Dataset Information

Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.

ABSTRACT: 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.

SUBMITTER: Guthrie B

PROVIDER: S-EPMC1744105 | biostudies-literature | 2005 Dec

REPOSITORIES: biostudies-literature

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Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.

Guthrie B B Love T T Fahey T T Morris A A Sullivan F F

Quality & safety in health care 20051201 6

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. T ...[more]

PMID: 16326793

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Project description:BackgroundA widely discussed design issue in patient satisfaction questionnaires is the optimal length and labelling of the answering scale. The aim of the present study was to compare intra-individually the answers on two response scales to five general questions evaluating patients' perception of hospital care.MethodsBetween November 2011 and January 2012, all in-hospital patients at a Swiss University Hospital received a patient satisfaction questionnaire on an adjectival scale with three to four labelled categories (LS) and five redundant questions displayed on an 11-point end-anchored numeric scale (NS). The scales were compared concerning ceiling effect, internal consistency (Cronbach's alpha), individual item answers (Spearman's rank correlation), and concerning overall satisfaction by calculating an overall percentage score (sum of all answers related to the maximum possible sum).ResultsThe response rate was 41% (2957/7158), of which 2400 (81%) completely filled out all questions. Baseline characteristics of the responders and non-responders were similar. Floor and ceiling effect were high on both response scales, but more pronounced on the LS than on the NS. Cronbach's alpha was higher on the NS than on the LS. There was a strong individual item correlation between both answering scales in questions regarding the intent to return, quality of treatment and the judgement whether the patient was treated with respect and dignity, but a lower correlation concerning satisfactory information transfer by physicians or nurses, where only three categories were available in the LS. The overall percentage score showed a comparable distribution, but with a wider spread of lower satisfaction in the NS.ConclusionsSince the longer scale did not substantially reduce the ceiling effect, the type of questions rather than the type of answering scale could be addressed with a focus on specific questions about concrete situations instead of general questions. Moreover, the low correlation in questions about information provision suggests that only three possible response choices are insufficient. Further investigations are needed to find a more sensitive scale discriminating high-end ratings. Otherwise, a longitudinal within-hospital or a cross-sectional between-hospital comparison of patient care is questionable.

Dataset Information

Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.

Publications

Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.

Similar Datasets

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