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Gan S, Kim C, Chang J, Lee DY, Park RW. Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study. Int J Nurs Stud 2024; 158:104850. [PMID: 39024965 DOI: 10.1016/j.ijnurstu.2024.104850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. OBJECTIVE This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DESIGN A retrospective observational cohort study. SETTING(S) This study was conducted at Ajou University School of Medicine in South Korea. PARTICIPANTS Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. METHODS Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. RESULTS Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. CONCLUSIONS This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.
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Affiliation(s)
- Sujin Gan
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
| | - Chungsoo Kim
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Junhyuck Chang
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | - Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
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Albashayreh A, Bandyopadhyay A, Zeinali N, Zhang M, Fan W, Gilbertson White S. Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives. JCO Clin Cancer Inform 2024; 8:e2300235. [PMID: 39116379 DOI: 10.1200/cci.23.00235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 08/10/2024] Open
Abstract
PURPOSE Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from negated symptoms and medication-related side effects. We evaluated the accuracy of NLP in (1) detecting 14 symptom groups (ie, pain, fatigue, swelling, depressed mood, anxiety, nausea/vomiting, pruritus, headache, shortness of breath, constipation, numbness/tingling, decreased appetite, impaired memory, disturbed sleep) and (2) distinguishing observed symptoms in EHR narratives among patients with cancer. METHODS We extracted 902,508 notes for 11,784 unique patients diagnosed with cancer and developed a gold standard corpus of 1,112 notes labeled for presence or absence of 14 symptom groups. We trained an embeddings-augmented NLP system integrating human and machine intelligence and conventional machine learning algorithms. NLP metrics were calculated on a gold standard corpus subset for testing. RESULTS The interannotator agreement for labeling the gold standard corpus was excellent at 92%. The embeddings-augmented NLP model achieved the best performance (F1 score = 0.877). The highest NLP accuracy was observed in pruritus (F1 score = 0.937) while the lowest accuracy was in swelling (F1 score = 0.787). After classifying the entire data set with embeddings-augmented NLP, we found that 41% of the notes included symptom documentation. Pain was the most documented symptom (29% of all notes) while impaired memory was the least documented (0.7% of all notes). CONCLUSION We illustrated the feasibility of detecting 14 symptom groups in EHR narratives and showed that an embeddings-augmented NLP system outperforms conventional machine learning algorithms in detecting symptom information and differentiating observed symptoms from negated symptoms and medication-related side effects.
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Affiliation(s)
| | | | | | - Min Zhang
- School of Economics and Management, Communication University of China, Beijing, China
| | - Weiguo Fan
- Tippie College of Business, University of Iowa, Iowa City, IA
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Li B, Du K, Qu G, Tang N. Big data research in nursing: A bibliometric exploration of themes and publications. J Nurs Scholarsh 2024; 56:466-477. [PMID: 38140780 DOI: 10.1111/jnu.12954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/14/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023]
Abstract
AIMS To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. DESIGN Bibliometric analysis. METHODS The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. RESULTS The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. CONCLUSIONS The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. CLINICAL RELEVANCE This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.
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Affiliation(s)
- Bo Li
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kun Du
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guanchen Qu
- School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China
| | - Naifu Tang
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Johnson EA, Dudding KM, Carrington JM. When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nurs Inq 2024; 31:e12583. [PMID: 37459179 DOI: 10.1111/nin.12583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/05/2023] [Accepted: 07/09/2023] [Indexed: 01/18/2024]
Abstract
Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision-making. The boundaries between human- and nonhuman-driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision-making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed.
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Affiliation(s)
- Elizabeth A Johnson
- Mark & Robyn Jones College of Nursing, Montana State University, Bozeman, Montana, USA
| | - Katherine M Dudding
- Department of Family, Community, and Health Systems, UAB School of Nursing, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jane M Carrington
- Department of Family, Community and Health System Science, University of Florida College of Nursing, Gainesville, Florida, USA
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Scharp D, Hobensack M, Davoudi A, Topaz M. Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review. J Am Med Dir Assoc 2024; 25:69-83. [PMID: 37838000 PMCID: PMC10792659 DOI: 10.1016/j.jamda.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVES To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings. DESIGN Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. SETTING AND PARTICIPANTS Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities). METHODS PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised. RESULTS Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators. CONCLUSIONS AND IMPLICATIONS The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.
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Affiliation(s)
| | | | - Anahita Davoudi
- VNS Health, Center for Home Care Policy & Research, New York, NY, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York, NY, USA
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Hobensack M, Song J, Oh S, Evans L, Davoudi A, Bowles KH, McDonald MV, Barrón Y, Sridharan S, Wallace AS, Topaz M. Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study. J Am Med Dir Assoc 2023; 24:1874-1880.e4. [PMID: 37553081 PMCID: PMC10839109 DOI: 10.1016/j.jamda.2023.06.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/23/2023] [Accepted: 06/25/2023] [Indexed: 08/10/2023]
Abstract
OBJECTIVE This study aimed to develop a natural language processing (NLP) system that identified social risk factors in home health care (HHC) clinical notes and to examine the association between social risk factors and hospitalization or an emergency department (ED) visit. DESIGN Retrospective cohort study. SETTING AND PARTICIPANTS We used standardized assessments and clinical notes from one HHC agency located in the northeastern United States. This included 86,866 episodes of care for 65,593 unique patients. Patients received HHC services between 2015 and 2017. METHODS Guided by HHC experts, we created a vocabulary of social risk factors that influence hospitalization or ED visit risk in the HHC setting. We then developed an NLP system to automatically identify social risk factors documented in clinical notes. We used an adjusted logistic regression model to examine the association between the NLP-based social risk factors and hospitalization or an ED visit. RESULTS On the basis of expert consensus, the following social risk factors emerged: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, Access to Care, and Housing and Economic Circumstances. Our NLP system performed "very good" with an F score of 0.91. Approximately 4% of clinical notes (33% episodes of care) documented a social risk factor. The most frequently documented social risk factors were Physical Environment and Social Environment. Except for Housing and Economic Circumstances, all NLP-based social risk factors were associated with higher odds of hospitalization and ED visits. CONCLUSIONS AND IMPLICATIONS HHC clinicians assess and document social risk factors associated with hospitalizations and ED visits in their clinical notes. Future studies can explore the social risk factors documented in HHC to improve communication across the health care system and to predict patients at risk for being hospitalized or visiting the ED.
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Affiliation(s)
| | - Jiyoun Song
- Columbia University School of Nursing, New York City, NY, USA
| | - Sungho Oh
- University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, VNS Health, New York, NY, USA
| | - Anahita Davoudi
- Center for Home Care Policy & Research, VNS Health, New York, NY, USA
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, VNS Health, New York, NY, USA; Department of Biobehavioral Health Sciences, NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | | | - Yolanda Barrón
- Center for Home Care Policy & Research, VNS Health, New York, NY, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, VNS Health, New York, NY, USA
| | - Andrea S Wallace
- The University of Utah College of Nursing, Salt Lake City, UT, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, NY, USA; Center for Home Care Policy & Research, VNS Health, New York, NY, USA; Data Science Institute, Columbia University, New York City, NY, USA
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7
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Min SH, Song J, Evans L, Bowles KH, McDonald MV, Chae S, Topaz M. Home Healthcare Patients With Distinct Psychological, Cognitive, and Behavioral Symptom Profiles and At-Risk Subgroup for Hospitalization and Emergency Department Visits Using Latent Class Analysis. Clin Nurs Res 2023; 32:1021-1030. [PMID: 37345951 PMCID: PMC11080676 DOI: 10.1177/10547738231183026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Abstract
One-third of home healthcare patients are hospitalized or visit emergency departments during a 60-day episode of care. Among all risk factors, psychological, cognitive, and behavioral symptoms often remain underdiagnosed or undertreated in older adults. Little is known on subgroups of older adults receiving home healthcare services with similar psychological, cognitive, and behavioral symptom profiles and an at-risk subgroup for future hospitalization and emergency department visits. Our cross-sectional study used data from a large, urban home healthcare organization (n = 87,943). Latent class analysis was conducted to identify meaningful subgroups of older adults based on their distinct psychological, cognitive, and behavioral symptom profiles. Adjusted multiple logistic regression was used to understand the association between the latent subgroup and future hospitalization and emergency department visits. Descriptive and inferential statistics were conducted to describe the individual characteristics and to test for significant differences. The three-class model consisted of Class 1: "Moderate psychological symptoms without behavioral issues," Class 2: "Severe psychological symptoms with behavioral issues," and Class 3: "Mild psychological symptoms without behavioral issues." Compared to Class 3, Class 1 patients had 1.14 higher odds and Class 2 patients had 1.26 higher odds of being hospitalized or visiting emergency departments. Significant differences were found in individual characteristics such as age, gender, race/ethnicity, and insurance. Home healthcare clinicians should consider the different latent subgroups of older adults based on their psychological, cognitive, and behavioral symptoms. In addition, they should provide timely assessment and intervention especially to those at-risk for hospitalization and emergency department visits.
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Affiliation(s)
- Se Hee Min
- Columbia University School of Nursing, New York, NY, USA
| | - Jiyoun Song
- Columbia University School of Nursing, New York, NY, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, USA
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, USA
- University of Pennsylvania School of Nursing, Philadelphia, USA
| | - Margaret V McDonald
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, USA
| | - Sena Chae
- University of Iowa College of Nursing, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York, NY, USA
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, USA
- Data Science Institute, Columbia University, New York, NY, USA
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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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Hobensack M, Song J, Chae S, Kennedy E, Zolnoori M, Bowles KH, McDonald MV, Evans L, Topaz M. Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2023; 2022:552-559. [PMID: 37128448 PMCID: PMC10148365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration.
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Affiliation(s)
| | - Jiyoun Song
- Columbia University School of Nursing, New York, NY, USA
| | - Sena Chae
- University of Iowa College of Nursing, Iowa City, IA, USA
| | - Erin Kennedy
- University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | | | - Kathryn H Bowles
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, USA
- University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | - Margaret V McDonald
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York, NY, USA
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, USA
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Bakken S, Dreisbach C. Informatics and data science perspective on Future of Nursing 2020-2030: Charting a pathway to health equity. Nurs Outlook 2022; 70:S77-S87. [PMID: 36446542 DOI: 10.1016/j.outlook.2022.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 11/27/2022]
Abstract
The Future of Nursing 2020 to 2030 report explicitly addresses the need for integration of nursing expertise in designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health (SDOH) and health equity. The metrics necessary to enable and evaluate progress on all recommendations require harnessing existing data sources and developing new ones, as well as transforming and integrating data into information systems to facilitate communication, information sharing, and decision making among the key stakeholders. We examine the recommendations of the 2021 report through an interdisciplinary lens that integrates nursing, biomedical informatics, and data science by addressing three critical questions: (a) what data are needed?, (b) what infrastructure and processes are needed to transform data into information?, and (c) what information systems are needed to "level up" nurse-led interventions from the micro-level to the meso- and macro-levels to address social determinants of health and advance health equity?
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Affiliation(s)
- Suzanne Bakken
- School of Nursing, Columbia University, New York, NY 10032, United States; Department of Biomedical Informatics, Columbia University, New York, NY, United States; Data Science Institute, Columbia University, New York, NY, United States.
| | - Caitlin Dreisbach
- Data Science Institute, Columbia University, New York, NY, United States
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Song J, Ojo M, Bowles KH, McDonald MV, Cato K, Rossetti SC, Adams V, Chae S, Hobensack M, Kennedy E, Tark A, Kang MJ, Woo K, Barrón Y, Sridharan S, Topaz M. Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit. Nurs Res 2022; 71:285-294. [PMID: 35171126 PMCID: PMC9246992 DOI: 10.1097/nnr.0000000000000586] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode. OBJECTIVE The aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits. METHODS This study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017. RESULTS A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions. CONCLUSION Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.
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12
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Hobensack M, Ojo M, Barrón Y, Bowles KH, Cato K, Chae S, Kennedy E, McDonald MV, Rossetti SC, Song J, Sridharan S, Topaz M. Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians. J Am Med Inform Assoc 2022; 29:805-812. [PMID: 35196369 PMCID: PMC9006696 DOI: 10.1093/jamia/ocac023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors. METHODS We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors. RESULTS Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%)). DISCUSSION Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them. CONCLUSION This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies.
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Affiliation(s)
- Mollie Hobensack
- Columbia University School of Nursing, New York City, New York, USA
| | - Marietta Ojo
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
| | - Yolanda Barrón
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Kenrick Cato
- Columbia University School of Nursing, New York City, New York, USA
- Emergency Medicine, Columbia University Irving Medical Center, New York City, New York, USA
| | - Sena Chae
- College of Nursing, University of Iowa, Iowa City, Iowa, USA
| | - Erin Kennedy
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Margaret V McDonald
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
| | - Sarah Collins Rossetti
- Columbia University School of Nursing, New York City, New York, USA
- Department of Biomedical Informatics, Columbia University, New York City, New York, USA
| | - Jiyoun Song
- Columbia University School of Nursing, New York City, New York, USA
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, New York, USA
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, New York, USA
- Data Science Institute, Columbia University, New York City, New York, USA
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Song J, Hobensack M, Bowles KH, McDonald MV, Cato K, Rossetti SC, Chae S, Kennedy E, Barrón Y, Sridharan S, Topaz M. Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care. J Biomed Inform 2022; 128:104039. [PMID: 35231649 PMCID: PMC9825202 DOI: 10.1016/j.jbi.2022.104039] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND/OBJECTIVE Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. METHODS Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. RESULTS During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. CONCLUSION All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.
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Affiliation(s)
- Jiyoun Song
- Columbia University School of Nursing, New York City, NY, USA,Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA,Corresponding author at: Columbia University School of Nursing, 560 West 168th Street, New York, NY 10032, USA. (J. Song)
| | | | - Kathryn H. Bowles
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA,University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USA
| | - Margaret V. McDonald
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA
| | - Kenrick Cato
- Columbia University School of Nursing, New York City, NY, USA,Emergency Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Sarah Collins Rossetti
- Columbia University School of Nursing, New York City, NY, USA,Columbia University, Department of Biomedical Informatics, New York City, NY, USA
| | - Sena Chae
- College of Nursing, University of Iowa, Iowa City, IA, USA
| | - Erin Kennedy
- University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USA
| | - Yolanda Barrón
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, NY, USA,Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City, NY, USA,Data Science Institute, Columbia University, New York City, NY, USA
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14
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Abstract
BACKGROUND Pain is a common but understudied symptom among patients with heart failure (HF) transported by emergency medical services (EMS). The aims were to determine explanatory factors of a primary complaint of pain and pain severity, and characterize pain among patients with HF transported by EMS. METHODS Data from electronic health records of patients with HF transported by EMS within a midwestern United States county from 2009 to 2017 were analyzed. Descriptive statistics, χ 2 , analysis of variance, and logistic and multiple linear regression analyses were used. RESULTS The sample (N = 4663) was predominantly women (58.1%) with self-reported race as Black (57.7%). The mean age was 64.2 ± 14.3 years. Pain was the primary complaint in 22.2% of the sample, with an average pain score of 6.8 ± 3.1 out of 10. The most common pain complaint was chest pain (68.1%). Factors associated with a primary pain complaint were younger age (odds ratio [OR], 0.97; 95% confidence interval [CI], 0.96-0.97), history of myocardial infarction (OR, 1.96; 95% CI, 1.55-2.49), and absence of shortness of breath (OR, 0.67; 95% CI, 0.58-0.77). Factors associated with higher pain severity were younger age ( b = -0.05, SE = 0.013), being a woman ( b = 1.17, SE = 0.357), and White race ( b = -1.11, SE = 0.349). CONCLUSIONS Clinical and demographic factors need consideration in understanding pain in HF during EMS transport. Additional research is needed to examine these factors to improve pain management and reduce transports due to pain.
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