Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.
Summarising the complex data generated by multiple cross sectional quality indicators in a way that patients, clinicians, managers and policymakers find useful is challenging. A common approach is aggregation to create summary measures such as star ratings and balanced score cards, but these may conceal the detail needed to focus quality improvement. We propose an alternative way of summarising and presenting multiple quality indicators, suitable for use for quality improvement and governance. This paper discusses (1) control charts for repeated measurements of single processes as used in industrial statistical process control (SPC); (2) control charts for cross sectional comparison of many institutions for a single quality indicator (rarely used in industry but commonly proposed for health care); and (3) small multiple graphics which combine control chart signal extraction with efficient graphical presentations for multiple indicators.
Guthrie
B
B
Department of Community Health Sciences, University of Dundee, Dundee DD2 4BF, UK. b.guthrie@chs.dundee.ac.uk
Love
T
T
Fahey
T
T
Morris
A
A
Sullivan
F
F
D000328:Adult; D002648:Child; D016001:Confidence Intervals; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000628:therapy; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007223:Infant; D008137:Longitudinal Studies; D011320:Primary Health Care; D011785:Quality Assurance, Health Care; D019984:Quality Indicators, Health Care; D012606:Scotland; D013745:Tetanus Toxoid / Q000008:administration & dosage
D006442:Glycated Hemoglobin; D013745:Tetanus Toxoid