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Yeganeh A, Johannssen A, Chukhrova N, Erfanian M, Azarpazhooh MR, Morovatdar N. A monitoring framework for health care processes using Generalized Additive Models and Auto-Encoders. Artif Intell Med 2023; 146:102689. [PMID: 38042610 DOI: 10.1016/j.artmed.2023.102689] [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: 05/12/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 12/04/2023]
Abstract
In recent years, there has been a considerable focus on developing effective methods for monitoring health care processes. Utilizing Statistical Process Monitoring (SPM) approaches, particularly risk-adjusted control charts, has emerged as a highly promising approach for achieving robust frameworks for this aim. Considering risk-adjusted control charts, longitudinal health care process data is typically monitored by establishing a regression relationship between various risk factors (explanatory variables) and patient outcomes (response variables). While the majority of prior research has primarily employed logistic models in risk-adjusted control charts, there are more intricate health care processes that necessitate the incorporation of both parametric and nonparametric risk factors. In such scenarios, the Generalized Additive Model (GAM) proves to be a suitable choice, albeit it often introduces higher computational complexity and associated challenges. Surprisingly, there are limited instances where researchers have proposed advancements in this direction. The primary objective of this paper is to introduce an SPM framework for monitoring health care processes using a GAM over time, coupled with a novel risk-adjusted control chart driven by machine learning techniques. This control chart is implemented on a data set encompassing two stroke types: ischemic and hemorrhagic. The key focus of this study is to monitor the stability of the relationship between stroke types and predefined explanatory variables over time within this data set. Extensive simulation results, based on real data from patients with acute stroke, demonstrate the remarkable flexibility of the proposed method in terms of its detection capabilities compared to conventional approaches.
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Affiliation(s)
- Ali Yeganeh
- University of Hamburg, 20146 Hamburg, Germany.
| | | | | | - Mahdiyeh Erfanian
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Mahmoud Reza Azarpazhooh
- Department of Neurology, Ghaem Hospital, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran; Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
| | - Negar Morovatdar
- Clinical Research Development Unit, Imam Reza Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Huang HF, Jerng JS, Hsu PJ, Lin NH, Lin LM, Hung SM, Kuo YW, Ku SC, Chuang PY, Chen SY. Monitoring the performance of a dedicated weaning unit using risk-adjusted control charts for the weaning rate in prolonged mechanical ventilation. J Formos Med Assoc 2023; 122:880-889. [PMID: 37149422 DOI: 10.1016/j.jfma.2023.04.021] [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: 11/25/2022] [Revised: 04/05/2023] [Accepted: 04/23/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Weaning rate is an important quality indicator of care for patients with prolonged mechanical ventilation (PMV). However, diverse clinical characteristics often affect the measured rate. A risk-adjusted control chart may be beneficial for assessing the quality of care. METHODS We analyzed patients with PMV who were discharged between 2018 and 2020 from a dedicated weaning unit at a medical center. We generated a formula to estimate monthly weaning rates using multivariate logistic regression for the clinical, laboratory, and physiologic characteristics upon weaning unit admission in the first two years (Phase I). We then applied both multiplicative and additive models for adjusted p-charts, displayed in both non-segmented and segmented formats, to assess whether special cause variation existed. RESULTS A total of 737 patients were analyzed, including 503 in Phase I and 234 in Phase II, with average weaning rates of 59.4% and 60.3%, respectively. The p-chart of crude weaning rates did not show special cause variation. Ten variables from the regression analysis were selected for the formula to predict individual weaning probability and generate estimated weaning rates in Phases I and II. For risk-adjusted p-charts, both multiplicative and additive models showed similar findings and no special cause variation. CONCLUSION Risk-adjusted control charts generated using a combination of multivariate logistic regression and control chart-adjustment models may provide a feasible method to assess the quality of care in the setting of PMV with standard care protocols.
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Affiliation(s)
- Hsiao-Fang Huang
- Center for Quality Management, National Taiwan University Hospital, Taipei, Taiwan
| | - Jih-Shuin Jerng
- Center for Quality Management, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
| | - Pei-Jung Hsu
- Center for Quality Management, National Taiwan University Hospital, Taipei, Taiwan
| | - Nai-Hua Lin
- Department of Nursing, National Taiwan University Hospital, Taipei, Taiwan
| | - Li-Min Lin
- Department of Nursing, National Taiwan University Hospital, Taipei, Taiwan
| | - Shu-Min Hung
- Department of Integrated Diagnostics & Therapeutics, National Taiwan University Hospital, Taipei, Taiwan
| | - Yao-Wen Kuo
- Department of Integrated Diagnostics & Therapeutics, National Taiwan University Hospital, Taipei, Taiwan
| | - Shih-Chi Ku
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Pao-Yu Chuang
- Center for Quality Management, National Taiwan University Hospital, Taipei, Taiwan; Department of Nursing, National Taiwan University Hospital, Taipei, Taiwan
| | - Shey-Ying Chen
- Center for Quality Management, National Taiwan University Hospital, Taipei, Taiwan; Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
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Control Charts Usage for Monitoring Performance in Surgery: A Systematic Review. J Patient Saf 2023; 19:110-116. [PMID: 36603595 DOI: 10.1097/pts.0000000000001103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVES The control chart is a graphical tool for data interpretation that detects aberrant variations in specific metrics, ideally leading to the identification of special causes that can be resolved. A clear assessment of control chart utilization and its potential impact in surgery is required to justify recommendations for its dissemination. This review aims to describe how performance monitoring using control charts was used over time in surgery. METHODS A systematic search of PubMed regarding statistical process control in surgery from its inception until December 2019 was performed using Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Information extracted from selected publications included study aim and population setting, monitored indicators, control charts methodological parameters, and implementation strategy. RESULTS One hundred thirteen studies met the selection criteria with a median of 1916 monitored patients. Overall, 57.5% of studies focused on control chart methodology, 24.8% aimed at evaluating performance changes using control charts retrospectively, and 17.7% implemented control charts for continuous quality improvement prospectively. Although there was a great diversity of used indicators and charting tools, the evaluation of patient safety (72.6%) or efficiency (15.9%) metrics based on Shewhart control chart (33.6%) or cumulative sum chart (54.9%) were common. To foster control charts implementation, 14 studies promoted their periodic review, but only three assessed their impact on patient outcomes. CONCLUSIONS The scientific literature supports the feasibility and utility of control chart to improve patient safety in multiple surgical settings. Additional studies are necessary to reveal the optimal manner in which to implement this affordable tool in surgical practice.
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Yeganeh A, Shadman A, Shongwe SC, Abbasi SA. Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08257-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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Alonazi WB, Altuwaijri EA. Health Policy Development During COVID-19 in Saudi Arabia: Mixed Methods Analysis. Front Public Health 2022; 9:801273. [PMID: 35360666 PMCID: PMC8963949 DOI: 10.3389/fpubh.2021.801273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/13/2021] [Indexed: 12/23/2022] Open
Abstract
Healthcare systems are increasingly required to utilize effective approaches, apply evidence-based practice, and consequently sustain successful strategic management. Document analysis provides insights into the effective management tools applied by agencies to respond to crises. This article provides a practical exploration of how the Saudi health authority applied effective measures to eventually reduce the administrative and clinical consequences while managing the COVID-19 pandemic. The conceptual descriptive framework was based on health policy triangle of Walt and Gilson. Official reports and supporting documents issued by the Saudi government toward COVID-19 were operationally analyzed. Moreover, five healthcare professional experts were invited in a semistructured interview to assess the strategic steps that have been utilized to minimize the health risk by conducting a healthcare risk analysis. Various documents showed that two major entities were responsible for managing regulations and medications of COVID-19 in addition to six other entities that were partially involved. Although each entity was approved to work independently, their efforts were cohesively associated with each other. Most documents were well-applied on personal, social, organizational, and national strata. However, it is unclear how lessons identified became affirmative, while the collaboration remains vague, especially under the emergence of a new entity such as the Public Health Authority. Healthcare professional experts also positively supported the effectiveness of such policies to confront COVID-19 through the following three domains: health guidelines, utilizing simulation (telehealth/telecommunication) services, and ensuring continuity of services.
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Affiliation(s)
- Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
- *Correspondence: Wadi B. Alonazi
| | - Eman A. Altuwaijri
- Department of Administrative and Human Sciences, College of Applied Studies and Community Service, King Saud University, Riyadh, Saudi Arabia
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Wittenberg P. Modeling the patient mix for risk-adjusted CUmulative SUM charts. Stat Methods Med Res 2022; 31:779-800. [PMID: 35139722 PMCID: PMC9014690 DOI: 10.1177/09622802211053205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The improvement of surgical quality and the corresponding early detection of its
changes is of increasing importance. To this end, sequential monitoring
procedures such as the risk-adjusted CUmulative SUM chart are frequently
applied. The patient risk score population (patient mix), which considers the
patients’ perioperative risk, is a core component for this type of quality
control chart. Consequently, it is important to be able to adapt different
shapes of patient mixes and determine their impact on the monitoring scheme.
This article proposes a framework for modeling the patient mix by a discrete
beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts.
Since the model-based approach is not limited by data availability,
any patient mix can be analyzed. We examine the effects on
the control chart’s false alarm behavior for more than 100,000 different
scenarios for a cardiac surgery data set. Our study finds a negative
relationship between the average risk score and the number of false alarms. The
results indicate that a changing patient mix has a considerable impact and, in
some cases, almost doubles the number of expected false alarms.
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Affiliation(s)
- Philipp Wittenberg
- Department of Mathematics and Statistics, Helmut Schmidt University, Germany
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Woodall WH, Rakovich G, Steiner SH. An overview and critique of the use of cumulative sum methods with surgical learning curve data. Stat Med 2020; 40:1400-1413. [PMID: 33316849 DOI: 10.1002/sim.8847] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 11/20/2020] [Accepted: 11/20/2020] [Indexed: 11/07/2022]
Abstract
Cumulative sum (CUSUM) plots and methods have wide-ranging applications in healthcare. We review and discuss some issues related to the analysis of surgical learning curve (LC) data with a focus on three types of CUSUM statistical approaches. The underlying assumptions, benefits, and weaknesses of each approach are given. Our primary conclusion is that two types of CUSUM methods are useful in providing visual aids, but are subject to overinterpretation due to the lack of well-defined decision rules and performance metrics. The third type is based on plotting the CUSUM of the differences between observations and their average value. We show that this commonly applied retrospective method is frequently interpreted incorrectly and is thus unhelpful in the LC application. Curve-fitting methods are more suitable for meeting many of the goals associated with the study of surgical LCs.
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Affiliation(s)
| | - George Rakovich
- Maisonneuve-Rosemont Hospital, University of Montreal School of Medicine, Montreal, Quebec, Canada
| | - Stefan H Steiner
- Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, Ontario, Canada
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Keshavarz M, Asadzadeh S, Niaki STA. Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1814775] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Maryam Keshavarz
- Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Shervin Asadzadeh
- Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
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Affiliation(s)
- Kai Yang
- Department of Biostatistics, University of Florida, Gainesville, FL
| | - Peihua Qiu
- Department of Biostatistics, University of Florida, Gainesville, FL
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