Search results

The purpose of this paper is to present the findings of a research study conducted to find the perceptions of medical professionals about reduction in medical errors using electronic medical records (EMRs). It presents the relationship between EMR use in medical facilities and the reduction in medical errors. The use of EMR can lead to competitive advantages in the health-care environment.

This paper is based upon the perceptions of 99 medical professionals who use EMR in their practice in Arizona, USA.

This paper presents the medical professionals who use EMR which reduces medical errors, wrong site surgery, improper dosage delivery to a patient, wrong medication, etc. by 50-60 per cent.

This paper is limited to perceived reductions in medical errors because the actual number of errors is either unavailable or medical professionals are unwilling to provide. Future research should seek conducting database searches to find medical malpractice lawsuits, unexpected costs or any reference to quantifying losses because of medical errors. Once the expenses are reported, relating to medical malpractice legal costs with the cost of investing in EMR system would prove an excellent observational study.

Medical professionals, medical facilities and patients should be aware of the impact EMRs have on the healthcare provided as well as the safety of patients enabled by the EMRs.

Health-care industry is operating in a crisis mode and before it turns chaotic, there needs to be a consistent product used by every health-care organization or practice. EMRs can automatically update patients’ information that is required on a routine basis via different computing systems such as cloud, minimizing the need for information technology professionals to handle the issues. This leads to reduced cost, increased efficiency, effectiveness and better management of the patients’ health and wellness outcomes, with perceived reduction in medical errors.

The value of this research report is to provide the various features EMR offers and how it helps to reduce medical mistakes that help in avoiding repetition of different tests, incorrect dosage delivery and interaction of various medicines a patient is taking.

The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

This study endeavours to (a) develop a comprehensive interpretive structural modelling (ISM) toolkit containing sufficient details about the suitability and procedural aspects of each ISM approach and offer points of reference for budding researchers, (b) highlight the compatibility of ISM approaches with other qualitative and quantitative approaches, and (c) chalk-out an agenda for future research.

This study is based on an extensive review of 74 studies where researchers have used one or more ISM approaches. These studies span across the different industry sectors.

There exists a huge void in terms of the methodological synthesis of ISM approaches. ISM approaches are frequently used in sync with other qualitative and quantitative approaches. Furthermore, it highlights the need of improving the robustness of the proposed ISM models by sharing the critical details of research process.

Being a review-based work, it could not illustrate the discussed ISM approaches with real data. However, it offers a research agenda for the prospective researchers.

The prerequisites, pitfalls, suitability and the procedural aspects of various ISM approaches contained in this toolkit are equally useful for the academicians as well as practitioners.

In the absence of a synthesized framework, this study contributes a comprehensive ISM toolkit which will help the researchers to choose a suitable ISM approach in a given case.