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Interoperability and Information-Sharing Paradigm for IoT-Enabled Healthcare
Published in Sanjay Kumar Biswash, Sourav Kanti Addya, Cloud Network Management, 2020
Brian Desnoyers, Kendall Weistroffer, Jenna Hallapy, Sandeep Pisharody
Evidence-based medicine is the practice of integrating the experiences and knowledge of a healthcare provider with external clinical evidence and patient needs [317, 318]. This integrated evidence comes in many forms with the responsibility for seeking out the best external evidence falling, at least partially, on healthcare providers. The “gold standard” source of external evidence is the randomized controlled trial (RCT) [318, 349], that often evaluates treatment effectiveness on the population scale through clinical epidemiology. Integration of external data is practiced by many healthcare providers today and has shortened the gap for new clinical research to be widely utilized in medical practice [252]. However, a significant challenge remains as healthcare providers must still decide how new studies pertain to their individual patients at the time of care [252]. Eric Topol uses the example of widely prescribed statin drugs for preventing endpoints, such as stroke and heart attack, to illustrate this challenge [4]: Instead of identifying the 1 person or 2 people out of every 100 who would benefit, the whole population with the criteria that were tested is deemed treatable with sufficient, incontrovertible statistical proof.
The Rational Diagnostician
Published in Pat Croskerry, Karen S. Cosby, Mark L. Graber, Hardeep Singh, Diagnosis, 2017
Like Stanovich [1], Bornstein and Emler [12] equated irrational decision making with the influence of biases and proposed that improved rationality might be achieved with an increased awareness of bias. Cognitive debiasing training, therefore, might mitigate the influence of biases and improve rationality. They further pointed out that evidence-based medicine (EBM) itself would facilitate improved decision making: “by providing the most relevant and objective empirical information available, and incorporating it with clinical expertise, test results and patient preferences, many of the biases associated with doctors’ relying too heavily on intuition and selectively attending to some information while ignoring other relevant information could be avoided.” In contrast, the treatment of rationality by Rao in Rational Medical Decision Making [13] contains no reference to biases other than those associated with study design: allocation of subjects and bias in the preparation of systematic reviews (publication bias, citation bias). There is no discussion of any of the common cognitive biases anywhere in the book. This is not to disparage Rao’s thorough treatment of the important area of clinical epidemiology and biostatistics, that is, quantitative medical decision making, which, as noted in Chapters 2 and 14 (Medical Decision Making; Medical Education and the Diagnostic Process), is a vital aspect of clinical decision making, but it serves to illustrate the widely differing views of what it is to be rational in medicine.
Biochemistry apps as enabler of compound and DNA computational: next-generation computing technology
Published in A. K. Haghi, Lionello Pogliani, Eduardo A. Castro, Devrim Balköse, Omari V. Mukbaniani, Chin Hua Chia, Applied Chemistry and Chemical Engineering, 2017
Etheredge concluded that using rapid learning techniques not only can improve patient safety but also can lead to substantial improvements in the quality and cost of care by turning all of the raw digital data into knowledge where these rapid learning health networks can enable doctors and researchers to better practice evidence-based medicine. Evidence-based medicine is the use of treatments judged to be the best practice for a certain population on the basis of scientific evidence of expected benefits and risks.
Dynamic multi-type patient advance scheduling for a diagnostic facility considering heterogeneous waiting time targets and equity
Published in IISE Transactions, 2022
Liping Zhou, Na Geng, Zhibin Jiang, Xiuxian Wang
Over the past two decades, advances in medical technology and evidence-based medicine have greatly improved the ability of physicians to detect, diagnose, and treat diseases and injuries (European Science Foundation, 2007; Hendee et al., 2010; Lavelle et al., 2015). For example, medical imaging can provide definitive diagnostic information about the physiological features of patients, and has revolutionized the diagnosis and treatment methods of many diseases (Hendee et al., 2010). Diagnostic imaging resources such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasonic diagnosis, and positron emission tomography are widely used in medical diagnoses and are critical resources in hospitals, due to their high procurement and operating costs.