Manual Data Entry Research Articles

Lab Inventory Management using QR Code

Laboratory inventory management is vital for the efficient operation of scientific research institutions. Conventional inventory tracking methods often involve manual data entry, which can be error-prone and time-consuming, leading to operational inefficiencies and potential research disruptions. To address these challenges, this project introduces a Lab Inventory Management System (LIMS) that leverages QR code technology. The LIMS assigns unique QR codes to each laboratory item, encoding critical information such as item name, quantity, and expiration date. This technology enables mobile devices equipped with QR code scanners to facilitate real-time updates, eliminating manual data entry errors and ensuring the accuracy of inventory information. Furthermore, a centralized database supports data integrity and enables seamless integration with existing laboratory systems. The project's advantages include enhanced accuracy, significant time and cost savings, and streamlined workflow efficiency. However, the successful implementation of the LIMS necessitates careful planning and comprehensive user training to ensure a smooth transition to this innovative inventory management solution. Key Words: Automated, Scientific Research, Data Integrity, Data Visualization, Barcode Scanning

Automatic Interpretation of Line Probe Assay Test for Tuberculosis

Line Probe Assay (LPA) is a widely used method for diagnosing drug-resistant tuberculosis (DRTB), but it is a time-consuming and labor-intensive process that requires expert interpretation. DRTB is a significant threat to global TB control efforts and its prompt diagnosis is critical for initiating appropriate treatment. In this paper, we present an automated LPA test interpretation solution that uses computer vision techniques to extract and analyze strips from LPA sheets and uses machine learning algorithms to produce drug sensitivity and resistivity outcomes with extremely high precision and recall. We also develop OCR models to eliminate manual data entry to further reduce the overall time. Our solution comprises a rejection module that flags ambiguous and novel samples that are then referred to experienced lab technicians. This results in increased trust in the solution. To evaluate our solution, we curate an extensive and diverse dataset of LPA strips annotated by multiple microbiologists across India. Our solution achieves more than 95% accuracy for all drugs on this dataset. The proposed solution has the potential to increase the efficiency, standardization of LPA test interpretation, and fast-tracking the dissemination of results to end-users via a designated Management Information System (MIS).

Abstract 3569: Using AI to automatically process data from unstructured health records of patients with lung cancer

Abstract Introduction: Structured databases created from electronic health records (EHR) are crucial for cancer research. Manual data entry into databases is both labor-intensive and error-prone. We aimed to develop an artificial intelligence (AI)-driven approach for automatically inputting patient information from EHRs. Methods: A REDCap database for 53 patients with advanced lung cancer treated with first line immuno+/-chemotherapy at Gustave Roussy between 02/2021-06/2023 was manually populated by physicians with demographics, risk factors, cancer history, and treatment data, with 137 variables/patient. Given unstructured medical letters and a schematic description of each variable, generative AI was used to find, quote and process variables into a structured form. We directed large language model actions with prompt engineering and tailored few-shot examples. Mortality data were auto-extracted from the French public registry, INSEE. We assessed consistency between manual (MDE) and automated data entry (ADE), with a secondary manual review for mismatches performed by a senior physician. Results: In total, 7,261 data points were assessed. ADE averaged 10 minutes per patient inclusive of quality checks vs 1 h for MDA. The concordance rate between ADE and MDE was 90.2% (6,550/7,261) with a discordance of 6.8% (496/7,261). Data was missing in 0.8% (59/7,261) of cases for both methods, 1% (73/7,261) for ADE and 1,1% (83/7,261) for MDA. After checking discordances, ADE correctness was 95.3% (6,922/7,261) (Table), MDE was 93.8% (6,809/7,261). Errors in ADE (207/7,261) were due to algorithm refinement needs (70%) and missing EHR information (30%). Conclusion: Generative AI has a strong potential for identifying and structuring data from EHRs, yielding consistency superior to manual entry by physicians and 85% reduced amount of time. This may enhance the efficiency, accuracy, and scalability of EHR-to-database conversions. ADE for >1000 patients will be presented at the meeting. ADE correctness 100% 95-99% 90-94% 80-89% Sex; Age; Life status; Last follow-up; Metastatic from diagnosis Molecular alterations; Metastatic sites; Progression sites; Treatment type Tobacco & cannabis consumption; Histology; PDL1 score; Progression event Pack years; Date of diagnosis & first metastasis; Stage; Date of start treatment Citation Format: Mihaela Aldea, Pierre Rolland, Solenne Simon, Aliette Poplu, Muriel Wartelle, Benjamin Vignal, Jean-Charles Louis, Francois Lion, Arnaud Borie, David Planchard, Caroline Robert, Stefan Michiels, Fabrice Andre, Fabrice Barlesi, Franck Le Ouay, Benjamin Besse. Using AI to automatically process data from unstructured health records of patients with lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3569.

Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.

Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience. Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified. A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields. Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate). Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.

Stationery Management System

The Stationery Management System is a comprehensive software solution designed to streamline and automate the processes involved in managing stationery inventory for organizations. This system aims to improve efficiency, reduce costs, and enhance overall productivity by providing a centralized platform to track, control, and replenish stationery supplies. The system offers a user-friendly interface that allows authorized personnel, such as office administrators or procurement managers, to manage all aspects of stationery inventory effectively. It provides the following key features: Inventory Management: The system maintains a comprehensive database of stationery items, including details such as item name, description, quantity, and unit price. It allows users to easily add new items, update existing ones, and track stock levels in real-time. Purchase and Requisition: Users can generate purchase orders or requisitions directly from the system, ensuring a streamlined procurement process. The system can also integrate with external vendors or suppliers to automate order placement and tracking. Stock Monitoring: The system provides real-time visibility into the current stock levels of stationery items. Users can set up automated alerts to notify them when stock levels fall below a predefined threshold, ensuring timely reordering and preventing stock outs. Issue and Return Tracking: The system facilitates the tracking of stationery items issued to employees or departments. It enables users to record item issuances, monitor their usage, and manage returns efficiently. This feature helps prevent misuse and ensures accountability. Reporting and Analytics: The system generates comprehensive reports and analytics, offering valuable insights into stationery usage, expenditure, and inventory trends. These reports assist in making data-driven decisions, optimizing procurement processes, and identifying cost-saving opportunities. Integration and Accessibility: The system can integrate with other existing systems within the organization, such as accounting or Enterprise Resource Planning (ERP) software, to ensure seamless data flow and minimize manual data entry. It also provides accessibility across multiple devices, enabling users to manage stationery inventory remotely. Overall, the Stationery Management System provides organizations with a centralized and efficient solution to manage their stationery inventory effectively. By automating key processes and providing real-time visibility, the system optimizes procurement, reduces waste, and enhances productivity, leading to cost savings and improved operational efficiency.

Perbandingan Penginputan Transaksi Secara Manual Menggunakan Accurate pada Laporan Keuangan Input Transaksi Manual dengan Accurate di Laporan Keuangan

This abstract discusses the comparison between manual transaction input and using Accurate in financial reports. Manual transaction input method involves the process of manually entering data into the accounting system, such as recording purchase transactions, sales, and other expenses. Although this method can produce accurate financial reports, it requires more time and effort from the inputting party. In addition, the risk of error is also higher because data entry is done manually. On the other hand, using Accurate as a transaction input system can improve the efficiency and accuracy of financial reports. Accurate is an accounting software designed to assist entrepreneurs or accountants in managing company financial transactions. By using Accurate, transaction input can be done automatically through integration with various data sources, such as cash register systems, purchase systems, and sales systems. This reduces the need for manual data entry, so the required time and effort can be minimized. In addition, Accurate also comes with automatic validation features that can reduce the risk of human error in inputting transactions. This feature will check the entered data and provide warnings or notifications if there are errors or non-compliance with applicable accounting rules. Thus, the accuracy of financial reports can be improved. In this study, a comparison will be made between manual transaction input and using Accurate in financial reports. By comparing these two methods, it is hoped that information can be found to help entrepreneurs or accountants in choosing the most efficient and accurate transaction input method for their company.

Implementation Analysis of Inventory Accounting at CV. Vivace

Inventory management is of paramount importance for trading companies, necessitating meticulous oversight to prevent potential losses. Adhering to inventory accounting principles is crucial, as failure to do so can result in critical errors. PSAK 14, established by the Indonesian Institute of Accountants (IAI), provides a comprehensive framework for these principles. Companies, such as CV Vivace in the entertainment industry, must scrutinize their inventory practices in strict accordance with PSAK 14 to ensure accuracy and compliance. This research adopts a multifaceted approach, incorporating informant interviews, direct observation, and a thorough review of prior studies. The primary objective is to assess CV Vivace's inventory practices, emphasizing the unique challenges faced by companies in the entertainment sector. The study delves into aspects like income generation and methodologies for inventory valuation, setting itself apart from previous research. While CV Vivace aligns with PSAK 14, there is room for improvement. Transitioning to automated systems can enhance efficiency, reducing reliance on manual data entry and mitigating the risk of fraudulent activities. This research anticipates further discussions and in-depth exploration to provide a comprehensive understanding of PSAK 14-related matters, shedding light on the intricacies of inventory management within the entertainment industry.

District health management and stillbirth recording and reporting: a qualitative study in the Ashanti Region of Ghana

BackgroundDespite global efforts to reduce maternal and neonatal mortality, stillbirths remain a significant public health challenge in many low- and middle-income countries. District health systems, largely seen as the backbone of health systems, are pivotal in addressing the data gaps reported for stillbirths. Available, accurate and complete data is essential for District Health Management Teams (DHMTs) to understand the burden of stillbirths, evaluate interventions and tailor health facility support to address the complex challenges that contribute to stillbirths. This study aims to understand stillbirth recording and reporting in the Ashanti Region of Ghana from the perspective of DHMTs.MethodsThe study was conducted in the Ashanti Region of Ghana. 15 members of the regional and district health directorates (RHD/DHD) participated in semi-structured interviews. Sampling was purposive, focusing on RHD/DHD members who interact with maternity services or stillbirth data. Thematic analyses were informed by an a priori framework, including theme 1) experiences, perceptions and attitudes; theme 2) stillbirth data use; and theme 3) leadership and support mechanisms, for stillbirth recording and reporting.ResultsUnder theme 1, stillbirth definitions varied among respondents, with 20 and 28 weeks commonly used. Fresh and macerated skin appearance was used to classify timing with limited knowledge of antepartum and intrapartum stillbirths. For theme 2, data quality checks, audits, and the district health information management system (DHIMS-2) data entry and review are functions played by the DHD. Midwives were blamed for data quality issues on omissions and misclassifications. Manual entry of data, data transfer from the facility to the DHD, limited knowledge of stillbirth terminology and periodic closure of the DHIMS-2 were seen to proliferate gaps in stillbirth recording and reporting. Under theme 3, perinatal audits were acknowledged as an enabler for stillbirth recording and reporting by the DHD, though audits are mandated for only late-gestational stillbirths (> 28 weeks). Engagement of other sectors, e.g., civil/vital registration and private health facilities, was seen as key in understanding the true population-level burden of stillbirths.ConclusionEffective district health management ensures that every stillbirth is accurately recorded, reported, and acted upon to drive improvements. A large need exists for capacity building on stillbirth definitions and data use. Recommendations are made, for example, terminology standardization and private sector engagement, aimed at reducing stillbirth rates in high-mortality settings such as Ghana.

Large Language Models as a Rapid and Objective Tool for Pathology Report Data Extraction.

Medical institutions continuously create a substantial amount of data that is used for scientific research. One of the departments with a great amount of archived data is the pathology department. Pathology archives hold the potential to create a case series of valuable rare entities or large cohorts of common entities. The major problem in creation of these databases is data extraction which is still commonly done manually and is highly laborious and error prone. For these reasons, we offer using large language models to overcome these challenges. Ten pathology reports of selected resection specimens were retrieved from electronic archives of Koç University Hospital for the initial set. These reports were de-identified and uploaded to ChatGPT and Google Bard. Both algorithms were asked to turn the reports in a synoptic report format that is easy to export to a data editor such as Microsoft Excel or Google Sheets. Both programs created tables with Google Bard facilitating the creation of a spreadsheet from the data automatically. In conclusion, we propose the use of AI-assisted data extraction for academic research purposes, as it may enhance efficiency and precision compared to manual data entry.

A Continuous Registry of Medical Record, Patient Input, and Epidemiological Data of Patients With Ulcerative Colitis: a Multicentre, Prospective, Observational Clinical Registry Study in Japan.

This registry aims to allow for a prospective non-interventional observational study of ulcerative colitis. This will facilitate monitoring of the current state of ulcerative colitis in Japan and improving the long-term disease course and adverse events associated with current treatment options. Inclusion of patients from five centres in Japan is planned. The study is expected to take place from July 15, 2020, to November 30, 2024. Background, demographics, and medical history/information will be collected from electronic medical records at enrolment. Medical information including medications, laboratory data, and disease activity will be collected automatically from electronic medical records throughout the study. Patient-reported quality of life data will be collected directly from patients via smartphone. Efficacy endpoints (clinical remission rate, clinical improvement rate, and endoscopic healing rate) and safety endpoints (incidence of adverse events and specific ulcerative colitis-related events) will be collected according to treatment administered. Treatment categories include no treatment, 5-aminosalicylic acids, corticosteroids, immunomodulators, immunosuppressants, anti-tumour necrosis alpha agents, cytapheresis, Janus kinase inhibitors, anti-integrin antibodies, and anti-interleukin-12/23 antibodies. The dataset will include cross-sectional and longitudinal data and is expected to capture the state of ulcerative colitis in Japan. Patients will be included on a large scale, and the registry will be established automatically from electronic medical records and direct patient input, facilitating the accurate recording of medical information for patients with ulcerative colitis in Japan and minimizing limitations intrinsic to databases that require manual data entry, such as the burden on participating investigators and entry of data with errors/typos.

Image-Based Extraction of Prescription Information using OCR-Tesseract

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care.

The Quebec Congenital Heart Disease Registry: A Model of Prospective Databank to Facilitate Research in Congenital Cardiology

BackgroundA national registry of congenital heart disease (CHD) would facilitate project initiation, decrease costs, increase statistical power, and avoid duplication. Establishing such registries poses numerous challenges, but the current Canadian research ecosystem in CHD is well positioned to meet them. We assessed the feasibility of building a province-wide CHD registry by automatically identifying people with CHD and extracting their native cardiac anatomy from multiple clinical data sources, without the need for manual data entry. MethodsWe designed a CHD registry of all fetuses and children with at least 1 echocardiographic report confirming CHD since 2000. We interfaced the registry with several clinical and echocardiography data sources from all paediatric cardiology programmes in Québec. ResultsWe extracted 885,287 echocardiogram reports and 70,121 clinical records. We identified CHD in 43,452 children and 4682 fetuses. There were 1128 (2.3%) cases with files in multiple institutions, and patients with more complex CHD were 3 times more likely to be seen in more than 1 institution. So far, the registry has been used to build and link CHD cohorts for 7 distinct projects. ConclusionsWe demonstrated the feasibility of a baseline CHD registry in Québec without the need for manual data entry, in which other CHD research projects could be nested. This could serve as a blueprint to expand the registry and to develop an integrated approach where data gathered in caring for patients with CHD serve as data layers that incrementally contribute to a national cohort, for which data remain easily accessible and usable.

A Study of The OCR Development History and Directions of Development

Optical character recognition (OCR) is a well-established technology that enables the conversion of scanned images and documents into editable and searchable electronic text. This technology has numerous applications across a range of industries and has proven to be a crucial tool for digitizing books, documents, and records. One of the main benefits of OCR technology is its ability to automate data entry processes, saving time and reducing errors that can be introduced through manual data entry. Additionally, OCR technology is used to extract text from images, which can be used as input data for machine learning and artificial intelligence applications. To better understand the current state of OCR technology, this systematic literature review is conducted that collected various research articles on the topic. The results of this paper provide insights into the strengths and limitations of OCR technology and also offer directions for future research in the field. In terms of language support, the paper found that OCR technology is capable of supporting a wide range of languages, including English, Spanish, Chinese, and many others. However, the accuracy of OCR technology can vary greatly based on the language, with some languages being more challenging to recognize than others. With continued advancements in technology and the increasing need for digitization, OCR technology will continue to play a crucial role in the development of many industries.

Modernization blood service’s specialized information systems

The object of the research is a mathematical apparatus for searching for the optimal variant of the project solution for the modernization blood service’s specialized medical information systems, which allows determining ways to increase the efficiency of blood services attached to limits of defined expendable resources by reducing the number of manual data entry operations during business processes with the help of appropriate organizational and technical components of specialized medical information systems. The purpose of the development of a mathematical apparatus for searching for a variant of a project solution for the modernization blood service’s specialized medical information systems is a formalized presentation of the problem finding an optimal variant of a project solution for the modernization blood service’s specialized medical information systems, taking into account the components of business processes, components of specialized medical information systems, performance indicators of blood services, partial and generalized indicators of the functioning of specialized medical information systems, expendable resources for the implementation of a variant of the project solution for the modernization specialized medical information systems or a separate organizational and technical component. To solve the problem, a process-oriented approach to the organization of a specialized medical information system with a decomposition into organizational and technical components was used, on the basis of which a formalized presentation of the optimal version of the project solution for the modernization a specialized medical information system by increasing the degree of automation of the relevant organizational and technical components was proposed, that provides the maximum integrated indicator of the functioning of the specialized medical information system, which correlates with the actual key socially significant indicators of the activity of the blood service. The ER model of the modernization of the specialized medical information system was designed, which allows to graphically present the structure of the search for the optimal variant of the project solution for the modernization of the specialized medical information system. The results of the study were used to find the optimal project solution for the modernization of the specialized medical information system of the CNE KHRC "Regional Blood Service Center", which made it possible to improve the key socially significant indicators of the blood service 's activity by determining the optimal data entry’s degree of automation on each organizational and technical component of the specialized medical information system considering the existing expendable resources for modernization.

Early and fair COVID-19 outcome risk assessment using robust feature selection

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.

Effortless Attendance Recording with MyOnTime: A Modern Approach for Educational Institutions

This study introduces MyOnTime, an innovative application designed to revolutionize student attendance management at Sekolah Indonesia Kota Kinabalu (SIKK) in response to the evolving educational landscape in the aftermath of the COVID-19 pandemic. The primary objective of this research was to develop an efficient and user-friendly solution that enhances attendance recording, promotes administrative efficiency, and aligns with current health and environmental guidelines. To achieve this goal, we employed the Extreme Programming (XP) and harnessed the AppSheet platform, a low-code application development tool. MyOnTime offers a streamlined approach to attendance recording through barcode scanning, minimizing manual data entry and reducing reliance on paper-based methods. We conducted a comprehensive user survey to evaluate the application's functionality, responsiveness, and reliability. The results indicate overwhelmingly positive user feedback, highlighting MyOnTime's effectiveness and its ability to meet the specific needs of students, teachers, and administrative staff. MyOnTime empowers educational institutions by providing real-time attendance data, comprehensive reporting capabilities, and a user-friendly interface. It supports sustainable practices by reducing paper waste and contributes to a more organized and disciplined learning environment. MyOnTime represents a promising tool for enhancing the educational experience in the post-pandemic era, promoting efficiency, convenience, and environmental friendliness in attendance management.

P042 Lean Six Sigma Approach to Improve Non-Invasive Ventilation Patient Care

Abstract Introduction A lean six-sigma (LSS) approach to clinical redesign looks at aspects of patient flow, value to consumer and process inefficiencies. We used LSS quality management approach for clinical redesign of our non-invasive ventilation patient care. Current recommendations suggest that patients using non-invasive ventilation (NIV) require therapy for a minimum of 4 hours per night for optimal health benefits, with less than this amount defined as low adherence. This information is collated for each patient at each clinic visit. The aims of this study were to improve the identification of patients on non-invasive ventilation that are poorly adherent to paediatric specialist support, decrease the time taken for entry of NIV adherence data for all patients and evaluate process/es for scalability for future growth. Methods A LSS approach was used with identification of variation, waste and patient flow concerns in diagnostic stage. Rapid improvement events, diagnostic and solution design workshops were conducted to determine root cause, key issues and possible solutions. A preferred solution was adopted through team collaboration. Results A diagnostic stage identified two key areas: a 45% increase in telehealth and 162 man-hours of manual data entry for patient care. The solution initiated was to extract NIV machine data remotely and automate data collection. Discussion Healthcare is well suited to using the lean six-sigma approach to facilitate team collaboration in all aspects of clinical redesign of a healthcare pathway. Automating aspects of patient care may improve patient health and staff satisfaction.

P092 Automation in Equipment Asset Management for Laboratory Efficiencies

Abstract Introduction Asset management of laboratory equipment is essential to patient safety. Traceability enables any patient affected by equipment that has been recalled, or subsequently been found faulty, to be retested. Sleep equipment in home, ward or laboratory needs to be clearly documented but this paperwork is tedious and manual. Methods A portable barcode scanner with software was added to the laboratory asset management and the time efficiencies were evaluated. Results The time reduction of using a barcode scanner to manual data entry was found to decrease time approximately fourfold, from 8 mins (std 1.5mins) to 2.2 mins (std 0.5mins). This is for each sleep laboratory testing bed and can equate to 35 minutes in a six-bed sleep laboratory. Accuracy of data is assured with a barcode scanner, compared to manual input. Discussion Using automation to replace manual tasks in a sleep laboratory saves time and ensures data accuracy. Further advances in automation for tagging medical devices would improve this in areas with high use of home or ward devices.

Text Extraction of Esports Summary Score Image in the Thai Language Using OCR Technology

The esports industry in Thailand has gained widespread attention, with several organizations starting to organize RoV tournaments to enhance the excitement of the competitions. Consequently, statistics of each match are collected and utilized as data for promoting each round of the competition. Conventionally, the method of data collection involves capturing images of the match results and manually inputting the information for further analysis. However, this process often leads to errors or delays, particularly when dealing with a large volume of data. To address these issues, the researchers explore the use of Optical Character Recognition (OCR) for data extraction from images, aiming to reduce errors associated with manual data entry and improve the convenience and efficiency of data collection. A comparative analysis of image data extraction performance between pytesseract and easyOCR reveals that pytesseract provides superior data extraction results and requires less time for the extraction process.

Challenges, Barriers, and Successes of Standardized Report Templates: Results of a Society of Interventional Radiology Survey

Registry data are being increasingly used to establish treatment guidelines, set benchmarks, allocate resources, and make payment decisions. Although many registries rely on manual data entry, the Society of Interventional Radiology (SIR) is using automated data extraction for its VIRTEX registry. This process relies on participants using consistent terminology with highly structured data in physician-developed standardized reports (SR). To better understand barriers to adoption, a survey was sent to 3,178 SIR members. Responses were obtained from 451 interventional radiology practitioners (14.2%) from 92 unique academic and 151 unique private practices. Of these, 75% used structured reports and 32% used the SIR SR. The most common barriers to the use of these reports include SR length (35% of respondents), lack of awareness about the SR (31%), and lack of agreement on adoption within practices (27%). The results demonstrated insights regarding barriers in the use and/or adoption of SR and potential solutions.