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Baskar D, Jarmul JA, Donnelly LF. Expenditure mapping of pediatric imaging costs using a resource utilization band analysis of claims data. Curr Probl Diagn Radiol 2024:S0363-0188(24)00129-4. [PMID: 39048500 DOI: 10.1067/j.cpradiol.2024.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs. METHODS A Claims data for children enrolled in a commercial value-based plan were categorized by RUB 0 non-user, 1 healthy user, 2 low morbidity, 3 moderate morbidity, 4 high morbidity, & 5 very high morbidity. The per member per year (PMPY) expense, total imaging spend, and imaging modality with the highest spend were assessed for each RUB. Diagnosis categories associated with high imaging costs were also evaluated. RESULTS There were 40,022 pediatric plan members. 14% had imaging-related claims accounting for approximately $2.8 million in expenditures. Member distribution and mean PMPY expenditure RUB was respectively: RUB 0 (3,037, $0), RUB 1 (6,604, $7), RUB 2 - 13,698, $27), RUB 3 - 13,341, $87), RUB 4 (2,810, $268), RUB 5 (532, $841). RUB 3 had the largest total imaging costs at $1,159,523. The imaging modality with the greatest mean PMPY expense varied by RUB with radiography highest in lower RUBs and MRI highest in higher RUBs. The top 3 diagnoses associated with the highest total imaging costs were developmental disorders ($443,980), asthma ($388,797), and congenital heart disease ($294,977) and greatest mean PMPY imaging expenditures malignancy/leukemia ($3,100), transplant ($2,639), and tracheostomy ($1,661). DISCUSSION Expense mapping using claims data allows for a better understanding of the distribution of imaging costs across a covered pediatric population. This tool may assist organizations in planning effective cost-reduction initiatives and learning how imaging utilization varies by patient complexity in their system.
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Affiliation(s)
- Danika Baskar
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Jamie A Jarmul
- University of North Carolina Health Alliance, Morrisville, NC, USA
| | - Lane F Donnelly
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA; Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA; University of North Carolina Health Alliance, Morrisville, NC, USA.
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Pan Y, Zhang X, Wen X, Yuan N, Guo L, Shi Y, Jia Y, Guo Y, Hao F, Qu S, Chen Z, Yang L, Wang X, Liu Y. Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1. Sleep Med 2024; 119:556-564. [PMID: 38810481 DOI: 10.1016/j.sleep.2024.05.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/04/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.
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Affiliation(s)
- Yuanhang Pan
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xinbo Zhang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xinyu Wen
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Na Yuan
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Li Guo
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yifan Shi
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yuanyuan Jia
- Encerebropathy Department, No.2, Baoji Hospital of Traditional Chinese Medicine, Baoji, PR China.
| | - Yanzhao Guo
- Encerebropathy Department, No.10, Xi'an Hospital of Traditional Chinese Medicine, Xi'an, PR China.
| | - Fengli Hao
- Department of Neurology, Xi'an Daxing Hospital, Xi'an, PR China.
| | - Shuyi Qu
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Ze Chen
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Lei Yang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xiaoli Wang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
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Pan Y, Zhao D, Zhang X, Yuan N, Yang L, Jia Y, Guo Y, Chen Z, Wang Z, Qu S, Bao J, Liu Y. Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness. Nat Sci Sleep 2024; 16:639-652. [PMID: 38836216 PMCID: PMC11149636 DOI: 10.2147/nss.s456903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/25/2024] [Indexed: 06/06/2024] Open
Abstract
Background Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1. Objective The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early. Methods Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP). Results Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA. Conclusion The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.
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Affiliation(s)
- Yuanhang Pan
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Di Zhao
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Xinbo Zhang
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Na Yuan
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Lei Yang
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Yuanyuan Jia
- Encephalopathy Department No.2, Baoji Hospital of Traditional Chinese Medicine, Baoji, People's Republic of China
| | - Yanzhao Guo
- Encephalopathy Department No.10, Xi'an Hospital of Traditional Chinese Medicine, Xi'an, People's Republic of China
| | - Ze Chen
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Zezhi Wang
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Shuyi Qu
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
| | - Junxiang Bao
- Department of Aerospace Hygiene, Air Force Medical University, Xi'an, People's Republic of China
| | - Yonghong Liu
- Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China
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Stewart J, Lu J, Goudie A, Arendts G, Meka SA, Freeman S, Walker K, Sprivulis P, Sanfilippo F, Bennamoun M, Dwivedi G. Applications of natural language processing at emergency department triage: A narrative review. PLoS One 2023; 18:e0279953. [PMID: 38096321 PMCID: PMC10721204 DOI: 10.1371/journal.pone.0279953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
INTRODUCTION Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data. METHODS All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided. RESULTS In total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice. CONCLUSION Unstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.
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Affiliation(s)
- Jonathon Stewart
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- Department of Emergency Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Juan Lu
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, Western Australia, Australia
| | - Adrian Goudie
- Department of Emergency Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Glenn Arendts
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Emergency Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Shiv Akarsh Meka
- HIVE & Data and Digital Innovation, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Sam Freeman
- Department of Emergency Medicine, St Vincent’s Hospital Melbourne, Melbourne, Victoria, Australia
- SensiLab, Monash University, Melbourne, Victoria, Australia
| | - Katie Walker
- School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Peter Sprivulis
- Western Australia Department of Health, East Perth, Western Australia, Australia
| | - Frank Sanfilippo
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, Western Australia, Australia
| | - Girish Dwivedi
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
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Mitha S, Schwartz J, Hobensack M, Cato K, Woo K, Smaldone A, Topaz M. Natural Language Processing of Nursing Notes: An Integrative Review. Comput Inform Nurs 2023; 41:377-384. [PMID: 36730744 DOI: 10.1097/cin.0000000000000967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.
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Affiliation(s)
- Shazia Mitha
- Author Affiliations : Columbia University School of Nursing, New York
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Wei Z, Wang X, Ren L, Liu C, Liu C, Cao M, Feng Y, Gan Y, Li G, Liu X, Liu Y, Yang L, Deng Y. Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A cross-sectional study. J Affect Disord 2023; 336:1-8. [PMID: 37209912 DOI: 10.1016/j.jad.2023.05.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 05/11/2023] [Accepted: 05/14/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND Anxiety and depression are the most prevalent comorbidities among epilepsy patients. The screen and diagnosis of anxiety and depression are quite important for the management of patients with epilepsy. In that case, the method for accurately predicting anxiety and depression needs to be further explored. METHODS A total of 480 patients with epilepsy (PWE) were enrolled in our study. Anxiety and Depressive symptoms were evaluated. Six machine learning models were used to predict anxiety and depression in patients with epilepsy. Receiver operating curve (ROC), decision curve analysis (DCA) and moDel Agnostic Language for Exploration and eXplanation (DALEX) package were used to evaluate the accuracy of machine learning models. RESULTS For anxiety, the area under the ROC curve was not significantly different between models. DCA revealed that random forest and multilayer perceptron has the largest net benefit within different probability threshold. DALEX revealed that random forest and multilayer perceptron were models with best performance and stigma had the highest feature importance. For depression, the results were much the same. CONCLUSIONS Methods created in this study may offer much help identifying PWE with high risk of anxiety and depression. The decision support system may be valuable for the everyday management of PWE. Further study is needed to test the outcome of applying this system to clinical settings.
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Affiliation(s)
- Zihan Wei
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China
| | - Xinpei Wang
- School of Aerospace Medicine, Fourth Military Medical University, 169 West Changle Road, Xi'an 710032, People's Republic of China
| | - Lei Ren
- Department of Clinical Psychology, Fourth Military Medical University, 169 West Changle Road, Xi'an 710032, People's Republic of China
| | - Chang Liu
- BrainPark, Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Clayton, Australia
| | - Chao Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China
| | - Mi Cao
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China
| | - Yan Feng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China; Xi'an Medical University, Xi'an 710021, People's Republic of China
| | - Yanjing Gan
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China
| | - Guoyan Li
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China; Xi'an Medical University, Xi'an 710021, People's Republic of China
| | - Xufeng Liu
- Department of Clinical Psychology, Fourth Military Medical University, 169 West Changle Road, Xi'an 710032, People's Republic of China
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China.
| | - Lei Yang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China.
| | - Yanchun Deng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China.
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Kraus R, Dresing K. Rational Usage of Fracture Imaging in Children and Adolescents. Diagnostics (Basel) 2023; 13:538. [PMID: 36766642 PMCID: PMC9914862 DOI: 10.3390/diagnostics13030538] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/28/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
In this paper, authors introduce the basic prerequisite for rational, targeted, and above all, child-oriented diagnosis of fractures and dislocations in children and adolescents is in-depth prior knowledge of the special features of trauma in the growth age group. This review summarizes the authors' many years of experience and the state of the current pediatric traumatology literature. It aims to provide recommendations for rational, child-specific diagnostics appropriate to the child, especially for the area of extremity injuries in the growth age. The plain radiograph remains the indispensable standard in diagnosing fractures and dislocations of the musculoskeletal system in childhood and adolescence. Plain radiographs in two planes are the norm, but in certain situations, one plane is sufficient. X-rays of the opposite side in acute diagnostics are obsolete. Images to show consolidation after conservative treatment is rarely necessary. Before metal removal, however, they are indispensable. The upcoming diagnostical tool in pediatric trauma is ultrasound. More and more studies show that in elected injuries and using standardized protocols, fracture ultrasound is as accurate as plain radiographs to detect and control osseous and articular injuries. In acute trauma, CT scans have only a few indications, especially in epiphyseal fractures in adolescents, such as transitional fractures of the distal tibia or coronal shear fractures of the distal humerus. CT protocols must be adapted to children and adolescents to minimize radiation exposure. MRI has no indication in the detection or understanding of acute fractures in infants and children. It has its place in articular injuries of the knee and shoulder to show damage to ligaments, cartilage, and other soft tissues. Furthermore, MRI is useful in cases of remaining pain after trauma without radiological proof of a fracture and in the visualization of premature closure of growth plates after trauma to plan therapy. Several everyday examples of rational diagnostic workflows, as the authors recommend them, are mentioned. The necessity of radiation protection must be taken into consideration.
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Affiliation(s)
- Ralf Kraus
- Department of Trauma and Orthopeadic Surgery, Klinikum Bad Hersfeld, 36251 Bad Hersfeld, Germany
| | - Klaus Dresing
- Department of Trauma Surgery, Orthopaedics and Plastic Surgery of the University Medical Center Göttingen, 37075 Göttingen, Germany
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Eysenbach G, Kleib M, Norris C, O'Rourke HM, Montgomery C, Douma M. The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review. JMIR Nurs 2023; 6:e41331. [PMID: 36637881 PMCID: PMC9883744 DOI: 10.2196/41331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Emergency departments use triage to ensure that patients with the highest level of acuity receive care quickly and safely. Triage is typically a nursing process that is documented as structured and unstructured (free text) data. Free-text triage narratives have been studied for specific conditions but never reviewed in a comprehensive manner. OBJECTIVE The objective of this paper was to identify and map the academic literature that examines triage narratives. The paper described the types of research conducted, identified gaps in the research, and determined where additional review may be warranted. METHODS We conducted a scoping review of unstructured triage narratives. We mapped the literature, described the use of triage narrative data, examined the information available on the form and structure of narratives, highlighted similarities among publications, and identified opportunities for future research. RESULTS We screened 18,074 studies published between 1990 and 2022 in CINAHL, MEDLINE, Embase, Cochrane, and ProQuest Central. We identified 0.53% (96/18,074) of studies that directly examined the use of triage nurses' narratives. More than 12 million visits were made to 2438 emergency departments included in the review. In total, 82% (79/96) of these studies were conducted in the United States (43/96, 45%), Australia (31/96, 32%), or Canada (5/96, 5%). Triage narratives were used for research and case identification, as input variables for predictive modeling, and for quality improvement. Overall, 31% (30/96) of the studies offered a description of the triage narrative, including a list of the keywords used (27/96, 28%) or more fulsome descriptions (such as word counts, character counts, abbreviation, etc; 7/96, 7%). We found limited use of reporting guidelines (8/96, 8%). CONCLUSIONS The breadth of the identified studies suggests that there is widespread routine collection and research use of triage narrative data. Despite the use of triage narratives as a source of data in studies, the narratives and nurses who generate them are poorly described in the literature, and data reporting is inconsistent. Additional research is needed to describe the structure of triage narratives, determine the best use of triage narratives, and improve the consistent use of triage-specific data reporting guidelines. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2021-055132.
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Affiliation(s)
| | - Manal Kleib
- Faculty of Nursing, University of Alberta, Edmonton, AB, Canada
| | - Colleen Norris
- Faculty of Nursing, University of Alberta, Edmonton, AB, Canada
| | | | | | - Matthew Douma
- School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin, Ireland
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Su D, Zhang X, He K, Chen Y, Wu N. Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches. Front Public Health 2022; 10:998549. [PMID: 36339144 PMCID: PMC9634246 DOI: 10.3389/fpubh.2022.998549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/20/2022] [Indexed: 01/26/2023] Open
Abstract
Background Chronic kidney disease (CKD) has become a major public health problem worldwide and has caused a huge social and economic burden, especially in developing countries. No previous study has used machine learning (ML) methods combined with longitudinal data to predict the risk of CKD development in 2 years amongst the elderly in China. Methods This study was based on the panel data of 925 elderly individuals in the 2012 baseline survey and 2014 follow-up survey of the Healthy Aging and Biomarkers Cohort Study (HABCS) database. Six ML models, logistic regression (LR), lasso regression, random forests (RF), gradient-boosted decision tree (GBDT), support vector machine (SVM), and deep neural network (DNN), were developed to predict the probability of CKD amongst the elderly in 2 years (the year of 2014). The decision curve analysis (DCA) provided a range of threshold probability of the outcome and the net benefit of each ML model. Results Amongst the 925 elderly in the HABCS 2014 survey, 289 (18.8%) had CKD. Compared with the other models, LR, lasso regression, RF, GBDT, and DNN had no statistical significance of the area under the receiver operating curve (AUC) value (>0.7), and SVM exhibited the lowest predictive performance (AUC = 0.633, p-value = 0.057). DNN had the highest positive predictive value (PPV) (0.328), whereas LR had the lowest (0.287). DCA results indicated that within the threshold ranges of ~0-0.03 and 0.37-0.40, the net benefit of GBDT was the largest. Within the threshold ranges of ~0.03-0.10 and 0.26-0.30, the net benefit of RF was the largest. Age was the most important predictor variable in the RF and GBDT models. Blood urea nitrogen, serum albumin, uric acid, body mass index (BMI), marital status, activities of daily living (ADL)/instrumental activities of daily living (IADL) and gender were crucial in predicting CKD in the elderly. Conclusion The ML model could successfully capture the linear and nonlinear relationships of risk factors for CKD in the elderly. The decision support system based on the predictive model in this research can help medical staff detect and intervene in the health of the elderly early.
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Affiliation(s)
- Dai Su
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China
| | - Xingyu Zhang
- Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, MI, United States,Thomas E. Starzl Transplantation Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Kevin He
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Yingchun Chen
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,Research Center for Rural Health Services, Hubei Province Key Research Institute of Humanities and Social Sciences, Wuhan, China
| | - Nina Wu
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China,*Correspondence: Nina Wu
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Chae S, Song J, Ojo M, Bowles KH, McDonald MV, Barrón Y, Hobensack M, Kennedy E, Sridharan S, Evans L, Topaz M. Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure. Heart Lung 2022; 55:148-154. [PMID: 35597164 PMCID: PMC11021173 DOI: 10.1016/j.hrtlng.2022.05.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 05/03/2022] [Accepted: 05/07/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management. OBJECTIVE To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management. METHODS An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management. RESULTS There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976-0.987, p < .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029- 1.043, p < .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3-1.67, p < .001) and depression (OR 1.36, 95% CI 1.09-1.68, p < .01), impaired decision-making (OR 1.64, 95% CI 1.37-1.95, p < .001), smoking (OR 1.7, 95% CI 1.4-2.04, p < .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1-1.42, p < .01) were associated with poor self-management. CONCLUSIONS Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.
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Affiliation(s)
- Sena Chae
- College of Nursing, University of Iowa, 50 Newton Rd, Iowa City, IA 52242, United States.
| | - Jiyoun Song
- Columbia University School of Nursing, New York, NY, United States
| | - Marietta Ojo
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Kathryn H Bowles
- Department of Biobehavioral Health Sciences Philadelphia PA, Center for Home Care Policy & Research, University of Pennsylvania School of Nursing, Visiting Nurse Service of New York, New York, NY, United States
| | - Margaret V McDonald
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Yolanda Barrón
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Mollie Hobensack
- Columbia University School of Nursing, New York, NY, United States
| | - Erin Kennedy
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, PA, United States
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Lauren Evans
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Maxim Topaz
- Center for Home Care Policy & Research, Columbia University School of Nursing, Data Science Institute, Columbia University, Visiting Nurse Service of New York, New York, NY, United States
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11
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Su D, Li Q, Zhang T, Veliz P, Chen Y, He K, Mahajan P, Zhang X. Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department. BMC Med Res Methodol 2022; 22:18. [PMID: 35026994 PMCID: PMC8759254 DOI: 10.1186/s12874-021-01490-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/08/2021] [Indexed: 11/12/2022] Open
Abstract
Background Early screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey. Methods We performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient’s ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms. Results Among the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69–0.75) for structured variables only, 0.72 (95% CI: 0.69–0.75) for unstructured variables only, and 0.78 (95% CI: 0.76–0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79–0.89) for including structured variables only, 0.78 (95% CI: 0.72–0.84) for unstructured variables, and 0.87 (95% CI: 0.83–0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model. Conclusions We developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01490-9.
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Affiliation(s)
- Dai Su
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China
| | - Qinmengge Li
- Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, USA
| | - Tao Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health School, Sichuan University, Chengdu, China
| | - Philip Veliz
- Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, USA
| | - Yingchun Chen
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Research Center for Rural Health Services, Hubei Province Key Research Institute of Humanities and Social Sciences, Wuhan, China
| | - Kevin He
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, USA
| | - Prashant Mahajan
- Department of Emergency Medicine, University of Michigan School of Medicine, Ann Arbor, USA
| | - Xingyu Zhang
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh Medical Center, Pittsburgh, USA.
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12
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Marin JR, Rodean J, Hall M, Alpern ER, Aronson PL, Chaudhari PP, Cohen E, Freedman SB, Morse RB, Peltz A, Samuels-Kalow M, Shah SS, Simon HK, Neuman MI. Racial and Ethnic Differences in Emergency Department Diagnostic Imaging at US Children's Hospitals, 2016-2019. JAMA Netw Open 2021; 4:e2033710. [PMID: 33512517 PMCID: PMC7846940 DOI: 10.1001/jamanetworkopen.2020.33710] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Diagnostic imaging is frequently performed as part of the emergency department (ED) evaluation of children. Whether imaging patterns differ by race and ethnicity is unknown. OBJECTIVE To evaluate racial and ethnic differences in the performance of common ED imaging studies and to examine patterns across diagnoses. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study evaluated visits by patients younger than 18 years to 44 US children's hospital EDs from January 1, 2016, through December 31, 2019. EXPOSURES Non-Hispanic Black and Hispanic compared with non-Hispanic White race/ethnicity. MAIN OUTCOMES AND MEASURES The primary outcome was the proportion of visits for each race/ethnicity group with at least 1 diagnostic imaging study, defined as plain radiography, computed tomography, ultrasonography, and magnetic resonance imaging. The major diagnostic categories classification system was used to examine race/ethnicity differences in imaging rates by diagnoses. RESULTS A total of 13 087 522 visits by 6 230 911 children and adolescents (mean [SD] age, 5.8 [5.2] years; 52.7% male) occurred during the study period. Diagnostic imaging was performed during 3 689 163 visits (28.2%). Imaging was performed in 33.5% of visits by non-Hispanic White patients compared with 24.1% of visits by non-Hispanic Black patients (odds ratio [OR], 0.60; 95% CI, 0.60-0.60) and 26.1% of visits by Hispanic patients (OR, 0.66; 95% CI, 0.66-0.67). Adjusting for confounders, visits by non-Hispanic Black (adjusted OR, 0.82; 95% CI, 0.82-0.83) and Hispanic (adjusted OR, 0.87; 95% CI, 0.87-0.87) patients were less likely to include any imaging study compared with visits by non-Hispanic White patients. Limiting the analysis to only visits by nonhospitalized patients, the adjusted OR for imaging was 0.79 (95% CI, 0.79-0.80) for visits by non-Hispanic Black patients and 0.84 (95% CI, 0.84-0.85) for visits by Hispanic patients. Results were consistent in analyses stratified by public and private insurance groups and did not materially differ by diagnostic category. CONCLUSIONS AND RELEVANCE In this study, non-Hispanic Black and Hispanic children were less likely to receive diagnostic imaging during ED visits compared with non-Hispanic White children. Further investigation is needed to understand and mitigate these potential disparities in health care delivery and to evaluate the effect of these differential imaging patterns on patient outcomes.
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Affiliation(s)
- Jennifer R. Marin
- Department of Pediatrics, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - Matt Hall
- Children’s Hospital Association, Lenexa, Kansas
| | - Elizabeth R. Alpern
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Paul L. Aronson
- Section of Pediatric Emergency Medicine, Departments of Pediatrics and Emergency Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Pradip P. Chaudhari
- Division of Emergency and Transport Medicine, Children’s Hospital Los Angeles, Keck School of Medicine of the University of Southern California, Los Angeles
| | - Eyal Cohen
- Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Stephen B. Freedman
- Sections of Pediatric Emergency Medicine and Gastroenterology, Department of Pediatrics, Alberta Children’s Hospital, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Emergency Medicine, Alberta Children’s Hospital, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Rustin B. Morse
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, Ohio
| | - Alon Peltz
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | | | - Samir S. Shah
- Divisions of Hospital Medicine and Infectious Diseases, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Harold K. Simon
- Division of Emergency Medicine, Departments of Pediatrics and Emergency Medicine, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia
| | - Mark I. Neuman
- Division of Emergency Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
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13
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Chamberlain JM, Chamberlain DB, Zorc JJ. Machine Learning and Clinical Prediction Rules: A Perfect Match? Pediatrics 2020; 146:peds.2020-012203. [PMID: 32855348 DOI: 10.1542/peds.2020-012203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/19/2020] [Indexed: 11/24/2022] Open
Affiliation(s)
| | | | - Joseph J Zorc
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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