de Lusignan S et al. 2002 "Does feedback improve the quality of computerized medical records in primary care?"
Reference
de Lusignan S, Stephens PN, Adal N, et al. Does feedback improve the quality of computerized medical records in primary care? J Am Med Inform Assoc 2002;9(4):395-401.
Abstract
"Objective: The [data quality feedback] database collects anonymized information from general practice computer systems in the United Kingdom, for research purposes. Data quality markers are collated and fed back to the participating general practitioners. The authors examined whether this feedback had a significant effect on data quality.
Methods: The data quality markers used since 1992 were examined. The authors determined whether the feedback of "useful" data quality markers led to a statistically significant improvement in these markers. Environmental influences on data quality from outside the scheme were controlled for by examination of the data quality scores of new entrants.
Results: Three quality markers improved significantly over the period of the study. These were the use of highly specific "lower-level" Read Codes (p=0.004) and the linkage of repeat prescriptions (p=0.03) and acute prescriptions (p=0.04) to diagnosis. Clinicians who fall below the target level for linkage of repeat prescriptions to diagnosis receive more detailed feedback; the effect of this was also statistically significant (p<0.01.)
Conclusions: The feedback of four of the ten markers had a significant effect on data quality. The effect of more detailed feedback appears to have had a greater effect. The lessons learned from this approach may help improve the quality of electronic medical records in the United Kingdom and elsewhere."
Methods: The data quality markers used since 1992 were examined. The authors determined whether the feedback of "useful" data quality markers led to a statistically significant improvement in these markers. Environmental influences on data quality from outside the scheme were controlled for by examination of the data quality scores of new entrants.
Results: Three quality markers improved significantly over the period of the study. These were the use of highly specific "lower-level" Read Codes (p=0.004) and the linkage of repeat prescriptions (p=0.03) and acute prescriptions (p=0.04) to diagnosis. Clinicians who fall below the target level for linkage of repeat prescriptions to diagnosis receive more detailed feedback; the effect of this was also statistically significant (p<0.01.)
Conclusions: The feedback of four of the ten markers had a significant effect on data quality. The effect of more detailed feedback appears to have had a greater effect. The lessons learned from this approach may help improve the quality of electronic medical records in the United Kingdom and elsewhere."
Objective
To analyze whether providing feedback to providers based on the collection of data quality markers had a significant effect on data quality.
Type Clinic
Primary care
Type Specific
Family practice
Size
not applicable
Geography
Urban, suburban, and rural
Other Information
The study was conducted using the MediPlus database, which includes 500 representative general practitioners across the United Kingdom. The system includes information on approximately 2 million patients.
Type of Health IT
Electronic health records (EHR)
Data feedback system
Type of Health IT Functions
"Data quality markers are used to ensure that only doctors supplying data that reaches specified quality standards are included in the database used by researchers. In total, ten data quality scores are used. These are calculated at individual doctor level and fed back to the participating practices quarterly... Doctors are given a small incentive (about £400 per doctor per year) to reach the target levels across the ten quality scores used."
Workflow-Related Findings
"The quality markers showing a significant improvement with time ... were (1) percentage of acute prescriptions linked to a diagnosis, (2) percentage of repeat prescriptions linked to a diagnosis, (3) percentage of problems defined by a Read Code of level 3 or lower [and] (4) percentage of notes in which Read Code is level 3 or lower. The quality markers that did not significantly change were (1) ratio of repeat to acute prescriptions, (2) percentage active patients seen in last 12 months, (3) percentage of patients with year of birth and sex recorded, (4) percentage of notes linked to diagnosis, (5) [number] of prescriptions per 1,000 patients, and (6) Percentage [of prescriptions] with dose details."
"Feedback of half the markers achieved significant improvement, while feedback of others did not. Feedback of this nature is not, therefore, in itself an effective mechanism, but it may represent a low-cost tool that can be used alongside other tools."
Study Design
Only postintervention (no control group)
Study Participants
The total number of general practitioners that joined the study was 576.