Introduction: Acute physiological deterioration is a major contributor to in-hospital morbidity and mortality. Early detection and intervention of deteriorating patients is key to improving patient outcomes. Prior research has demonstrated the effectiveness of Early Warning Systems and other algorithmic approaches in automatically identifying these patients from passively monitoring vital signs.
Methods: In this work, we conduct a prospective pilot study of clinical deployment of the Mayo Clinic Bedside Patient Rescue (BPR) system using an escalating alerting logic enabled by machine learning. Among four units where the BPR system was deployed, time to response and time to intervention for deteriorating patients were significantly reduced relative to matched control units.
Results: In pilot units, time to response decreased by 35.4% (from 63.2 minutes to 40.8 minutes) and time to intervention decreased by 48.5% (from 106.3 minutes to 55.9 minutes). No significant differences were observed in counterbalance metrics of mortality, ICU transfer rate, and Rapid Response Team activation rate. Furthermore, the automated alerting system was well-received by clinicians participating in the pilot study, as assessed by survey.
Discussion: These results demonstrate a successful clinical deployment of a practice-changing machine learning alert system with demonstrable impact on improving patient care.
Objectives: This study was conducted to describe patients at risk for prolonged time alone in the emergency department (ED) and to determine the relationship between clinical outcomes, specifically 30-day hospitalization, and patient alone time (PAT) in the ED.Methods: An observational cohort design was used to evaluate PAT and patient characteristics in the ED. The study was conducted in a tertiary academic ED that has both adult and pediatric ED facilities and of patients placed in an acute care room for treatment between May 1 and July 31, 2016, excluding behavioral health patients. Simple linear regression and t tests were used to evaluate the relationship between patient characteristics and PAT. Logistic regression was used to evaluate the relationship between 30-day hospitalization and PAT.Results: Pediatric patients had the shortest total PAT compared with all older age groups (86.4 minutes versus 131 minutes, P < 0.001). Relationships were seen between PAT and patient characteristics, including age, geographic region, and the severity and complexity of the health condition. Controlling for Charlson comorbidity index and other potentially confounding variables, a logistic regression model showed that patients are more likely to be hospitalized within 30 days after their ED visit, with an odds ratio (95% confidence interval) of 1.056 (1.017-1.097) for each additional hour of PAT.Conclusions: Patient alone time is not equal among all patient groups.Study results indicate that PAT is significantly associated with 30-day hospitalization. This conclusion indicates that PAT may affect patient outcomes and warrants further investigation.
Across the United States, many patients, including veteran patients, face barriers in accessing appropriate, timely, and affordable healthcare. When developing or modifying healthcare systems to improve patient access, we can consider strategies (e.g., telehealth) in which lower-cost, more abundant resources are used for services often performed with more constrained and/or more expensive, specialized resources. We propose a framework in which hierarchical care networks allow patients to receive frontline care from lower cost, more accessible alternatives, while specialized resources are reserved for more complex care. We use operations research tools, including mixed-integer programming and facility location models, to design and analyze these networks. We present a case study applying these methods to improve veterans’ access to eye care screenings. The case study results demonstrate that using different providers in locations throughout the system can increase the number of patients screened by nearly four times the number currently screened while increasing the budget by only 2.5%. When designing healthcare networks to improve access, decision makers must consider several trade-offs, including how resources are distributed. Operations research tools are effective methods for understanding, designing, and evaluating these trade-offs to best understand the wide-ranging impacts of resource (re)distribution.
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.