Data Quality Research Articles

Enhancing healthcare data integrity: fraud detection using unsupervised learning techniques

Data in healthcare forms the backbone of any treatment and decision-making for patients. However, the data from a healthcare institution can sometimes be prone to abnormalities, thereby putting treatment and patient safety in jeopardy. This paper points to the dire need for reliable anomaly detection systems in the healthcare industry. It employs various unsupervised learning methods, including Isolation Forest, Local Outlier Factor (LOF), K-Nearest Neighbours (KNN), and autoencoder models for detecting abnormalities in healthcare data with better accuracy. Anomaly detection capabilities also allow health providers to reduce risks and provide some assurance of the integrity of the data, as these capabilities are more likely to indicate unusual patient profiles or incorrect test results. Isolation Forest, LOF, and KNN are the preliminary methods for performing anomaly detection in this work, with Isolation Forest yielding the best results. Then, autoencoder models that learn subtle variations and complex patterns in data are employed. This paper seeks to enhance anomaly detection in terms of accuracy and reliability to ensure better healthcare data quality and patient safety.

GenAI for Scientific Discovery in Electrochemical Energy Storage: State-of-the-Art and Perspectives from Nano- and Micro-Scale.

The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium-ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges. This research introduces a novel integration of Generative AI (GenAI) within electrochemical energy storage systems to address these issues. By leveraging advanced GenAI techniques like Generative Adversarial Networks, autoencoders, diffusion and flow-based models, and multimodal large language models, this paper demonstrates significant improvements in material discovery, battery design, performance prediction, and lifecycle management across different types of electrochemical storage systems. The research further emphasizes the importance of nano- and micro-scale interactions, providing detailed insights into optimizing these interactions for improved efficiency and longevity. Additionally, the paper discusses the challenges and future directions for integrating GenAI in energy storage research, highlighting the importance of data quality, model transparency, workflow integration, scalability, and ethical considerations. By addressing these aspects, this research sets a new benchmark for the use of GenAI in battery development, promoting sustainable, efficient, and safer energy solutions.

Long term changes in Chalk groundwater quality in East Anglia, UK

Groundwater abstraction quantity from the Chalk is limited to what is sustainable. Water quality data collected in the 1970s and 1980s have been compared with modern water quality monitoring data for 25 public supply abstraction sites. This comparison has established that there has been both substantial (magnitude) and significant (statistical) change in composition at all sites. Identified changes generally represent real changes in groundwater composition. At some sites in the south of the area considered, water that is not the result of present-day recharge processes is being replaced by water from present-day recharge. It is also clear that, at those sites where ancient brackish water is known to be present at depth, it is generally not being mobilised by pumping. In the north of the area, changes are more subtle but there is a tendency of groundwater composition to approach the regional average, apart from calcium which is trending higher. Nitrate has not increased here, suggesting that reduction is a still-active process. Thus, although quantity has been sustained, quality is changing but not to a degree that impacts potable use.

Incidence trends, histological subtypes, and topographical distribution of bladder cancer in Iran: a study based on the Iranian National Cancer Registry during 2006-2015.

Bladder cancer (BCa) is a significant public health concern. This study aimed to analyze the incidence trends, histological subtypes, and topographical distribution of BCa in Iran over a decade. This retrospective registry-based study analyzed data on BCa patients diagnosed between March 20, 2006, and March 20, 2015. Following data quality control, age-standardized incidence rates (ASIRs) were calculated for BCa overall, by sex and histological subtype using the new World Health Organization (WHO) standard population. We identified 51,379 BCa cases (81.97% male) with a mean age of 65.10 ± 14.89 years. The overall ASIR was 8.92 per 100,000 (95% CI: 8.84-9.00). While a modest increase in ASIR was observed overall (8.77 in 2006 to 9.64 in 2015) and among males (14.13 in 2006 to 15.95 in 2015) during the study period, males consistently had a significantly higher ASIR compared to females (approximately 4.5:1 ratio). BCa incidence showed a progressive increase across older age groups, particularly those aged 40-44 to 80-84 years. Urothelial cell carcinoma (UCC) was the most prevalent histological type (ASIR: 8.19 to 7.93), followed by adenocarcinoma (AC; ASIR: 0.13 to 0.11). Squamous cell carcinoma (SCC) displayed a decreasing trend (ASIR: 0.11 to 0.06). Both UCC and AC were more frequent in males (approximately 5 and 3 times higher than females, respectively). Malignant neoplasm of the bladder, unspecified, constituted over 95% of BCa topography classifications. This study identified a modest rise in BCa incidence, with male predominance and a higher burden in older adults. Further investigation into potential risk factors contributing to this increase is warranted.

Memory-type Mean Estimators for Surveys with Non-response: A Regression-imputation and Ewma Approach

Sampling survey mostly faces the challenge of missing information or non-response, Missing data naturally occurs in sample surveys when a few sampling units refuse to respond or are unable to participate in the survey. In time based survey, this could happen due to relocation or system malfunctioning after some period. This missing information can bring about a substantial amount of bias, make the handling and analysis of the data more arduous and create reductions in efficiency of the estimator. Surmounting this challenge bring about the development of various imputation-based mean estimation methods. Some of these, ratio-type regression estimators have been devised to compute population parameters using only current sample data. However, recent researches has changed this approach by integrating both past and current sample information through the application of exponentially weighted moving averages (EWMA). This new invented methodology has given rise to the creation of memory-type estimators tailored for surveys conducted over time. In this paper, we present regression- imputation and EWMA based memory-type mean estimators in the presence of non- response. For the performance assessment between existing and proposed estimators, two real-life time-scaled data sets are considered. In the first data set proposed estimators (Tp1 and Tp2) have the minimum MSE and higher PRE and also in the second data set, results show that the proposed estimators (estimators (Tp1 and Tp2) have the minimum MSE and higher PRE when compared with the existing estimators at all levels of smoothing parameter. Therefore the proposed estimator will perform better in estimating the EWMA-based mean in the presence non response for time based survey and longitudinal survey. Most especially in quality control data that are collected over time. The current work is limited to simple random sampling which assumes homogeneity of the population units. Further work can be done on stratify random sampling which assumes heterogeneity of the population units.

Pilot study for the development of an automatically generated and wearable-based early warning system for the detection of deterioration of hospitalized patients of an acute care hospital.

Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland-Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. January 26, 2023, NCT05699967.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Cardiac sarcoidosis: diagnosis and management.

Non-caseating granulomatous infiltration of the myocardium is the hallmark of cardiac sarcoidosis (CS). CS manifests clinically as conduction disturbance, ventricular arrhythmia, sudden cardiac death and/or heart failure with reduced ejection fraction. Other than confirmation through endomyocardial biopsy, a diagnosis of probable CS can be established by histological evidence of systemic sarcoidosis in addition to characteristic clinical or advanced imaging findings. Cardiac Magnetic Resonance imaging (CMR) and 18F-flurodeoxyglycose positron emission tomography (FDG-PET) are imaging modalities indispensable in the diagnosis and monitoring of CS. FDG-PET is the method of choice for identifying the active inflammatory phase of CS and in the monitoring and modifying of immunosuppressive treatment. CMR is better suited for assessing cardiac morphology and function. Both modalities are more effective in detecting CS when used in combination than either is alone. Management of CS is primarily based upon observational data of low quality due to a paucity of randomized controlled trials. Corticosteroid therapy and/or tiered-immunosuppression are the mainstays of treatment in reducing myocardial inflammation. Steroid-sparing agents aim to limit the unfavorable side-effects of a significant steroid burden. Antiarrhythmics and guideline-directed medical therapies are utilized for control of ventricular arrhythmia and left ventricular dysfunction respectively. CS necessitates multidisciplinary care in specialized centers to most effectively diagnose and manage the disease. Additional randomized trials are warranted to further our understanding of medical optimization in CS.

Effect of sacubitril and valsartan combined with conventional therapy on patients with heart failure

Purpose: To investigate the clinical effectiveness of the combination of sacubitril and valsartan with conventional therapy in the treatment of patients with heart failure. Methods: This was a retrospective study comprising 100 heart failure patients randomized into study (n = 57) and control groups (n = 43). The study group received sacubitril/valsartan along with conventional drug therapy while control group received only conventional drugs, viz, irbesartan, metoprolol slow release, and furosemide tablets. Echocardiogram showing left ventricular end-systolic diameter (LVESD), left ventricular end-diastolic diameter (LVEDD), left anterior descending artery (LAD), Nterminal pro-b-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), glomerular filtration rate (eGFR), and homocysteine (HCY) of the two groups were compared before and after treatment. Multiple regression was used to analyze the correlation between re-hospitalization and sacubitril/valsartan intervention. Results: The study group showed significantly lower LVEF, LVESD, LVEDD, LAD, NT-proBNP, and homocysteine levels after treatment compared to control group (p &lt; 0.05). Re-hospitalization for abnormal cardiovascular events between the two groups was significantly different in the adjusted Cox proportional hazards regression model. Furthermore, multiple regression analysis showed that sacubitril/valsartan treatment was the independent variable (p &lt; 0.001). Conclusion: Sacubitril/valsartan improves heart function, with reduced incidence of adverse effects without affecting renal function. Further studies are required to validate these findings by expanding sample size, strictly controlling data quality and strengthening follow-up.

Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors

ABSTRACTAgricultural Internet of Things has become one of the most important data sources of agricultural big data. However, the wireless sensors equipped with agricultural Internet of Things is affected by many factors, and anomalous data inevitably exists during the data collection process. The existence of anomalous data leads to the deterioration of the agricultural data quality, which hinders the efficient development of agricultural data analysis. In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. The model first adds Gaussian noise to the input sequence and converts it into standard time series and image data. Subsequently, it is processed by different modal encoders and fuses the image and sequence modal data using Temporal Cross‐modal Attention module to enhance the perception of anomalous modal information. Finally, the fused data is reconstructed, and anomalous data are identified by the image and sequence decoders. Comparison experiments with several baselines prove the validity of the model proposed in this article, ablation experiments prove the necessity of the key modules in the model, and multiple sets of experiments are also designed to discuss the effects of hyperparameters.

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.

Accelerated Data Engine: A faster dataset construction workflow for computer vision applications in commercial livestock farms

Large-scale, high-quality dataset was the foundation of developing advanced artificial intelligence applications. However, creating such a benchmark dataset in a professional field, such as precision management of animals, was always a challenge because of the costly and labor-intensive process of annotation and review. This study introduced a novel workflow named Accelerated Data Engine (ADE), designed to efficiently produce representative and high-quality computer vision datasets from raw animal surveillance footage. By incorporating referring and grounding models (R&G models) as auto-annotators, along with a distillation mechanism for dataset-auditors, ADE significantly speeded up the dataset construction process. The new workflow received natural language inputs as referrals to identify animal instances, delineated their body shapes, and then refined the auto-annotated data through a selection process. To demonstrate the efficacy of ADE, three 30-minute surveillance video samples featuring pigs, sheep, and cattle were discussed in this study. The results indicated the R&G models effectively annotated animals across various farms, while distillation mechanisms could identify various detection errors, balance the data representations, refine annotations, and verify the data quality. Two high-quality cattle datasets (6.5 k and 486 frames), including 26 k and 2.5 k cattle instances, were generated through the ADE workflow from 24-hour surveillance videos on a commercial cattle farm and made publicly available. The proposed dataset has achievable performance between 74.6 %∼84.1 %. The ADE workflow saved 78.4 % of manual work compared to the traditional dataset construction workflow (approximately 141 h). This pioneering approach empowered the fast creation of benchmark animal datasets and would enhance computer vision applications in the livestock production industry in the future.

The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

The healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues . This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery.

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

What aspects of outcome measurement instruments are important to parents and caregivers in child health trials: Survey protocol

Background Trials of interventions to prevent illness and/or improve health outcomes in children play a crucial role in the advancement of paediatric healthcare, research, and policy. It is important for researchers and trialists involved in such trials to understand factors which are important to parents/caregivers when deciding to participate and provide data about their child. There is little evidence to date surrounding the impact of outcome measurement instruments (OMIs) on parents/caregivers’ decisions about participation and provision of data in trials. The aim of this project is to examine what characteristics of OMIs are important in parents/caregivers’ decisions to engage with and participate in trials of interventions to prevent childhood illness and/or improve child health outcomes. Methods An online cross-sectional survey is being conducted. Survey recruitment began July 2024 and will be completed in October 2024. Parents/caregivers are eligible to participate if they have at least one child up to 12 years of age; there are no restrictions based on factors such as gender or nationality. Survey participants are being recruited using convenience sampling via social media, parenting websites, and online forums. This survey includes questions about parents/caregivers’ preferences for how, when, where, and with whom data is collected in child health research, as well as the types of OMIs parents/caregivers would be most comfortable using. Data will be analysed using descriptive statistics. Conclusions Findings from this study will address the current gap in knowledge related to preferences of parents/caregivers in how data are collected in trials of interventions to improve health outcomes in children. Study findings will inform trialists in the design and conduct of child health research in the future. Implementation of study findings in future trials can also improve experiences of children and their caregivers engaged in research and enhance the quality and quantity of data being collected.

Privately evaluating sensitive population record linkage without ground truth data

AbstractData containing personal information allow detailed studies in the health and social sciences, such as population-related analysis. However, such studies often require the linking of two or more databases because information about a person can be scattered across multiple data sources. To address this issue of data being scattered, researchers have been working on linking records across multiple data sources to identify records that refer to the same person, or the same group of individuals (known as group linkage) using quasi-identifiers such as names and addresses which can be missing, out of date or contain errors or variations, making record linkage a very challenging task. Record linkage applications often also lack ground truth data in the form of matching and non-matching record pairs, which challenges the assessment of the quality of linkage algorithms. Furthermore, when linkage is conducted on sensitive data, for example personal health records, due to privacy concerns ground truth can generally not be obtained using methods such as crowd sourcing. This study therefore aims to develop methods to assess the linkage quality of sensitive data by using publicly available data sets, such as census or voter data, in a privacy-preserving manner, with a focus on the group linkage problem. Assuming that distinct groups, such as siblings in a family, are identifiable in both the sensitive and public data sets, we develop a novel method to estimate linkage quality using public data by encoding information that is commonly available in both sensitive and public data sets into a common representation using Bloom filters. Comparing these Bloom filters then allows the estimation of linkage quality. An evaluation using a real sensitive birth data set and a public census data set from Scotland shows the effectiveness of our proposed method for quality estimation, which achieves a median correlation of 98% with linkage quality calculated based on ground truth data.

Health-related quality of life and utility of maternity health states amongst post-partum Australians.

This study aimed to measure patient-reported health-related quality of life amongst post-partum women in Queensland, Australia. Patient-reported health-related quality of life data was prospectively collected from 134 post-partum women using the EQ-5D-5L at weekly intervals during the first six weeks following birth. Data across the five health domains of the EQ-5D-5L was converted to a single health utility value to represent overall health status. Linear mixed modelling and regression analysis were used to examine changes in utility over the first six weeks post-birth and determine associations between utility and clinical and demographic characteristics of post-partum women. Gestation at birth and weeks post-partum were significantly associated with utility values when considered in a multivariate linear mixed model. Mean utility values increased by 0.01 for every week increase in gestation at birth, and utility values were 0.70 at one week post-partum and increased to 0.85 at six weeks post-partum, with the largest increase occurring between one- and two-weeks post-birth. When controlling for variables that were found to predict utility values across the first six weeks post-partum, no single state of health predicted utility values at one-week post-partum. Maternity services can use our data and methods to establish norms for their own service, and researchers and maternity services can partner to conduct cost-effectiveness analysis using our more relevant utility values than what is currently available. Time since birth and gestational age of the woman's baby should be considered when selecting post-partum health state utility values for maternity services cost-effectiveness analyses.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Evaluating User Acceptance of Mobile Payment Systems in Uganda

Purpose: This research evaluates user acceptance of mobile payment systems. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: Mobile payment providers should prioritize user-centric design principles, focusing on simplicity, usability, and intuitive interfaces. Policymakers should develop regulatory frameworks that establish clear standards for mobile payment systems, focusing on data protection, privacy, and security. Keywords: User Acceptance, Mobile Payment Systems

Evaluation of alternative methods for forecasting the Aboriginal and Torres Strait Islander population of Australia

Assessing future demand for a wide range of services requires good quality population forecasts. Unfortunately, many past forecasts of the Aboriginal and Torres Strait Islander (Indigenous) population of Australia have proved highly inaccurate. This is due to poor data quality, missing data, and demographers’ incomplete understanding of Indigenous population change. In addition, because Indigenous population estimates are published only every 5 years and long after the reference date, forecasts are often used as preliminary population estimates. These form the denominators of various metrics used to monitor programmes aimed at improving health and social outcomes. The aim of this paper is to present an evaluation of alternative forecasting models and forecasts of the Indigenous population of Australia’s States and Territories by age and sex. Four models, differing substantially in complexity, were evaluated: (1) the simple Hamilton–Perry method, (2) the synthetic migration cohort-component model, (3) a uniregional cohort-component model with net migration, and (4) a bi-regional cohort-component model. The population forecasting methods were evaluated against several criteria, including forecast accuracy over the 2016–21 period, input data requirements, conceptual adequacy, output detail, time required to prepare, ability to create scenarios and select alternative assumptions, and ease of implementation. The Hamilton–Perry and synthetic migration cohort-component models provided greater forecast accuracy and scored well against the evaluation criteria. In the challenging data environment for modelling Indigenous populations, simpler forecasting methods offer several practical advantages and are likely to produce more accurate forecasts than more data-intensive models.