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Haleem A, Garcia A, Khan S, Shakelly P, Lee DJ. Access to Sudden Sensorineural Hearing Loss Care at Private Equity-Owned Otolaryngology Clinics. Otolaryngol Head Neck Surg 2024; 170:1705-1711. [PMID: 38327257 DOI: 10.1002/ohn.665] [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: 07/13/2023] [Revised: 12/14/2023] [Accepted: 01/13/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVE Characterizing access to sudden sensorineural hearing loss (SSNHL) care at private practice otolaryngology clinics of varying ownership models. STUDY DESIGN Cross-sectional prospective review. SETTING Private practice otolaryngology clinics. METHODS We employed a Secret Shopper study design with private equity (PE) owned and non-PE-owned clinics within 15 miles of one another. Using a standardized script, researchers randomly called 50% of each clinic type between October 2021 and January 2022 requesting an appointment on behalf of a family member enrolled in either Medicaid or private insurance (PI) experiencing SSNHL. Access to timely care was assessed between clinic ownership and insurance type. RESULTS Seventy-eight total PE-owned otolaryngology clinics were identified across the United States. Only 40 non-PE clinics could be matched to the PE clinics; 39 PE and 28 non-PE clinics were called as Medicaid patients; 39 PE and 25 non-PE clinics were called as PI patients; 48.7% of PE and 28.6% of non-PE clinics accepted Medicaid. The mean wait time to new appointment ranged between 9.55 and 13.21 days for all insurance and ownership types but did not vary significantly (P > .480). Telehealth was significantly more likely to be offered for new Medicaid patients at non-PE clinics compared to PE clinics (31.8% vs 0.0%, P = .001). The mean cost for an appointment was significantly greater at PE clinics than at non-PE clinics ($291.18 vs $203.75, P = .004). CONCLUSIONS Patients seeking SSNHL care at PE-owned otolaryngology clinics are likely to face long wait times prior to obtaining an initial appointment and reduced telehealth options.
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Affiliation(s)
- Afash Haleem
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
- Department of Otolaryngology-Head and Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Alejandro Garcia
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
| | - Sophia Khan
- Department of Biology, The College of New Jersey, Ewing, New Jersey, USA
| | - Purvi Shakelly
- Department of Biology, The College of New Jersey, Ewing, New Jersey, USA
| | - Daniel J Lee
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
- Division of Otolaryngology-Head and Neck Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
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Yang TH, Chen YF, Cheng YF, Huang JN, Wu CS, Chu YC. Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S. BioData Min 2023; 16:35. [PMID: 38098102 PMCID: PMC10722728 DOI: 10.1186/s13040-023-00351-z] [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: 11/04/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
OBJECTIVES The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. METHODS We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model-which had the greatest AUC of 0.83 (95% CI 0.81-0.85)-was then only used on the holdout testing data. RESULTS On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79-0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques-specifically, the LGBM classifier-with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. CONCLUSIONS Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
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Affiliation(s)
- Tzong-Hann Yang
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan
- General Education Center, University of Taipei, Taipei, 10671, Taiwan
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Fu Chen
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan
| | - Chuan-Song Wu
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan.
- College of Science and Engineering, Fu Jen University, Taipei, 243, Taiwan.
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Big Data Center, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 112, Taiwan.
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Tang SY, Chen TH, Kuo KL, Huang JN, Kuo CT, Chu YC. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. J Chin Med Assoc 2023; 86:1020-1027. [PMID: 37713313 DOI: 10.1097/jcma.0000000000000994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
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Affiliation(s)
- Shao-Yu Tang
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Tz-Heng Chen
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Ko-Lin Kuo
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chen-Tsung Kuo
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
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Buhl M, Akin G, Saak S, Eysholdt U, Radeloff A, Kollmeier B, Hildebrandt A. Expert validation of prediction models for a clinical decision-support system in audiology. Front Neurol 2022; 13:960012. [PMID: 36081868 PMCID: PMC9446152 DOI: 10.3389/fneur.2022.960012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
For supporting clinical decision-making in audiology, Common Audiological Functional Parameters (CAFPAs) were suggested as an interpretable intermediate representation of audiological information taken from various diagnostic sources within a clinical decision-support system (CDSS). Ten different CAFPAs were proposed to represent specific functional aspects of the human auditory system, namely hearing threshold, supra-threshold deficits, binaural hearing, neural processing, cognitive abilities, and a socio-economic component. CAFPAs were established as a viable basis for deriving audiological findings and treatment recommendations, and it has been demonstrated that model-predicted CAFPAs, with machine learning models trained on expert-labeled patient cases, are sufficiently accurate to be included in a CDSS, but it requires further validation by experts. The present study aimed to validate model-predicted CAFPAs based on previously unlabeled cases from the same data set. Here, we ask to which extent domain experts agree with the model-predicted CAFPAs and whether potential disagreement can be understood in terms of patient characteristics. To these aims, an expert survey was designed and applied to two highly-experienced audiology specialists. They were asked to evaluate model-predicted CAFPAs and estimate audiological findings of the given audiological information about the patients that they were presented with simultaneously. The results revealed strong relative agreement between the two experts and importantly between experts and the prediction for all CAFPAs, except for the neural processing and binaural hearing-related ones. It turned out, however, that experts tend to score CAFPAs in a larger value range, but, on average, across patients with smaller scores as compared with the machine learning models. For the hearing threshold-associated CAFPA in frequencies smaller than 0.75 kHz and the cognitive CAFPA, not only the relative agreement but also the absolute agreement between machine and experts was very high. For those CAFPAs with an average difference between the model- and expert-estimated values, patient characteristics were predictive of the disagreement. The findings are discussed in terms of how they can help toward further improvement of model-predicted CAFPAs to be incorporated in a CDSS for audiology.
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Affiliation(s)
- Mareike Buhl
- Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- *Correspondence: Mareike Buhl
| | - Gülce Akin
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Department of Psychological Methods and Statistics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Samira Saak
- Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Ulrich Eysholdt
- Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Universitätsklinik für Hals-Nasen-Ohren-Heilkunde, Evangelisches Krankenhaus Oldenburg, Oldenburg, Germany
| | - Andreas Radeloff
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Universitätsklinik für Hals-Nasen-Ohren-Heilkunde, Evangelisches Krankenhaus Oldenburg, Oldenburg, Germany
| | - Birger Kollmeier
- Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Hörzentrum Oldenburg gGmbH, Oldenburg, Germany
- Hearing Speech and Audio Technology, Fraunhofer Institute for Digital Media Technology (IDMT), Oldenburg, Germany
| | - Andrea Hildebrandt
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Department of Psychological Methods and Statistics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- Andrea Hildebrandt
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Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional Parameters (CAFPAs). Diagnostics (Basel) 2022; 12:diagnostics12020463. [PMID: 35204556 PMCID: PMC8870744 DOI: 10.3390/diagnostics12020463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 12/19/2022] Open
Abstract
Common Audiological Functional Parameters (CAFPAs) were previously introduced as abstract, measurement-independent representation of audiological knowledge, and expert-estimated CAFPAs were shown to be applicable as an interpretable intermediate layer in a clinical decision support system (CDSS). Prediction models for CAFPAs were built based on expert knowledge and one audiological database to allow for data-driven estimation of CAFPAs for new, individual patients for whom no expert-estimated CAFPAs are available. Based on the combination of these components, the current study explores the feasibility of constructing a CDSS which is as interpretable as expert knowledge-based classification and as data-driven as machine learning-based classification. To test this hypothesis, the current study investigated the equivalence in performance of predicted CAFPAs compared to expert-estimated CAFPAs in an audiological classification task, analyzed the importance of different CAFPAs for high and comparable performance, and derived explanations for differences in classified categories. Results show that the combination of predicted CAFPAs and statistical classification enables to build an interpretable but data-driven CDSS. The classification provides good accuracy, with most categories being correctly classified, while some confusions can be explained by the properties of the employed database. This could be improved by including additional databases in the CDSS, which is possible within the presented framework.
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