Mathematical Prediction Models Applied to Health Management

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 12696

Special Issue Editors


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Guest Editor
Research Unit for Health Economics and Management, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: health management; health economics & outcome research (HEOR); health services research; health econometrics; healthcare expenditure; multimorbidity; risk adjustment

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Guest Editor
Research Centre for Economics Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: health econometrics; health costs; case-mix systems; quality of life

E-Mail Website
Guest Editor
Research Centre for Economics Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: health econometrics; health costs; case-mix systems; quality of life

Special Issue Information

Dear Colleagues,

This Special Issue will gather a set of high-quality multidisciplinary papers showing applications of mathematical prediction models to health management and economics, in the broadest sense.

Therefore, there is room for both theoretical and, above all, practical work that contributes to:

  1. Predicting costs in health care,
  2. Modeling the cost-effectiveness of treatments,
  3. Measurement of the efficiency of health production,
  4. Clinical decision-making.

Studies comparing classical prediction methods with machine learning approaches are welcome.

Finally, during this global SARS-CoV-2 epidemic, applications in this field will be subject to special recognition.

Prof. Dr. David Vivas-Consuelo
Prof. Dr. Natividad Guadalajara-Olmeda
Prof. Dr. Isabel Barrachina Martínez
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • mathematical modeling
  • healthcare
  • clinical risk adjustment
  • operations research
  • uncertainty quantification
  • first probability density function
  • random variable transformation technique
  • Markov model
  • machine learning

Published Papers (6 papers)

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Research

17 pages, 532 KiB  
Article
A Novel Strategy to Classify Chronic Patients at Risk: A Hybrid Machine Learning Approach
by Fabián Silva-Aravena, Hugo Núñez Delafuente and César A. Astudillo
Mathematics 2022, 10(17), 3053; https://doi.org/10.3390/math10173053 - 24 Aug 2022
Cited by 2 | Viewed by 1650
Abstract
Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently [...] Read more.
Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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13 pages, 1482 KiB  
Article
Validation of a New Telenursing Questionnaire: Testing the Test
by Julio Emilio Marco-Franco, Margarida Reis-Santos, Isabel Barrachina-Martínez, Silvia González-de-Julián and Ramón Camaño-Puig
Mathematics 2022, 10(14), 2463; https://doi.org/10.3390/math10142463 - 15 Jul 2022
Cited by 3 | Viewed by 1469
Abstract
Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire [...] Read more.
Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire was designed with 18 paired (to avoid leading) questions (Likert-5) plus three dichotomous questions (randomly ordered, inspired by existing validated tests) to analyse the dimensions of: acceptance, usefulness and appropriateness of telenursing from the nursing point of view (7 min test). The questionnaire was validated by classical tests and item response tests (Rasch) using six computer-generated databases with different response profiles (tendency to be positioned against, neutral and positioned in favour) with two degrees of agreement between each pair of responses for each option. Results: Classical testing: Cronbach’s alphas (from 0.8 to 0.95), Kaiser–Meyer–Olkin (KMO) (0.93 to 0.95) and a significant p < 0.0001 for Bartlett’s test of sphericity were obtained. Rasch analysis: Reliability coefficients (0.94). Warm’s mean weighted likelihood estimates (0.94). Extreme infit-t and outfit-t values (+1.61 to −1.98). Conclusions: Both the classical test and the Rasch approaches confirm the usefulness of the new test for assessing nurses’ positioning in relation to telenursing. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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14 pages, 700 KiB  
Article
Modelling Deprivation Level and Multimorbidity in a Health District
by María Pilar Botija Yagüe, Sofía Sorbet-Santiago, Javier Díaz-Carnicero, Silvia González-de-Julián and Ruth Usó-Talamantes
Mathematics 2022, 10(4), 659; https://doi.org/10.3390/math10040659 - 20 Feb 2022
Cited by 1 | Viewed by 1620
Abstract
Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the [...] Read more.
Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the subsequent use of healthcare resources in one health district, using the socioeconomic, clinical and geographical data from its administrative health records. Eight socioeconomic indicators were identified and weighted using the methodology of two-phase principal component analysis, providing an index that allowed each census section to be classified into seven deprivation groups. Secondly, the possible relation between the IPID and the variables for multimorbidity and healthcare resources was analysed using the theory of multiple comparisons. It was observed that places with a greater proportion of healthy people presented lower values of deprivation and that, at lower levels of deprivation, there were fewer hospital admissions. The results show that living in an area with a higher deprivation index is associated with greater consumption of healthcare resources and disease burden. Identifying areas of sociosanitary vulnerability can help to identify health inequalities and allow intervention by clinical practices and healthcare management to reduce them. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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31 pages, 1537 KiB  
Article
Effective Optimisation of the Patient Circuits of an Oncology Day Hospital: Mathematical Programming Models and Case Study
by Adrián González-Maestro, Elena Brozos-Vázquez, Balbina Casas-Méndez, Rafael López-López, Rosa López-Rodríguez and Francisco Reyes-Santias
Mathematics 2022, 10(1), 62; https://doi.org/10.3390/math10010062 - 25 Dec 2021
Cited by 1 | Viewed by 2255
Abstract
In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model [...] Read more.
In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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16 pages, 2387 KiB  
Article
Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic
by Francisco Gabriel Morillas-Jurado, María Caballer-Tarazona and Vicent Caballer-Tarazona
Mathematics 2022, 10(1), 46; https://doi.org/10.3390/math10010046 - 23 Dec 2021
Cited by 6 | Viewed by 2401
Abstract
In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August [...] Read more.
In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August 2020), the disparity in the recording criteria and in the timing of providing these figures to the central government created controversy and confusion regarding the real dimension of the pandemic. It is therefore necessary to have objective and homogeneous criteria at the national level to guide health managers in the correct recording and evaluation of the magnitude of the pandemic. Within this context, we propose using Benford’s Law as an auditing tool to monitor the reliability of the number of daily COVID-related deaths to identify possible deviations from the expected trend. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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17 pages, 355 KiB  
Article
Predicting the Reputation of Pharmaceutical Firms with Financing and Geographical Location Data
by Mª Ángeles Alcaide, Elena de la Poza and Mª Natividad Guadalajara
Mathematics 2021, 9(16), 1893; https://doi.org/10.3390/math9161893 - 09 Aug 2021
Cited by 2 | Viewed by 1839
Abstract
Reputation is a strategic asset for firms, but has been poorly studied in the pharmaceutical industry, particularly in relation to their financial and stock-market performance. This work aimed to predict the probability of a firm being included in a pharmaceutical reputation index (Merco [...] Read more.
Reputation is a strategic asset for firms, but has been poorly studied in the pharmaceutical industry, particularly in relation to their financial and stock-market performance. This work aimed to predict the probability of a firm being included in a pharmaceutical reputation index (Merco and PatientView), and the position it occupies, according to its economic–financial and stock-market outcomes and its geographical location. Fifty firms with excellent sales in 2019 and their rankings in 2017–2019 were employed. The methodology followed was logistic regression. Their research and development (R&D) expenditures and dividends strongly influenced them being included in both rankings. Non-Asian pharmaceutical companies were more likely to belong to the two reputation indices than Asian ones, and to occupy the best positions in the Merco ranking. Although no large differences appeared in the firms in both indices, differences were found in the position that pharmaceutical companies occupied in rankings and in the variables that contribute to them occupying these positions. Being in PatientView influenced dividends, sales, and income, while appearing in Merco showed accounting aspects like value in books and debt ratio. Full article
(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)
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