Data Entry Errors Research Articles

Integrating Biomarker Testing into EHR Systems: Implications for the Laboratory Based on Findings from a Multistakeholder Summit

Abstract Introduction/Objective Guideline-driven tumor biomarker testing remains standard of care for precision oncology due to the increasing number of treatment decisions that can be made based on tumor-specific alterations. However, most electronic health record (EHR) systems lack bidirectional interfaces with labs that enable computerized order entry and tumor biomarker testing results. Without these integrations, cancer centers may experience delays in test ordering and resulting, manual data entry errors, and operational inefficiencies that impact patient care and survival outcomes. Methods/Case Report To explore how cancer centers, hospitals, oncology practices, and labs integrate EHRs to streamline cancer biomarker testing processes, the Association of Cancer Care Centers (ACCC), in partnership with LUNGevity, convened a multistakeholder summit on Oct. 4, 2023. The summit brought together clinicians, pathologists, genetic counselors, and administrators to discuss practical solutions to issues surrounding interoperability, clinical workflows, and access to clinical data. Results (if a Case Study enter NA) Key findings from the summit include: -EHR-integrated orders and results save time, reduce errors, and streamline clinical workflow. These electronic interfaces enable clear communication between multidisciplinary teams system-wide and ensure that required patient information is readily available at the time of therapy planning. -Standardized workflows that define test ordering, tracking, and resulting protocols for in-house and send-out testing offer important downstream benefits. Staff should clearly understand how specimens are handled for in-house and send-out testing. -Integrations should be planned and built based on the EHR/laboratory information systems (LIS) in place can incorporate solutions to integrate with reference labs using HL7 standards and/or add-on modules to enable discrete genomic data elements. Conclusion Clinicians, pathologists, and administrators should prioritize EHR/LIS integrations to enable more effective cancer biomarker test ordering and resulting. These pre/post-analytic processes have significant implications for patient care. Findings from this summit can enable cancer programs and labs to evaluate current processes, work collaboratively to build integrations, and streamline biomarker testing workflows to enhance the quality and efficiency of delivering precision oncology.

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

Insights from ARCOS-V’s Transition to Remote Data Collection during the COVID-19 Pandemic: A Descriptive Study

Introduction: The ARCOS-V study, an epidemiological study on stroke and transient ischaemic attack (TIA), faced the challenge of continuing data collection amidst the COVID-19 pandemic. This study aimed to describe the methodological changes and challenges encountered during the transition from paper-based methods to digital data collection for the ARCOS-V study and to provide insights into the potential of using digital tools to transform epidemiological research. Methods: The study adapted to remote data collection using REDCap and Zoom, involving daily health record reviews, direct data entry by trained researchers, and remote follow-up assessments. The process was secured with encryption and role-based access controls. The transition period was analysed to evaluate the effectiveness and challenges of the new approach. Results: The digital transition allowed for uninterrupted monitoring of stroke and TIA cases during lockdowns. Using REDCap and Zoom improved data reach, accuracy, and security. However, it also revealed issues such as the potential for systematic data entry errors and the need for robust security measures to protect sensitive health information. Conclusion: The ARCOS-V study’s digital transformation exemplifies the resilience of epidemiological research in the face of a global crisis. The successful adaptation to digital data collection methods highlights the potential benefits of such tools, particularly as we enter a new age of artificial intelligence (AI).

Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records.

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean-shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real-world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis.

Sistem Manajemen Kepegawaian dan Pencatatan Pekerja Migran Indonesia di BP3MI Riau dengan Metode Prototipe

Balai Pelayanan Pelindungan Pekerja Migran Indonesia (BP3MI) Riau, a government agency dedicated to protecting Indonesian migrant workers. The agency faces challenges with its unstructured and inefficient data management system. The use of Microsoft Excel for documenting the data of Indonesian Migrant Workers (PMI) and employees at BP3MI Riau is prone to data entry errors, time inefficiencies, unstructured data, potential damage, limited access, restricted analysis, difficulties in tracking changes, and a high risk of data loss. To address these issues, research was conducted to develop a more effective employee management and PMI data recording system using the prototype method. This method emphasizes user collaboration in understanding system requirements, resulting in practical solutions. The prototype method stages include user requirements gathering, designing input, process, and output needs, followed by rapid UML model design, program code implementation, and finally, testing. The system development results show that users have a very positive impression from the usability testing, achieving a score of 97.76%. This indicates the effectiveness of the prototype. The system significantly improves manual methods, enhancing the efficiency, accuracy, and accessibility of data at BP3MI Riau, and lays the foundation for future advancements with greater integration and automation prospects in BP3MI Riau's operations.

The Effects of Age and Treatment Intensity on Behavioral Target Mastery With Applied Behavior Analysis (ABA) Intervention Using Causal Moderation Models.

Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensityrefers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and massand naturalistic training. Data on the mastery of target behaviors were collected using the Catalystsoftware (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions between age and treatment intensity within ABAinterventions. Practitioners and researchers can develop more tailored and effective therapeutic strategies by identifying and leveraging these interactions. This approach optimizes the treatment process and ensures that interventions are personalized to meet the unique needs of each individual. Ultimately, this leads to more successful outcomes in behavioral therapy, fostering improved adaptive behaviors and overall development.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

Audit of Genito-Anal Injuries in male and female sexual abuse cases - A retrospective study.

Background: Sexual assault is a global issue wherein timely examination of sexual assault victims by medico-legal officers and an understanding of the characteristics and factors influencing their injuries have a crucial impact on medico-legal evidence collection and its use in the court. Genital injury findings improve the level of justice for victims, but the lack of injury does not negate the possibility of sexual violence incurred on the victim. The following study describes in great detail the different factors associated with injury characteristics and the medico-legal importance of all these factors. Methodology: It was a retrospective descriptive study conducted at the Forensic Medicine Department of King Edward Medical University, Lahore, Pakistan. All the cases included that fulfill the predefined inclusion criteria were included. A record chart was designed, and six years’ data was recorded in it from the medical records of the medico-legal clinic. It was entered and edited manually. Methods used to prevent data entry errors included double entry and validation following data entry Difference of proportions between variables, where appropriate, has been calculated by Pearson Chi-Square test and Fischer Exact test at 95% confidence interval. Data were analyzed using SPSS v23. All procedures performed in this study involving human participants were following the ethical standards of the institutional and/or national research committee and the Helsinki declaration. Results: Genito anal injury was present in 28.2% cases and was absent in 71.8% cases. Most of the victims were aged 11 to 15 years. The most common injury in the victims was a tear. In females, the most common injury site was the vagina, while the perianal area was the most injured site in males. There was a significant relation (p<0.05) between type and site of injury, gender, site of injury, and prior sexual intercourse experience and prevalence rate of injury. Conclusion: This study concludes that the presence of injuries in the Genito anal area is suggestive of sexual assault and provides useful court evidence.

LIO-PAY: Sustainable low-cost offline payment solution

Offline payment solutions exist today to enable customers to avail themselves of the privileges of the cashless economy. Financial inclusion is visualized as a key factor in encouraging digital transactions in rural and semi-urban regions. Recently, the Government of India, along with the Reserve Bank of India, announced a framework that allows offline payments with finite transaction limits. This aims at providing access to formal financial services in an affordable manner to deepen financial inclusion. However, the remote population rarely access their digital bank accounts due to intermittent network connectivity issues. Hence, the majority of these accounts are in a dormant state, which demeans the impact of financial inclusion. The aim of this research is to design and develop a sustainable, low-cost, highly efficient offline payment solution using a LoRa-enabled IoT ecosystem to connect with Space Information Network (SIN). A novel offline payment system is designed with features of a) recharge and b) payment with offline wallet technology, which can be availed of in the absence of Internet or telecommunication services. The proposed solution, LIO-PAY, allows merchants and end users across the ecosystem to run seamless transactions. We developed a proof of concept and tested it in a lab setup with volunteers to analyze the usability of the application for initiating transactions. We examined 46 customer-initiated transactions and found that 16 (around 34.8%) failed due to merchant data entry errors, likely typos. In a separate analysis of these transactions, 4 customers (approximately 11.5%) incorrectly entered their PIN. The average time to complete a transaction was 35.42 s. Bringing satellite payments to life faces hurdles but holds immense promise for the unbanked. Getting the green light from banking authorities and the satellite network provider is no easy feat. Even if approved, launching a whole new satellite network just for low-value transactions might be too expensive. A more realistic approach could be using existing telecom satellites to bounce transaction data to banks. Regulatory approval is needed from both banking authorities and the satellite network provider. Hence, we simulated the SIN environment with seven LEO (Low Earth Orbit) satellites using the Matlab tool. These simulations revealed a transmission time range of 2 min to 1 h, indicating a reasonable timeframe for delivering information to a ground station via SIN. Despite terrestrial networks expanding their reach, satellite-based solutions hold promise for areas with no or weak internet connectivity. This becomes even more attractive if launch costs go down or if banking authorities help subsidize the service to bring financial inclusion to remote regions. Thus, we hope this approach may help researchers and policymakers visualize the possibilities of bridging the gap in financial inclusion.

Optimalisasi Pendataan Anak Serta Donatur Yayasan Kasih Mandiri Bersinar dengan Metode Prototype

The Yayasan Kasih Mandiri Bersinar (YKMB) is a child welfare institution established in May 1996. However, data management for foster children and donors is still performed manually using notebooks or Microsoft Excel, leading to challenges such as data entry errors, difficulties in report generation, data loss, and limitations in information retrieval. This study employs a prototyping method to develop a web-based application using PHP and MySQL. The prototyping approach allows iterative testing and refinement of the application based on user feedback throughout the development process. The results indicate that the application meets the foundation’s requirements, enhances data management efficiency, and reduces the risks of errors and data loss. Additionally, the application facilitates faster and more structured report generation, supporting better data management practices at YKMB.

Case validation of bloodstream infections with an antibiotic-resistant organism

Background: Bloodstream infections (BSIs) are an important cause of morbidity and mortality in severely ill patients, contributing to increased length of hospital stay and higher cost of care. Alberta Health Services Infection Prevention and Control (IPC) conducts inpatient surveillance of new episodes of BSIs with methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE) or carbapenemase-producing organisms (CPO) in 112 acute care facilities. A case-finding process was undertaken to verify the accuracy of BSI data entry. Methods: All positive MRSA, VRE or CPO blood cultures in 2021 were linked to the Inpatient Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) to identify new cases during acute care admissions. The results were then compared to surveillance records captured by infection control professionals (ICPs). Cases with unmatched culture date and/or encounter date and cases not identified by ICPs were screened by the study team with final decision made by ICPs. Results were analyzed by ARO and by % increase in number of surveillance records. Results: The laboratory linkage identified 286 new cases. Comparing to surveillance records (n = 248) captured by ICPs, 137 (57.3%) had matching collection dates and encounter dates, 85 (35.6%) had close matches on collection dates and encounter dates, 17 (7.1%) records had either matching collection dates or encounter dates, and 1 (0.4%) record did not have any matches on dates. There were 46 records identified in the laboratory data that were not in the surveillance system and 8 records that were in the surveillance system but not matched to the laboratory data. After review, 22 Surveillance records had data entry errors (1 CPO BSI, 20 MRSA BSI, and 1 VRE BSI), and there were 14 BSI records found to be missing (13 MRSA BSI, 1 VRE BSI). This represents a 6% increase in MRSA BSI and a 3% increase in VRE BSI identified in 2021 and no increase in CPO BSI. Conclusions: A laboratory validation to determine if BSIs with an ARO were missed during routine IPC surveillance identified a small proportion of missed bloodstream infections. The most common reason for the miss was admission through the emergency department with multiple blood cultures collected during a single admission. These results will be shared with the Infection Control program to facilitate correct BSI capture.

1107 Facilitating Accurate Sleep Diary Data Collection and Processing Based on Common Participant Data Entry Errors

1107 Facilitating Accurate Sleep Diary Data Collection and Processing Based on Common Participant Data Entry Errors

Measuring the cost-effectiveness of treating rectovaginal and vesicovaginal fistulas: A multicenter global study by the Fistula Foundation.

Surgery for obstetric fistula is a highly effective treatment to restore continence and improve quality of life. However, a lack of data on the cost-effectiveness of this procedure limits prioritization of this essential treatment. This study measures the effectiveness of fistula surgeries using disability-adjusted life years (DALYs) averted. In 2021 and 2022, the Fistula Foundation funded 20 179 fistula surgeries and related procedures at 143 hospitals among 27 countries. We calculated DALYs averted specifically for vesicovaginal fistula and rectovaginal fistula procedure types (n = 13 235 surgeries) by using disability weights from the 2019 Global Burden of Disease study. We based cost calculations on direct treatment expenses, including medical supplies, health provider fees, and preoperative and postoperative care. We measured effectiveness using data on the risk of permanent disability, country-specific average life spans, and treatment outcomes. The total treatment cost was $7.6 million, and a total of 131 433 DALYs were averted. Thus, the cost per DALY averted-the cost to restore 1 year of healthy life-was $58. For this analysis, we took a cautious approach and weighted only surgeries that resulted in a closed fistula with restored continence. We calculated DALYs averted by country. Limitations of the study include data entry errors inherent in patient logs and lack of long-term outcomes. The current study demonstrates that obstetric fistula surgery, along with having a significant positive impact on maternal health outcomes, is highly cost-effective in comparison with other interventions. The study therefore highlights the benefits of prioritizing fistula treatment as part of the global agenda for maternal health care.

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

Local Validation of a National Orthopaedic Registry.

Registries are limited by the quality of the data they collect. We aimed to measure the data entry error rate at a regional orthopaedic unit in a national arthroplasty registry and to assess a proposed intervention of restricting data entry to senior trainees. A total of 200 primary and revision arthroplasty cases (119 hips, 81 knees) were randomly selected from a single year, 2020. The Irish National Orthopaedic Registry was examined for the grade of the trainee that populated the form and the accuracy of 24 parameters by comparison with data recorded elsewhere in the patient record. The mean number of errors per form was 2.17 (95% confidence interval (CI): 1.95-2.39), giving an overall error rate of 9% (95% CI: 8%-10.0%). Eighty-seven percent of forms examined contained inaccuracies, ranging from one to nine errors (4%-38%). Some parameters were more prone to errors, ranging from 1% to 28%. There was no evidence of total errors varying by trainee grade (analysis of variance (ANOVA) p-value: 0.34). Error rates were in line with the literature. Results did not support restricting data entry to senior trainees.

Risk factors for mortality in children with hemorrhagic cystitis after hematopoietic transplant

Hemorrhagic cystitis (HC) is a devastating complication of bone marrow (BMT) and stem cell transplant (SCT). Much of the literature has focused on exclusively adult patient populations, with limited evidence regarding risk factors for mortality and morbidity among pediatric HC patients. To examine factors associated with all-cause mortality in children with HC after BMT/SCT. The Pediatric Health Information System database was queried for patients with ICD-9/10 codes for hematopoietic transplant and gross hematuria, hematuria unspecified, or cystitis with hematuria. Multivariable logistic regression examined association of medical and surgical interventions frequently employed for hemorrhagic cystitis with mortality and genitourinary morbidity, defined as having received instillation of any bladder medication or having undergone any genitourinary procedure. A total of 811 patients, mean age of 12.4 years and 62% male, were included. Primary diagnosis included 388 (49%) leukemia/lymphoma, 182 (22%) blood dyscrasia, 99 (12%) solid organ tumor, 27 (3%) metabolic disease, 115 (14%) unknown. Transplant type included 377 (46%) bone marrow, 329 (41%) stem cell, 105, and (13%) unknown. Performing any bladder instillation (p<0.0001) or any type of GU procedure (p<0.0001) was significantly associated with mortality. On multivariate analysis, dialysis (OR=10.7, 95% CI=5.7-20.2), genitourinary morbidity (OR=4, 95% CI=2.2-6.8) and intravenous cidofovir (OR=2.0, 95% CI=1.2-3.3) were significantly associated with all cause mortality. Having an underlying diagnosis of blood dyscrasia was protective against mortality (OR=0.425, CI=0.205-0.88). In this large retrospective study evaluating factors associated with mortality in children with HC, all cause mortality was found to be 11%. This is probably an underrepresentation of true mortality in this population, as many patients discharged from the hospital likely die outside the hospital at home or hospice care. This study supports the current literature that invasive GU procedures are not associated with increased survival in patients with severe HC. This study is limited by retrospective use of a billing database that has the potential for errors in data entry and missing data. Patients who were discharged from the hospital were not captured by the PHIS which only collects data from inpatient stays. Patients with HC who received dialysis, intravenous cidofovir, or underwent GU intervention had significantly higher all-cause mortality. High grade HC is a marker of disease severity and efforts should be made by urologists and oncologists to maximize quality of life and limit futile treatments in this patient population.

Machine learning assisted analysis and prediction of rubber formulation using existing databases

Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.

Evaluating and improving the usability of a mHealth platform to assess postoperative dental pain.

The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application. The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and in situ pilot testing (Phase III). The study team conducted Phase I, while providers and patients participated in Phase II and III. The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by patients and 57% by dentists. Usability problems encountered during pilot testing included undelivered messages due to mobile phone carrier and service-related issues, errors in patients' phone number data entry, and problems in provider training. Through collaborative and iterative work with the vendor, usability issues were addressed before launching a trial to assess its efficacy. The usability of the mHealth application for postoperative dental pain was remarkably improved by the iterative analysis and interdisciplinary collaboration.

Support needs of survivors of violence against women in urban India: a prospective analysis of client records

Background Violence against women harms individuals, communities, and society. Supporting survivors requires a combination of skills and resources and involvement of multiple institutions. We aimed to document the range of responses that a counselling service should be able to provide, based on direct experience of working with survivors predominantly from urban informal settlements. Methods At non-government counselling centres in Mumbai, we analysed unlinked electronic records collected by counsellors supporting survivors of violence. We examined how women knew of the services, how they described their concerns, what they said they expected, and what was provided. We quantified the proportions of clients who required crisis intervention, police action, legal input, and medical, psychological and psychiatric support. Results Counsellors met with 2278 women clients in 2019, almost half of whom had been encouraged to attend through community outreach. Clients described intimate partner violence (37%), domestic violence by a family member other than their partner (22%), or both (27%). Common forms of violence reported were emotional (88%), economic (73%), and physical (71%); 68% of clients reported episodes of neglect, 59% of coercive control, and 36% of sexual violence, while 77% had survived three or more forms of violence. Over a median seven consultations, 32% required crisis intervention, 31% home visits from counsellors, and 17% legal support; 13% saw a clinical psychologist, 7% were assisted in consulting the police, and 5% required medical care. Discussion Demand for services was substantial at 200 new clients each month. Key concerns for counsellors were coping with this heavy workload, skills in responding to women’s experience of multiple forms of violence and their desire to stay in relationships, skills in emotional support, ability to undertake and act on risk and mental health assessment, and effective engagement with health, police, and legal services. Future work Counsellors need to take a systematic approach to interacting with survivors of violence that covers all the considerations and activities needed in order of priority. We have developed a package of guidelines to meet this requirement, including trauma-informed counselling and mental health assessment and support (https://garima.snehamumbai.org/). Limitations The record system may have been subject to errors in data entry or systematic differences between counsellor choices, with the possibility of over- or under-identification of need and different counsellors’ propensities for and choices of referral. The spectrum of concerns relates to women residents of urban informal settlements, and we should be cautious about their application to other populations. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 17/63/47. A plain language summary of this research article is available on the NIHR Journals Library Website https://doi.org/10.3310/LKNH2423.

Development of Desktop-Based Employee Payroll: A Case Study on PT. Bio Pilar Utama

As a result of periodic shifts, technological advancements are becoming more varied. The payroll system for employees is one of them. Payroll must follow the company’s standard operating procedures (SOP) to determine several issues. Thus, the system can be customized to match the company’s specific needs and SOP. The administration and finance departments encounter difficulty processing employee payroll due to frequent errors in employee data entry, including position, department, income, and deductions. The absence of a payroll information system that can reliably and rapidly calculate employee salaries and provide timely reports has become the primary source of the difficulty. The designed employee payroll information system only processed PT. Bio Pilar Utama’s payroll data. Facilitating the administration and finance departments to input data, calculate salaries, store data, and generate reports on employee payroll could be achieved through the newly developed employee payroll information system.