Background Quality improvement professionals often choose between patient-specific interventions, like clinical decision support (CDS), and population-based interventions, like registries or care management. In this paper, we explore the synergy of these two strategies, targeting the problem of procedure documentation for patients with a history of splenectomy.
Methods We developed a population health documentation (PHD) intervention and a CDS intervention to improve splenectomy documentation within our electronic health record. Rates of splenectomy documentation were collected before and after the implementation of both interventions to assess their impact on the rate of procedure documentation.
Results Both the PHD and CDS interventions led to statistically significant (p<0.001) increases in the baseline rate of splenectomy documentation of 27.4 documentations per month. During the PHD intervention, 444.7 splenectomies were documented per month, while 40.8 splenectomies per month were documented during the CDS intervention.
Discussion Both approaches were successful, with the PHD intervention leading to a larger number of incremental procedure documentations, in batches, and the CDS intervention augmenting procedure documentation on an ongoing basis. Our results suggest that population health and CDS strategies complement each other and, where possible, should be used in conjunction.
Conclusions PHD and CDS strategies may best be used in conjunction to create a symbiotic relationship in which current problem and procedure documentation gaps are closed using PHD strategies, while new gaps are prevented through ongoing CDS interventions
- Decision Support, Clinical
- Decision Support, Computerised
- Information Technology
- Quality Improvement
- Quality Measurement
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Contributors DSM assisted in the analysis and interpretation of data and drafted the initial draft of the manuscript with supervision from AW. TG designed and oversaw the population health documentation intervention. AT designed and validated the natural language processing algorithm used for the population health documentation intervention. AW designed and conducted the clinical decision support intervention. All authors provided critical revisions to the manuscript, approved the final version of the manuscript and take responsibility for the integrity and accuracy of this work.
Competing interests None declared.
Ethics approval Partners HealthCare Human Subjects Committee.
Provenance and peer review Not commissioned; externally peer reviewed.
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