An empirical study of the antecedents of data completeness in electronic medical records

Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir

doi:10.1016/j.ijinfomgt.2019.05.001

Cited by 13 publications

(26 citation statements)

References 53 publications

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“…Therefore, measurement items were measured using a five-point Likert scale. This is also in line with several existing studies (for example, Dwivedi et al, 2013;Kapoor et al, 2014;Kim et al, 2009;Liu et al, 2020;Shareef et al, 2016;2017;Sharma & Sharma, 2019) that have successfully utilised five-point Likert scale.…”

Section: Methodssupporting

confidence: 90%

Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness

Alzubaidi

Slade

Dwivedi

2021

Journal of Business Research

217

140

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Major environmental issues facing our planet are considered to be partly rooted in consumer overconsumption that has resulted from high economic growth. Pro-environmental behaviours (PEBs) have been studied extensively in developed Western countries but more research is needed in developing non-Western countries. Additionally, there are increasing calls for research providing broader theoretical and behavioural explanations of consumers' intentions to adopt PEBs. Therefore, the aim of this research was to examine the factors affecting consumers' PEBs in Saudi Arabia. Quantitative data (n=613) were collected using a survey method. The proposed conceptual model and associated hypotheses were tested using structural equation modelling. The results revealed that consumers' intentions to adopt direct-and indirect-PEBs are affected by innovativeness, materialism, perceived consumer effectiveness, and environmental concern, but not by social influence. Evidence was also found of differences between younger and older respondents. These findings can be used to formulate effective marketing strategies to benefit the environment, society, and sustainable companies in the country.

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Section: Methodssupporting

confidence: 90%

Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness

Alzubaidi

Slade

Dwivedi

2021

Journal of Business Research

217

140

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“…Complementing the study by [25], the three most important factors influencing accounting data quality suggested by [24] through quantitative study namely 1) top management commitment, 2) the nature of the systems, and 3) input controls. Further, in the context of health data, [26] suggested six factors influencing data quality which are 1) top management support, 2) resources, 3) regulatory capability, 4) business-IT alignment, 5) staff participation, and 6) data/system integration.…”

Section: A Data Qualitymentioning

confidence: 99%

“…Sufficient communication, understanding, and involvement between technical and business employees across the department are the provision of effective teamwork [24]- [26], [28]. The effective teamwork can be facilitated using business friendly approach [69].…”

Section: B) Teamworkmentioning

confidence: 99%

“…c) Continuous improvement: Continuous improvement of master data quality is normally driven by the changes in the internal and external environment such as technology and regulation that requires a proper response by the organization [24], [25]. Continuous improvement is an ongoing process for assuring the quality of master data, which involves the implementation of a preventive measure focusing more on the business process betterment [26], [59]. In particular, involving the installation of data quality elements in every phase of the data life cycle is required that include data collection, processing, deletion, and archiving [21], [45].…”

Section: B) Teamworkmentioning

confidence: 99%

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Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

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“…Data quality is “the ability of EHR-derived data to produce an accurate, reliable, and consistent aggregate-level picture of what is happening at the point-of-care” [ 9 ]. For secondary use of data to be used by researchers, it is vital to ensure that EHR data are high in quality [ 2 , 10 ], which improves the quality of care and organization overall performance [ 11 ], and ensures that accurate and valid conclusions are derived from the EHR. EHR users, “generators of data” and “consumers of data” [ 9 ], should understand EHR dataset limitations before its use by identifying sources of errors and recognizing the underline causes of errors [ 2 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

et al. 2021

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Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients’ records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) “confirmed cases”, 10 (3.05%) “probable cases”, 9 (2.74%) “suspected cases”, and 91 (27.74%) “no sufficient evidence”. The identification of “probable cases” and “suspected cases” was more challenging than “confirmed cases” where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients’ records. “No sufficient evidence” records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

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An empirical study of the antecedents of data completeness in electronic medical records

Cited by 13 publications

References 53 publications

Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness

Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness

Factors Influencing Master Data Quality: A Systematic Review

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

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