Towards an implementation framework for business intelligence in healthcare

Foshay, Neil; Kuziemsky, Craig

doi:10.1016/j.ijinfomgt.2013.09.003

Cited by 98 publications

(74 citation statements)

References 32 publications

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“…Despite the KUVA aims to emphasize the used data quality, there were no specific indicators for data quality. It is obvious when the source data is generated for the operational use of the intended use of the data items is changed when data are joined to statistical compilation [53]. Incomplete and erroneous data decrease the used data value, statistical significance, and usability for further use [16,54].…”

Section: Discussionmentioning

confidence: 99%

Voices behind indicators for welfare and healthcare in Finland

Mussalo

Hotti²,

Mussalo³

2019

FinJeHeW

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The voices of different kinds (i.e., stated or unstated expectations of the entities) affect the functioning of the systems. Voices of authorities (VoA), processes (VoP) and shareholders (VoS) are seen in controls interacting with an environment. Therefore, control-related indicators are prescriptive, and they provide expectations for the functioning of the systems. In the study, the entity-related approach of the voices (VoA, VoP, and VoS) adapted to formalize rules for the evaluation metadata of the KUVA and SOTKANET indicators the meaning of which is to control welfare and health in Finland. The KUVA indicators are meant to control especially cost-effectiveness. The region classifications of the KUVA and SOTKANET indicators used to figure out whether responsible information providers (VoP) and information consumers (VoA and VoS) can be established. When 15 region classifications mapped within the voices by the nine rules the result of which was that nine region classifications mapped within VoA, three within VoS, and two within VoP. The main information providers are municipalities and hospital districts and municipalities. Despite our metadata-based KUVA and SOTKANET content research, without the deployment instructions of the indicators, the municipalities and other service providers do not get a complete picture of how the authorities and shareholders see them and what is expected of them, i.e., control-related cost-effectiveness will not be transparent.

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Section: Discussionmentioning

confidence: 99%

Voices behind indicators for welfare and healthcare in Finland

Mussalo

Hotti²,

Mussalo³

2019

FinJeHeW

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“…In this conceptualization, IQ was directly related to decision-making in healthcare; poor quality in healthcare datasets leads to adverse decision-making [21], impact on decision-making at national and regional levels (e.g., reported healthcare data for funding and policy making), and clinical decisions on patient level [e.g., 33,58].…”

Section: Framework Of Conceptualizations and Relationsmentioning

confidence: 99%

“…One study reported 42 percent of interviewees citing poor data quality in healthcare to be a major barrier in decision-making [21]. The same study reported cumbersome processes in healthcare for requesting information (mostly adhoc), no standards in terms of empirical measures of core processes, lack of understanding of information needs, and labor intensive and time consuming processes of obtaining value from data, often by manually exporting and manipulating data in spreadsheets.…”

Section: Introductionmentioning

confidence: 99%

The Role of Information Quality in Healthcare Organizations: A Multi-Disciplinary Literature Review

Hausvik

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…In health Care Industries, Information has been referred to as the life blood of healthcare as it is essential for effective clinical and administrative decision making [21]. Healthcare decision making is complex and requires access to a wide array of high-quality information [22]. University of Tennessee [23] Medical Center hospitals have introduced a new tool that takes data from the hospital's electronic health record system and clusters patients into different risk levels.…”

Section: Related Workmentioning

confidence: 99%

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

Mwanza¹

2016

International Journal of Advanced Research in Computer and Comm

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Zambia Revenue Authority (ZRA) generates large volumes of data that need complex mechanisms in order to extract useful tax information. The purpose of the study was to develop a data mining model for detection of fraud on tax and taxpayer data for ZRA. This study focused on two areas. These were (1) the baseline study that helped to establish the extent of the challenges in fraud detection for the tax payers and (2) the automation and development of the fraud detection tool using the results from the baseline study. Our baseline study showed that the current methodologies, processes, architectures, and technologies that were being used to transform raw data into meaningful and useful information were tedious and time consuming. In order to detect fraud they depended on random audits, informants and under-cover operations. A model which implements outlier algorithms for fraud detection, Continuous Monitoring of Distance Based and Distance Based Outlier Queries was then developed. We used both algorithms to analyse the domestic tax payments to detect underpayments and overpayments according to business rules. Underpayments and overpayments are marked as outliers. Results generated by our tool showed improved accuracy and takes less time in order to detect under and over payments as outliers when compared to the older methods.

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Towards an implementation framework for business intelligence in healthcare

Cited by 98 publications

References 32 publications

Voices behind indicators for welfare and healthcare in Finland

Voices behind indicators for welfare and healthcare in Finland

The Role of Information Quality in Healthcare Organizations: A Multi-Disciplinary Literature Review

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

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