Prospective systematic risk analysis of the digital technology use within pharmaceutical care.

BACKGROUND: Digital technologies are present in every phase of a drug lifecycle, from drug design and development to its dispensing and use. However, given the rapid development and implementation of digital solutions, their monitoring, evaluation and risk assessment are limited and lacking. OBJECTIVE: This research is aiming to identify potential errors, quantify and prioritize associated risks in the context of certain technologies used in pharmaceutical care, as well as define corrective measures to improve patient safety and the quality of pharmaceutical care. METHODS: A ten-member multidisciplinary team conducted Failure Mode & Effect Analysis (FMEA) to identify critical risks, their causes and effects, along with developing corrective measures within the selected digital health components: Telepharmacy, mHealth, Artificial intelligence (AI) and Software infrastructure and systems. Critical risks were determined by calculating risk priority numbers (RPNs) from severity, occurence, and detectability scores. RESULTS: This study identified 42 risks regarding the 4 components. After calculating RPNs and the threshold RPN (RPN=30), 8 critical risks were identified. Corrective measures were proposed for these failure modes, after which the risks were re-evaluated (RPN sum was reduced from 414 to 156). The risk with the highest RPN value was Internet/identity fraud, while the rest included inadequate and incomplete data entry and management, flawed implementation, human and technology errors, and lack of transparency, personalization and infrastructure. For the critical risks, 42 different causes were recognized on a system, technological and individual level while their effects were discussed in terms of patient safety and business management in pharmacies. CONCLUSION: Digitalization of pharmaceutical practice promises greater effectiveness of pharmaceutical care, but in order to achieve this, efforts, resources and initiatives must be directed towards timely identification of problems, appropriate monitoring and building adequate infrastructure that can support safe implementation of digital tools and services despite the swift development of innovations.

Bioelectrical Impedance Analysis and Mid-Upper Arm Muscle Circumference Can Be Used to Detect Low Muscle Mass in Clinical Practice.

Identification of low muscle mass becomes increasingly relevant due to its prognostic value in cancer patients. In clinical practice, mid-upper arm muscle circumference (MAMC) and bioelectrical impedance analysis (BIA) are often used to assess muscle mass. For muscle-mass assessment, computed tomography (CT) is considered as reference standard. We investigated concordance between CT, BIA, and MAMC, diagnostic accuracy of MAMC, and BIA to detect low muscle mass and their relation with the clinical outcome malnutrition provided with the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF). This cross-sectional study included adult patients with advanced esophageal and gastrointestinal cancer. BIA, MAMC, and PG-SGA-SF were performed. Routine CT-scans were used to quantify psoas muscle index (PMI) and skeletal muscle area. Good concordance was found between CTPMI and both BIAFFMI (fat free mass index) (ICC 0.73), and BIAASMI (appendicular skeletal muscle index) (ICC 0.69) but not with MAMC (ICC 0.37). BIAFFMI (94%), BIAASMI (86%), and MAMC (86%) showed high specificity but low sensitivity. PG-SGA-SF modestly correlated with all muscle-mass measures (ranging from -0.17 to -0.43). Of all patients with low muscle mass, 62% were also classified with a PG-SGA-SF score of ≥4 points. Although CT remains the first choice, since both BIA and MAMC are easy to perform by dieticians, they have the potential to be used to detect low muscle mass in clinical practice.

Improvement of the Interaction Model Aimed to Reduce the Negative Effects of Cybersickness in VR Rehab Applications.

Virtual reality (VR) has the potential to be applied in many fields, including medicine, education, scientific research. The e-health impact of VR on medical therapy for people cannot be ignored, but participants reported problems using them, as the capabilities and limitations of users can greatly affect the effectiveness and usability of the VR in rehabilitation. Previous studies of VR have focused on the development and use of the technology itself, and it is only in recent years that emphasis has been placed on usability problems that include the human factor. In this research, different ways of adapting interaction in VR were tested. One approach was focused on means of navigating through a VR, while the second dealt with the impact of the amount of animation and moving elements through a series of tests. In conclusion, the way of navigation and the amount of animation and moving elements, as well as their combination, are proven to have a great influence on the use of VR systems for rehabilitation. There is a possibility to reduce the occurrence of problems related to cybersickness if the results of this research are taken into consideration and applied from an early stage of designing VR rehabilitation applications.

On Entropy of Probability Integral Transformed Time Series.

The goal of this paper is to investigate the changes of entropy estimates when the amplitude distribution of the time series is equalized using the probability integral transformation. The data we analyzed were with known properties-pseudo-random signals with known distributions, mutually coupled using statistical or deterministic methods that include generators of statistically dependent distributions, linear and non-linear transforms, and deterministic chaos. The signal pairs were coupled using a correlation coefficient ranging from zero to one. The dependence of the signal samples is achieved by moving average filter and non-linear equations. The applied coupling methods are checked using statistical tests for correlation. The changes in signal regularity are checked by a multifractal spectrum. The probability integral transformation is then applied to cardiovascular time series-systolic blood pressure and pulse interval-acquired from the laboratory animals and represented the results of entropy estimations. We derived an expression for the reference value of entropy in the probability integral transformed signals. We also experimentally evaluated the reliability of entropy estimates concerning the matching probabilities.

Cloud based metalearning system for predictive modeling of biomedical data.

Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.