Data Quality and Indicators

Geospatial Data Quality Indicators

Indicators which summarise the characteristics of spatiotemporal data coverages significantly simplify quality evaluation, decision making and justification processes by providing a number of quality cues that are easy to manage and avoiding information overflow. Criteria which are commonly prioritised in evaluating spatial data quality and assessing a dataset’s fitness for use include lineage,...

Data Quality Sensitivity Analysis on Aggregate Indicators

Decision making activities stress data and information quality requirements. The quality of data sources is frequently very poor, therefore a cleansing process is required before using such data for decision making processes. When alternative (and more trusted) data sources are not available data can be cleansed only using business rules derived from domain knowledge. Business rules focus on fi...

AHRQ Quality Indicators Toolkit - Applying the AHRQ Quality Indicators to Hospital Data

• Quality and safety staff, as well as clinical and other staff (e.g., quality and/or patient safety officer at the hospital) involved in quality improvement work, should be involved in assessing the hospital’s performance on the QIs and making decisions on priorities for improvement. • Statisticians, data analysts, and programmers can help to calculate the QIs using data available from the hos...

Quality and Quality Indicators in Peritoneal Dialysis

Colonoscopy Quality Improvement: Quality Indicators and Benchmarks

Introduction Increasing efforts to reform health care and contain medical expenditures have accelerated the push to define, capture, and enforce quality measures for delivered care. This trend is evident particularly for procedural and preventative measures that can have a marked effect on patient outcomes and health care costs, such as the use of colonoscopy for screening and surveillance for ...