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Data and Information
Published in Nikhilesh Krishnamurthy, Amitabh Saran, Building Software, 2007
Nikhilesh Krishnamurthy, Amitabh Saran
Paperless offices and societies have not been attained. Paper continues to be an efficient method of capturing information. It requires low setup and support costs (a pen and a paper), allows cheaper scalable and parallel processing (at a DMV office, for example, it is less expensive to have many people fill in paper forms in parallel than to provide an entry terminal for each person), and, in many cases, doubles as both an application and a proof of application because of the formal signature on it. Carbon copies can be retained by the applicant for his or her records. Paper, however, is less efficient in getting it processed downstream. Data entry can introduce errors and has a cost. Paper forms do not provide the intelligence and validation that electronic forms can offer. However, a system designer should consider paper forms as a valid design option. They are not outdated. (Many Web sites still basically serve up forms supplying them as downloads or screen forms.)
Human Factors Engineering
Published in Richard C. Fries, Handbook of Medical Device Design, 2019
Manual data entry functions should be designed to establish consistency of data entry transactions, minimize user’s input actions and memory load, ensure compatibility of data entry with data display, and provide flexibility of user control of data entry. The system should provide feedback to the user about acceptance or rejection of an entry.
Human Factors
Published in Paul H. King, Richard C. Fries, Arthur T. Johnson, Design of Biomedical Devices and Systems, 2018
Paul H. King, Richard C. Fries, Arthur T. Johnson
Manual data entry functions should be designed to establish consistency of data entry transactions, minimize user’s input actions and memory load, ensure compatibility of data entry with data display, and provide flexibility of user control of data entry. The system should provide feedback to the user about acceptance or rejection of an entry.
Six Sigma to reduce claims processing errors in a healthcare payer firm
Published in Production Planning & Control, 2020
Vijaya Sunder M, Nidhin R. Kunnath
A large US-based healthcare payer firm has its back-office operations offshored to their outsourced center at India. With a staff count of approximately 1200 full-time employees, the operations center handles some key processes that include data entry, claims adjudication, payments, call center operations, and reconciliation. One of the chief aims of offshoring is to realize the cost arbitration benefits in the Indian market. However, this is not at a cost of quality delivery to the clients. Hence, quality and responsiveness targets have been established in the service level agreements and the performance is monitored periodically. The value chain of the operations begins when the claimants submit their medical claims along with the supporting documents. The supporting documents include doctor’s prescriptions, discharge summaries, surgical diagnosis, medical bills, special diagnosis like X-rays, scan reports, etc. The received claims reach the outsourced center as scanned images. The relevant data from these scanned images is entered into the firm’s inhouse database through a manual data entry process. Then, claims are checked for correctness and relevancy based on the respective medical plans and then a payment decision is made for eligible claimants.
Shop floor to cloud connect for live monitoring the production data of CNC machines
Published in International Journal of Computer Integrated Manufacturing, 2020
Prathima B A, Sudha P N, Suresh P M
Accuracy of data capture improved considerably as the different types of idle time are authentically captured on the above screen. Probable causes of idle time and probable causes of rejection are already built into the system, enabling a broad framework to the operators. This helped the data flow in a structured manner, which improved operator involvement through the data entry into the HMI. It was not possible for the operator to fill up the data unless he knew the type of defect enlisted in the HMI screen. The expected time saving, after a study of about 6 months of implementation, is found to be an average of 20 min per shift, subject to standard operating conditions. The calculation of increase in production due to the accuracy in the idle time computation is indicated in Table 2.
Personal protective equipment doffing practices of healthcare workers
Published in Journal of Occupational and Environmental Hygiene, 2019
Linh T. Phan, Dayana Maita, Donna C. Mortiz, Rachel Weber, Charissa Fritzen-Pedicini, Susan C. Bleasdale, Rachael M. Jones
Data were recorded on paper forms and entered into a database using double data entry (Access, 2016; Microsoft, Redmond, WA). All data analysis was performed with the R project for Statistical Computing (The R Foundation for Statistical Computing, Vienna, Austria). Differences in proportions among HCW job role groups, hospital unit groups, and patient isolation categories were tested with the χ2 test where expected values were determined using the overall mean proportion. Statistical significance was set at α = 0.05 for all tests. Although HCWs could participate more than one time, observations were treated as independent in the statistical analyses because HCWs performed different type of care activities and/or on different patients during each observation.