Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Samuels‐Kalow, Margaret; Faridi, Mohammad Kamal; Espinola, Janice A.; Klig, Jean E.; Camargo, Carlos A.

doi:10.1111/acem.13342

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…In addition, the prediction target was limited to asthma hospital visits at Intermountain Healthcare rather than asthma hospital visits anywhere. It would be interesting to see how the automatically generated explanations of the prediction results of the model would differ if we have access to more complete clinical and administrative data [50]. 2. Our study used 1 predictive modeling problem, predicting asthma hospital visits as the test case.…”

Section: Limitationsmentioning

confidence: 99%

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Luo¹,

Johnson²,

Nkoy³

et al. 2020

JMIR Med Inform

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Background Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. Objective This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. Methods Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. Results Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. Conclusions This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.

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Section: Limitationsmentioning

confidence: 99%

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Luo¹,

Johnson²,

Nkoy³

et al. 2020

JMIR Med Inform

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show abstract

“…We identified repeat users of the ED for psychiatric care through a unique identifier called “visitlink” (details provided in Data Supplement , Appendix A.1, available as supporting information in the online version of this paper, which is available at http://onlinelibrary.wiley.com/doi/10.1111/acem.13812/full) . Prior research utilizes this variable to examine high users of EDs for psychiatric and somatic conditions …”

Section: Methodsmentioning

confidence: 99%

Psychiatric‐related Revisits to the Emergency Department Following Rapid Expansion of Community Mental Health Services

Singh

Chakravarthy

Yoon

et al. 2019

Academic Emergency Medicine

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Objectives: Repeat visits (revisits) to emergency departments (EDs) for psychiatric care reflect poor continuity of care and impose a high financial cost. We test whether rapid expansion of community health centers (CHCs)-which provide regional, low-cost primary care-correspond with fewer repeat psychiatric-related ED visits (PREDVs).Methods: We obtained repeated cross-sectional time-series data for 7.8 million PREDVs from the State Emergency Department Database for four populous U.S. states (California, Florida, North Carolina, and New York) from 2006 to 2011. We specified as the outcome variable the count of repeat visits per ED visitor with a psychiatric diagnosis. We retrieved aggregate-level mental health visits at CHCs from the Uniform Data System. Negative binomial regression methods controlled for individual-level confounders, county health system and sociodemographic attributes, year fixed effects, and county fixed effects.Results: The risk of a repeat PREDV decreased with a county-level increase in mental health patients seen at CHCs (incidence rate ratio = 0.986, 95% confidence interval = 0.98 to 0.99). Conversion of this rate ratio to the number of revisits averted indicated 34,000 fewer repeat PREDVs in these four states statistically associated with a 1% expansion in CHC mental health visits. Exploratory analyses found that revisits decline for relatively mild/moderate illnesses (e.g., mood, anxiety disorders) but not for severe illnesses (e.g., schizophrenia/ psychoses).

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Missed Screening for Adverse Social Determinants of Health and Emergency Department Utilization

Samuels-Kalow,

Mayes,

Cash

et al. 2024

Annals of Emergency Medicine

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Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Cited by 9 publications

References 22 publications

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Psychiatric‐related Revisits to the Emergency Department Following Rapid Expansion of Community Mental Health Services

Missed Screening for Adverse Social Determinants of Health and Emergency Department Utilization

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