Optimizing Laboratory Test Utilization in Long-Term Acute Care Hospitals

Mora, Adan; Krug, Brian; Grigonis, Antony; Dawson, Amanda; Jing, Yuqing; Hammerman, Samuel

doi:10.1080/08998280.2017.11929516

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…A reflection of the continuing international focus on laboratory cost reductions and appropriate resource utilization, and indeed an indication of the generalizability of our study, is the fact that during the time since our study was performed, interventions with analogous outcomes have been reported from the USA [23,24,26] and Italy [27,28]. However, it is the Canadian intervention described by Gottheil et al in 2016 [29] that perhaps most closely resembles our work in combining educational activities and complementary modifications to the IT system that successfully generated a reduction of 40% reduction on lab over-utilization, results that are comparable with our outcomes.…”

Section: Accepted Manuscript Discussionmentioning

confidence: 80%

Clustered interventions to reduce inappropriate duplicate laboratory tests in an Irish tertiary hospital

Brady

Piggott

Dunne

et al. 2018

Clinical Biochemistry

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Section: Accepted Manuscript Discussionmentioning

confidence: 80%

Clustered interventions to reduce inappropriate duplicate laboratory tests in an Irish tertiary hospital

Brady

Piggott

Dunne

et al. 2018

Clinical Biochemistry

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“…Variations that exist in patients’ conditions and disease progression cannot be fully explained by patients’ demographics, laboratory results, and therapeutic (surgical) data [ 61 ]. Therefore, we include an additional parameter matrix , which serves as a latent regulator to account for disease progression information or underlying mechanisms related to complication phenotypes.…”

Section: Methodsmentioning

confidence: 99%

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complication Phenotypes

Sheng

Liu

et al. 2021

J Med Internet Res

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Background Acute diseases present severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians’ care and management of patients with acute diseases by predicting crucial complication phenotypes for a timely diagnosis and treatment. However, effective phenotype predictions require several challenges to be overcome. First, patient data collected in the early stages of an acute disease (eg, clinical data and laboratory results) are less informative for predicting phenotypic outcomes. Second, patient data are temporal and heterogeneous; for example, patients receive laboratory tests at different time intervals and frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for predicting complication phenotypes. Objective To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning–based method that uses recurrent neural network–based sequence embedding to represent disease progression while considering temporal heterogeneities in patient data. Our method incorporates a latent regulator to alleviate data insufficiency constraints by accounting for the underlying mechanisms that are not observed in patient data. The proposed method also includes cost-sensitive learning to address imbalanced outcome distributions in patient data for improved predictions. Methods From a major health care organization in Taiwan, we obtained a sample of 10,354 electronic health records that pertained to 6545 patients with peritonitis. The proposed method projects these temporal, heterogeneous, and clinical data into a substantially reduced feature space and then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Moreover, our method employs cost-sensitive learning to further increase the predictive performance. Results We evaluated the efficacy of the proposed method for predicting two hepatic complication phenotypes in patients with peritonitis: acute hepatic encephalopathy and hepatorenal syndrome. The following three benchmark techniques were evaluated: temporal multiple measurement case-based reasoning (MMCBR), temporal short long-term memory (T-SLTM) networks, and time fusion convolutional neural network (CNN). For acute hepatic encephalopathy predictions, our method attained an area under the curve (AUC) value of 0.82, which outperforms temporal MMCBR by 64%, T-SLTM by 26%, and time fusion CNN by 26%. For hepatorenal syndrome predictions, our method achieved an AUC value of 0.64, which is 29% better than that of temporal MMCBR (0.54). Overall, the evaluation results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and AUC while maintaining comparable precision values. Conclusions The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes and offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios that are characterized by insufficient patient clinical data availability, temporal heterogeneities, and imbalanced distributions of important patient outcomes.

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“…Variations that exist in patients' conditions and disease progression cannot be fully explained by patients' demographics, laboratory results, and therapeutic (surgical) data [61].…”

Section: Model Constructionmentioning

confidence: 99%

Predictive Analytics for Caring and Managing Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complications Phenotypes (Preprint)

Sheng¹,

Hu²,

Liu³

et al. 2020

Preprint

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BACKGROUND Acute diseases have severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians’ care and management of acute disease patients by predicting crucial complication phenotypes for timely diagnoses and treatment. However, ensuring the clinical effectiveness and value for predicting complication phenotypes requires overcoming several challenges. First, patient data collected in early stages (e.g., risk factors) are less informative for predicting phenotypic outcomes. Second, patient data are temporally heterogeneous; e.g., patients receiving laboratory tests at different intervals and with varying frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for complication phenotype predictions. OBJECTIVE To predict severe complication phenotypes among patients suffering acute diseases, we propose a novel, deep learning–based method that employs recurrent neural network–based sequence embedding to represent patients’ disease progressions, with the consideration of temporal heterogeneities in patient data. The method incorporates a latent regulator to alleviate data insufficiency constraints, by accounting for the dynamics of underlying mechanisms that are not observed in patient data. A cost-sensitive analysis also addresses imbalanced outcome distributions in patient data for better predictions. METHODS From a major healthcare organization in Taiwan, we obtain a sample of 10,354 electronic health records that pertain to 6545 peritonitis patients. The proposed method projects these heterogeneous, temporal, clinical data into a substantially reduced feature space, then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Finally, our method applies a cost-sensitive analysis to improve predictive performance further. RESULTS To evaluate the proposed method, we examine its efficacy for predicting two hepatic complication phenotypes for peritonitis patients: acute hepatic encephalopathy (A-HE) and hepatorenal syndrome (HRS). We compare our method with three benchmark techniques. For A-HE predictions, our method attains an area under curve (AUC) of 0.74, which outperforms temporal case-based reasoning (T-MMCBR) by 48%, temporal short long-term memory (T-SLTM) network by 28%, and time fusion convolutional neural network (CNN) by 12%. For HRS predictions, it achieves an AUC of 0.65, which is 20%, 14%, and 18% better than that of T-MMCBR (0.54), T-LSTM (0.57), and time fusion CNN (0.55), respectively. Overall, the proposed method achieves higher recall values for predicting A-HE and HRS than the benchmark techniques. CONCLUSIONS The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes, and it offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios characterized by insufficient patient clinical data availability, temporal heterogeneities, or imbalanced distributions of important patient outcomes.

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Optimizing Laboratory Test Utilization in Long-Term Acute Care Hospitals

Cited by 6 publications

References 11 publications

Clustered interventions to reduce inappropriate duplicate laboratory tests in an Irish tertiary hospital

Clustered interventions to reduce inappropriate duplicate laboratory tests in an Irish tertiary hospital

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complication Phenotypes

Predictive Analytics for Caring and Managing Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complications Phenotypes (Preprint)

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