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Deina C, Fogliatto FS, da Silveira GJC, Anzanello MJ. Decision analysis framework for predicting no-shows to appointments using machine learning algorithms. BMC Health Serv Res 2024; 24:37. [PMID: 38183029 PMCID: PMC10770919 DOI: 10.1186/s12913-023-10418-6] [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: 08/09/2023] [Accepted: 11/30/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. METHODS In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. RESULTS From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. CONCLUSION This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.
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Affiliation(s)
- Carolina Deina
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil.
| | - Flavio S Fogliatto
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil
| | - Giovani J C da Silveira
- Haskayne School of Business, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
| | - Michel J Anzanello
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil
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Fystro JR, Feiring E. Mapping out the arguments for and against patient non-attendance fees in healthcare: an analysis of public consultation documents. JOURNAL OF MEDICAL ETHICS 2023; 49:844-849. [PMID: 36944503 PMCID: PMC10715470 DOI: 10.1136/jme-2022-108856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Patients not attending their appointments without giving notice burden healthcare services. To reduce non-attendance rates, patient non-attendance fees have been introduced in various settings. Although some argue in narrow economic terms that behavioural change as a result of financial incentives is a voluntary transaction, charging patients for non-attendance remains controversial. This paper aims to investigate the controversies of implementing patient non-attendance fees. OBJECTIVE The aim was to map out the arguments in the Norwegian public debate concerning the introduction and use of patient non-attendance fees at public outpatient clinics. METHODS Public consultation documents (2009-2021) were thematically analysed (n=84). We used a preconceived conceptual framework based on the works of Grant to guide the analysis. RESULTS A broad range of arguments for and against patient non-attendance fees were identified, here referring to the acceptability of the fees' purpose, the voluntariness of the responses, the effects on the individual character and institutional norms and the perceived fairness and comparative effectiveness of patient non-attendance fees. Whereas the aim of motivating patients to keep their appointments to avoid poor utilisation of resources and increased waiting times was widely supported, principled and practical arguments against patient non-attendance fees were raised. CONCLUSION A narrow economic understanding of incentives cannot capture the breadth of arguments for and against patient non-attendance fees. Policy makers may draw on this insight when implementing similar incentive schemes. The study may also contribute to the general debate on ethics and incentives.
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Affiliation(s)
- Joar Røkke Fystro
- Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
| | - Eli Feiring
- Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
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Tarabichi Y, Higginbotham J, Riley N, Kaelber DC, Watts B. Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative. J Gen Intern Med 2023; 38:2921-2927. [PMID: 37126125 PMCID: PMC10150669 DOI: 10.1007/s11606-023-08209-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/11/2023] [Indexed: 05/02/2023]
Abstract
BACKGROUND Appointment no shows are prevalent in safety-net healthcare systems. The efficacy and equitability of using predictive algorithms to selectively add resource-intensive live telephone outreach to standard automated reminders in such a setting is not known. OBJECTIVE To determine if adding risk-driven telephone outreach to standard automated reminders can improve in-person primary care internal medicine clinic no show rates without worsening racial and ethnic show-rate disparities. DESIGN Randomized controlled quality improvement initiative. PARTICIPANTS Adult patients with an in-person appointment at a primary care internal medicine clinic in a safety-net healthcare system from 1/1/2022 to 8/24/2022. INTERVENTIONS A random forest model that leveraged electronic health record data to predict appointment no show risk was internally trained and validated to ensure fair performance. Schedulers leveraged the model to place reminder calls to patients in the augmented care arm who had a predicted no show rate of 15% or higher. MAINE MEASURES The primary outcome was no show rate stratified by race and ethnicity. KEY RESULTS There were 5840 appointments with a predicted no show rate of 15% or higher. A total of 2858 had been randomized to the augmented care group and 2982 randomized to standard care. The augmented care group had a significantly lower no show rate than the standard care group (33% vs 36%, p < 0.01). There was a significant reduction in no show rates for Black patients (36% vs 42% respectively, p < 0.001) not reflected in white, non-Hispanic patients. CONCLUSIONS In this randomized controlled quality improvement initiative, adding model-driven telephone outreach to standard automated reminders was associated with a significant reduction of in-person no show rates in a diverse primary care clinic. The initiative reduced no show disparities by predominantly improving access for Black patients.
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Affiliation(s)
- Yasir Tarabichi
- Center for Clinical Informatics Research and Education, MetroHealth, Cleveland, OH, USA.
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | | | - Nicholas Riley
- Center for Clinical Informatics Research and Education, MetroHealth, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - David C Kaelber
- Center for Clinical Informatics Research and Education, MetroHealth, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Brook Watts
- School of Medicine, University of Michigan, Ann Arbor, MI, USA
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Oikonomidi T, Norman G, McGarrigle L, Stokes J, van der Veer SN, Dowding D. Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review. J Am Med Inform Assoc 2022; 30:559-569. [PMID: 36508503 PMCID: PMC9933067 DOI: 10.1093/jamia/ocac242] [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: 10/04/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Outpatient no-shows have important implications for costs and the quality of care. Predictive models of no-shows could be used to target intervention delivery to reduce no-shows. We reviewed the effectiveness of predictive model-based interventions on outpatient no-shows, intervention costs, acceptability, and equity. MATERIALS AND METHODS Rapid systematic review of randomized controlled trials (RCTs) and non-RCTs. We searched Medline, Cochrane CENTRAL, Embase, IEEE Xplore, and Clinical Trial Registries on March 30, 2022 (updated on July 8, 2022). Two reviewers extracted outcome data and assessed the risk of bias using ROB 2, ROBINS-I, and confidence in the evidence using GRADE. We calculated risk ratios (RRs) for the relationship between the intervention and no-show rates (primary outcome), compared with usual appointment scheduling. Meta-analysis was not possible due to heterogeneity. RESULTS We included 7 RCTs and 1 non-RCT, in dermatology (n = 2), outpatient primary care (n = 2), endoscopy, oncology, mental health, pneumology, and an magnetic resonance imaging clinic. There was high certainty evidence that predictive model-based text message reminders reduced no-shows (1 RCT, median RR 0.91, interquartile range [IQR] 0.90, 0.92). There was moderate certainty evidence that predictive model-based phone call reminders (3 RCTs, median RR 0.61, IQR 0.49, 0.68) and patient navigators reduced no-shows (1 RCT, RR 0.55, 95% confidence interval 0.46, 0.67). The effect of predictive model-based overbooking was uncertain. Limited information was reported on cost-effectiveness, acceptability, and equity. DISCUSSION AND CONCLUSIONS Predictive modeling plus text message reminders, phone call reminders, and patient navigator calls are probably effective at reducing no-shows. Further research is needed on the comparative effectiveness of predictive model-based interventions addressed to patients at high risk of no-shows versus nontargeted interventions addressed to all patients.
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Affiliation(s)
- Theodora Oikonomidi
- Corresponding Author: Theodora Oikonomidi, PhD, Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK;
| | - Gill Norman
- National Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK,Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Laura McGarrigle
- National Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK,Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Jonathan Stokes
- Centre for Primary Care & Health Services Research, The University of Manchester, Manchester, UK,MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Sabine N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK,National Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK
| | - Dawn Dowding
- National Institute for Health and Care Research Applied Research Collaboration Greater Manchester, Manchester, UK,Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
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Sotudian S, Afran A, LeBedis CA, Rives AF, Paschalidis IC, Fishman MDC. Social determinants of health and the prediction of missed breast imaging appointments. BMC Health Serv Res 2022; 22:1454. [PMID: 36451240 PMCID: PMC9714014 DOI: 10.1186/s12913-022-08784-8] [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: 05/15/2022] [Accepted: 11/03/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment. METHODS This HIPAA compliant retrospective cohort study was IRB approved. Informed consent was waived. After data preprocessing steps, the dataset contained 9,970 patients and 36,606 appointments from 1/1/2015 to 12/31/2019. We identified 57 potentially impactful variables used in the initial prediction model and assessed each patient for MIA. We then developed a parsimonious model via recursive feature elimination, which identified the 25 most predictive variables. We utilized linear and non-linear models including support vector machines (SVM), logistic regression (LR), and random forest (RF) to predict MIA and compared their performance. RESULTS The highest-performing full model is the nonlinear RF, achieving the highest Area Under the ROC Curve (AUC) of 76% and average F1 score of 85%. Models limited to the most predictive variables were able to attain AUC and F1 scores comparable to models with all variables included. The variables most predictive of missed appointments included timing, prior appointment history, referral department of origin, and socioeconomic factors such as household income and access to caregiving services. CONCLUSIONS Prediction of MIA with the data available is inherently limited by the complex, multifactorial nature of MIA. However, the algorithms presented achieved acceptable performance and demonstrated that socioeconomic factors were useful predictors of MIA. In contrast with non-modifiable demographic factors, we can address SDH to decrease the incidence of MIA.
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Affiliation(s)
- Shahabeddin Sotudian
- grid.189504.10000 0004 1936 7558Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA USA
| | - Aaron Afran
- grid.189504.10000 0004 1936 7558Department of Radiology, Boston University School of Medicine, Boston, MA USA
| | - Christina A. LeBedis
- grid.189504.10000 0004 1936 7558Department of Radiology, Boston University School of Medicine, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA USA
| | - Anna F. Rives
- grid.189504.10000 0004 1936 7558Department of Radiology, Boston University School of Medicine, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA USA
| | - Ioannis Ch. Paschalidis
- grid.189504.10000 0004 1936 7558Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Biomedical Engineering, and Faculty of Computing & Data Sciences, Boston University, Boston, MA USA ,Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston, MA USA
| | - Michael D. C. Fishman
- grid.189504.10000 0004 1936 7558Department of Radiology, Boston University School of Medicine, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA USA
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