Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature

Liaw, Siaw‐Teng; Rahimi, Alireza; Ray, Pradeep; Taggart, Jane; Dennis, Sarah; Lusignan, Simon de; Jalaludin, Bin; Yeo, Anthony E. T.; Talaei-Khoei, Amir

doi:10.1016/j.ijmedinf.2012.10.001

Cited by 152 publications

(155 citation statements)

References 51 publications

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“…Firstly, the identification of T2D patients relied on searching the "Past History" data table within Best Practice TM and HbA1c readings compared to more complex ontological approaches [23]. However, it is important to note that the "Past History" table includes both "reason for visit" and "past history "fields and that entries were searched for free text as well as coded values.…”

Section: General Practitioner Questionnairementioning

confidence: 99%

“…This study demonstrates that the completeness of data in Australian general practice is improving although it is doing so from a relatively low base and the availability of historical data is somewhat limited. This study has only addressed one dimension of data quality and other dimensions such as accuracy, correctness and timeliness [23] need to be considered to fully assess the potential usefulness of applying computer model techniques to predict clinically significant outcomes for patients with T2D in the Australian general practice setting.…”

Section: General Practitioner Questionnairementioning

confidence: 99%

“…Several Ontological techniques have also shown promise and been demonstrated to improve case finding for the creation of T2D disease registers [16,21,22]. A recent literature review relevant to routinely collected electronic clinical data and chronic disease management identified completeness, accuracy, correctness and timeliness as major dimensions that need to be considered when assessing data quality for both research and patient care purposes [23].…”

Section: Introductionmentioning

confidence: 99%

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The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

Staff

Roberts

March

2016

Primary Care Diabetes

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Background: Computer models predicting outcomes among patients with Type 2 Diabetes (T2D) can be used as disease management program evaluation tools. The clinical data required as inputs for these models can include annually updated measurements such as blood pressure and glycated haemoglobin (HbA1c). These data can be extracted from primary care physician office systems but there are concerns about their completeness. Primary care practitioners were surveyed to identify barriers to recording data and strategies to improve its completeness. Results:Over the study period completeness of data improved substantially from less than 20% for some parameters up to a level of approximately 80% complete, except for the recording of weight.T2D patients with Ischaemic Heart Disease were more likely to have their blood pressure recorded (OR 1.6, p=0.02). Practitioners' responses suggest they were not experiencing any major barriers to using their electronic medical record system but did agree with some suggested strategies to improve record completeness. Conclusion:The completeness of routinely collected data suitable for input into computerised predictive models is improving although other dimensions of data quality need to be addressed.

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Section: General Practitioner Questionnairementioning

confidence: 99%

Section: General Practitioner Questionnairementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

Staff

Roberts

March

2016

Primary Care Diabetes

View full text Add to dashboard Cite

show abstract

“…In another study, [40] indicated that completeness, accuracy, consistency, timeliness, and correctness are essential aspects of data quality. at the same study Reference [41] indicated the positive effects of Completeness, Accuracy, Correctness, Consistency, Timeliness on data quality Reference [42] indicated that Six quality metrics, including the currency, availability, information to noise ratio, authority, popularity, and cohesiveness are effective on the search effectiveness in Centralized/Distributed Information Retrieval on the World Wide Web. Reference [43] have identified the Decision-maker quality; Data quality of the BD sources; Staff; Flexible infrastructure; Routinizing and standardization; Process integration and standardization; Collaboration; Knowledge exchange; BDA capabilities; Relational governance; Contractual governance as the effective factors on the big data decision-making quality.…”

Section: International Journal Of Trade Economics and Finance Vol mentioning

confidence: 97%

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

Hejazi¹,

Abdolvand²,

Harandi³

2017

IJTEF

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Abstract-Business Intelligence (BI) is defined as the intelligent transfer of data from all variety of sources with a view to providing a real time quantification of business performance. Moreover, by utilizing BI senior managers are able to make better-informed decisions, guided by the integration of comprehensive information from all facets of an organization in real time. Further, the quality of corporate decision making is directly dependent upon the quality of data. Quality dimensions have proven to be of importance in evaluating how an organization understands and manages its data. Hence, in this study we employed the data quality model of Wang and Strong as our benchmark, and by questioning experts five additional factors (Usefulness, Efficiency, Availability, Navigation, and Usability) were added to the model to improve the reference model. Furthermore, the importance of these factors (and how they relate to each other, in terms of their dimensions) will be measured by the AHP method and an expert panel. Finally, we will reveal how the findings demonstrate that the intrinsic, contextual, representational and accessibility IQs in terms of their dimensions and reputation, accuracy, believability, and reliability, proved to be the most important factors.. Index Terms-Business intelligence, data, data quality, data quality model.

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“…Each of these expressions can lead to a different diagnose alongside with other symptoms. Several proposals had been made by researchers to use fuzzy variable in medical area some of these researches used semantic web to describe the knowledge and others used ontology engineering to represent the classes and their relations (Ismail et al, 2003;Parry, 2004;Kaya et al, 2011;Liaw et al, 2012;Orsi et al, 2014). To the best of our knowledge, there is a lack in the literature in determining the cause of CP and propose a treatment rather than the researches that focused on angina or cardiovascular CP.…”

Section: Introductionmentioning

confidence: 99%

Chest-Pain Consultation System Based on Fuzzy Semantic Rules

Al-Hamadani¹

2016

CIS

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Chest pain is considered to be one of the most important prognoses to diagnose several heart and chest diseases. This domain of knowledge suffers from vague information especially when non specialist people interact with it. This paper presents a consultation system to be used by patients with chest pain or discomfort in order to diagnose the cause of the pain and provide suitable recommendations. The system consists of two subsystems. Determining the caused of the pain is the first stage based on the fuzzy concepts and fuzzy OWL to deal with vague information. Depending on the result of the first stage, the second stage provides recommendations to be accomplished by the patient to save their lives according to SWRL and Ontology engineering. The system has been tested using 55 patients to validate its sensitivity and specificity. The results were compared with the specialists' opinion and the results emphasized the efficiency and the reliability of the system.

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Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature

Cited by 152 publications

References 51 publications

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

Chest-Pain Consultation System Based on Fuzzy Semantic Rules

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