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Gotowe bibliografie tematyczne / Healthcare Data Quality / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Healthcare Data Quality”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Charnock, Victoria. "Electronic healthcare records and data quality." Health Information & Libraries Journal 36, no. 1 (2019): 91–95. http://dx.doi.org/10.1111/hir.12249.

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Berndt, D. J., J. W. Fisher, A. R. Hevner, and J. Studnicki. "Healthcare data warehousing and quality assurance." Computer 34, no. 12 (2001): 56–65. http://dx.doi.org/10.1109/2.970578.

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Warwick, William. "A Framework to Assess Healthcare Data Quality." European Journal of Social and Behavioural Sciences 13, no. 2 (2015): 1730–35. http://dx.doi.org/10.15405/ejsbs.156.

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Cadarette, S. M., S. B. Jaglal, L. Raman-Wilms, D. E. Beaton, and J. M. Paterson. "Osteoporosis quality indicators using healthcare utilization data." Osteoporosis International 22, no. 5 (2010): 1335–42. http://dx.doi.org/10.1007/s00198-010-1329-8.

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Deghati, Shirin. "Impact of Data Governance on Data Quality in Healthcare Institutions." American Journal of Data, Information and Knowledge Management 5, no. 1 (2024): 39–48. http://dx.doi.org/10.47672/ajdikm.2351.

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Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: 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: The study found that institutions with robust data governa

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Kerr, Karolyn A., Tony Norris, and Rosemary Stockdale. "The strategic management of data quality in healthcare." Health Informatics Journal 14, no. 4 (2008): 259–66. http://dx.doi.org/10.1177/1460458208096555.

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C., S. Sindhu, and P. Hegde Nagaratna. "A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2798–805. https://doi.org/10.11591/ijece.v7i5.pp2798-2805.

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With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in on

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Sumaedi, Sik, Medi Yarmen, and I. Gede Mahatma Yuda Bakti. "Healthcare service quality model." International Journal of Productivity and Performance Management 65, no. 8 (2016): 1007–24. http://dx.doi.org/10.1108/ijppm-08-2014-0126.

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Purpose The purpose of this paper is to develop and test a multi-level healthcare service quality (HSQ) model in Jakarta, Indonesia. Design/methodology/approach The research used a quantitative research method. Data were collected via a survey with questionnaire. The respondents are 154 patients of a healthcare institution in Jakarta, Indonesia. Findings The research result shows a multi-level HSQ model. The HSQ model consists of three primary dimensions, namely, healthcare service outcome, healthcare service interaction, and healthcare service environment. Healthcare service outcome has three

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Nandish Shivaprasad. "Metadata Repositories in Healthcare Data Architecture." Journal of Sustainable Solutions 1, no. 4 (2024): 176–86. https://doi.org/10.36676/j.sust.sol.v1.i4.50.

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Meta databases are also very important in managing healthcare information since they provide an effective framework for archiving different varieties of healthcare data. These repositories enhance data sharing and usage, aiming at data correspondence, accuracy, and security of the data in the health sector. Due to rising tendencies of developing complicated health care models, the management of metadata is crucial for enhancing the quality of the health care delivery and maintaining the legal requirements. In this paper, we examine technologies for metadata repositories and potential applicati

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Mehmood, Ali Mohammed, Ali Mohammed Murtuza, and Ali Mohammed Vazeer. "Impact of Artificial Intelligence on the Automation of Digital Health System." International Journal of Software Engineering & Applications (IJSEA) 13, no. 6 (2022): 23–29. https://doi.org/10.5281/zenodo.7417611.

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Automating digital systems in healthcare plays a significant role in transforming the quality-of-care services delivered to patients across the board. This role is anticipated to be accomplished by the development and implementation of artificial intelligence in healthcare which has the potential to impact the provision of healthcare services. This paper sought to investigate the impact of adopting and implementing artificial intelligence on the automation of digital health systems within the different levels of healthcare.

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Noël, Guillermina, Janet Joy, and Carmen Dyck. "Improving the quality of healthcare data through information design." Information Design Journal 23, no. 1 (2017): 104–22. http://dx.doi.org/10.1075/idj.23.1.11noe.

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Improving the quality of patient care, generally referred to as Quality Improvement (QI), is a constant mission of healthcare. Although QI initiatives take many forms, these typically involve collecting data to measure whether changes to procedures have been made as planned, and whether those changes have achieved the expected outcomes. In principle, such data are used to measure the success of a QI initiative and make further changes if needed. In practice, however, many QI data reports provide only limited insight into changes that could improve patient care. Redesigning standard approaches

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Robbins, Richard. "Improving Quality in Healthcare." Southwest Journal of Pulmonary, Critical Care & Sleep 26, no. 1 (2023): 8–10. http://dx.doi.org/10.13175/swjpccs002-23.

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No abstract available. Article truncated after 150 words. Everyone is in favor of quality healthcare and improving it. However, to date, initially highly touted quality measures prove to be meaningless metrics in about 5-10 years. That is, when the measures are scientifically studied, they are found to be of little worth. The cycle is then repeated, i.e., new and highly touted measures are again selected and found to be useless in 5-10 years. The latest in this cycle may be the Centers for Medicare and Medicaid’s (CMS) Merit-based Incentive Payment System (MIPS). The theory underlying MIPS has

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Shahian, David M. "Clinical data registries and the future of healthcare quality." Progress in Pediatric Cardiology 32, no. 2 (2011): 71–74. http://dx.doi.org/10.1016/j.ppedcard.2011.10.003.

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Halaweh, Mohanad, and Fathi Fayeq Salameh. "Using Social Media Data for Exploring Healthcare Service Quality." International Journal of Healthcare Information Systems and Informatics 18, no. 1 (2023): 1–13. http://dx.doi.org/10.4018/ijhisi.325064.

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The provision of quality services in the healthcare sector has become a highly prioritized goal, as it is seen as a key factor in the satisfaction and loyalty of patients. This study aims to explore patients' perception of service quality in the UAE, a topic that has not yet been extensively studied. A qualitative approach, using social media data, was employed. Grounded theory techniques were used to analyze online feedback and comments on clinical services posted by patients. Results revealed five key factors that shape patients' perception of service quality: time efficiency, human interact

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Kalra, Jawahar (Jay), Zoher Rafid-Hamed, and Patrick Seitzinger. "Autopsy Data to Refine Healthcare Quality: A Fresh Perspective." Pathology and Laboratory Medicine – Open Journal 3, no. 1 (2021): e1-e3. http://dx.doi.org/10.17140/plmoj-3-e004.

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Mehmood, Ali Mohammed, Ali Mohammed Murtuza, and Ali Mohammed Vazeer. "Impact of Artificial Intelligence on the Automation of Digital Health System." International Journal of Software Engineering & Applications (IJSEA) 13, no. 6 (2023): 23–29. https://doi.org/10.5281/zenodo.7948021.

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Automating digital systems in healthcare plays a significant role in transforming the quality-of-care services delivered to patients across the board. This role is anticipated to be accomplished by the development and implementation of artificial intelligence in healthcare which has the potential to impact the provision of healthcare services. This paper sought to investigate the impact of adopting and implementing artificial intelligence on the automation of digital health systems within the different levels of healthcare. The general objective of the research study was to investigate the imp

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Jeremiah, Olawumi Arowoogun, Babawarun Oloruntoba, Chidi Rawlings, Oyeyemi Adeniyi Adekunle, and Anthonia Okolo Chioma. "A comprehensive review of data analytics in healthcare management: Leveraging big data for decision-making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1810–21. https://doi.org/10.5281/zenodo.14042126.

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This research paper presents a comprehensive review of data analytics in healthcare management, focusing on leveraging big data for decision-making. The literature review explores the historical evolution of data analytics, emphasizing its growing importance in clinical support, resource allocation, and operational efficiency within the healthcare sector. The paper discusses fundamental concepts, methodologies, and emerging trends, including integrating artificial intelligence, real-time analytics, and the impact of wearable technologies. Challenges such as data quality, privacy, and interoper

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Nidhi, Bhattacherjee D. Mustafi &. V. Bhattacharjee*. "A BIG DATA ARCHITECTURE FOR HEALTHCARE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 9, no. 4 (2020): 108–12. https://doi.org/10.5281/zenodo.3778449.

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These are testing times for organizations in the healthcare industry as it is transforming at an incredible velocity. The industry has been focusing on delivering better quality care and outcome at better prices in terms of affordability i.e. the industry is focusing more on value. The shift of healthcare towards digitization, ever increasing due to implementation and adoption of digital medical records, is now being utilized and combined with new age technologies like mobile communication, social networking, cloud computing and analytics. This paper presents an overview of applying Big data i

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Zhang, Yili, and Güneş Koru. "Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions." Journal of the American Medical Informatics Association 27, no. 3 (2019): 386–95. http://dx.doi.org/10.1093/jamia/ocz201.

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Abstract Objective Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States. Materials and Methods There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature

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Otokiti, Ahmed. "Using informatics to improve healthcare quality." International Journal of Health Care Quality Assurance 32, no. 2 (2019): 425–30. http://dx.doi.org/10.1108/ijhcqa-03-2018-0062.

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Purpose The purpose of this paper is to provide insights into contemporary challenges associated with applying informatics and big data to healthcare quality improvement. Design/methodology/approach This paper is a narrative literature review. Findings Informatics serve as a bridge between big data and its applications, which include artificial intelligence, predictive analytics and point-of-care clinical decision making. Healthcare investment returns, measured by overall population health, healthcare operation efficiency and quality, are currently considered to be suboptimal. The challenges p

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S Raj, Shrisha. "AI in Healthcare Quality: Advances and Ethical Concerns." Journal of Quality in Health Care & Economics 7, no. 5 (2024): 1–6. http://dx.doi.org/10.23880/jqhe-16000413.

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The discovery of fire by early humans initiated a technological revolution, laying the foundation for advancements that have dramatically transformed society. Today, artificial intelligence (AI) is at the forefront of this evolution, driving innovation across multiple sectors. From early mechanical tools to modern AI-driven systems, technology has become more autonomous and efficient. In healthcare, AI plays a pivotal role in diagnostics, precision medicine, and telemedicine, enabling real-time patient monitoring and personalized treatment plans. Its ability to process vast amounts of data sig

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Alshammari, Abdullah Thamer A., Maryam Adel Alghmgham, Zainab Abdulaziz M. Hamadah, et al. "The Benefits of Accreditation for Healthcare Quality." International Journal Of Pharmaceutical And Bio-Medical Science 02, no. 12 (2022): 627–38. http://dx.doi.org/10.47191/ijpbms/v2-i12-09.

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Background: Accreditation is widely regarded as a reliable method for assessing and improving the quality of medical care provided. However, the effect that it has on performance and outcomes is not yet fully understood. The purpose of this review was to locate and assess the available evidence regarding the effects of hospital accreditation. Methods: We conducted in-depth searches of a variety of electronic databases, including PubMed, CINAHL, PsycINFO, EMBASE, MEDLINE (OvidSP), CDSR, CENTRAL, ScienceDirect, SSCI, RSCI, and SciELO, as well as other sources, using subject headings that were pe

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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.

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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.

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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.

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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.

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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.

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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.

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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.

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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.

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Siddhartha, Nuthakki. "Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 75–79. https://doi.org/10.5281/zenodo.13470691.

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The integration of data science in healthcare has revolutionized the industry, offering innovative solutions for data collection, management, and predictive analytics. This paper explores the multifaceted role of data science in healthcare, from the initial stages of data collection to the implementation of predictive modeling techniques. By examining current methodologies, challenges, and future directions, we aim to highlight the transformative impact of data science on healthcare outcomes.

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Kondasani, Rama Koteswara Rao, Rajeev Kumar Panda, and R. Basu. "Better healthcare setting for better healthcare service quality." International Journal of Quality & Reliability Management 36, no. 10 (2019): 1665–82. http://dx.doi.org/10.1108/ijqrm-05-2018-0120.

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Purpose The purpose of this paper is to assess and compare different private healthcare settings based on perceived service quality in Indian context using analytical hierarchy process (AHP). The Indian private healthcare sector has been controlled by three categories of healthcare settings, namely, nursing clinics (NCs), non-corporate hospitals (NCHs) and corporate hospitals (CHs). Design/methodology/approach AHP was used to rank order of healthcare setting regarding the service quality dimensions and relative standings of every service provider with respect to its competitors. The authors co

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Wolf, Tessa, Wolfgang Greiner, and Peter Stegmaier. "Keine nachhaltige Gesundheitsversorgung ohne Daten." Monitor Versorgungsforschung 2025, no. 01 (2025): 22–25. https://doi.org/10.24945/mvf.01.25.1866-0533.2684.

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In addition to the several hundred quality indicators and key figures in the hospital quality reports, there are now a further 292 "measurable indicators that are specifically tailored to the requirements of the healthcare system" and which are intended to provide "a detailed and measurable picture of the sustainability and performance of the system". The people involved in the NHI Sustainability Index - Tessa Wolf (Head Corporate Affairs AstraZeneca Germany) and Prof Dr Wolfgang Greiner (holder of the Chair of Health Economics and Health Management at Bielefeld University and scientific advis

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Kaduk, D.Ye., T.M. Aleksandrova, P.S. Talapova, et al. "Current state and prospects of implementation of data standardization in the health care system of Ukraine (literature review)." Medicni perspektivi 28, no. 3 (2023): 190–98. https://doi.org/10.26641/2307-0404.2023.3.289226.

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The orientation of the world medical community, and Ukraine in particular, towards the improvement of the quality of medical services includes the introduction of modern tools and methods for quality regulation in the healthcare system. The focus of research on the modernization of medical services, methods and forms of treatment and diagnosis, as well as focus on global experience in regulating the quality of services provided by relevant institutions, undoubtedly contributes to the transition of the Ukrainian healthcare system to a new level. One of the most powerful and modern ways to impro

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Jarwar, Muhammad Aslam, and Ilyoung Chong. "Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application." Applied Sciences 10, no. 6 (2020): 2181. http://dx.doi.org/10.3390/app10062181.

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contex

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Kim, Ki-Hoon, Seol Whan Oh, Soo Jeong Ko, Kang Hyuck Lee, Wona Choi, and In Young Choi. "Healthcare data quality assessment for improving the quality of the Korea Biobank Network." PLOS ONE 18, no. 11 (2023): e0294554. http://dx.doi.org/10.1371/journal.pone.0294554.

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Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and wer

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van der Veer, S. N., K. J. Jager, N. Peek, N. F. de Keizer, and A. Koetsier. "Control Charts in Healthcare Quality Improvement." Methods of Information in Medicine 51, no. 03 (2012): 189–98. http://dx.doi.org/10.3414/me11-01-0055.

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SummaryObjectives: Use of Shewhart control charts in quality improvement (QI) initiatives is increasing. These charts are typically used in one or more phases of the Plan Do Study Act (PDSA) cycle to monitor summaries of process and outcome data, abstracted from clinical information systems, over time. We summarize methodological criteria of Shewhart control charts and investigate adherence of published QI studies to these criteria.Methods: We searched Medline, Embase and CINAHL for studies using Shewhart control charts in QI processes in direct patient care. We extracted methodological criter

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Paolo, Pietro Biancone, Secinaro Silvana, and Brescia Valerio. "A Review of Big Data Quality and an Assessment Method and features of Data Quality for Public Health Information Systems." International Journal of Management Sciences and Business Research 7, no. 1 (2018): 19–33. https://doi.org/10.5281/zenodo.3474561.

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Data governance Refers to the management of public health information systems and data. The article tries to give an updated definition of big data quality through a review and systematic approach. We Identified publications by searching several eletronic bibliographic databases. The articles were confined to Inglese and italian leanguage. We performed the litterature advanced between January 2015 and December 2016. The group of study would propose a method for handle Big Data Quality System and the features That the system must have to be a management tool. The study investigates and provides

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Al-Assaf, Karam, Zied Bahroun, and Vian Ahmed. "Transforming Service Quality in Healthcare: A Comprehensive Review of Healthcare 4.0 and Its Impact on Healthcare Service Quality." Informatics 11, no. 4 (2024): 96. https://doi.org/10.3390/informatics11040096.

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This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and 2023. The selection process used clearly defined inclusion and exclusion criteria to identify studies focusing on advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Rayyan software facilitated systemat

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Bell, Ralph, and Michuel J. Knuich. "How to Use Patient Satisfaction Data to Improve Healthcare Quality." Journal For Healthcare Quality 23, no. 4 (2001): 45. http://dx.doi.org/10.1097/01445442-200107000-00016.

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Reiner, Bruce I. "Improving Healthcare Delivery Through Patient Informatics and Quality Centric Data." Journal of Digital Imaging 24, no. 2 (2011): 177–78. http://dx.doi.org/10.1007/s10278-011-9363-4.

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McLennan, Stuart, Roxanne Maritz, David Shaw, and Bernice Elger. "The inconsistent ethical oversight of healthcare quality data in Switzerland." Swiss Medical Weekly 148, no. 2728 (2018): w14637. http://dx.doi.org/10.57187/smw.2018.14637.

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Strotbaum, Veronika, Monika Pobiruchin, Björn Schreiweis, Martin Wiesner, and Brigitte Strahwald. "Your data is gold – Data donation for better healthcare?" it - Information Technology 61, no. 5-6 (2019): 219–29. http://dx.doi.org/10.1515/itit-2019-0024.

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Abstract Today, medical data such as diagnoses, procedures, imaging reports and laboratory tests, are not only collected in context of primary research and clinical studies. In addition, citizens are tracking their daily steps, food intake, sport exercises, and disease symptoms via mobile phones and wearable devices. In this context, the topic of “data donation” is drawing increased attention in science, politics, ethics and practice. This paper provides insights into the status quo of personal data donation in Germany and from a global perspective. As this topic requires a consideration of se

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Emons, Matthew F. "Integrated Patient Data for Optimal Patient Management: The Value of Laboratory Data in Quality Improvement." Clinical Chemistry 47, no. 8 (2001): 1516–20. http://dx.doi.org/10.1093/clinchem/47.8.1516.

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Abstract Managed care organizations are shifting from traditional utilization management programs to focus on initiatives that improve the health of an insured population. This strategy requires sophisticated data integration to identify at-risk individuals and track outcomes. Laboratory data are becoming increasingly valuable tools for managed care organizations and healthcare providers. The HEDIS® Effectiveness of Care measures have incorporated laboratory data into several key performance indicators. By building a comprehensive repository of laboratory data that includes both procedure code

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Höög, Elisabet, Jack Lysholm, Rickard Garvare, Lars Weinehall, and Monica Elisabeth Nyström. "Quality improvement in large healthcare organizations." Journal of Health Organization and Management 30, no. 1 (2016): 133–53. http://dx.doi.org/10.1108/jhom-10-2013-0209.

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Purpose – The purpose of this paper is to investigate the obstacles and challenges associated with organizational monitoring and follow-up (M & F) processes related to health care quality improvement (QI) and development. Design/methodology/approach – A longitudinal case study of a large health care organization during a system-wide QI intervention. Content analysis was conducted of repeated interviews with key actors and archival data collected over a period of four years. Findings – The demand for improved M & F strategies, and what and how to monitor were described by the respondent

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Kuo, Nai Wen. "Healthcare Information System and Data Mining." Applied Mechanics and Materials 55-57 (May 2011): 561–66. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.561.

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This paper is to integrate information technology and medical-related technologies to develop a healthcare information system for comprehensive geriatric assessment. This system not only can process geriatric consultation services and ensure that all patient’s information are stored in standardized format , but also provide medical personnel for statistical analysis and processing purposes. This paper uses the Apriori algorithm of data mining for helping doctors to find out the relationship of geriatric syndrome. The systems of this paper can improve increase the timeliness and accuracy of pat

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Polubriaginof, Fernanda C. G., Patrick Ryan, Hojjat Salmasian, et al. "Challenges with quality of race and ethnicity data in observational databases." Journal of the American Medical Informatics Association 26, no. 8-9 (2019): 730–36. http://dx.doi.org/10.1093/jamia/ocz113.

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Abstract Objective We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy. Materials and Methods We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative

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Bonde, Morten, Claus Bossen, and Peter Danholt. "Data-work and friction: Investigating the practices of repurposing healthcare data." Health Informatics Journal 25, no. 3 (2019): 558–66. http://dx.doi.org/10.1177/1460458219856462.

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The focus on digital data for improved management and quality of healthcare is paramount. In particular, the vast volumes of accumulated data in clinical systems have created high hopes for repurposing data to serve secondary purposes beyond the practices of direct clinical care, such as research, improvement and efficiency. This article contributes with an understanding of the pivotal, but often unnoticed “data-work” involved in such efforts. The article is based on a regional project in Danish healthcare, in which nine hospital departments were given the task of developing new indicators for

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Venkat Mounish Gundla. "Demystifying data engineering for AI in Healthcare: A strategic beginner’s guide." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 819–27. https://doi.org/10.30574/wjaets.2025.15.2.0611.

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The integration of artificial intelligence into healthcare represents a transformative force with potential to revolutionize patient care, operational efficiency, and clinical outcomes. Data engineering forms the indispensable foundation of this revolution, yet remains poorly understood by many healthcare stakeholders. This introduction to data engineering in AI-powered healthcare illuminates the complex ecosystem of data pipelines, architectures, and quality frameworks essential for successful implementation. Healthcare generates extraordinarily diverse data across structured, semi-structured

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Boyapati, Phanindra Sai, Godavarthi Kranthi, and Ashik Kumar. "Advancing Quality Management in using Scalable Transaction Validation." International Journal of Computing and Engineering 7, no. 2 (2025): 21–38. https://doi.org/10.47941/ijce.2625.

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The migration of healthcare data represents a foundational step in the ongoing transformation and modernization of healthcare systems worldwide. As institutions increasingly digitize records and integrate advanced technologies into their operations, the ability to efficiently and accurately migrate data becomes crucial. This process, however, is fraught with challenges tied to maintaining the accuracy, consistency, and security of data—a concern that is heightened by the sensitive nature of healthcare information and the intricate nature of existing health IT infrastructures. Given these compl

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Sudeep, Dhivya. "Enhancing Healthcare Claims and Membership Data Quality: SPSS Modeler Predictive Analysis." International Journal of Health Sciences 7, no. 8 (2024): 51–63. http://dx.doi.org/10.47941/ijhs.2369.

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Purpose: This paper explores the use of SPSS Modeler predictive analysis to enhance healthcare claims and membership data quality. The analysis uses advanced analytical techniques and algorithms to identify discrepancies and improve data accuracy, improving decision-making and operational efficiencies within healthcare organizations. The paper also provides insights to optimize data integrity, streamline claims processing, and ultimately improve patient care outcomes by ensuring that accurate and reliable data can sustain all healthcare operations. Methodology: This paper explores the use of a

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Atta Ur Rahman, Bibi Saqia, Yousef S. Alsenani, and Inam Ullah. "Data Quality, Bias, and Strategic Challenges in Reinforcement Learning for Healthcare: A Survey." International Journal of Data Informatics and Intelligent Computing 3, no. 3 (2024): 24–42. http://dx.doi.org/10.59461/ijdiic.v3i3.128.

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Data quality is a critical aspect of data analytics since it directly influences the accuracy and effectiveness of insights and predictions generated from data. Artificial Intelligence (AI) schemes have grown in the existing era of technological advancement, which provides innovative exposure to healthcare applications. Reinforcement Learning (RL) is a subfield and an influential Machine Learning (ML) model aimed at optimizing decision-making by association with dynamic environments. In healthcare applications, RL can modify conduct strategies, enhance source application, and improve patient i

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Mishra, Amit, Tushar Mokashi, Arun Nair, and Maulik Chokshi. "Mapping Healthcare Data Sources in India." Journal of Health Management 24, no. 1 (2022): 146–59. http://dx.doi.org/10.1177/09720634221077322.

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Healthcare data sources collect and report various kinds of health data related to routine service delivery, patient-based care, resources related to infrastructure, human resources and finance. Typically, in developing countries, multiple sources are used for the provision of healthcare data, and these include national health surveys, census and civil registration systems, and routine reporting systems. In addition, rapid infusion of information technology has increased adoption of management information systems in public health programs. During the last decade, India has witnessed a sharp ri

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Singu, Santosh Kumar. "Elevating Healthcare ETL Quality: The Role of Automated Testing in Ensuring Data Excellence." Journal of Artificial Intelligence & Cloud Computing 2, no. 1 (2023): 1–5. http://dx.doi.org/10.47363/jaicc/2023(2)392.

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Data quality significantly impacts patient care, operational efficiency, and regulatory compliance in healthcare. Extract, Transform, and Load (ETL) processes connect and manage healthcare data, yet data quality is difficult to maintain

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