Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland

Brüngger, Beat; Blozik, Eva

doi:10.1136/bmjopen-2018-028409

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…29 Similarly, the strongest predictor of readmission (and therefore survival) is previous emergency hospital admission. 30 All coded frailty syndromes included in the risk models are significantly associated with upper quartile length of hospital stay after multivariable risk adjustment, with coded functional dependence, delirium and pressure ulcers being the strongest predictors (OR >2). In contrast, anxiety and depression, as well as incontinence was not associated with upper quartile length of hospital stay when the cohort is restricted to non-elective patients.…”

Section: Performance Metricsmentioning

confidence: 94%

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

et al. 2020

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Background Identifying older people with clinical frailty, reliably and at scale, is a research priority. We measured frailty in older people using a novel methodology coding frailty syndromes on routinely collected administrative data, developed on a national English secondary care population, and explored its performance of predicting inpatient mortality and long length of stay at a single acute hospital. Methodology We included patient spells from Secondary User Service (SUS) data for those ≥65 years with attendance to the emergency department or admission to West Middlesex University Hospital between 01 July 2016 to 01 July 2017. We created eight groups of frailty syndromes using diagnostic coding groups. We used descriptive statistics and logistic regression to explore performance of diagnostic coding groups for the above outcomes. Results We included 17,199 patient episodes in the analysis. There was at least one frailty syndrome present in 7,004 (40.7%) patient episodes. The resultant model had moderate discrimination for inpatient mortality (area under the receiver operating characteristic curve (AUC) 0.74; 95% confi dence interval (CI) 0.72-0.76) and upper quartile length of stay (AUC 0.731; 95% CI 0.722-0.741). There was good negative predictive value for inpatient mortality (98.1%). Conclusions Coded frailty syndromes signifi cantly predict outcomes. Model diagnostics suggest the model could be used for screening of elderly patients to optimise their care.

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Section: Performance Metricsmentioning

confidence: 94%

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

et al. 2020

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“…However, LR with lasso was shown to perform better than GBM in another research [ 97 ]. A poor discrimination of 0.60 was observed with LR prediction based on claims data available during admission [ 98 ]. Flaks-Manov et al .…”

Section: Application To Readmissionmentioning

confidence: 99%

Current Trends in Readmission Prediction: An Overview of Approaches

et al. 2021

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Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

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“…Approach Horizon AUC [51] Tensor decomposition 1 year 0.68-0.78 [53] EHR embedding 6 months 0.79 [7] Logistic regression + medical claims (Switzerland) 30 days 0.60-0.61 [49] TopicRNN on EHR 30 days 0.61 [47] CNN + MLP on EMR 30 days 0.70 [35] RNN on EHR To account for the inherent label skewness in the final 1.2M dataset, we tuned the classification threshold 𝛼 as follows:…”

Section: Papermentioning

confidence: 99%

Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder

Lahlou,

Crayton,

Trier

et al. 2021

Preprint

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In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge 1 seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) issued by CMS. Moreover, we propose a comprehensive modeling solution centered on a deep learning framework for this data. To demonstrate the framework, we train an attentionbased Transformer to learn Medicare semantics in support of performing downstream prediction tasks thereby achieving 0.91 AUC and 0.91 recall on readmission classification. We also introduce a novel data pre-processing pipeline and discuss pertinent deployment considerations surrounding model explainability and bias. CCS CONCEPTS• Computing methodologies → Natural language processing; Classification and regression trees; • Applied computing → Health care information systems.

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Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland

Cited by 11 publications

References 31 publications

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

Validating a methodology to measure frailty syndromes at hospital level utilising administrative data

Current Trends in Readmission Prediction: An Overview of Approaches

Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder

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