Design and evaluation of hospital-based business intelligence system (HBIS): A foundation for design science research methodology

Digital Transformation in Healthcare 4.0: Critical Factors for Business Intelligence Systems

The health sector is one of the most knowledge-intensive and complicated globally. It has been proven repeatedly that Business Intelligence (BI) systems in the healthcare industry can help hospitals make better decisions. Some studies have looked at the usage of BI in health, but there is still a lack of information on how to develop a BI system successfully. There is a significant research gap in the health sector because these studies do not concentrate on the organizational determinants that impact the development and acceptance of BI systems in different organizations; therefore, the aim of this article is to develop a framework for successful BI system development in the health sector taking into consideration the organizational determinants of BI systems’ acceptance, implementation, and evaluation. The proposed framework classifies the determinants under organizational, process, and strategic aspects as different types to ensure the success of BI system deployment. Concerning practical implications, this paper gives a roadmap for a wide range of healthcare practitioners to ensure the success of BI system development.

Extension to 'combined SNA and LDA methods to understand adverse medical events': Doctor and nurse perspectives

BACKGROUND

Physicians and nurses are responsible for reporting medical adverse events. Each views these events through a different lens subject to their role-based perceptions and barriers. Physicians typically engage with diagnosis and treatment while nurses primarily care for patients' daily lives and mental well-being. This results in reporting and describing medical adverse events differently.

OBJECTIVE

We aimed to compare adverse medical event reports generated by physicians and nurses to better understand the differences and similarities in perspective as well as the nature of adverse medical events using social network analysis (SNA) and latent Dirichlet allocation (LDA).

METHODS

The current study examined data from the Maccabi Healthcare Community. Approximately 17,868 records were collected from 2000 to 2017 regarding medical adverse events. Data analysis used SNA and LDA to perform descriptive text analytics and understand underlying phenomenon.

RESULTS

A significant difference in harm levels reported by physicians and nurses was discovered. Shared topic keyword lists broken down by physicians and nurses were derived. Overall, communication, lack of attention, and information transfer issues were reported in medical adverse events data. Specialized keywords, more likely to be used by a physician were determined as: repeated prescriptions, diabetes complications, and x-ray examinations. For nurses, the most common special adverse event behavior keywords were vaccine problem, certificates of fitness, death and incapacity, and abnormal dosage.

CONCLUSIONS

Communication and inattentiveness appeared most frequently in medical adverse events reports regardless of whether doctors or nurses did the reporting. Findings suggest feedback and information sharing processes could be implemented as a step toward alleviating many issues. Institutional management, healthcare managers and government officials should take actions to decrease medical adverse events, many of which may be preventable.

Combined SNA and LDA methods to understand adverse medical events

OBJECTIVE

To compare primary medical adverse event keywords from reporters (e.g. physicians and nurses) and harm level perspectives to explore the underlying behaviors of medical adverse events using social network analysis (SNA) and latent Dirichlet allocation (LDA) leading to process improvements.

DESIGN

Used SNA methods to explore primary keywords used to describe the medical adverse events reported by physicians and nurses. Used LDA methods to investigate topics used for various harm levels. Combined the SNA and LDA methods to discover common shared topic keywords to better understand underlying behaviors of physicians and nurses in different harm level medical adverse events.

SETTING

Maccabi Healthcare Community is the second largest healthcare organization in Israel.

DATA

17,868 medical adverse event data records collected between 2000 and 2017.

METHODS

Big data analysis techniques using social network analysis (SNA) and latent Dirichlet allocation (LDA).

RESULTS

Shared topic keywords used by both physicians and nurses were determined. The study revealed that communication, information transfer, and inattentiveness were the most common problems reported in the medical adverse events data.

CONCLUSIONS

Communication and inattentiveness were the most common problems reported in medical adverse events regardless of healthcare professional reporting or harm levels. Findings suggested that an information-sharing and feedback mechanism should be implemented to eliminate preventable medical adverse events. Healthcare institutions managers and government officials should take targeted actions to decrease these preventable medical adverse events through quality improvement efforts.

Methodologies for designing healthcare analytics solutions: A literature analysis

Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field.

An Artifact for Evaluating the Quality of Health Service Providers: Evidence From Brazil

The aim of this article is to present an artifact for evaluating the quality and performance of service providers in the field of health care: the UNIPLUS Program. To verify the scientific nature of the artifact and ensure that it meets the criteria set by the community and the environment, the premises of Design Science Research (DSR) were used. As this research field lacks empirical evidence, the artifact was tested from 2013 to 2015 with 25 health care service providers from different categories, with an emphasis on hospitals and clinics located in 7 cities in the south of Brazil. This article makes 3 main contributions to the field: (1) the artifact can be applied to any health insurance operator in Brazil and other countries, as it meets the legal norms and requirements established by current legislation; (2) it helps health service providers by generating information that identifies shortfalls and possibilities for improvement for every aspect analyzed in the evaluation process; and (3) it uses the DSR methodology in an evaluation artifact that evaluates the quality and performance of services in the field of health care. The artifact proved to be adequate for the purpose in question, helping to improve the quality of care and institutional performance.