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Decision Modelling for Healthcare Evaluation: 2nd Edition
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Decision Modelling for Healthcare Evaluation: 2nd Edition

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Policy".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 27082

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,

At present, the economic climate has meant that health economic modelling is increasingly used to inform the decisions of health care systems about which health care interventions to finance from accessible funds. The study of health decision modelling is rapidly growing because governments, insurers, healthcare organisations and the pharmaceutical industry recognise the need to estimate the costs, clinical outcomes and benefits of healthcare systems. These developments have increased due to the important changes in clinical decision-making processes that have been carried out in response to the COVID-19 pandemic.

This Special Issue aims to bring together state-of-the-art research and practical applications carried out on clinical decision-making processes or on Decision Modelling for Healthcare Evaluation.

Topics of interest include but are not limited to:

  • 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 semimonthly 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 2700 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
  • healthcare and its socio-economic impact
  • intertemporal decisions on healthcare
  • clinical decisions under uncertainty
  • technology and healthcare

 

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Published Papers (9 papers)

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Research

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9 pages, 816 KiB  
Article
Analysis of Operational Efficiency and Cost Differences between Local and General Anesthesia for Vitreoretinal Surgery
by Mohammad Z. Siddiqui, Muhammad Z. Chauhan, Alvin F. Stewart and Ahmed B. Sallam
Healthcare 2022, 10(10), 1918; https://doi.org/10.3390/healthcare10101918 - 30 Sep 2022
Cited by 3 | Viewed by 2333
Abstract
There has been a growing trend of using local anesthesia (LA) compared to general anesthesia (GA) over the last two decades in VR surgery. We aim to answer the following question: what is the institutional benefit of LA versus GA use in operation-room [...] Read more.
There has been a growing trend of using local anesthesia (LA) compared to general anesthesia (GA) over the last two decades in VR surgery. We aim to answer the following question: what is the institutional benefit of LA versus GA use in operation-room time, anesthesia duration, earlier discharge from an outpatient surgery facility, and the estimated cost savings? We conducted a retrospective analysis of 1476 eyes that underwent vitreoretinal surgery over a 6-year period from a single site; 61.8% of patients received GA and 38.2% received LA for VR surgery. Anesthesia, surgical, and recovery times were significantly shorter with LA (100.49, 66.47, 66.47 mins) vs. GA (145.53, 100.14, 75.08 mins). Anesthesia, surgical, and recovery costs were significantly lower for eyes that received LA, with an estimated adjusted cost reduction of USD 1516 per surgery using LA instead of GA. Use of LA for vitreoretinal surgery is associated with better operational efficiency, earlier patient discharge, and significant cost reduction. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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16 pages, 902 KiB  
Article
Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
by Nancy G. Rios, Paige E. Oldiges, Marcela S. Lizano, Danielle S. Doucet Wadford, David L. Quick, John Martin, Michael Korvink and Laura H. Gunn
Healthcare 2022, 10(8), 1368; https://doi.org/10.3390/healthcare10081368 - 23 Jul 2022
Cited by 4 | Viewed by 1912
Abstract
Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is [...] Read more.
Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 (n = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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16 pages, 7611 KiB  
Article
Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm
by Mohan Debarchan Mohanty, Abhishek Das, Mihir Narayan Mohanty, Ayman Altameem, Soumya Ranjan Nayak, Abdul Khader Jilani Saudagar and Ramesh Chandra Poonia
Healthcare 2022, 10(7), 1275; https://doi.org/10.3390/healthcare10071275 - 9 Jul 2022
Cited by 12 | Viewed by 2873
Abstract
Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and [...] Read more.
Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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19 pages, 1558 KiB  
Article
Assessing Hospital Resource Utilization with Application to Imaging for Patients Diagnosed with Prostate Cancer
by Yazmine Lunn, Rudra Patel, Timothy S. Sokphat, Laura Bourn, Khalil Fields, Anna Fitzgerald, Vandana Sundaresan, Greeshma Thomas, Michael Korvink and Laura H. Gunn
Healthcare 2022, 10(2), 248; https://doi.org/10.3390/healthcare10020248 - 28 Jan 2022
Cited by 1 | Viewed by 3903
Abstract
Resource utilization measures are typically modeled by relying on clinical characteristics. However, in some settings, those clinical markers are not available, and hospitals are unable to explore potential inefficiencies or resource misutilization. We propose a novel approach to exploring misutilization that solely relies [...] Read more.
Resource utilization measures are typically modeled by relying on clinical characteristics. However, in some settings, those clinical markers are not available, and hospitals are unable to explore potential inefficiencies or resource misutilization. We propose a novel approach to exploring misutilization that solely relies on administrative data in the form of patient characteristics and competing resource utilization, with the latter being a novel addition. We demonstrate this approach in a 2019 patient cohort diagnosed with prostate cancer (n = 51,111) across 1056 U.S. healthcare facilities using Premier, Inc.’s (Charlotte, NC, USA) all payor databases. A multivariate logistic regression model was fitted using administrative information and competing resources utilization. A decision curve analysis informed by industry average standards of utilization allows for a definition of misutilization with regards to these industry standards. Odds ratios were extracted at the patient level to demonstrate differences in misutilization by patient characteristics, such as race; Black individuals experienced higher under-utilization compared to White individuals (p < 0.0001). Volume-adjusted Poisson rate regression models allow for the identification and ranking of facilities with large departures in utilization. The proposed approach is scalable and easily generalizable to other diseases and resources and can be complemented with clinical information from electronic health record information, when available. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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10 pages, 255 KiB  
Article
County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
by Sean Daley, Bakthameera Kajendrakumar, Samyuktha Nandhakumar, Christine Personett, Michael Sholes, Swornim Thapa, Chen Xue, Michael Korvink and Laura H. Gunn
Healthcare 2021, 9(11), 1424; https://doi.org/10.3390/healthcare9111424 - 22 Oct 2021
Cited by 4 | Viewed by 1836
Abstract
The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to [...] Read more.
The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for n = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
16 pages, 3013 KiB  
Article
Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease
by Zhengyan Li, Huichun Li, Xue Zhang and Chengli Zhao
Healthcare 2021, 9(9), 1224; https://doi.org/10.3390/healthcare9091224 - 16 Sep 2021
Cited by 3 | Viewed by 2598
Abstract
Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm [...] Read more.
Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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Review

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12 pages, 3084 KiB  
Review
Cardiovascular Risk Prediction Parameters for Better Management in Rheumatic Diseases
by Abhinav Sharma, Ruxandra Christodorescu, Ahmad Agbariah, Daniel Duda-Seiman, Diala Dahdal, Dana Man, Nilima Rajpal Kundnani, Octavian Marius Cretu and Simona Dragan
Healthcare 2022, 10(2), 312; https://doi.org/10.3390/healthcare10020312 - 7 Feb 2022
Cited by 12 | Viewed by 2808
Abstract
The early detection of cardiovascular disease (CVD) serves as a key element in preventive cardiology. The risk of developing CVD in patients with rheumatic disease is higher than that of the general population. Thus, the objective of this narrative review was to assess [...] Read more.
The early detection of cardiovascular disease (CVD) serves as a key element in preventive cardiology. The risk of developing CVD in patients with rheumatic disease is higher than that of the general population. Thus, the objective of this narrative review was to assess and describe updated risk-prediction parameters for CVD in patients suffering from rheumatic diseases, and, additionally, to evaluate therapeutic and risk management possibilities. The processes of recognizing CVD risk factors in rheumatic diseases, establishing diagnoses, and discovering CV risk assessments are currently displeasing in clinical practice; they have a limited clinical impact. A large number of references were found while screening PUBMED, Scopus, and Google scholar databases; the 47 most relevant references were utilized to build up this study. The selection was limited to English language full text articles, RCTs, and reviews published between 2011 and 2021. Multiple imaging techniques, such as ECG, ultrasound, and cIMT, as well as biomarkers like osteoprotegerin cytokine receptor and angiopoietin-2, can be beneficial in both CV risk prediction and in early subclinical diagnosis. Physical exercise is an essential non-pharmacological intervention that can maintain the health of the cardiovascular system and, additionally, influence the underlying disease. Lipid-lowering drugs (methotrexate from the non-biologic DMARDs family as well as biologic DMARDs such as anti-TNF) were all associated with a lower CV risk; however, anti-TNF medication can decrease cardiac compliance and promote heart failure in patients with previously diagnosed chronic HF. Although they achieved success rates in reducing inflammation, glucocorticoids, NSAIDs, and COX-2 inhibitors were correlated with an increased risk of CVD. When taking all of the aforementioned points into consideration, there appears to be a dire need to establish and implement CVD risk stratification models in rheumatic patients. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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Other

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16 pages, 320 KiB  
Essay
Effect of Collaborative Governance on Medical and Nursing Service Combination: An Evaluation Based on Delphi and Entropy Method
by Beiquan Chang, Yansui Yang, Guillermo Andres Buitrago Leon and Yuzhong Lu
Healthcare 2021, 9(11), 1456; https://doi.org/10.3390/healthcare9111456 - 27 Oct 2021
Cited by 7 | Viewed by 2592
Abstract
[Background]: Improvement of synergies in medical and nursing services can help governments to optimize the allocation of medical resources; however, an appropriate evaluation method is critical for a suitable decision process in this regard. [Method]: To assess the medical and nursing service combination [...] Read more.
[Background]: Improvement of synergies in medical and nursing services can help governments to optimize the allocation of medical resources; however, an appropriate evaluation method is critical for a suitable decision process in this regard. [Method]: To assess the medical and nursing service combination (MNSC) at a regional level, this study applied a five-dimension evaluation index composed of 28 basic response areas related to the MNSC development status in China, determining its respective weight through the Delphi and entropy methods. [Result]: This empirical exercise analyzed the MNSC supply system by interviewing nine heads of medical and nursing institutions and eleven healthcare-related government personnel during August of 2020 in Xinxiang City, Henan province, P.R China. Results showed: (1) public satisfaction with the fees charged by Medical and Nursing service Institutions (MNSI); (2) Medicare and supply services’ policy publicity; (3) the external financing situation of MNSI; (4) the medical staff’s professional quality; (5) the medical facilities and supply of MNSI; and (6) that the recognition level of the development plan of MNSI scored the highest effect on the synergy of MNSC supply among the assessed factors. [Conclusion]: These results showed that an evaluation based on the Delphi and entropy methods can effectively integrate the opinions of experts and related institutions to evaluate synergies on the medical and nursing service supply. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
14 pages, 1314 KiB  
Systematic Review
Determinants of Healthcare Use Based on the Andersen Model: A Systematic Review of Longitudinal Studies
by André Hajek, Benedikt Kretzler and Hans-Helmut König
Healthcare 2021, 9(10), 1354; https://doi.org/10.3390/healthcare9101354 - 12 Oct 2021
Cited by 15 | Viewed by 5002
Abstract
The aim was to give an overview of longitudinal observational studies investigating the determinants of healthcare use explicitly using the Andersen model. To this end, three electronic databases (Medline, PsycINFO and CINAHL) were searched (and an additional hand search was performed). Longitudinal observational [...] Read more.
The aim was to give an overview of longitudinal observational studies investigating the determinants of healthcare use explicitly using the Andersen model. To this end, three electronic databases (Medline, PsycINFO and CINAHL) were searched (and an additional hand search was performed). Longitudinal observational studies examining the determinants of healthcare use (outpatient physician services and hospital stays) based on the Andersen model were included, whereas disease-specific samples were excluded. Study quality was evaluated. The selection of studies, extraction of data and assessment of the studies were conducted by two reviewers. The following determinants of healthcare use were displayed based on the (extended) Andersen model: predisposing characteristics, enabling resources, need factors and psychosocial factors. In sum, n = 10 longitudinal studies have been included in our systematic review. The included studies particularly showed a longitudinal association between increased needs and higher healthcare use. Study quality was rather high. However, several studies did not conduct robustness checks or clarify the handling of missing data. In conclusion, this systematic review adds to our current understanding of the factors associated with healthcare use (mainly based on cross-sectional studies). It showed mixed evidence with regard to the association between predisposing characteristics, enabling resources and healthcare use longitudinally. In contrast, increased need factors (in particular, self-rated health and chronic conditions) were almost consistently associated with increased healthcare use. This knowledge may assist in managing healthcare use. Since most of the studies were conducted in North America or Europe, future longitudinal studies from other regions are urgently required. Full article
(This article belongs to the Special Issue Decision Modelling for Healthcare Evaluation: 2nd Edition)
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