Prevalence and Nature of Financial Considerations Documented in Narrative Clinical Records in Intensive Care Units

Gordon, Deborah D.; Patel, Ihsaan; Pellegrini, Amelia M.; Perlis, Roy H.

doi:10.1001/jamanetworkopen.2018.4178

Cited by 8 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a continuously supplemented dynamic data system that reflects the diagnosis and treatment process of critically ill patients in the real world. It has become a resource that researchers in critical care medicine often utilize (23), and many important discoveries have been made and analyses have been conducted using this database (24)(25)(26)(27). Based on the latest version of the database, we retrospectively analyzed the impact of TTE on the short-term prognosis of elderly patients admitted to the ICU.…”

Section: Discussionmentioning

confidence: 99%

The impact of transthoracic echocardiography on the short-term prognosis of elderly patients in the intensive care unit: a retrospective analysis based on the MIMIC-III database

Shi

Zhu

et al. 2021

Ann Palliat Med

View full text Add to dashboard Cite

Background: Elderly patients in the intensive care unit (ICU) often suffer from cardiac function impairment. Real-time monitoring of cardiac function and structural changes has important clinical significance. Transthoracic echocardiography (TTE) is a convenient, intuitive, and non-invasive real-time examination of the heart, and it has been widely used for intensive care patients. This study aims to analyze the impact of TTE on the prognosis of elderly patients in ICU.Methods: Data from elderly patients in the ICU was obtained from the MIMIC-III 1.4 database, and they were divided into a TTE examination group and a non-TTE examination group. The baseline data of the two groups were compared, and multiple regression analysis, propensity score (PS), compatibility analysis, and other methods were used to analyze the influence of TTE on the prognosis of elderly patients in ICU.Results: A total of 8,952 elderly cases were included, comprising 3,280 cases (36.6%) in the TTE group and 5,672 cases (63.4%) in the non-TTE group. The SAPS score (20.34±5.34 vs. 18.74±5.2, t=13.889, P<0.001) and SOFA score (5.10±3.38 vs. 3.82±2.81, t=19.250, P<0.001) of patients in the TTE group were higher than those of non-TTE group. The rate of patients in the TTE group receiving mechanical ventilation (52.10% vs. 34.80%) and vasoactive drugs (29.30% vs. 15.00%) was significantly higher than that in the non-TTE group. In the PS score compatibility cohort, the 28-day mortality rate of patients in the TTE group was 23.4%, and the 28-day mortality rate of patients in the non-TTE group was 28.7%.The adjusted odd ratio (OR) value was 0.76 (95% CI: 0.65-0.87, P<0.001). Analysis of secondary endpoints showed that patients in the TTE group did not use mechanical ventilation and hypertension drugs for a longer period of time than those in the non-TTE group, and the TTE group patients had significantly more fluid input in the first three days after admission to the ICU than in the non-TTE group.Conclusions: TTE examination can reduce the 28-day mortality risk of elderly critically ill patients.

show abstract

Section: Discussionmentioning

confidence: 99%

The impact of transthoracic echocardiography on the short-term prognosis of elderly patients in the intensive care unit: a retrospective analysis based on the MIMIC-III database

Shi

Zhu

et al. 2021

Ann Palliat Med

View full text Add to dashboard Cite

show abstract

“…We have previously described the derivation of the natural language processing (NLP) model used to classify notes. 6 In brief, the notes labeled for the presence of financial conversations from the previous study were used to train a random forest classifier with Python’s scikit-learn package (sklearn.ensemble.RandomForestClassifier, version 0.20.0). 9 These notes were randomly split into a training set of 5021 of 5579 notes (90.0%) for model development and tuning, and a testing set of 558 (10.0%) was used to confirm the model’s ability to generalize.…”

Section: Methodsmentioning

confidence: 99%

“…As an alternative to these existing methods, in our previous work, 6 we applied machine learning to electronic health records to develop a highly discriminative model identifying the presence and nature of financial considerations in intensive care unit (ICU) clinical notes. Here, we sought to understand the prevalence of such conversations in a cohort likely to be more representative of medicine as a whole, particularly outpatient medicine.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods

et al. 2019

Self Cite

View full text Add to dashboard Cite

Key Points Question How often are cost considerations documented in narrative clinical notes in a primary care setting? Findings In this cohort study of a data set including 222 457 outpatient primary care notes for 46 244 patients at a large academic medical center, 13.1% of patients had at least 1 note indicating a financial conversation with their physician. Specific socioeconomic features were associated with the presence of documented cost considerations. Meaning Although the literature suggests that patients desire to discuss health costs with their physicians, these conversations remain infrequent.

show abstract

“…We developed accurate and robust machine learning models for detecting social distress (AUROC = 0.98) and spiritual pain (AUROC = 0.90) in terminally ill patients with cancer. Gordon et al 29 developed a machine learning model for financial consideration based on clinical notes that had an AUROC of 0.89. Glauser et al 30 developed a machine learning model for detecting illness-induced religious struggles using chapel notebooks that had an AUROC of 0.73.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records

et al. 2022

View full text Add to dashboard Cite

Background: Few studies have developed automatic systems for identifying social distress, spiritual pain, and severe physical and phycological symptoms from text data in electronic medical records. Aim: To develop models to detect social distress, spiritual pain, and severe physical and psychological symptoms in terminally ill patients with cancer from unstructured text data contained in electronic medical records. Design: A retrospective study of 1,554,736 narrative clinical records was analyzed 1 month before patients died. Supervised machine learning models were trained to detect comprehensive symptoms, and the performance of the models was tested using the area under the receiver operating characteristic curve (AUROC) and precision recall curve (AUPRC). Setting/participants: A total of 808 patients was included in the study using records obtained from a university hospital in Japan between January 1, 2018 and December 31, 2019. As training data, we used medical records labeled for detecting social distress ( n = 10,000) and spiritual pain ( n = 10,000), and records that could be combined with the Support Team Assessment Schedule (based on date) for detecting severe physical/psychological symptoms ( n = 5409). Results: Machine learning models for detecting social distress had AUROC and AUPRC values of 0.98 and 0.61, respectively; values for spiritual pain, were 0.90 and 0.58, respectively. The machine learning models accurately identified severe symptoms (pain, dyspnea, nausea, insomnia, and anxiety) with a high level of discrimination (AUROC > 0.8). Conclusion: The machine learning models could detect social distress, spiritual pain, and severe symptoms in terminally ill patients with cancer from text data contained in electronic medical records.

show abstract

Prevalence and Nature of Financial Considerations Documented in Narrative Clinical Records in Intensive Care Units

Cited by 8 publications

References 13 publications

The impact of transthoracic echocardiography on the short-term prognosis of elderly patients in the intensive care unit: a retrospective analysis based on the MIMIC-III database

The impact of transthoracic echocardiography on the short-term prognosis of elderly patients in the intensive care unit: a retrospective analysis based on the MIMIC-III database

Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods

Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records

Contact Info

Product

Resources

About