A review of the literature, mainly since 1975, examining ways and means of assessing the quality and consistency of nursing care, and maintenance of high standards. Reference is made to audits, tests and peer reviews.
BackgroundUnnecessary testing for Clostridium difficile infection (CDI) can be both wasteful and contra productive—retesting the same positive patient after transfer to a new nursing unit will only to confirm the patient has CDI (already known) and likely be classified as a new case of hospital-onset (HO) CDI. Yet, it is also important to recognize community-onset (CO) CDI in hospital, not only because it prevents late recognition of CO CDI as being classified as an HO event, will also to afford appropriate contact precautions and therapeutic measures are instituted in a timely fashion. Laboratory stewardship (LS) can be helpful in improving appropriateness of C. difficile testing.MethodsWe developed 2 CDI testing algorithms. One focused on hospital days 1–3, the other for all C. difficile testing after hospital day 3 (AHD3). The LS quality improvement (QI) project was rolled out in 2 stages. During the first 6 months we focused on improving early detection of CO-CDI, while during the next 6 months a mandatory review of all C. difficile testing orders AHD3 was conducted by a 10 person team. Testing that concurred with the algorithm was approved. Nonapproval was communicated to the care teams. Appeals could be made on a case-by-case basis to the medical director of infection control. Validation audits of nonapproved cases were performed to determine whether testing algorithms were sound.ResultsCO-CDI detection steadily increased over the yearlong LS QI period (average of 6 cases/week at start vs. 12 cases/week at year’s end). During the 6 months of the AHD3 mandatory order review 678 C. difficile orders were placed, 428 (63.1%) were approved, 250 (36.9%) were rejected. Reduced use of laboratory resources is estimated to have saved $14,950. LS and frequent communication with care teams contributed better recognition of CO-CDI, decreased inappropriate repeat testing, avoidance of diagnosing colonized patients as HO-CDI and was associated with a significantly drop our CDI SIR (Figure 1).ConclusionAn algorithm-based guideline for a 2-step LS QI program focused on reviews of all C. difficile orders AFHD3 as well as improving early detection of CO-CDI and was associated with better laboratory resource utilization and markedly decreased C. difficile SIR. Efforts are currently underway to automate much of the review process. Disclosures J. P. Parada, Merck: Speaker’s Bureau, Speaker honorarium. A. Harrington, Biofire: Grant Investigator and Scientific Advisor, Consulting fee, Research grant and Speaker honorarium. Cepheid: Grant Investigator and Speaker’s Bureau, Research grant.
BACKGROUND In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions. METHODS A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children’s hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions. RESULTS The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%–12.8%) and 11.1% (95% CI: 10.8%–11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431–$2 735 364). CONCLUSIONS A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.