Characterizing Potentially Preventable Cancer- and Chronic Disease–Related Emergency Department Use in the Year After Treatment Initiation: A Regional Study

Panattoni, Laura; Fedorenko, Catherine R.; Greenwood-Hickman, Mikael Anne; Kreizenbeck, Karma L.; Walker, Julia Rose; Martins, Renato; Eaton, Keith D.; Rieke, J.; Conklin, Ted; Smith, Bruce R.; Lyman, Gary H.; Ramsey, Scott D.

doi:10.1200/jop.2017.028191

Cited by 86 publications

(100 citation statements)

References 19 publications

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“…We have identified a need for improved symptom control before and during the ED visit. Pain is a driver of ED presentation among patients with cancer, 4 , 12 and appropriately managing pain is a significant contributor to improving quality of life in these patients. 25 , 26 Pain in the ED was present in 62.1% of study participants, whereas 249 (23.2%; 95% CI, 20.7%-25.8%) had no pain assessment documented in the ED medical record.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Diagnoses, Symptoms, Medications, and Admissions Among Patients With Cancer Presenting to Emergency Departments

et al. 2019

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Key Points Question What are the characteristics of patients with active cancer presenting to US emergency departments? Findings In this multicenter cohort study of 1075 adult patients with active cancer in the Comprehensive Oncologic Emergencies Research Network (CONCERN), patients commonly presented with symptoms such as pain (62.1%) and nausea (31.3%), were frequently treated for potential infection (26.5%), and were admitted (57.2%; 25.0% for <2 days) or placed in observation (7.6%). Meaning Opportunities for improving emergency department care for patients with cancer include establishing protocols and processes for prompt and appropriate symptom control, creating improved risk stratification tools, and improving outpatient management to prevent ED visits.

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Section: Discussionmentioning

confidence: 99%

Analysis of Diagnoses, Symptoms, Medications, and Admissions Among Patients With Cancer Presenting to Emergency Departments

et al. 2019

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“…A recent study of visits by patients with cancer to the ED found that nearly one half were associated with potentially preventable diagnoses. 42 In addition, medical home models, patient navigation, and related interventions to improve outpatient support (eg, community-based palliative care, after-hours telephone support, evidence-based treatment guidelines, out-ofhospital emergency teams) have been shown to address patients' urgent needs while reducing acute care use and improving quality of care. [43][44][45] One oncology practice found that implementing an oncology nurse practitioner-led supportive care clinic to address urgent care needs reduced symptom-related care admissions by 31%.…”

Section: Discussionmentioning

confidence: 99%

Unplanned Hospitalization Among Individuals With Cancer in the Year After Diagnosis

Whitney

Bell

Tancredi

et al. 2019

JOP

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PURPOSE: Reducing acute care use is an important strategy for improving value in cancer care. However, little information is available to describe and compare population-level hospital use across cancer types. Our aim was to estimate unplanned hospitalization rates and to describe the reasons for hospitalization in a population-based cohort recently diagnosed with cancer. MATERIALS AND METHODS: California Cancer Registry data linked with administrative inpatient data were used to examine unplanned hospitalization among individuals diagnosed with cancer between 2009 and 2012 (n = 412,850). Hospitalizations for maintenance chemotherapy, radiotherapy, or planned surgery were excluded. Multistate models were used to estimate age-adjusted unplanned hospitalization rates, accounting for survival. RESULTS: Approximately 67% of hospitalizations in the year after diagnosis were unplanned, 35% of newly diagnosed individuals experienced an unplanned hospitalization, and 67% of unplanned hospitalizations originated in the emergency department (ED). Nonmalignancy principal diagnoses most frequently associated with unplanned hospitalization included infection (15.8%) and complications of a medical device or care (6.5%). Unplanned hospitalization rates were highest for individuals with hepatobiliary or pancreatic cancer (2.08 unplanned hospitalizations per person-year at risk), lung cancer (1.58 unplanned hospitalizations), and brain or CNS cancer (1.47 unplanned hospitalizations), and were lowest among individuals with prostate cancer (0.18 unplanned hospitalizations) and melanoma (0.25 unplanned hospitalizations). CONCLUSION: The population burden of unplanned hospitalization among individuals newly diagnosed with cancer is substantial. Many unplanned hospitalizations originate in the ED and are associated with potentially preventable admission diagnoses. Efforts to reduce unplanned hospitalization might target subgroups at higher risk and focus on the ED as a source of admission.

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“…Identification of high-risk patients may direct supportive care and prevent such events, 2 and it has been estimated that approximately half of ED visits during cancer therapy are potentially preventable. 7 A recent literature review recommended identification of high-risk patients as a best practice to direct interventions and resources to reduce unplanned acute care for patients with cancer. 6 Given the predictive complexity of this actionable, unmet clinical need, there is an opportunity to leverage artificial intelligence and machine learning (ML), which has recently demonstrated successes in many areas of medicine.…”

Section: Introductionmentioning

confidence: 99%

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm

Hong

Niedzwiecki

Palta

et al. 2018

JCO Clinical Cancer Informatics

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Purpose Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events. Methods A total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data. Results All methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment). Conclusion ML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.

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Characterizing Potentially Preventable Cancer- and Chronic Disease–Related Emergency Department Use in the Year After Treatment Initiation: A Regional Study

Cited by 86 publications

References 19 publications

Analysis of Diagnoses, Symptoms, Medications, and Admissions Among Patients With Cancer Presenting to Emergency Departments

Analysis of Diagnoses, Symptoms, Medications, and Admissions Among Patients With Cancer Presenting to Emergency Departments

Unplanned Hospitalization Among Individuals With Cancer in the Year After Diagnosis

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm

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