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International Journal of Nursing Studies May 2021In the face of pressure to contain costs and make best use of scarce nurses, flexible staff deployment (floating staff between units and temporary hires) guided by a...
Beyond ratios - flexible and resilient nurse staffing options to deliver cost-effective hospital care and address staff shortages: A simulation and economic modelling study.
BACKGROUND
In the face of pressure to contain costs and make best use of scarce nurses, flexible staff deployment (floating staff between units and temporary hires) guided by a patient classification system may appear an efficient approach to meeting variable demand for care in hospitals.
OBJECTIVES
We modelled the cost-effectiveness of different approaches to planning baseline numbers of nurses to roster on general medical/surgical units while using flexible staff to respond to fluctuating demand.
DESIGN AND SETTING
We developed an agent-based simulation, where hospital inpatient units move between being understaffed, adequately staffed or overstaffed as staff supply and demand (as measured by the Safer Nursing Care Tool patient classification system) varies. Staffing shortfalls are addressed by floating staff from overstaffed units or hiring temporary staff. We compared a standard staffing plan (baseline rosters set to match average demand) with a higher baseline 'resilient' plan set to match higher than average demand, and a low baseline 'flexible' plan. We varied assumptions about temporary staff availability and estimated the effect of unresolved low staffing on length of stay and death, calculating cost per life saved.
RESULTS
Staffing plans with higher baseline rosters led to higher costs but improved outcomes. Cost savings from lower baseline staff mainly arose because shifts were left understaffed and much of the staff cost saving was offset by costs from longer patient stays. With limited temporary staff available, changing from low baseline flexible plan to the standard plan cost £13,117 per life saved and changing from the standard plan to the higher baseline 'resilient' plan cost £8,653 per life saved. Although adverse outcomes from low baseline staffing reduced when more temporary staff were available, higher baselines were even more cost-effective because the saving on staff costs also reduced. With unlimited temporary staff, changing from low baseline plan to the standard cost £4,520 per life saved and changing from the standard plan to the higher baseline cost £3,693 per life saved.
CONCLUSION
Shift-by-shift measurement of patient demand can guide flexible staff deployment, but the baseline number of staff rostered must be sufficient. Higher baseline rosters are more resilient in the face of variation and appear cost-effective. Staffing plans that minimise the number of nurses rostered in advance are likely to harm patients because temporary staff may not be available at short notice. Such plans, which rely heavily on flexible deployments, do not represent an efficient or effective use of nurses.
STUDY REGISTRATION
ISRCTN 12307968 Tweetable abstract: Economic simulation model of hospital units shows low baseline staff levels with high use of flexible staff are not cost-effective and don't solve nursing shortages.
Topics: Cost-Benefit Analysis; Hospitals; Humans; Nurses; Nursing Staff, Hospital; Personnel Staffing and Scheduling; Workforce
PubMed: 33677251
DOI: 10.1016/j.ijnurstu.2021.103901 -
Value in Health : the Journal of the... Aug 2010The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using...
BACKGROUND
The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using baselines of perfect health are not comparable with those generated using baselines adjusted to reflect the HSUVs associated with the health condition. Despite this, there is no guidance on the preferred techniques and little research describing the effect on cost per quality adjusted life-year (QALY) results when using the different methods.
METHODS
Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health-state utility profiles (perfect health, no history of CVD, general population) in conjunction with models (minimum, additive, multiplicative) frequently used to approximate scores for health states with multiple health conditions. HSUVs are calculated using the EQ-5D UK preference-based algorithm.
RESULTS
Assuming a baseline of perfect health ignores the natural decline in quality of life associated with age, overestimating the benefits of treatment. The results generated using baselines from the general population are comparable to those obtained using baselines from individuals with no history of CVD. The minimum model biases results in favor of younger-aged cohorts. The additive and multiplicative models give similar results.
CONCLUSION
Although further research in additional health conditions is required to support our findings, our results highlight the need for analysts to conform to an agreed reference case. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation.
Topics: Adolescent; Adult; Age Factors; Aged; Aged, 80 and over; Algorithms; Cardiovascular Diseases; Data Collection; Decision Making; England; Female; Health Status; Health Status Indicators; Humans; Male; Markov Chains; Middle Aged; Models, Economic; Pilot Projects; Quality of Life; Quality-Adjusted Life Years; Research Design; Surveys and Questionnaires; Young Adult
PubMed: 20230546
DOI: 10.1111/j.1524-4733.2010.00700.x -
NDSS Symposium 2023When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i)...
When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i) privacy guarantees for the data entries and (ii) liability guarantees (via fingerprinting) in case of unauthorized redistribution. However, (i) and (ii) are orthogonal objectives, because when sharing a database with multiple recipients, privacy via data sanitization requires adding noise once (and sharing the same noisy version with all recipients), whereas liability via unique fingerprint insertion requires adding different noises to each shared copy to distinguish all recipients. Although achieving (i) and (ii) together is possible in a naïve way (e.g., either differentially-private database perturbation or synthesis followed by fingerprinting), this approach results in significant degradation in the utility of shared databases. In this paper, we achieve privacy and liability guarantees simultaneously by proposing a novel entry-level differentially-private (DP) fingerprinting mechanism for relational databases without causing large utility degradation. The proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature of fingerprinting and transforming it into provable privacy guarantees. Specifically, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an -entry-level DP fingerprinting mechanism. We theoretically analyze the connections between privacy, fingerprint robustness, and database utility by deriving closed form expressions. We also propose a sparse vector technique-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients. We experimentally show that our mechanism achieves strong fingerprint robustness (e.g., the fingerprint cannot be compromised even if the malicious database recipient modifies/distorts more than half of the entries in its received fingerprinted copy), and higher database utility compared to various baseline methods (e.g., application-dependent database utility of the shared database achieved by the proposed mechanism is higher than that of the considered baselines).
PubMed: 37275390
DOI: 10.14722/ndss.2023.24693 -
Brain : a Journal of Neurology Jan 2019The proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. To the extent that this is true, recovery from stroke can...
The proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. To the extent that this is true, recovery from stroke can be predicted accurately from baseline measures of acute post-stroke impairment alone. Reports that baseline scores explain more than 80%, and sometimes more than 90%, of the variance in the patients' recoveries, are rapidly accumulating. Here, we show that these headline effect sizes are likely inflated. The key effects in this literature are typically expressed as, or reducible to, correlation coefficients between baseline scores and recovery (outcome scores minus baseline scores). Using formal analyses and simulations, we show that these correlations will be extreme when outcomes are significantly less variable than baselines, which they often will be in practice regardless of the real relationship between outcomes and baselines. We show that these effect sizes are likely to be over-optimistic in every empirical study that we found that reported enough information for us to make the judgement, and argue that the same is likely to be true in other studies as well. The implication is that recovery after stroke may not be as proportional as recent studies suggest.
Topics: Humans; Recovery of Function; Statistics as Topic; Stroke
PubMed: 30535098
DOI: 10.1093/brain/awy302 -
Perspectives on Behavior Science Sep 2022Multiple baseline designs-both concurrent and nonconcurrent-are the predominant experimental design in modern applied behavior analytic research and are increasingly...
Multiple baseline designs-both concurrent and nonconcurrent-are the predominant experimental design in modern applied behavior analytic research and are increasingly employed in other disciplines. In the past, there was significant controversy regarding the relative rigor of concurrent and nonconcurrent multiple baseline designs. The consensus in recent textbooks and methodological papers is that nonconcurrent designs are less rigorous than concurrent designs because of their presumed limited ability to address the threat of coincidental events (i.e., history). This skepticism of nonconcurrent designs stems from an emphasis on the importance of across-tier comparisons and relatively low importance placed on replicated within-tier comparisons for addressing threats to internal validity and establishing experimental control. In this article, we argue that the primary reliance on across-tier comparisons and the resulting deprecation of nonconcurrent designs are not well-justified. In this article, we first define multiple baseline designs, describe common threats to internal validity, and delineate the two bases for controlling these threats. Second, we briefly summarize historical methodological writing and current textbook treatment of these designs. Third, we explore how concurrent and nonconcurrent multiple baselines address each of the main threats to internal validity. Finally, we make recommendations for more rigorous use, reporting, and evaluation of multiple baseline designs.
PubMed: 36249165
DOI: 10.1007/s40614-022-00326-1 -
Brain Sciences Aug 2021Event-related mu-rhythm activity has become a common tool for the investigation of different socio-cognitive processes in pediatric populations. The estimation of the...
Event-related mu-rhythm activity has become a common tool for the investigation of different socio-cognitive processes in pediatric populations. The estimation of the mu-rhythm desynchronization/synchronization (mu-ERD/ERS) in a specific task is usually computed in relation to a baseline condition. In the present study, we investigated the effect that different types of baseline might have on toddler mu-ERD/ERS related to an action observation (AO) and action execution (AE) task. Specifically, we compared mu-ERD/ERS values computed using as a baseline: (1) the observation of a static image (BL1) and (2) a period of stillness (BL2). Our results showed that the majority of the subjects suppressed the mu-rhythm in response to the task and presented a greater mu-ERD for one of the two baselines. In some cases, one of the two baselines was not even able to produce a significant mu-ERD, and the preferred baseline varied among subjects even if most of them were more sensitive to the BL1, thus suggesting that this could be a good baseline to elicit mu-rhythm modulations in toddlers. These results recommended some considerations for the design and analysis of mu-rhythm studies involving pediatric subjects: in particular, the importance of verifying the mu-rhythm activity during baseline, the relevance of single-subject analysis, the possibility of including more than one baseline condition, and caution in the choice of the baseline and in the interpretation of the results of studies investigating mu-rhythm activity in pediatric populations.
PubMed: 34573178
DOI: 10.3390/brainsci11091159 -
BMC Medical Informatics and Decision... Oct 2023There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support...
BACKGROUND
There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons.
OBJECTIVE
The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events.
MATERIALS AND METHODS
The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3.
RESULTS
Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI.
CONCLUSION
In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care.
Topics: Humans; Creatinine; Retrospective Studies; Models, Statistical; Prognosis; Acute Kidney Injury
PubMed: 37814311
DOI: 10.1186/s12911-023-02306-0 -
International Journal of Medical... Mar 2020To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically and predict diagnoses by the similar/dissimilar patients.
OBJECTIVE
To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically and predict diagnoses by the similar/dissimilar patients.
METHODS
We proposed a novel patient-similarity-based framework for diagnostic prediction, which is inspired by the structure-mapping theory about analogy reasoning in psychology. Patient similarity is defined as the similarity between two patients' diagnoses sets rather than a dichotomous (absence/presence of just one disease). The multilabel classification problem is converted to a single-value regression problem by integrating the pairwise patients' clinical features into a vector and taking the vector as the input and the patient similarity as the output. In contrast to the common k-NN method which only considering the nearest neighbors, we not only utilize similar patients (positive analogy) to generate diagnostic hypotheses, but also utilize dissimilar patients (negative analogy) are used to reject diagnostic hypotheses.
RESULTS
The patient-similarity-based models perform better than the one-vs-all baseline and traditional k-NN methods. The f-1 score of positive-analogy-based prediction is 0.698, significantly higher than the scores of baselines ranging from 0.368 to 0.661. It increases to 0.703 when the negative analogy method is applied to modify the prediction results of positive analogy. The performance of this method is highly promising for larger datasets.
CONCLUSION
The patient-similarity-based model provides diagnostic decision support that is more accurate, generalizable, and interpretable than those of previous methods and is based on heterogeneous and incomplete data. The model also serves as a new application for the use of clinical big data through artificial intelligence technology.
Topics: Artificial Intelligence; Cluster Analysis; Diagnosis; Female; Humans; Male; Middle Aged; Patients
PubMed: 31923816
DOI: 10.1016/j.ijmedinf.2019.104073 -
Cureus Sep 2023The aim of this study was to assess the efficacy and safety of efpeglenatide in patients with type 2 diabetes (T2D). The study was reported according to the 2020... (Review)
Review
The aim of this study was to assess the efficacy and safety of efpeglenatide in patients with type 2 diabetes (T2D). The study was reported according to the 2020 guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Web of Science, PubMed, and Scopus databases were searched by two authors independently, with no restriction on language and year of publication, using the following key terms: (efpeglenatide) OR (glucagon-like peptide-1 receptor agonist) AND (type 2 diabetes) OR (diabetes) OR (T2DM) AND (HbA1c) OR (FSG) OR (fasting serum glucose) OR (weight) OR (bodyweight) OR (adverse events) OR (safety) OR (AE). Outcomes assessed in this meta-analysis included change in hemoglobin A1C (HbA1C) from baseline (%), change in weight from baseline (Kg), and change in fasting serum glucose (FSG) from baselines. For the safety analysis, we assessed total adverse events and gastrointestinal (GI) adverse events. A total of four studies fulfilled the inclusion and exclusion criteria and were included in this meta-analysis, encompassing six randomized controlled trials (RCTs). Compared with a control group, efpeglenatide lowered the HbA1c (mean difference (MD): -0.81, 95% confidence interval (CI): -1.01 to -0.60), body weight (MD: -1.15, 95% CI: -1.82 to -0.47), and FSG (MD: -0.98, 95% CI: -1.19 to -0.77). However, the risk of GI-related adverse events was significantly higher in the efpeglenatide group compared to the control group.
PubMed: 37885518
DOI: 10.7759/cureus.45927 -
Scientific Reports Nov 2023In this paper, we consider the problem of batch classification and propose a novel framework for achieving fairness in such settings. The problem of batch classification...
In this paper, we consider the problem of batch classification and propose a novel framework for achieving fairness in such settings. The problem of batch classification involves selection of a set of individuals, often encountered in real-world scenarios such as job recruitment, college admissions etc. This is in contrast to a typical classification problem, where each candidate in the test set is considered separately and independently. In such scenarios, achieving the same acceptance rate (i.e., probability of the classifier assigning positive class) for each group (membership determined by the value of sensitive attributes such as gender, race etc.) is often not desirable, and the regulatory body specifies a different acceptance rate for each group. The existing fairness enhancing methods do not allow for such specifications and hence are unsuited for such scenarios. In this paper, we define a configuration model whereby the acceptance rate of each group can be regulated and further introduce a novel batch-wise fairness post-processing framework using the classifier confidence-scores. We deploy our framework across four real-world datasets and two popular notions of fairness, namely demographic parity and equalized odds. In addition to consistent performance improvements over the competing baselines, the proposed framework allows flexibility and significant speed-up. It can also seamlessly incorporate multiple overlapping sensitive attributes. To further demonstrate the generalizability of our framework, we deploy it to the problem of fair gerrymandering where it achieves a better fairness-accuracy trade-off than the existing baseline method.
PubMed: 37919372
DOI: 10.1038/s41598-023-45943-1