Purpose
Clinical decision support (CDS) systems could be valuable tools in reducing aminoglycoside prescribing errors. We evaluated the impact of CDS on initial dosing, interval, and pharmacokinetic outcomes of amikacin and tobramycin therapy.
Methods
A complex CDS advisor to provide guidance on initial dosing and monitoring, using both traditional and extended interval dosing strategies, was integrated into computerized provider order entry (CPOE) and compared to a control group which featured close pharmacy monitoring of all aminoglycoside orders. A random sample of 118 patients from an academic, tertiary care medical center prescribed amikacin and tobramycin prior to advisor implementation was compared to 98 patients admitted following advisor implementation. Primary outcome was an initial dose within 10% of a dose calculated to be adherent to published dose guidelines. Secondary outcomes were a guideline-adherent interval, trough and peak concentrations in goal range, and incidence of nephrotoxicity.
Results
Of 216 patients studied, 97 were prescribed amikacin and 119 were prescribed tobramycin. The primary outcome of initial dosing consistent with guideline-based care increased from 40% in the pre-advisor arm to 80% in the post-advisor arm (p<0.001), with a number needed to treat of 3 patients to prevent one incorrect dose. Correct initial interval based on renal function also increased from 63% to 87% (p<0.001). The changes in initial dosing and interval resulted in an increase of trough concentrations in the goal range from 59% pre-advisor to 89% post-advisor implementation (p=0.0004). There was no significant difference in peak concentrations in goal range or incidence of nephrotoxicity (25% vs. 17%, p=0.2).
Conclusion
An advisor for aminoglycoside dosing and monitoring integrated into CPOE significantly improves initial dosing, selection of interval, and trough concentrations at goal compared to unassisted physician dosing.
Crowdsourcing is an effective, inexpensive method for generating a knowledge base of problem-medication pairs that is automatically mapped to local terminologies, up-to-date, and reflective of local prescribing practices and trends.
For the select small number of constipated patients that cannot be managed medically, surgical options should be considered. Increases in our knowledge of colorectal physiology and experience have fostered improvements in patient evaluation and surgical management. Currently, patients with refractory colonic inertia are offered total abdominal colectomy and ileorectal anastomosis, often with laparoscopic techniques. With proper patient selection, the results have been excellent for resolving the frequency and quality of bowel movements. However, symptoms such as bloating and abdominal pain, which may be related to irritable bowel syndrome rather than the colonic inertia, may persist.
SummaryBackground: Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. Objective: We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. Methods: We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problemmedication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. Results: The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. Conclusions: We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.
Research Article
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