Overall, PPVs were moderate for most of the PSIs. Implementing POA codes and using more specific ICD-9-CM codes would improve their validity. Our results suggest that additional coding improvements are needed before the PSIs evaluated herein are used for hospital reporting or pay for performance.

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Our results suggest that interventions that focus on minimizing preventable inpatient safety events as well as improving coordination of care between and across settings may decrease the likelihood of readmission.

Although almost 42% of unplanned readmissions were identified as clinically related, the majority of unplanned readmissions were unrelated to the index hospitalization. Quality improvement interventions targeted at processes of care associated with the index hospitalization are likely to be most effective in reducing clinically related readmissions. It is less clear how to reduce nonclinically related readmissions; these may involve broader factors than inpatient care.

Key Points Question How can coded and free-text data from electronic medical records be used to support infection tracking and other patient safety surveillance following common but understudied cardiac device procedures? Findings In this national cohort study of 19 212 patients who underwent cardiovascular implantable electronic device procedures in the US Department of Veterans Affairs health care system, an algorithm to reliably identify cases with a true 90-day infection by combining coded data (eg, diagnosis of a comorbid condition) and free-text data extracted from clinical notes (eg, documentation of an infection by a cardiologist) was developed and validated. Text note searching was a useful and straightforward adjunct to coded data for surveillance. Meaning The findings of this study suggest that the algorithm to detect patients who received cardiovascular implantable electronic device and developed an infection has the potential to significantly enhance surveillance in an underserved area.

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The authors therefore propose a systematic methodology to develop trigger tools that takes into consideration previously published work, end-user preferences and expert opinion.