Special Issue "Statistical Process Monitoring in Healthcare"

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Prof. Michele Scagliarini
Website
Guest Editor
Department of Statistical Sciences, University of Bologna
Interests: Statistical Quality Control; Design of Experiments; Measurement System Analysis

Special Issue Information

Dear Colleagues,

Healthcare organizations are continuously involved in quality improvement programs, and performance indicators are often suggested as key elements to capture information on clinical, organizational, and financial processes. The challenge is thus to select useful statistical tools for accurately monitoring and providing benchmarks for such indicators.

Statistical process control (SPC) provides a methodological approach both for assessing undisturbed, or steady-state, process behaviour and for detecting positive or negative changes in performance. Control charts are one of the primary tools used in SPC and are being increasingly adopted in healthcare to aid in process understanding, to assess process stability and to identify changes that indicate either the improvement or deterioration in quality.

This Special Issue aims to bring together state-of-the-art research and practical applications of statistical process control in the healthcare framework.

Prof. Michele Scagliarini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adverse events
  • CUSUM control charts
  • EWMA control charts
  • disease monitoring
  • key performance indicators
  • mortality rate
  • risk adjustment

Published Papers (7 papers)

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Research

Open AccessArticle
Developing a Transnational Health Record Framework with Level-Specific Interoperability Guidelines Based on a Related Literature Review
Healthcare 2021, 9(1), 67; https://doi.org/10.3390/healthcare9010067 - 13 Jan 2021
Viewed by 375
Abstract
With the advent of digital healthcare without borders, enormous amounts of health information are captured and computerized. As healthcare quality largely depends on the reliability of given health information, personal health records should be accessible according to patients’ mobility, even as they travel [...] Read more.
With the advent of digital healthcare without borders, enormous amounts of health information are captured and computerized. As healthcare quality largely depends on the reliability of given health information, personal health records should be accessible according to patients’ mobility, even as they travel or migrate to other countries. However, since all the health information is scattered in multiple places, it is an onerous task to carry it whenever people move to other countries. To effectively and efficiently utilize health information, interoperability, which is the ability of various healthcare information technologies to exchange, to interpret, and to use data, is needed. Hence, building a robust transnational health information infrastructure with clear interoperability guidelines considering heterogeneous aspects is necessary. For this purpose, this study proposes a Transnational Health Record framework, which enables access to personal health records anywhere. We review related literature and define level-specific interoperability guidelines, business processes, and requirements for the Transnational Health Record system framework. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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Open AccessArticle
Analysis of Altmetrics in Social Recognition of Neurology and Neurological Disorders
Healthcare 2020, 8(4), 367; https://doi.org/10.3390/healthcare8040367 - 25 Sep 2020
Cited by 1 | Viewed by 516
Abstract
This study used Altmetric analysis to rank neurological articles and assessed the implications in relation to the social recognition of neurology and neurological disorders. An Altmetric Explorer search was conducted on 25 May 2018 for articles published in the 91 journals included in [...] Read more.
This study used Altmetric analysis to rank neurological articles and assessed the implications in relation to the social recognition of neurology and neurological disorders. An Altmetric Explorer search was conducted on 25 May 2018 for articles published in the 91 journals included in the 2015 InCites™ Journal Citation Report®. We identified and analyzed the 100 articles with the highest Altmetric Attention Scores (AASs). A major proportion of the social impact (high AASs) was focused on neurodegenerative disorders such as dementia and neurodegenerative disorders. About half of the high-ranking articles provided academic information such as disease information (29 articles, 29%), new or advanced treatments (17%), and side effects of treatment (8%). The journal with largest number of top 100 articles was the New England Journal of Medicine (29 articles). Some of the data gathered via altmetrics can change a field of study, the public’s health, or a larger society. This is the first report on the impact of academic articles in neurological disorder on the general public living in our altered information society. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
Open AccessArticle
Promotion of Healthy Lifestyles to Teenagers with Mobile Devices: A Case Study in Portugal
Healthcare 2020, 8(3), 315; https://doi.org/10.3390/healthcare8030315 - 02 Sep 2020
Viewed by 761
Abstract
Educating teenagers about nutrition and promoting active lifestyles is essential in reducing the long-term health risks and one idea to achieve this is by using mobile applications. Previous studies showed that the existing mobile applications have similar functionalities, such as intervention with questionnaires, [...] Read more.
Educating teenagers about nutrition and promoting active lifestyles is essential in reducing the long-term health risks and one idea to achieve this is by using mobile applications. Previous studies showed that the existing mobile applications have similar functionalities, such as intervention with questionnaires, and the use of gamification techniques to improve interactiveness. However, unlike our study, some studies are not validated and verified by healthcare professionals. Additionally, this study intends to promote the interaction between the teenagers and the medical communities. In this study, we analyze the benefits of the proposed mobile application, which features monitoring of physical activity, daily tips and curiosities, questionnaires, and gamification through earning points. Most of the teenagers were satisfied with the physical activity monitoring and found the tips, curiosities, and weekly questionnaires useful. The study started with 26 teenagers from two schools in the center of Portugal that would use the mobile application for five weeks. Still, at the end of the study, only 7 teenagers finalized the study. The decreasing number of teenagers in the study was affected by the lack of social interaction caused by the pandemic situation. During the period, the mobile application would engage the users with notifications on nutrition and physical activity, challenges concerning the number of steps and calories they would have to spend, and questionnaires related to the curiosities and suggestions from the previous week. We used Fisher’s test to investigate the relationship between the assessment obtained in the responses to the questionnaires, and the adoption of healthier eating and sports practices. In summary, participants were satisfied with the mobile application and experienced some improvements in diet and habits. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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Open AccessArticle
Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model
Healthcare 2020, 8(3), 247; https://doi.org/10.3390/healthcare8030247 - 31 Jul 2020
Cited by 5 | Viewed by 926
Abstract
In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor [...] Read more.
In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order to improve the prediction effect of existing models. The combination of convenient sampling and snowball sampling in Xicheng District, Beijing was used to conduct a questionnaire survey on the personal data, eating habits, exercise status and family medical history of 380 middle-aged and elderly people. Then, we trained the models and obtained the disease risk index for each sample with 10-fold cross-validation. Experiments were made to compare the commonly used machine learning algorithms mentioned above and we found that XGBoost had the best prediction effect, with an average accuracy of 0.8909 and the area under the receiver’s working characteristic curve (AUC) was 0.9182. Therefore, due to the superiority of its architecture, XGBoost has more outstanding prediction accuracy and generalization ability than existing algorithms in predicting the risk of type 2 diabetes, which is conducive to the intelligent prevention and control of diabetes in the future. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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Open AccessArticle
Improving Efficiency Assessment of Psychiatric Halfway Houses: A Context-Dependent Data Envelopment Analysis Approach
Healthcare 2020, 8(3), 189; https://doi.org/10.3390/healthcare8030189 - 28 Jun 2020
Viewed by 772
Abstract
This study proposes the approach of context-dependent data envelopment analysis (DEA) to measure operating performance in halfway houses to enable suitable adjustments at the current economic scale. The proposed approach can be used to discriminate the performance of efficient halfway houses and provide [...] Read more.
This study proposes the approach of context-dependent data envelopment analysis (DEA) to measure operating performance in halfway houses to enable suitable adjustments at the current economic scale. The proposed approach can be used to discriminate the performance of efficient halfway houses and provide more accurate DEA results related to the performance of all halfway houses in a region or a country. The relative attractiveness and progress were also evaluated, and individual halfway houses’ competitive advantage and potential competitors could be determined. A case study of 38 halfway houses in Taiwan was investigated by our proposed approach. Findings suggest that fifteen halfway houses belong to the medium level, which can be classified into a quadrant by examining both their attractiveness score and progress score. The results can be used to allocate community resources to improve the operational directions and develop incentives for halfway houses with attractive and progressive values, which can reduce the institutionalization and waste of medical resources caused by the long-term hospitalization of patients with mental illnesses. Our proposed approach can also provide references for operators and policy makers to improve the management, accreditation, and resource allocation of institutions. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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Open AccessArticle
Applying Data Mining to Investigate Cancer Risk in Patients with Pyogenic Liver Abscess
Healthcare 2020, 8(2), 141; https://doi.org/10.3390/healthcare8020141 - 22 May 2020
Viewed by 960
Abstract
Pyogenic liver abscess is usually a complication of biliary tract disease. Taiwan features among the countries with the highest incidence of colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Few studies have investigated whether patients with pyogenic liver abscess (PLA) have higher incidence rates [...] Read more.
Pyogenic liver abscess is usually a complication of biliary tract disease. Taiwan features among the countries with the highest incidence of colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Few studies have investigated whether patients with pyogenic liver abscess (PLA) have higher incidence rates of CRC and HCC. However, these findings have been inconclusive. The risks of CRC and HCC in patients with PLA and the factors contributing to cancer development were assessed in these patients. The clinical tests significantly associated with cancers in these patients with PLA were determined to assist in the early diagnosis of these cancers. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using binary logistic regression Cancer classification models were constructed using the decision tree algorithm C5.0 to compare the accuracy among different models with those risk factors of cancers and then determine the optimal model. Thereafter, the rules were summarized using the decisi8on tree model to assist in the diagnosis. The results indicated that CRC and HCC (OR, 3.751; 95% CI, 1.149–12.253) and CRC (OR, 6.838; 95% CI, 2.679–17.455) risks were higher in patients with PLA than those without PLA. The decision tree analysis demonstrated that the model with the PLA variable had the highest accuracy, and that classification could be conducted using fewer factors, indicating that PLA is critical in HCC and CRC. Two rules were determined for assisting in the diagnosis of CRC and HCC using the decision tree model. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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Open AccessArticle
A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications
Healthcare 2020, 8(2), 111; https://doi.org/10.3390/healthcare8020111 - 26 Apr 2020
Cited by 3 | Viewed by 1722
Abstract
In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast [...] Read more.
In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer. Full article
(This article belongs to the Special Issue Statistical Process Monitoring in Healthcare)
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