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Zolnoori M, Sridharan S, Zolnour A, Vergez S, McDonald MV, Kostic Z, Bowles KH, Topaz M. Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare. J Am Med Inform Assoc 2024; 31:435-444. [PMID: 37847651 PMCID: PMC10797261 DOI: 10.1093/jamia/ocad195] [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: 07/08/2023] [Accepted: 09/21/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients. OBJECTIVES To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes. METHODS This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses. RESULTS Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more "sadness" and "anxiety," and have extended periods of silence during conversation. CONCLUSION This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record.
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Affiliation(s)
- Maryam Zolnoori
- School of Nursing, Columbia University, New York, NY 10032, United States
- Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States
| | | | - Ali Zolnour
- School of Electrical and Computer Engineering, University of Tehran, Tehran 14395-515, Iran
| | - Sasha Vergez
- Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States
| | - Margaret V McDonald
- Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States
| | - Zoran Kostic
- Electrical Engineering Department, Columbia University, New York, NY 10027, United States
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States
- School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Maxim Topaz
- School of Nursing, Columbia University, New York, NY 10032, United States
- Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States
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Song J, Min SH, Chae S, Bowles KH, McDonald MV, Hobensack M, Barrón Y, Sridharan S, Davoudi A, Oh S, Evans L, Topaz M. Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care. J Am Med Inform Assoc 2023; 30:1801-1810. [PMID: 37339524 PMCID: PMC10586044 DOI: 10.1093/jamia/ocad101] [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: 03/06/2023] [Revised: 05/04/2023] [Accepted: 06/02/2023] [Indexed: 06/22/2023] Open
Abstract
OBJECTIVE This study aimed to identify temporal risk factor patterns documented in home health care (HHC) clinical notes and examine their association with hospitalizations or emergency department (ED) visits. MATERIALS AND METHODS Data for 73 350 episodes of care from one large HHC organization were analyzed using dynamic time warping and hierarchical clustering analysis to identify the temporal patterns of risk factors documented in clinical notes. The Omaha System nursing terminology represented risk factors. First, clinical characteristics were compared between clusters. Next, multivariate logistic regression was used to examine the association between clusters and risk for hospitalizations or ED visits. Omaha System domains corresponding to risk factors were analyzed and described in each cluster. RESULTS Six temporal clusters emerged, showing different patterns in how risk factors were documented over time. Patients with a steep increase in documented risk factors over time had a 3 times higher likelihood of hospitalization or ED visit than patients with no documented risk factors. Most risk factors belonged to the physiological domain, and only a few were in the environmental domain. DISCUSSION An analysis of risk factor trajectories reflects a patient's evolving health status during a HHC episode. Using standardized nursing terminology, this study provided new insights into the complex temporal dynamics of HHC, which may lead to improved patient outcomes through better treatment and management plans. CONCLUSION Incorporating temporal patterns in documented risk factors and their clusters into early warning systems may activate interventions to prevent hospitalizations or ED visits in HHC.
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Affiliation(s)
- Jiyoun Song
- Columbia University School of Nursing, New York City, New York, USA
| | - Se Hee Min
- Columbia University School of Nursing, New York City, New York, USA
| | - Sena Chae
- College of Nursing, University of Iowa, Iowa City, Iowa, USA
| | - Kathryn H Bowles
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | | | - Mollie Hobensack
- Columbia University School of Nursing, New York City, New York, USA
| | - Yolanda Barrón
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Anahita Davoudi
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Sungho Oh
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, New York, USA
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
- Data Science Institute, Columbia University, New York City, New York, USA
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Sterling MR, Lau J, Rajan M, Safford M, Akinyelure OP, Kern LM. Self-reported gaps in care coordination and preventable adverse outcomes among older adults receiving home health care. J Am Geriatr Soc 2023; 71:810-820. [PMID: 36468538 PMCID: PMC10023304 DOI: 10.1111/jgs.18135] [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: 08/22/2022] [Revised: 10/19/2022] [Accepted: 10/24/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Older adults see multiple outpatient providers and increasingly use home health care (HHC) services. Previous studies attempting to draw inferences about the association between HHC use and patient outcomes have been mixed. Whether HHC is associated with care coordination and how both influence outcomes are unknown. In addition, prior studies have not taken the patient perspective into account. We examined the association between receiving HHC and self-reported gaps in care coordination and separately, preventable adverse outcomes. METHODS The analysis for this cross-sectional study was conducted between October 2021 and June 2022, using data on 4296 Medicare beneficiaries from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study who completed a survey on care coordination from 2017 to 2018. The associations between the receipt of HHC and two outcomes (a gap in care coordination, and separately, a preventable adverse event) were examined with Poisson models with robust standard errors. Potential confounders were accounted for through propensity score-based inverse probability weighting. RESULTS Among 4296 participants, 430 (10%) received HHC and they were older and had more comorbidities and ambulatory visits than those without HHC. HHC was not associated with differences in self-reported gaps in care coordination (33.3% HHC vs. 32.5% no-HHC, p = 0.70). HHC recipients reported more preventable drug-drug interactions (9.1% vs. 4.0%, p < 0.001) but not more preventable ED visits or hospital admissions. In IPW-adjusted models, HHC was not associated with gaps in care coordination (p = 0.60) but was associated with double the risk of a preventable adverse outcome (aRR 2.06; CI: 1.37, 3.10, p < 0.001). CONCLUSIONS HHC recipients were significantly more likely (than those without HHC) to report a potentially preventable adverse event (particularly a drug-drug interaction), suggesting an opportunity to improve patient safety by leveraging the observations of older adults receiving HHC.
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Affiliation(s)
| | - Jennifer Lau
- Division of General Internal Medicine, Weill Cornell Medicine, New York, NY
| | - Mangala Rajan
- Division of General Internal Medicine, Weill Cornell Medicine, New York, NY
| | - Monika Safford
- Division of General Internal Medicine, Weill Cornell Medicine, New York, NY
| | | | - Lisa M. Kern
- Division of General Internal Medicine, Weill Cornell Medicine, New York, NY
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Song J, Chae S, Bowles KH, McDonald MV, Barrón Y, Cato K, Collins Rossetti S, Hobensack M, Sridharan S, Evans L, Davoudi A, Topaz M. The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care. J Adv Nurs 2023; 79:593-604. [PMID: 36414419 PMCID: PMC10163408 DOI: 10.1111/jan.15498] [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: 05/30/2022] [Revised: 09/30/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022]
Abstract
AIMS To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits. DESIGN A retrospective cohort study. METHODS This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan-Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care. RESULTS A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to "impaired physical comfort with pain," defined as situations where patients may experience increased pain. Cluster 2 is characterized by "high comorbidity burden" defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by "impaired cognitive/psychological and skin integrity" including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001). CONCLUSION Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes. IMPACT Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events. PATIENT OR PUBLIC CONTRIBUTION There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.
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Affiliation(s)
- Jiyoun Song
- Columbia University School of Nursing, New York City, New York, USA
| | - Sena Chae
- College of Nursing, The University of Iowa, Iowa City, Iowa, USA
| | - Kathryn H. Bowles
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
- Center for Home Care Policy & Research, VNS Health, New York, New York City, USA
| | - Margaret V. McDonald
- Center for Home Care Policy & Research, VNS Health, New York, New York City, USA
| | - Yolanda Barrón
- Center for Home Care Policy & Research, VNS Health, New York, New York City, 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
| | - 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
| | - Mollie Hobensack
- Columbia University School of Nursing, New York City, New York, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, VNS Health, New York, New York City, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, VNS Health, New York, New York City, USA
| | - Anahita Davoudi
- Center for Home Care Policy & Research, VNS Health, New York, New York City, USA
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, New York, USA
- Center for Home Care Policy & Research, VNS Health, New York, New York City, 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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Probabilistic linguistic WASPAS method for patients’ prioritization by developing prioritized Maclaurin symmetric mean aggregation operators. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02807-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zolnoori M, Song J, McDonald MV, Barrón Y, Cato K, Sockolow P, Sridharan S, Onorato N, Bowles KH, Topaz M. Exploring Reasons for Delayed Start-of-Care Nursing Visits in Home Health Care: Algorithm Development and Data Science Study. JMIR Nurs 2021; 4:e31038. [PMID: 34967749 PMCID: PMC8759020 DOI: 10.2196/31038] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/31/2021] [Accepted: 10/28/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Delayed start-of-care nursing visits in home health care (HHC) can result in negative outcomes, such as hospitalization. No previous studies have investigated why start-of-care HHC nursing visits are delayed, in part because most reasons for delayed visits are documented in free-text HHC nursing notes. OBJECTIVE The aims of this study were to (1) develop and test a natural language processing (NLP) algorithm that automatically identifies reasons for delayed visits in HHC free-text clinical notes and (2) describe reasons for delayed visits in a large patient sample. METHODS This study was conducted at the Visiting Nurse Service of New York (VNSNY). We examined data available at the VNSNY on all new episodes of care started in 2019 (N=48,497). An NLP algorithm was developed and tested to automatically identify and classify reasons for delayed visits. RESULTS The performance of the NLP algorithm was 0.8, 0.75, and 0.77 for precision, recall, and F-score, respectively. A total of one-third of HHC episodes (n=16,244) had delayed start-of-care HHC nursing visits. The most prevalent identified category of reasons for delayed start-of-care nursing visits was no answer at the door or phone (3728/8051, 46.3%), followed by patient/family request to postpone or refuse some HHC services (n=2858, 35.5%), and administrative or scheduling issues (n=1465, 18.2%). In 40% (n=16,244) of HHC episodes, 2 or more reasons were documented. CONCLUSIONS To avoid critical delays in start-of-care nursing visits, HHC organizations might examine and improve ways to effectively address the reasons for delayed visits, using effective interventions, such as educating patients or caregivers on the importance of a timely nursing visit and improving patients' intake procedures.
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Affiliation(s)
- Maryam Zolnoori
- School of Nursing, Columbia University, New York, NY, United States
| | - Jiyoun Song
- School of Nursing, Columbia University, 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
| | - Kenrick Cato
- School of Nursing, Columbia University, New York, NY, United States
| | - Paulina Sockolow
- College of Nursing and Health Professions, Drexel University, Philadelphia, PA, United States
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Nicole Onorato
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, United States.,Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, United States
| | - Maxim Topaz
- School of Nursing, Columbia University, New York, NY, United States
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