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Blockchain Technology and 6G
Published in Vinay Rishiwal, Sudeep Tanwar, Rashmi Chaudhry, Blockchain for 6G-Enabled Network-Based Applications, 2023
Shyam Mohan, S. Ramamoorthy, Vedantham Hanumath Sreeman, Venkata Chakradhar Vanam, Kota Harsha Surya Abhishek
Smart healthcare comprises numerous members, such as experts and patients, clinical centers, and assessment foundations. It characteristically incorporates various estimations, including sickness neutralization and checking, finding, and treatment; a crisis facility on the board, a prosperity dynamic, and clinical investigation. Information developments, such as IoT, convenient Internet, disseminated figuring, enormous data, 6G, microelectronics, and artificial thinking, alongside present-day biotechnology, establish rigorous human administrations. These developments are commonly employed in all aspects of clearly defined social protection. From patients’ viewpoint, they will utilize portable gadgets to track their success accurately and check for therapeutic assistance from associates, even as homes conduct affairs with far-off organizations. From the professionals’ perspective, there is a structure of watchful professional decision making, using solid procedures to assist and even enhance confidence in the decision making. Professionals can administer clinical information through an organized information vehicle that consolidates the laboratory information management system, such as Picture Archiving and Communication Systems (PACS), electronic medical records, etc. Continuously precise clinical techniques can be cultivated by means of cautious robots and the development of mixed reality. Securing 6G healthcare should go one step further than 5G frameworks to unwind the in-patient problem [41].
Introduction to Trace Environmental Quantitative Analysis (TEQA)
Published in Paul R. Loconto, Trace Environmental Quantitative Analysis, 2020
One answer to this question can be found in Figure 1.8. The scenario “from human specimen to analytical result” is listed in terms of five essential and sequential steps, each linked by a chain-of-custody protocol. The arrows show that the relationship between steps must include a chain-of-custody protocol. This protocol might take the form of a written document. If, however, a Laboratory Information Management System (LIMS) is in place, the protocol would include data entry into a computer that utilizes an LIMS. Referring to Figure 1.8, the sample prep lab may give to the analyst a complete sample extract along with a signed chain-of-custody form to provide evidence as to where the sample extract is headed next. This five-step approach to biomonitoring is also applicable to trace enviro-chemical quantitative analysis.
Laboratory Controls
Published in Graham P. Bunn, Good Manufacturing Practices for Pharmaceuticals, 2019
For instrumental analytical systems and computer systems such as Laboratory Information Management System (LIMS), attention should be paid to curbing the ability to alter data in temporary memory before it becomes permanent. For example: data that is entered into LIMS should be automatically saved if the user attempts to leave the data-entry area without explicitly saving it; if a user attempts to change a previously entered value, they should be immediately prompted to provide a reason for the change. Audit trail reviews must be performed and should include all data—both the explicitly saved data and any data left in temporary memory—and there should be evidence available to confirm that review of the relevant audit trails have taken place.
A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company
Published in Production Planning & Control, 2023
Amine Belhadi, Sachin S. Kamble, Angappa Gunasekaran, Karim Zkik, Dileep Kumar M., Fatima Ezahra Touriki
In this step, the team aimed to develop and validate the measurement system to collect the baseline data of the process. For this purpose, a metadata repository containing more than 100 process variables was prepared. Then, data were extracted using the PI system from two primary sources:Distributed Control System (DCS): for variables with direct information issued from sensors at the process level. The extraction was made at a sampling frequency of 1 min.Laboratory Information Management System (LIMS): laboratory testing and analyses at the workshop level. The frequencies of extraction vary from one variable to another.
Profiling Readmissions Using Hidden Markov Model - the Case of Congestive Heart Failure
Published in Information Systems Management, 2021
Ofir Ben-Assuli, Tsipi Heart, Joshua R. Vest, Roni Ramon-Gonen, Nir Shlomo, Robert Klempfner
We drew on six hospital information systems for demographic, encounter, and clinical data. Sheba Medical Center is an intensively computerized hospital, using six information systems including: (1) a comprehensive Chameleon® EMR system that communicates with (2) the Laboratory Information Management System (LIMS) (biochemistry, blood counts, serology, microbiology cultures, biomarkers, genetic tests, etc.) and (3) Picture Archiving and Communication System (PACS) for imaging result notifications and access. The system is used in the ED and in all out- and inpatient departments including Cardiology, and fully replaces all paper-based medical records. This EMR communicates with (4) the administrative ATD (Admit-Transfer-Discharge) system, which is connected to (5) the Israeli Population Registry to facilitate prompt authentication of patients upon presentation at the hospital. In addition, there are specific departmental ISs, such as (6) the Magic system, describing imagery results and manually monitored parameters (patient temperature, blood pressure, pulse, saturation, weight, height etc.).
Pharmaceutical quality control laboratory digital twin – A novel governance model for resource planning and scheduling
Published in International Journal of Production Research, 2020
Miguel R. Lopes, Andrea Costigliola, Rui Pinto, Susana Vieira, Joao M.C. Sousa
The demand for analytical work can be quantified as the time-varying volume of incoming samples. Data procured from the existing Laboratory Information Management System (LIMS) was processed to develop a data-driven Sample Generation Framework (SGF) that accurately emulates the arrival of samples, considering important factors such as the effect of seasonality, the actual inter-arrival times between consecutive samples and whether the samples arrive one at the time or grouped in a batch (Table 1).