Quantifying the impact of patient boarding on emergency department length of stay: All admitted patients are negatively affected by boarding

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case

BACKGROUND

Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

METHODS

Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two.

RESULTS

All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores.

CONCLUSIONS

As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

Original Research: Breaking Through the Bottleneck: Acuity Adaptability in Noncritical Trauma Care

BACKGROUND

Achieving efficient throughput of patients is a challenge faced by many hospital systems. Factors that can impede efficient throughput include increased ED use, high surgical volumes, lack of available beds, and the complexities of coordinating multiple patient transfers in response to changing care needs. Traditionally, many hospital inpatient units operate via a fixed acuity model, relying on multiple intrahospital transfers to move patients along the care continuum. In contrast, the acuity-adaptable model allows care to occur in the same room despite fluctuations in clinical condition, removing the need for transfer. This model has been shown to be a safe and cost-effective approach to improving throughput in populations with predictable courses of hospitalization, but has been minimally evaluated in other populations, such as patients hospitalized for traumatic injury.

PURPOSE

This quality improvement project aimed to evaluate implementation of an acuity-adaptable model on a 20-bed noncritical trauma unit. Specifically, we sought to examine and compare the pre- and postimplementation metrics for throughput efficiency, resource utilization, and nursing quality indicators; and to determine the model's impact on patient transfers for changes in level of care.

METHODS

This was a retrospective, comparative analysis of 1,371 noncritical trauma patients admitted to a level 1 trauma center before and after the implementation of an acuity-adaptable model. Outcomes of interest included throughput efficiency, resource utilization, and quality of nursing care. Inferential statistics were used to compare patients pre- and postimplementation, and logistic regression analyses were performed to determine the impact of the acuity-adaptable model on patient transfers.

RESULTS

Postimplementation, the median ED boarding time was reduced by 6.2 hours, patients more often remained in their assigned room following a change in level of care, more progressive care patient days occurred, fall and hospital-acquired pressure injury index rates decreased respectively by 0.9 and 0.3 occurrences per 1,000 patient days, and patients were more often discharged to home. Logistic regression analyses revealed that under the new model, patients were more than nine times more likely to remain in the same room for care after a change in acuity and 81.6% less likely to change rooms after a change in acuity. An increase of over $11,000 in average daily bed charges occurred postimplementation as a result of increased progressive care-level bed capacity.

CONCLUSIONS

The implementation of an acuity-adaptable model on a dedicated noncritical trauma unit improved throughput efficiency and resource utilization without sacrificing quality of care. As hospitals continue to face increasing demand for services as well as numerous barriers to meeting such demand, leaders remain challenged to find innovative ways to optimize operational efficiency and resource utilization while ensuring delivery of high-quality care. The findings of this study demonstrate the value of the acuity-adaptable model in achieving these goals in a noncritical trauma care population.

Emergency Department Overcrowding: Understanding the Factors to Find Corresponding Solutions

It is certain and established that overcrowding represents one of the main problems that has been affecting global health and the functioning of the healthcare system in the last decades, and this is especially true for the emergency department (ED). Since 1980, overcrowding has been identified as one of the main factors limiting correct, timely, and efficient hospital care. The more recent COVID-19 pandemic contributed to the accentuation of this phenomenon, which was already well known and of international interest. Considering what would appear to be a trivial definition of overcrowding, it may seem simple for the reader to hypothesize solutions for what seems to be one of the most avoidable problems affecting the hospital system. However, proposing solutions to overcrowding, as well as their implementation, cannot be separated from a correct and precise definition of the issue, which must consider the main causes and aggravating factors. In light of the need of finding solutions that can put an end to hospital overcrowding, this review aims, through a review of the literature, to summarize the triggering factors, as well as the possible solutions that can be proposed.