Production Error Rate
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Human Error Probability Table
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improvement approach reduces errors in records Tweet Print Janet Jacobsen, American Society for Quality Tags: continuous improvement, lean manufacturing Routine floor audits and data-gathering protocols uncovered some surprises at MEDRAD:
Acceptable Error Rate Six Sigma
Up to 20 percent of all in-process device history record (DHR) packets contained how to calculate human error percent an error. This rate was particularly troubling because MEDRAD employees were double- and even triple-checking the required paperwork for acceptable error rate manufacturing the medical devices manufactured by the company to ensure accuracy prior to submission to the product release coordinator. The company, located in Pittsburgh, is a global leader in developing, manufacturing, selling and http://www.bls.gov/web/empsit/cesvarpe.htm servicing diagnostic imaging and therapeutic medical devices used to diagnose and treat cardiovascular and other diseases. A 2003 winner of the prestigious Malcolm Baldrige National Quality Award, MEDRAD employs more than 2,000 people. Recognition of Errors Leads to Team Improvement Project Operating in a government-regulated environment, MEDRAD places great importance on product and operational quality as well as on specific paper trail requirements such as http://www.reliableplant.com/Read/27007/Continuous-improvement-reduces-errors DHRs. Created for each medical product assembled, DHRs contain records of the manufacture date, assemblers responsible for manufacturing, test results and more. DHRs are also known as traveler packages, according to Matt Boyle, plant quality manager. “The traveler package is simply the paper documentation that travels with the unit during assembly,” says Boyle, an American Society for Quality senior member and the chair-elect for ASQ’s Pittsburgh Section. “The manufacturing steps are recorded, signed and dated. This becomes our objective evidence that the unit was built to specification,” In 2004, routine floor audits uncovered that up to 20 percent of all in-process DHR packets contained an error. Additionally, data collected during a subsequent protocol confirmed that processes for assuring DHR quality prior to product release were ineffective and causing unnecessary rework and delays during the product release process. The data captured the attention of company leaders in the compliance and manufacturing areas and led to a continuous improvement project to reduce the error rates. Aligning Project Goals to Corporate Goals At MEDRAD, the impact of all continuous improvement projects is assessed against five corporate objectives as follows: Exceed financials. Grow the comp
flagged as suspicious by conventional OCR engines. These errors cannot be cost effectively found by manual review, and are often not found by other error correction technology, hence they http://www.primerecognition.com/clean_data.htm flow through to the end user's application. For most applications, errors in the application data are extremely costly. This document shows how Prime Recognition’s High Accuracy OCR engine, called PrimeOCR, can reduce the number of errors left in recognized data, AFTER manual error correction, by more than 75%. OCR error rates are highly variable based on the quality of images, font types, error rate etc. This analysis uses a single, relatively typical, example to make it easy to follow. Feel free to contact Prime Recognition for a more elaborate model of error rates, or to ask how your particular situation may differ from this model. For many applications this analysis will understate the quality improvement of using Prime Recognition’s OCR engine. This analysis uses Prime Recognition's human error rate entry level engine, which produces 65%, or two thirds fewer errors than conventional OCR. Prime Recognition also offers higher accuracy options that produce 82%, or four fifths fewer errors than conventional OCR engines. Image Conversion Alternatives An image of a document, i.e., a piece of paper converted into pixels in computer memory, is of little value unless you also electronically capture information about the image’s content. Ideally you want to capture all the text that appears on the document. The fast growth of imaging systems in recent years for automated processing of insurance forms, medical claims, legal documents, and other types of data on paper suggests that there is tremendous value in electronically capturing the text information of an image. Currently, the most common way to capture this information is multiple pass manual data entry. Multiple passes (i.e., typing in the same text 2-3 times, comparing the results, and fixing the discrepancies) are required because single pass is not accurate enough for most applications. A common accuracy target is 99.95% accuracy, or .5 errors in 1000 characters. Three pass manual data entry can usually
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