Delay in reviewing test results prolongs hospital length of stay: a retrospective cohort study

Background: Failure in the timely follow-up of test results has been widely documented, contributing to delayed medical care. Yet, the impact of delay in reviewing test results on hospital length of stay (LOS) has not been studied. We examine the relationship between laboratory tests review time and hospital LOS. Methods: A retrospective cohort study of inpatients admitted to a metropolitan teaching hospital in Sydney, Australia, between 2011 and 2012 (n = 5804). Generalized linear models were developed to examine the relationship between hospital LOS and cumulative clinician read time (CRT), defined as the time taken by clinicians to review laboratory test results performed during an inpatient stay after they were reported in the computerized test reporting system. The models were adjusted for patients’ age, sex, and disease severity (measured by the Charlson Comorbidity index), the number of test panels performed, the number of unreviewed tests pre-discharge, and the cumulative laboratory turnaround time (LTAT) of tests performed during an inpatient stay. Results: Cumulative CRT is significantly associated with prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of prolonged LOS by 13.2% (p < 0.0001). Restricting the analysis to tests with abnormal results strengthened the relationship between cumulative CRT and prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of delayed discharge by 33.6% (p < 0.0001). Increasing age, disease severity and total number of tests were also significantly associated with prolonged LOS. Increasing number of unreviewed tests was negatively associated with prolonged LOS. Conclusions: Reducing unnecessary hospital LOS has become a critical health policy goal as healthcare costs escalate. Preventing delay in reviewing test results represents an important opportunity to address potentially avoidable hospital stays and unnecessary resource utilization. Electronic supplementary material The online version of this article (10.1186/s12913-018-3181-z) contains supplementary material, which is available to authorized users

Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter

The manner in which people preferentially interact with others like themselves suggests that information about social connections may be useful in the surveillance of opinions for public health purposes. We examined if social connection information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers that identify antivaccine opinions. From 42,533 tweets posted between October 2013 and March 2014, 2,098 were sampled at random and two investigators independently identified anti-vaccine opinions. Machine learning methods were used to train classifiers using the first three months of data, including content (8,261 text fragments) and social connections (10,758 relationships). Connection-based classifiers performed similarly to content-based classifiers on the first three months of training data, and performed more consistently than content-based classifiers on test data from the subsequent three months. The most accurate classifier achieved an accuracy of 88.6% on the test data set, and used only social connection features. Information about how people are connected, rather than what they write, may be useful for improving public health surveillance methods on Twitter

Common Variable Immunodeficiency Non-Infectious Disease Endotypes Redefined Using Unbiased Network Clustering in Large Electronic Datasets

Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described {high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)} and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression

Nocturnal Lifestyle Behaviours and Risk of Poor Sleep during Pregnancy

The extent to which lifestyle practices at night influence sleep quality in pregnant women remains unknown. This study aimed to examine whether nocturnal behaviours were associated with poor sleep during pregnancy. We performed a cross-sectional analysis of a prospective cohort of pregnant women at 18–24 gestation weeks recruited from KK Women’s and Children’s Hospital, Singapore, between 2019 and 2021. Nocturnal behaviours were assessed with questionnaires, and sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) with a global score ≥5 indicative of poor sleep quality. Modified Poisson regression and linear regression were used to examine the association between nocturnal behaviour and sleep quality. Of 299 women, 117 (39.1%) experienced poor sleep. In the covariate-adjusted analysis, poor sleep was observed in women with nocturnal eating (risk ratio 1.51; 95% confidence interval [CI] 1.12, 2.04) and nocturnal artificial light exposure (1.63; 1.24, 2.13). Similarly, nocturnal eating (β 0.68; 95% CI 0.03, 1.32) and light exposure (1.99; 1.04, 2.94) were associated with higher PSQI score. Nocturnal physical activity and screen viewing before bedtime were not associated with sleep quality. In conclusion, reducing nocturnal eating and light exposure at night could potentially improve sleep in pregnancy.publishedVersionPeer reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Systematic review of failures in handoff communication during intrahospital transfers

Background: Handoffs serve a critical function in ensuring patient care continuity during transitions of care. Studies to date have predominantly focused on intershift handoffs, with relatively little attention given to intrahospital transfers. A systematic literature review was conducted to characterize the nature of handoff failures during intrahospital transfers and to examine factors affecting handoff communication and the effectiveness of current interventions. Methods: Primary studies investigating handoff communication between care providers during intrahospital transfers were sought in the English-language literature between 1980 and February 2011. Data for study design, population characteristics, sample size, setting, intervention specifics, and relevant outcome measures were extracted. Data Synthesis: Study results were summarized by the impact of communication breakdown during intrahospital transfer of patients, and the current deficiencies in the process. Results of interventions were summarized by their effect on the quality of handoff communication and patient safety. Findings: The initial search identified 516 individual articles, 24 of which satisfied the inclusion criteria. Some 19 were primary studies on handoff practices and deficiencies, and the remaining 5 were interventional studies. The studies were categorized according to the clinical settings involved in the intrahospital patient transfers. Conclusions: There is consistent evidence on the perceived impact of communication breakdown on patient safety during intrahospital transfers. Exposure of handoffs at patient transfers presents challenges that are not experienced in intershift handoffs. The distinct needs of the specific clinical settings involved in the intrahospital patient transfer must be considered when deciding on suitable interventions.11 page(s

Safety through redundancy : a case study of in-hospital patient transfers

Objectives To study the extent and execution of redundant processes during inpatient transfers to Radiology, and their impact on errors during the transfer process; to explore the use of causal and reliability analyses for modelling error detection and redundancy in the transfer process; and to provide guidance on potential system improvements. Methods A prospective observational study at a metropolitan teaching hospital. 101 patient transfers to Radiology were observed over a 6-month period, and errors in patient transfer process were recorded. Fault Tree Analysis was used to model error paths and identify redundant steps. Reliability Analysis was used to quantify system reliability. Results 420 errors were noted, an average of four errors per transfer. No incidents of patient harm were recorded. Inadequate handover was the most common error (43.1%), followed by failure to perform patient identification checks (41.9%), patient inadequately prepared for transfer (7.4%), inadequate infection control precautions (2.9%), inadequate clinical escort (2.1%), inadequate transport vehicle (2.1%) and equipment failure (0.2%). Four redundant steps for communicating patients' infectious status were identified (reliability=0.07, 0.37, 0.26, 0.31). Collectively, these yielded a system reliability of 0.7. The low reliability of each individual step was due to its low rate of execution. Conclusions Analysis of the transfer process revealed a number of redundancies that safeguard against transfer errors. However, they were relatively ineffective in preventing errors, due to the poor compliance rate. Thus, the authors advocate increasing compliance to existing redundant processes as an improvement strategy, before investing resources on new processes.7 page(s

Evaluating the Effectiveness of Clinical Alerts: A Signal Detection Approach

Clinical alerts are widely used in healthcare to notify caregivers of critical information. Alerts can be presented through many different modalities, including verbal, paper and electronic. Increasingly, information technology is being used to automate alerts. Most applications, however, fall short in achieving the desired outcome. The objective of this study is twofold. First, we examine the effectiveness of verbal and written alerts in promoting adherence to infection control precautions during inpatient transfers to radiology. Second, we propose a quantitative framework based on Signal Detection Theory (SDT) for evaluating the effectiveness of clinical alerts. Our analysis shows that verbal alerts are much more effective than written alerts. Further, using precaution alerts as a case study, we demonstrate the application of SDT to evaluate the quality of alerts, and human behavior in handling alerts. We hypothesize that such technique can improve our understanding of computerized alert systems, and guide system redesign

Breast cancer diagnoses : the authors reply [letter to the editor]

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National expenditure for false-positive mammograms and breast cancer overdiagnoses estimated at $4 billion a year

Populationwide mammography screening has been associated with a substantial rise in false-positive mammography findings and breast cancer overdiagnosis. However, there is a lack of current data on the associated costs in the United States. We present costs due to false-positive mammograms and breast cancer overdiagnoses among women ages 40-59, based on expenditure data from a major US health care insurance plan for 702,154 women in the years 2011-13. The average expenditures for each false-positive mammogram, invasive breast cancer, and ductal carcinoma in situ in the twelve months following diagnosis were $852,$51,837 and $12,369, respectively. This translates to a national cost of$4 billion each year. The costs associated with false-positive mammograms and breast cancer overdiagnoses appear to be much higher than previously documented. Screening has the potential to save lives. However, the economic impact of false-positive mammography results and breast cancer overdiagnoses must be considered in the debate about the appropriate populations for screening.8 page(s