Sentence examples for data quality outcomes from inspiring English sources

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In terms of operational circumstances, which have been described in detail elsewhere, the registry may be considered as a typical example for an urban developing country cancer registry with appropriate data quality outcomes (Sankaranarayanan et al, 1998; Parkin et al, 2003; Chokunonga et al, 2004).

Similar(59)

A cross-sectional design employing hospital admission data from 76 general practices in Northamptonshire, England for 2006 08, including demographic data, quality and outcomes framework points and GP patient survey outcomes.

The new Medicare Prescription Drug, Improvement, and Modernization Act (2003) mandates the collection and use of data for quality, outcomes measurement, program administration, and facilitating consumer choice.

Lenders like Skills Fund now fund about 20% of coding bootcamp students and as a result collect limited but very high quality outcomes data.

Study methods were assessed using criteria adapted from Wasson et al 15 and Royston et al, 16 including sampling, predictors, follow-up, outcomes, data quality and performance of the rule.

Some sources of routine data such as the Quality Outcomes Framework were not as useful as Hospital Episode Statistics because changes made to the indicators in different years made comparisons over time difficult.

As a template for others to generate next generation DNA sequencing courses, here we describe the ecological metagenomics course, results of student affective surveys, learning outcomes, data quality, and initial findings of the first marine mammal genome sequenced and annotated by undergraduate students.

Analysis of Quality Outcomes Framework data in the National Primary Care Database and exception reporting data from the Information Centre from 8407 practices in England in 2005 – 6.

Furthermore, we assessed data quality of patient-related outcome scores.

Inovalon had designed large-scale systems able to aggregate that data and analyze that data to speak to things like quality outcomes and disease identification.

Issues obstructing progress in data mining for improved health outcomes include data quality problems, data redundancy, data inconsistency, repeated measures, temporal (time-contextual) measures, and data volume.

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