 responses from Meyer and 6585 from CCUH, collected from 1st December 2018 to 21st January 2020. Quantitative and qualitative experience analyses are included, using Pearson chi-square tests, Fisher’s exact tests and narrative evidence from free text responses. Results The large populations reached in both countries suggest the continuous, digital collection of paediatric PREMs described is feasible for collecting paediatric PREMs at scale. Overall response rates were 59% in Meyer and 45% in CCUH. There was very low variation in mean scores between the hospitals, with greater clustering of Likert scores around the mean in CCUH and a wider spread in Meyer for a number of items. The significant majority of responses represent the carers’ point of view or the perspective of children and adolescents expressed through proxy reporting by carers. Conclusions Very similar reported scores may reflect broadly shared preferences among children, adolescents and carers in the two countries, and the ability of both hospitals in this study to meet their expectations. The model has several interesting features: inclusion of a narrative element; electronic administration and completion after discharge from hospital, with high completion rates and easy data management; access for staff and researchers through an online platform, with real time analysis and visualization; dual implementation in two sites in different settings, with comparison and shared learning. These bring new opportunities for the utilization of PREMs for more person-centered and better quality care, although further research is needed in order to access direct reporting by children and adolescents.

scholarly journals Analysis of the Risk Management Process on the Development of the Public Sector Information Technology Master Plan

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 248 ◽  
Author(s):  
Sérgio Andrade deFreitas ◽  
Edna Canedo ◽  
Rodrigo Santos Felisdório ◽  
Heloise Leão
Keyword(s):  
Risk Management ◽  
Public Sector ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Master Plan ◽  
Public And Private ◽  
The Public ◽  
Management Processes ◽  
Risk Management Process ◽  
Information And Communication

The Information and Communication Technology Master Plan—ICTMP—is an important tool for the achievement of the strategic business objectives of public and private organizations. In the public sector, these objectives are closely related to the provision of benefits to society. Information and Communication Technology (ICT) actions are present in all organizational processes and involves size-able budgets. The risks inherent in the planning of ICT actions need to be considered for ICT to add value to the business and to maximize the return on investment to the population. In this context, this work intends to examine the use of risk management processes in the development of ICTMPs in the Brazilian public sector.

scholarly journals The place of artificial intelligence in the risk management process

2021 ◽  
Vol 120 ◽  
pp. 02013
Author(s):  
Petya Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Risk Identification ◽  
Management Process ◽  
Whole Process ◽  
Risk Management Process ◽  
Problematic Issue ◽  
The Individual

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

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