Using Informatics to Improve Medical Device Safety And Systems Thinking

Witz, Steven M.; Buening, Natalie R.; Catlin, Ann Christine; Malloy, William X.; Kindsfater, Julie; Walroth, Todd; Washington, Alana; Zink, Richard J.

doi:10.2345/0899-8205-48.s2.38

Cited by 4 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The knowledge management system for healthcare is essential for improving the services and providing the best possible treatment [7] [8]. It implies the combination of internal knowledge and externally generated information.…”

Section: Industry Knowledgementioning

confidence: 99%

“…Such an approach can help clinical experts clarify the process of pathogenesis and classification of complex diseases appropriately, which is caused by several factors that are mutually involved [6]. Deep learning has also been recognized as an initial step toward precision medicine [7]. However, the process of integrating molecular science into clinical medicine requires great progress before big data can make a maximum contribution to health care.…”

Section: Introductionmentioning

confidence: 99%

“…[8]. In order to address this challenge, the application of big data is used to develop statistical techniques as well as computational infrastructure that is able to integrate and analyze large and heterogeneous data sets, so that ultimately the information presented has clinically relevant insights that are able to handle unseen diagnostic and therapeutic needs in all broad medical disciplines [7].…”

Section: Introductionmentioning

confidence: 99%

“…Big data refers to large amounts of information consolidated analyzed by certain technologies and applied to the health sector, it will use certain health data from a population and potentially help prevent epidemics and cure disease [8]. In addition, big data can also play a role in obtaining patient's personal data and will reduce hospital errors in providing medication [7]. This usually occurs because the hospital lacks data from these patients, with big data this will not happen [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Tutuko¹,

Nurmaini²,

Rachmatullah³

et al. 2020

ComEngApp

View full text Add to dashboard Cite

Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services.

show abstract

Section: Industry Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Tutuko¹,

Nurmaini²,

Rachmatullah³

et al. 2020

ComEngApp

View full text Add to dashboard Cite

show abstract

“…5,6 Member hospitals upload alert data generated by their smart pumps to the IPI collaborative database for data sharing and comparative analytics (Figure 1). IPI supports data from several smart pump vendors, including Hospira and Alaris.…”

mentioning

confidence: 99%

Changing Smart Pump Vendors: Lessons Learned

et al. 2016

Self Cite

View full text Add to dashboard Cite

Smart infusion pump technology is a mainstay in health care, and the integration and use of those pumps is crucial for patient safety. An institution purchasing smart infusion pumps has the ability to trial the various vendors before purchase, however literature that documents a conversion from one pump to another is lacking. This article describes the conversion from one smart infusion pump platform to another at a government institution and a large multisite facility. The differences in 2 smart infusion pumps are described as well as lessons learned following the conversion in both organizations.

show abstract