Special Issue "Decision Modelling for Healthcare Evaluation"

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 13158

Special Issue Editor

Dr. Rocío De Andrés Calle
E-Mail Website
Guest Editor
Foundations of Economic Analysis (PTUN), University of Salamanca, 37080 Salamanca, Spain
Interests: decision-making; social choice
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the economic climate means that health economic modelling is increasingly used to inform the decision of health care systems about which health care interventions to finance. The study of health decision modelling is rapidly growing due to the fact that governments, insurers, healthcare organisations and the pharmaceutical industry recognise the need to estimate the costs, clinical outcomes and benefits of healthcare systems. This Special Issue will bring together state-of-the-art research and practical applications of Decision Modelling for Healthcare Evaluation.

Topics of interest include but are not limited to the following:

  • Decision analytic models for healthcare evaluation
  • Decision probabilistic models for healthcare evaluation
  • Analysing and presenting simulation output from decision modelling for healthcare evaluation
  • Decision modelling for healthcare under uncertainty
  • Technology and decision modelling for healthcare
  • Other related topics

Dr. Rocío De Andrés Calle
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 submissions that pass pre-check are 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 1800 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

  • Healthcare Preferences
  • Socio-economic impact
  • Inter-temporal healthcare
  • Healthcare under uncertainty
  • Technology and healthcare

Published Papers (11 papers)

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Research

Article
Mortality Trends by Causes of Death and Healthcare during a Period of Global Uncertainty (1990–2017)
Healthcare 2021, 9(6), 748; https://doi.org/10.3390/healthcare9060748 - 18 Jun 2021
Cited by 1 | Viewed by 839
Abstract
In this study we aim to highlight the spatial differences, intensity and frequency of causes of death associated with a range of diseases and the implications of the socio-economic impact on healthcare worldwide between 1990 and 2017: (1) Background: At the same time, [...] Read more.
In this study we aim to highlight the spatial differences, intensity and frequency of causes of death associated with a range of diseases and the implications of the socio-economic impact on healthcare worldwide between 1990 and 2017: (1) Background: At the same time, an attempt was made to find regional spatial patterns that may be typical for a given geographical area, based on the assumption that global health care is in a permanent state of uncertainty as developed countries have a different morbidity profile than emerging or developing countries. (2) Methods: Using information provided by Global Burden of Disease Collaborative Network, Our World in Data and the World Bank, a multidimensional analysis was carried out, comprising four types of statistical models: grouping analysis, principal component analysis (PCA) Bravais–Pearson linear correlation and multivariate regression. (3) Results: The results confirm the hypothesis of significant correlations between the frequency of causes of death, quality of health care and quality of public health infrastructure, validated by incidence with socio-economic indicators. The study contributes to the literature by analysing trends in the spatial distribution of causes of death worldwide, detecting regional differentiations and testing how socio-economic factors may limit the incidence of morbidity. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Development of Clinical Referral Score Model for Early Diagnosis of Hirschsprung’s Disease in Suspected Pediatric Patients
Healthcare 2021, 9(6), 678; https://doi.org/10.3390/healthcare9060678 - 04 Jun 2021
Viewed by 941
Abstract
The diagnosis of Hirschsprung’s disease (HSCR) relies on history, physical examination, and investigations. Some of investigation modalities could not be done in primary hospital. This study was aimed to develop the clinical score model for diagnosing and early referrals of HSCR, especially in [...] Read more.
The diagnosis of Hirschsprung’s disease (HSCR) relies on history, physical examination, and investigations. Some of investigation modalities could not be done in primary hospital. This study was aimed to develop the clinical score model for diagnosing and early referrals of HSCR, especially in areas where investigations were not available. Overall 483 consecutive suspected HSCR patients who were under 15 years old from January 2006 to December 2020 were included in this study, with 207 (42.86%) patients diagnosed with HSCR and 276 (51.14%) patients in the non-HSCR group. Five clinical parameters were included in the prediction model. The AuROC of clinical parameters, which included having an age younger than one month, male gender, the term infant, history of delayed meconium passage, and history of enterocolitis, was 72%. The prediction score ranged from 0–7, with a score 0–3 meaning a low risk to be HSCR (LHR+ = 0.37). We concluded that patients with suspected HSCR who had clinical score 4–7 had a high probability to be HSCR and, thus, it was suggested that these patients have an early referral for further investigations, which were contrast enema and rectal suction biopsy. In the case of a low probability of HSCR, clinical observation is still warranted. This clinical scoring system can be used as a screening tool to prevent delay diagnosis and complications. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Predicting Hospital Overall Quality Star Ratings in the USA
Healthcare 2021, 9(4), 486; https://doi.org/10.3390/healthcare9040486 - 20 Apr 2021
Viewed by 918
Abstract
The U.S. Centers for Medicare and Medicaid Services (CMS) assigns quality star ratings to hospitals upon assessing their performance across 57 measures. Ratings can be used by healthcare consumers for hospital selection and hospitals for quality improvement. We provide a simpler, more intuitive [...] Read more.
The U.S. Centers for Medicare and Medicaid Services (CMS) assigns quality star ratings to hospitals upon assessing their performance across 57 measures. Ratings can be used by healthcare consumers for hospital selection and hospitals for quality improvement. We provide a simpler, more intuitive modeling approach, aligned with recent criticism by stakeholders. An ordered logistic regression approach is proposed to assess associations between performance measures and ratings across eligible (n = 4519) U.S. hospitals. Covariate selection reduces the double counting of information from highly correlated measures. Multiple imputation allows for inference of star ratings when information on all measures is not available. Twenty performance measures were found to contain all the relevant information to formulate star rating predictions upon accounting for performance measure correlation. Hospitals can focus their efforts on a subset of model-identified measures, while healthcare consumers can predict quality star ratings for hospitals ineligible under CMS criteria. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
Article
How Do Middle-Aged Chinese Men and Women Balance Caregiving and Employment Income?
Healthcare 2021, 9(4), 415; https://doi.org/10.3390/healthcare9040415 - 03 Apr 2021
Cited by 1 | Viewed by 787
Abstract
Unpaid family caregivers might suffer losses in income as a result of care provision. Here we used data from the baseline survey of the China Health and Retirement Longitudinal Study to assess the relationship between hours of weekly caregiving provided to grandchildren/parents/parents-in-law and [...] Read more.
Unpaid family caregivers might suffer losses in income as a result of care provision. Here we used data from the baseline survey of the China Health and Retirement Longitudinal Study to assess the relationship between hours of weekly caregiving provided to grandchildren/parents/parents-in-law and individual’s monthly employment income. Our study sample comprised 3718 middle-aged Chinese adults who were of working age (45–60 years). For women and men separately, we used a likelihood-based method to determine a caregiving threshold in a two-stage Heckman selection procedure. Instrumental variables were used to rule out the endogeneity of caregiving hours. Our analysis revealed a negative association between caregiving and income for women that depended on a caregiving threshold of 63 h per week. There was an absence of caregiving-income relationship among men. These results offer new insights into the opportunity costs of unpaid caregiving and support tailored policies to protect the financial well-being of female caregivers. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
Article
Shedding Light on the Main Characteristics and Perspectives of Romanian Medicinal Oxygen Market
Healthcare 2021, 9(2), 155; https://doi.org/10.3390/healthcare9020155 - 03 Feb 2021
Viewed by 864
Abstract
Medicinal oxygen plays an important role in healthcare, being essential for the existence and maintenance of the health of millions of people, who depend on medicinal oxygen every day, both in hospitals and at home. Medicinal oxygen is the primary treatment administrated to [...] Read more.
Medicinal oxygen plays an important role in healthcare, being essential for the existence and maintenance of the health of millions of people, who depend on medicinal oxygen every day, both in hospitals and at home. Medicinal oxygen is the primary treatment administrated to the majority of patients suffering from respiratory problems and low levels of oxygen in the blood, and in the context of the actual health crisis caused by the new COVID-19, the challenge is represented by increasing the supply of medicinal oxygen while reducing cost so that it is accessible where it is needed most, free at the point of use. It will take increased investment and commitment to put oxygen at the center of strategies for universal health coverage. In this context, it becomes essential to investigate the main characteristics of the Romanian market of medicinal oxygen, highlighting top key players, market development, key driving factors, types of products, market perspectives as well as shedding light on the segmentation of this particular market based on considerations regarding regions, hospital competence class and hospital specialization. Also, the research aims to explore the regional disparities in the decision of using O93%medicinal oxygen, revealing the main factors related to the usage of this type of product among Romanian public hospitals. The research relies on the first quantitative survey regarding medicinal oxygen usage among 121 public hospital units from a total of 461 public hospitals in 2018, which meet the specific requirements: includes the entire population according to the list published on the website of the Ministry of Health, is the most recent data and does not show repetition. The sampling was of probabilistic stage-type stratification, with the following sampling layers: hospital county distribution, hospital competence class officially assigned by the Ministry of Health and also area of residence (urban/rural). In order to analyze the main characteristics of the Romanian oxygen market, the following methods have been used: analysis of variance (ANOVA) together with Kruskal–Wallis, Pearson correlation coefficient as well as Goodman and Kruskal gamma, Kendall’s tau-b and Cramer’s V, as well as multilevel logistic regression analysis using hierarchical data (hospitals grouped in regions). The Romanian market of medicinal oxygen is rather an oligopoly market characterized by the existence of a small number of producers and two types of products currently used for the same medical purpose and having a substitutable character: medicinal oxygen O99.5%, and medicinal oxygen O93%. An overwhelming proportion of public hospitals agree that both types of medicinal oxygen serve the same therapeutic purpose. The Romanian market of medicinal oxygen highlighted a significant segmentation on considerations based on regions, hospital competence class and hospital specialization. Regarding the main perspectives, the Romanian market of medical oxygen keeps the growth trend registered globally, with development perspectives for competitors. Exploring the regional disparities in the decision of using O93 medicinal oxygen, the empirical results acknowledged the important role of unitary price, hospital capacity and the relevance of this product seen as a medicine. Medicinal oxygen is vital in sustaining life, proving its utility mainly in the context of the actual health crisis. In this context, the Romanian local market exhibits prospects for further development, being characterized by an important segmentation depending on regions, hospital competence class and hospital specialization. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Sociodemographic Factors Affecting Older People’s Care Dependency in Their Daily Living Environment According to Care Dependency Scale (CDS)
Healthcare 2021, 9(2), 114; https://doi.org/10.3390/healthcare9020114 - 21 Jan 2021
Viewed by 736
Abstract
The aim of the research was to determine the influence of sociodemographic factors on older people’s care dependency in their living environment according to the Care Dependency Scale (CDS). Methods: The research was conducted in a group of 151 older people staying in [...] Read more.
The aim of the research was to determine the influence of sociodemographic factors on older people’s care dependency in their living environment according to the Care Dependency Scale (CDS). Methods: The research was conducted in a group of 151 older people staying in their own homes. The methods applied in the research included a sociodemographic questionnaire and scales including the Abbreviated Mental Test Score (AMTS), CDS, Katz Index of Independence in Activities of Daily Living (ADL), Lawton’s Instrumental Activities of Daily Living (I-ADL), Mini Nutritional Assessment (MNA), and Geriatric Depression Scale (GDS). Results: Gender had a significant impact on the level of care dependency. The surveyed females obtained the medium or high level of dependency more often than males (22.4% vs. 6.1%), and the low level of dependency was significantly more frequent among men than women (p = 0.006). Moreover, the age of the respondents determined their level of care dependency. The subjects with a medium or high level of care dependency were significantly older (p = 0.001). The subjects with a low level of care dependency were more likely to be married than people with a medium/high level (p < 0.001). The level of education had a significant impact on care dependency. A higher level of education correlated with a medium/high level of dependency (p = 0.003). Conclusions: The survey results confirmed that sociodemographic factors have a significant impact on the level of care dependency. When planning care in the home environment, special attention should be paid to older women, who are more likely to lose their independence than men. These women should be given additional support. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
Article
Public Healthcare: Citizen’s Preferences in Spain
Healthcare 2020, 8(4), 467; https://doi.org/10.3390/healthcare8040467 - 08 Nov 2020
Cited by 1 | Viewed by 977
Abstract
This paper analyzes the stability of citizens’ preferences on public healthcare services in Spain. Nowadays, the increasing privatization of some healthcare services and the rapid emergence of private hospitals have caused changes in people’s preferences on public healthcare systems. This paper focuses on [...] Read more.
This paper analyzes the stability of citizens’ preferences on public healthcare services in Spain. Nowadays, the increasing privatization of some healthcare services and the rapid emergence of private hospitals have caused changes in people’s preferences on public healthcare systems. This paper focuses on analyzing the preferences of Spaniards on their healthcare system over time under the assumption that citizens’ preferences are represented by complete pre-orders. Data for this study were collected from the Spanish Health Barometer survey, and they were searched from 1995 until 2018. The results show that preferences on the public healthcare system are very stable along time. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Prioritization of Medical Errors in Patient Safety Management: Framework Using Interval-Valued Intuitionistic Fuzzy Sets
Healthcare 2020, 8(3), 265; https://doi.org/10.3390/healthcare8030265 - 12 Aug 2020
Cited by 6 | Viewed by 1413
Abstract
Medical errors negatively affect patients, healthcare professionals, and healthcare establishments. Therefore, all healthcare service members should be attentive to medical errors. Research has revealed that most medical errors are caused by the system, rather than individuals. In this context, guaranteeing patient safety and [...] Read more.
Medical errors negatively affect patients, healthcare professionals, and healthcare establishments. Therefore, all healthcare service members should be attentive to medical errors. Research has revealed that most medical errors are caused by the system, rather than individuals. In this context, guaranteeing patient safety and preventing medical faults appear to be basic elements of quality in healthcare services. Healthcare institutions can create internal regulations and follow-up plans for patient safety. While this is beneficial for the dissemination of patient safety culture, it poses difficulties in terms of auditing. On the other hand, the lack of a standard patient safety management system, has led to great variation in the quality of the provided service among hospitals. Therefore, this study aims to create an index system to create a standard system for patient safety by classifying medical errors. Due to the complex nature of healthcare and its processes, interval-valued intuitionistic fuzzy logic is used in the proposed index system. Medical errors are prioritized, based on the index scores that are generated by the proposed model. Because of this systematic study, not only can the awareness of patient safety perception be increased in health institutions, but their present situation can also be displayed, on the basis of each indicator. It is expected that the outcomes of this study will motivate institutions to identify and prioritize their future improvements in the patient safety context. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Developing and Evaluating A One-Stop Patient-Centered Interprofessional Collaboration Platform in Taiwan
Healthcare 2020, 8(3), 241; https://doi.org/10.3390/healthcare8030241 - 29 Jul 2020
Cited by 2 | Viewed by 1576
Abstract
(1) Background: Effective healthcare collaboration not only improves the outcomes of patients, but also provides benefits to healthcare providers. A patient-centered communication platform, a so-called “one-stop platform”, is necessary to promote interprofessional collaboration (IPC) for optimal patient care. (2) Methods: Chi Mei Medical [...] Read more.
(1) Background: Effective healthcare collaboration not only improves the outcomes of patients, but also provides benefits to healthcare providers. A patient-centered communication platform, a so-called “one-stop platform”, is necessary to promote interprofessional collaboration (IPC) for optimal patient care. (2) Methods: Chi Mei Medical Center developed a patient-centered computerized platform to fulfill interprofessional collaboration needs. The platform features a spiral-shaped integrated care area and a communication area that allows the medical team to access patients’ information including the medical care they received within seven days, and veritably shows whether the team members have read communication messages. After pilot adoption, an online survey was conducted. (3) Results: A one-stop IPC platform was implemented and promoted for patient care. The online survey revealed that medical team members have high positive appraisal of the platform. It also pointed out that resistance to change among the medical team still has a significant impact on behavioral intention. (4) Conclusions: The interprofessional collaboration platform was recognized by the medical teams of Chi Mei Medical Center as an effective and convenient tool for assisting clinical decision making. However, actions to reduce user resistance to change and encourage collaboration among team members still need to be continued. Shared decision making within physicians and patients will be valuable to develop in the platform in the future. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions
Healthcare 2020, 8(3), 234; https://doi.org/10.3390/healthcare8030234 - 26 Jul 2020
Cited by 11 | Viewed by 1872
Abstract
Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors [...] Read more.
Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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Article
Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble
Healthcare 2020, 8(2), 121; https://doi.org/10.3390/healthcare8020121 - 01 May 2020
Cited by 4 | Viewed by 1519
Abstract
The rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue [...] Read more.
The rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson’s disease with normal cognition (PD-NC) = 48; Parkinson’s disease with mild cognitive impairment (PD-MCI) = 199; Parkinson’s disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model’s variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson’s Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson’s disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation)
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