Data Entry Mistakes Research Articles

Timed Genetic Process Mining for Robust Tracking of Processes under Incomplete Event Log Conditions

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms.

On the Provenance of Field Reports of the 1886 Charleston, South Carolina, Earthquake: A Seismo-Historical Whodunnit

Abstract Much of what is known about the effects of the 1886 Charleston, South Carolina, earthquake throughout the epicentral region can be attributed to meticulous field investigations by an individual with training in geology and engineering, Earle Sloan (Clendenin, 1926). In a recent study, Bilham and Hough (2024) undertook a detailed analysis of the effects of the earthquake on railroads in the Charleston region, drawing heavily from Sloan’s reports. This exercise identified several inconsistencies in Sloan’s field reports, including understandable measurement imprecision, inferred data entry mistakes, and transcription errors. The study also begged the question, where was Sloan at the time of the mainshock and over the following week? And to what extent did he draw from secondhand information in compiling his reports? On this question Sloan’s reports were sometimes enigmatic, lending themselves to misinterpretation in contemporaneous as well as modern interpretations. Beyond the details that were germane for, and briefly summarized by, the studies of Bilham and Hough (2023, 2024), in this report we don our historical seismologist caps to chronicle Sloan’s activities following the earthquake. We summarize our inferences here for the benefit of future scholars who might attempt to retrace either Sloan’s footsteps or our own. This study also serves to highlight Sloan’s singular contributions to earthquake science, which were never published separately.

Axiomatizing SNOMED CT Disorders: Should There Be Room for Interpretation?

SNOMED CT is a large concept-based terminology designed according to epistemic, semantic and pragmatic principles relevant to clinicians. Its goal is structured clinical reporting in electronic healthcare records (EHRs). The Basic Formal Ontology (BFO) is an ontology designed on the basis of types claimed to exist in reality based on a domain-independent ontological theory. Its goal is faithful representation of reality within that theory. The Ontology for General Medical Science (OGMS) extends the BFO by providing definitions for types relevant within the clinical domain. Combining SNOMED CT with the ontological rigor of BFO and OGMS might improve clinical reporting by, f.i., preventing data entry mistakes and inconsistencies, and make EHRs more comparable. To that end, we are developing a logical framework capable of exploiting what SNOMED CT offers terminologically and realism-based ontologies such as the BFO and the OGMS ontologically by means of bridging axioms compatible with the BFO, and expressed in the same CLIF-dialect as used in its axiomatization in first order logic. In this paper, we report on our attempts to detect in the combinations of binary relations that are used in the definition of SNOMED CT's definitions of disorder concepts patterns which might at least partially automate the construction of such axioms. Our findings suggest that this partial automation is indeed possible, but to a smaller extent than we had hoped for. We compare our approach with a recent proposal that seeks to bring SNOMED CT and BFO closer together by reinterpreting SNOMED CT disorders as clinical occurrents. The proposal has its merit in providing a realist underpinning for that part of SNOMED CT's concept model in terms of the BFO, but is not discriminatory enough for an automatic translation into OGMS. Key problem is the lack of face validity of SNOMED CT disorder terms as compared to the formal definitions they are given and this in absence of textual definitions.

P-775 Responsible Artificial Intelligence in Clinical Decision Making: Anomaly Detection in Trigger Day Optimization

Abstract Study question Is there a benefit of using outlier detection when developing an AI algorithm for predicting the number of oocytes retrieved in different trigger days? Summary answer Using an outlier detection as a pre-step for other algorithms helps to determine if a prediction\decision should be made for a specific scenario. What is known already Outlier detection is a process that helps in identifying unusual or unexpected observations in the data. Outliers can arise for a variety of reasons, such as: measurement errors, data entry mistakes, genuine rare events or data the model did not observe when training. In recent years multiple AI algorithms have been developed across a variety of areas in reproductive health (embryos, sperm, etc.), however not many publications have been made on the importance of implementing outlier detection when assisting doctors in clinical decisions. Study design, size, duration The data used for developing this algorithm consists of 9,618 antagonist protocol cycles performed in a large center serving over 50 physicians, between August 2017 and November 2022. The data was divided into three subsets, representing cases in which the physician performed the trigger 0, 1 or 2 days after a blood and ultrasound test day. Participants/materials, setting, methods Three outlier detection models were developed for a pre-developed trigger day selection algorithm. Each outlier model was developed for a different trigger day subset. Those models were built using Local Outlier Factor algorithm, an unsupervised method. Each model was applied to its specific data set that had been standardized and preprocessed using principal component analysis (PCA). Additionally, A rule-based outlier detection method was implemented, based on thresholds for different hormones. Main results and the role of chance The three outlier detection models were evaluated using train, validation, and test sets, identifying approximately 1.5%, 1.5%, and 2% of the data as outliers, respectively. Additionally, each model was evaluated using other datasets, for example, the "trigger today" outlier detection model was evaluated using "trigger tomorrow" and "trigger in two days" datasets, which resulted in ∼12% and ∼87% of the data being identified as outliers. To further assess the models, additional subsets were created for different trigger times, such as in 3 days, 4 days, 5 days, and 6 or more days. The results of the "trigger today" outlier detection model were highly accurate in identifying outliers, with at least 99.5% of the data being detected as outliers in all of the additional subsets. Instances with a closer relative trigger time will result in similar instances, therefore, detecting a lower percentage of outliers. Limitations, reasons for caution The outlier detection models were developed specifically for antagonist protocol cycles. Additionally, there may be rare cycles that occur infrequently and may not be well represented in the data, resulting in them being incorrectly identified as outliers, even though they are a legitimate scenario. Wider implications of the findings Outlier detection is a crucial aspect of AI algorithms to ensure accurate results. It recognizes cases that are outside of the model's training set as well as abnormal values and typos, thus avoiding predictions on untested cases. This is especially important when first deploying algorithms on diverse populations. Trial registration number Not applicable

DOCUMENTS ISSUANCE VALIDATION USING 2D BARCODE

The daily procedures in both official and non-official organizations include the printing and archiving of thousands of paper-based documents, either as issued, incoming, or as attachment documents; as well as the verification of the authenticity of the issuance of such documents at the receiving party, this leads to the production of further papers that are referred to as "issuance validation", This procedure costs more time, effort, and money. Furthermore, the process of archiving and storing these documents electronically requires considerable effort and time in terms of manually entering the information or saving them as images, as well as the possibility of data entry mistakes. This paper proposes employing authenticated PDF417 barcodes in paper-based document publication procedures by adding an encrypted barcode (signature) to the issued document and making it simpler to authenticate the information. During the document verification process, neither internet access nor real-time interaction with the document issuer are required. Furthermore, the proposed system offers a way to digitally archive documents using a barcode scanner or smartphones, which enables accuracy and speed in entering the information of documents received or issued by the institution.

The Successful Synchronized Orchestration of an Investigator-Initiated Multicenter Trial Using a Clinical Trial Management System and Team Approach: Design and Utility Study

BackgroundAs the cost of clinical trials continues to rise, novel approaches are required to ensure ethical allocation of resources. Multisite trials have been increasingly utilized in phase 1 trials for rare diseases and in phase 2 and 3 trials to meet accrual needs. The benefits of multisite trials include easier patient recruitment, expanded generalizability, and more robust statistical analyses. However, there are several problems more likely to arise in multisite trials, including accrual inequality, protocol nonadherence, data entry mistakes, and data integration difficulties.ObjectiveThe Biostatistics & Data Science department at the University of Kansas Medical Center developed a clinical trial management system (comprehensive research information system [CRIS]) specifically designed to streamline multisite clinical trial management.MethodsA National Institute of Child Health and Human Development–funded phase 3 trial, the ADORE (assessment of docosahexaenoic acid [DHA] on reducing early preterm birth) trial fully utilized CRIS to provide automated accrual reports, centralize data capture, automate trial completion reports, and streamline data harmonization.ResultsUsing the ADORE trial as an example, we describe the utility of CRIS in database design, regulatory compliance, training standardization, study management, and automated reporting. Our goal is to continue to build a CRIS through use in subsequent multisite trials. Reports generated to suit the needs of future studies will be available as templates.ConclusionsThe implementation of similar tools and systems could provide significant cost-saving and operational benefit to multisite trials.Trial RegistrationClinicalTrials.gov NCT02626299; https://tinyurl.com/j6erphcj

An Australian collector's authority file, 1973-2020.

BackgroundBiodiversity databases contain omissions and errors, including those resulting from data entry mistakes and from the use of outdated or incorrect data sources. Some of these omissions and errors can be minimised by the use of authority files, such as expert-compiled taxonomic name databases. However, there are few publicly available authority files for collecting events, and the "where", "when" and "by whom" of specimen data are typically entered into biodiversity databases separately and directly, item by item from specimen labels.New informationHere I describe a publicly available compilation of 3829 of my own collecting events over a 48-year period in Australia. Each record contains a unique combination of date, georeferenced location and location notes.

The Double Bind of Qualitative Comparative Analysis

Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer simulations to show that researchers in the QCA tradition face a vexing double bind. On the one hand, QCA algorithms often require large data sets in order to recover an accurate causal model, even if that model is relatively simple. On the other hand, as data sets increase in size, it becomes harder to guarantee data integrity, and QCA algorithms can be highly sensitive to measurement error, data entry mistakes, or misclassification.

PDG13 CAN THE PURCHASING PERFORMANCE OF HEALTHCARE INSTITUTIONS BE ASSESSED VIA THE DATA DECLARED FOR REIMBURSEMENT?

French health authorities rely on information reported by healthcare facilities in E-PMSI to assess hospital activity and ensure their reimbursement. Some of these data are restored in open access (ScanSante database) and therefore can be used to improve purchasing performance through benchmark studies. Nevertheless, a previous study suggested obvious misreporting errors regarding expensive drug prices. We aim to explore the confidence that can be placed in these declarative data. This retrospective study assesses open data robustness based on reported annual quantities and spending of expensive drugs reimbursed in addition to the diagnosis related group tariff. We deduced the 2017 average purchasing prices (prices are confidential) for APHP (Paris hospitals, e-PMSI database) and for French hospitals formerly under overall allocation (ex-UOA, ScanSante database) and compared it to the national reimbursement price cap (RPC) (the oldest of the year, if so). The relative price differential (RPD%) should be negative or zero. Then, we compared (as an external validation) APHP monthly declared data in 2018 with accurate market data and with monthly RPC applicable in 2018. Results: APHP and ex-UOA hospitals consumed 216 expensive drugs of the additional list in 2017. Significant errors affected 25% of it (RPD>1%) that indicates data entry mistakes (RPD min: +1,01%; max: +152,2%; average:+7,3%). Reported biases concerned primarily immunosuppressors and antihemorragic agents. In 2018 in APHP, 48% of the declared purchasing price (n=222) were superior to the RPC and 35% were higher than the accurate market prices. Deviations were particularly explained by the price declaration of old contracts, repealed references and declared consumption unit errors. Conclusions: Despite the conservative methodology adopted, our study highlights the poor quality of the declarative data that limit their use. Improved data capture is a major challenge because of the financial consequences for institutions in terms of refunds granted.

DEVELOPMENT OF A NEW SMART SURVEILLANCE SYSTEM FOR MONITORING SPORTS INJURIES

BackgroundThere are several methods for monitoring sports injuries including simple questionnaires, text messaging and sophisticated surveillance systems. However, some problems related to inconsistency of injury definitions and classifications, high costs,...

CamtrapR: an R package for efficient camera trap data management

SummaryCamera trapping is a widely applied method to study mammalian biodiversity and is still gaining popularity. It can quickly generate large amounts of data which need to be managed in an efficient and transparent way that links data acquisition with analytical tools.We describe the free and open‐source R packagecamtrapR, a new toolbox for flexible and efficient management of data generated in camera trap‐based wildlife studies. The package implements a complete workflow for processing camera trapping data. It assists in image organization, species and individual identification, data extraction from images, tabulation and visualization of results and export of data for subsequent analyses. There is no limitation to the number of images stored in this data management system; the system is portable and compatible across operating systems.The functions provide extensive automation to minimize data entry mistakes and, apart from species and individual identification, require minimal manual user input. Species and individual identification are performed outside the R environment, either via tags assigned in dedicated image management software or by moving images into species directories.Input for occupancy and (spatial) capture–recapture analyses for density and abundance estimation, for example in the R packagesunmarkedorsecr, is computed in a flexible and reproducible manner. In addition, survey summary reports can be generated, spatial distributions of records can be plotted and exported togissoftware, and single‐ and two‐species activity patterns can be visualized.camtrapRallows for streamlined and flexible camera trap data management and should be most useful to researchers and practitioners who regularly handle large amounts of camera trapping data.

The impact of ankle-foot orthoses one year post orthopaedic surgery in children with cerebral palsy

Introduction: Physical examination of neuromusculoskeletal and movement-related functions is commonly performed in children with cerebral palsy (CP). Next to instrumented gait analysis, neuromusculoskeletal physical examination (NMS-PE) is an essential element in clinical decision-making, especially when orthopaedic or neurosurgery is indicated. Taking a complete NMSPE is rather extensive, so the examination and its registration can take quite some time. Accordingly, not all NMS-PE items are always performed, leading to incompleteness of data. Also paper based forms are subjected to data entry mistakes, and might even get lost. Altogether, thismay result in suboptimaldecision-making. The quality of NMSPE will be improved by technology that supports taking a PE. The aim of this project was to develop an innovative, easy touse tool (an “app”) for data entry anddata storageofNMS-PE findings in children with CP. Patients/materials and methods: The content of the NMS-PE tool is based on a recently published guideline book [1]. Requirements and a functional design of the app were drafted in close cooperationwithhealth-careprofessionals. Basedon these requirements, a user interfacewas developed, aswell as a database to store the entered data. A prototype of the NMS-PE app was tested in a pilot study. During the pilot study, NMS-PEs were performed in children with CP whom were seen for a first appointment at the outpatient clinic of a university hospital. Examinations were performed with the old method (manual data entry on paper sheets) and with the new method (digital data entry) on a second cohort. Factors of success and failure were documented during the pilot. Furthermore, efficiency (time to perform the examination), completeness of data registration (% completed items) and satisfaction were evaluated. RESULTS

A tablet computer “app” for neuromusculoskeletal physical examination in children with cerebral palsy

Introduction: Physical examination of neuromusculoskeletal and movement-related functions is commonly performed in children with cerebral palsy (CP). Next to instrumented gait analysis, neuromusculoskeletal physical examination (NMS-PE) is an essential element in clinical decision-making, especially when orthopaedic or neurosurgery is indicated. Taking a complete NMSPE is rather extensive, so the examination and its registration can take quite some time. Accordingly, not all NMS-PE items are always performed, leading to incompleteness of data. Also paper based forms are subjected to data entry mistakes, and might even get lost. Altogether, thismay result in suboptimaldecision-making. The quality of NMSPE will be improved by technology that supports taking a PE. The aim of this project was to develop an innovative, easy touse tool (an “app”) for data entry anddata storageofNMS-PE findings in children with CP. Patients/materials and methods: The content of the NMS-PE tool is based on a recently published guideline book [1]. Requirements and a functional design of the app were drafted in close cooperationwithhealth-careprofessionals. Basedon these requirements, a user interfacewas developed, aswell as a database to store the entered data. A prototype of the NMS-PE app was tested in a pilot study. During the pilot study, NMS-PEs were performed in children with CP whom were seen for a first appointment at the outpatient clinic of a university hospital. Examinations were performed with the old method (manual data entry on paper sheets) and with the new method (digital data entry) on a second cohort. Factors of success and failure were documented during the pilot. Furthermore, efficiency (time to perform the examination), completeness of data registration (% completed items) and satisfaction were evaluated. RESULTS

Development of Data Translators for Interfacing Power-Flow Programs With EMTP-Type Programs: Challenges and Lessons Learned

This paper describes the challenges and lessons learned when developing industrial-grade data translators aimed for the interfacing of power-flow programs with Electromagnetic Transients Program-type programs. It has been found that the greatest challenges to overcome include: the lack, in the databases used in power-flow programs, of vital pieces of information necessary to perform transient studies; inconsistency in the format of data files; the presence of data entry mistakes in very large databases; the validation of the translated data; and the analysis of the large amount of data that transient simulations provide. Several examples are presented to show the implemented solutions. Finally, recommendations based on experience are made to help future developers of interfacing tools.

OVERCOMING SPREADSHEET RISK IN SUPPLY CHAIN MODELING

Spreadsheet optimization modeling plays an important role in the management of today's sophisticated supply chains. However, despite the advantages of spreadsheets (or in some cases because of these advantages) there are significant risks associated with spreadsheet models. Spreadsheet risk is defined as the chance of adverse operational or financial consequences due to erroneous creation, maintenance, and/or use of spreadsheet models. Such errors arise because the intrinsic complexity of optimization models is beyond the expertise of many non‐technical managers. In this article we discuss procedures that we developed for managing the substantial risks associated with the use of spreadsheets for supply chain optimization. The issues we address go far beyond simple data entry mistakes, which are possible in virtually every aspect of business and can never be completely eliminated.

Comparison between web-based and paper versions of a self-administered anthropometric questionnaire

Online data collection could advantageously replace paper-and-pencil questionnaires in epidemiological studies by reducing the logistic burden, the cost and the duration of data processing. However, there is a need for studies comparing these new instruments to traditional ones. Our objective was to compare the web-based version of the NutriNet-Santé self-administered anthropometric questionnaire to the paper-based version. The questionnaire included 17 questions divided into subquestions (55 variables in all) dealing with height, weight, hip and waist circumferences, weight history, restrictive diet and weight self-perception. Both versions of the questionnaire were filled out by 147 volunteers (paper version first, N = 76, or web-based version first, N = 71) participating in the SU.VI.MAX ("Supplémentation en VItamines Minéraux et AntioXydants") cohort (age-range: 49-75 years; men: 46.3%). At the end of the test, subjects filled in a "satisfaction" questionnaire giving their opinions and feelings about each version. Agreement was assessed by intraclass correlation coefficients (ICCs) and kappas. We also quantified the number of errors inherent in the paper version. Agreement between the two versions was high. ICCs ranged from 0.86 to 1.00. Kappas ranged from 0.69 to 1.00 for comparable variables. A total of 82 data entry mistakes (1.5% of total entries), 60 missing values (1.1%), 57 inconsistent values (1.1%) and 3 abnormal values (0.1%) were counted in the paper version (non-existent in the web-based version due to integrated controls). The web-based version was preferred by 92.2% of users. In conclusion, the quality of information provided by the web-based anthropometric questionnaire used in the NutriNet-Santé Study was equal to, or better than, that of the paper version, with substantial logistic and cost advantages.

Error minimization strategy in clinical and translational studies (CTS) through the development of a data management routine (DMR)

17567 Background: Clinical studies are the basis for the decision making process in health care. So quality assurance is mandatory. In CTS, of intervention or descriptive nature, the use of spreadsheets as databases is frequent among investigators favoring mistakes during data entry, mainly due to the matricial distribution of data, urging strategies to error minimization. Creation of complex systems by informatics technologists is viable, but expensive and unnecessary for medium and small sized studies. Otherwise, there are systems that allow databases to be built by non-programmers upon training. But the customization of those tools requires knowledge on CTS. Our aim wasto ensure the quality of CTS conducted at Instituto Nacional de Cancer of Brazil (INCA) through the development of a DMR. Methods: DMR is based upon MS Word 2003, MS Access 2003 and SPSS 13.0. Content variables were defined based upon studies running at Clinical and Translational Research Division (CTRD). For its development, 8 different studies (retrospective, molecular correlation, formal clinical trials) were used. Results: We generated a DMR based upon two pillars: a) a multi-step standard operational procedure that includes meetings with the investigators and data collection training; establishment of clinical report forms supported by guidelines for its use; a pivotal pilot data collection for identification and clarification of eventual misunderstanding; a revision of 30% of the cases enrolled; b) a backbone relational database customized for each study by health professionals trained in our service with a friendly easy-to-use interface and several validation rules to reduce data entry mistakes. It stores, organizes, encodes data, filters eligibility criteria, calculates data, generates reports to investigators for proper follow-up and generates spreadsheets easily imported by SPSS for statistical analysis. Since 2004 this routine has been applied to all the studies referred to CTRD, decreasing the intra-mural historical 15% mistake rate in patient inclusion to nearly 0%. Conclusions: In a low-cost and simple manner an effective DMR has been generated, decreasing the likelihood of mistakes with great impact on the quality of the data generated in CTS at INCA. No significant financial relationships to disclose.

Improving data quality: eliminating dupes & I-D-ing those spurious links

Dirty data arise as a result of abbreviations, data entry mistakes, duplicate records, missing fields and so forth. This problem is aggravated when multiple data sources need to be integrated. Data cleaning refers to a series of processes employed to deal with detecting and removing errors and inconsistencies from data. Given the "garbage in, garbage out" principle, clean data is crucial for database integration, data warehousing and data mining. Data cleaning is a necessary step prior to the knowledge discovery process. We have reviewed a knowledge-based framework that provides for the definition of duplicate identification rules. We have described a context-based approach to identify these spurious links in the data.

Cleaning the spurious links in data

Data quality problems can arise from abbreviations, data entry mistakes, duplicate records, missing fields, and many other sources. These problems proliferate when you integrate multiple data sources in data warehousing, federated databases, and global information systems. A newly discovered class of erroneous data is spurious links, where a real-world entity has multiple links that might not be properly associated with it. The existence of such spurious links often leads to confusion and misrepresentation in the data records representing the entity. Although the data set is well known for its high-quality bibliographic information, collecting and maintaining the data from diverse sources requires enormous effort. Errors, including spurious links, are inevitable. To solve this problem, we use context information to identify spurious links. First, we identify data records that contain potential spurious links. We then determine the set of attributes that constitute each record's context. Experiments with three real-world databases have demonstrated that our approach can accurately identify spurious links. Comparing context information between data records can help solve the data quality problem of spurious links-that is, multiple links between data entries and real-world entities.

Workload, error detection, and experienced stress in a simulated pharmacy verification task.

The relationships among workload, stress, and performance efficiency are topics of applied interest and theoretical importance to researchers concerned with human performance. Such interest extends to a variety of occupational areas including inpatient, outpatient, and community pharmacies. In that context, these relationships have become a consumer health issue given concerns that workload contributes to job stress and a significant decline in dispensing accuracy. In the present study, 102 trained college-aged individuals evaluated simulated pharmacy prescriptions for errors under conditions of either low workload (72 orders over 120 min. on task) or high workload (120 orders over 120 min. on task) in a high-fidelity simulated pharmacy environment. Overall, cumulative and detection theory indices of error detection were compatible with estimates from pharmacy field studies. When rates of sensitivity and specificity for detection were examined, substantial variations in the identification of errors (sensitivity) and difficulties with detection of data-entry mistakes were observed in the high workload condition, but only modest effects emerged for the low workload condition. Although increases in objective workload were associated with modest declines in detection accuracy, objective workload did not significantly affect negative mood (Mood Adjective Checklist) or perceived workload (NASA-Task Load Index) as expected.