Setbacks in Clinical Laboratory Innovations

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dc.contributor.advisorCarter, Teresa
dc.creatorGrindstaff, Lori
dc.date.accessioned2021-11-02T17:09:29Z
dc.date.available2021-11-02T17:09:29Z
dc.date.issued2021-10-25
dc.description.abstractTechnological advancements are soaring in healthcare as a whole; however, advancements in the clinical laboratory have fallen behind. Innovation in the clinical laboratory can be complicated and comes with high risks. Consequently, new technology and its acceptance have fallen short. The slow growth of artificial intelligence (AI), machine learning (ML), and deep learning (DL) keeps laboratory technicians working harder and healthcare costs soaring. This technological lag perpetuates analysis delays, unnecessary testing, human errors, suboptimal patient care, and in an untimely fashion. The goal of this research is to prove the significant need for innovations in laboratory medicine and establish a means to accomplish them. This paper will include analyses of articles, peer reviewed articles, websites, and journals that are no more than three years old to show how laboratory innovations can enhance turnaround times, lower healthcare costs, increase the level of patient care, and allow for greater patient outcomes.en_US
dc.identifier.urihttp://hdl.handle.net/11558/5905
dc.language.isoen_USen_US
dc.subjectLaboratory, clinicalen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectHealth record (electronic)en_US
dc.subjectClinical decision supporten_US
dc.subjectPoint of care testingen_US
dc.subjectTest recommendation toolsen_US
dc.subjectDeep learningen_US
dc.titleSetbacks in Clinical Laboratory Innovationsen_US
dc.typeThesisen_US

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