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Health and Medical Policy in the Era of Big Data Analytics

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 19900

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Special Issue Editors

Prof. Dr. Naoru Koizumi

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Guest Editor

Schar School of Policy and Government, George Mason University, Fairfax, VA 22030, USA
Interests: health/medical policies; organ transplantation and other chronic disease treatments; biostatistics

Prof. Dr. Megumi Inoue

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Guest Editor

Department of Social Work, College of Heatlth and Human Services, George Mason University, Fairfax, VA 22030, USA
Interests: end-of-life issues: advance care planning and grief process; patients’ autonomy and dignity in health care settings; personalized music intervention for dementia

Prof. Dr. Ali Andalibi

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Guest Editor

School of Systems Biology, College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: microbiology and molecular genetics; inflammatory process; innate immune molecules in the etiology of diseases

Special Issue Information

Dear Colleagues,

Over the last few years, the breathtaking pace of advances in translational medical knowledge has been generating vast quantities of digitized biomedical and health care data that more than match the quintessential five Vs of Big Data—Volume, Velocity, Variety, Variability, and Veracity.

It is only natural that proven and new techniques and solutions based on Big Data analytics are developed and employed in processing zetta- and yotta-bytes of digitized biomedical/health data. Some of the key objectives of using Big Data analytics for the biomedical and health sectors are:

To generate sensible knowledge that provides fresh insights;
To help craft actionable personalized diagnostics, medical care, and precision treatment;
To potentially create new revenue streams for the future growth of the health care industry while saving operational costs.

As the fusion of biomedical research with Big Data continues to break new grounds, a fresh examination of the adequacy of existing health and medical policies is warranted. Such an examination would involve in-depth analyses with Big Data to identify loopholes, address burdensome statutes, and discover what may be missing and what is required.

These essential steps would greatly help in formulating sound health and medical policies that would not only promote continued and balanced growth of the sector, but also enhance critical aspects such as basic adherence to the Hippocratic Oath, the assurance of privacy, and accessible and equitable biomedical and health care.

To that end, this Special Issue on “Health and Medical Policy in the Era of Big Data Analytics” seeks original research, reports, and reviews that highlight challenges posed in developing actionable policies with Big Data analytics to provide state-of-the-art, affordable, and equitable biomedical health care to all. Research contributions showing how augmenting AI/machine learning techniques with Big Data analytics could help mitigate health care disparity are also welcome.

Prof. Dr. Naoru Koizumi
Prof. Dr. Megumi Inoue
Prof. Dr. Ali Andalibi
Guest Editors

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

artificial intelligence/machine learning
medical and health care policy
fairness and disparity in the health care system

Published Papers (9 papers)

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Research

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14 pages, 660 KiB

Open AccessArticle

Cerebrovascular Disease Hospitalization Rates in End-Stage Kidney Disease Patients with Kidney Transplant and Peripheral Vascular Disease: Analysis Using the National Inpatient Sample (2005–2019)

by Tyler John Canova, Rochell Issa, Patrick Baxter, Ian Thomas, Ehab Eltahawy and Obi Ekwenna

Healthcare 2024, 12(4), 454; https://doi.org/10.3390/healthcare12040454 - 10 Feb 2024

Viewed by 617

Abstract

Individuals with end-stage kidney disease (ESKD) face higher cerebrovascular risk. Yet, the impact of peripheral vascular disease (PVD) and kidney transplantation (KTx) on hospitalization rates for cerebral infarction and hemorrhage remains underexplored. Analyzing 2,713,194 ESKD hospitalizations (2005–2019) using the National Inpatient Sample, we investigated hospitalization rates for ischemic and hemorrhagic cerebrovascular diseases concerning ESKD, PVD, KTx, or their combinations. Patients hospitalized with cerebral infarction due to thrombosis/embolism/occlusion (CITO) or artery occlusion resulting in cerebral ischemia (AOSI) had higher rates of comorbid ESKD and PVD (4.17% and 7.29%, respectively) versus non-CITO or AOSI hospitalizations (2.34%, p < 0.001; 2.29%, p < 0.001). Conversely, patients hospitalized with nontraumatic intracranial hemorrhage (NIH) had significantly lower rates of ESKD and PVD (1.64%) compared to non-NIH hospitalizations (2.34%, p < 0.001). Furthermore, hospitalizations for CITO or AOSI exhibited higher rates of KTx and PVD (0.17%, 0.09%, respectively) compared to non-CITO or AOSI hospitalizations (0.05%, p = 0.033; 0.05%, p = 0.002). Patients hospitalized with NIH showed similar rates of KTx and PVD (0.04%) versus non-NIH hospitalizations (0.05%, p = 0.34). This nationwide analysis reveals that PVD in ESKD patients is associated with increased hospitalization rates with cerebral ischemic events and reduced NIH events. Among KTx recipients, PVD correlated with increased hospitalizations for ischemic events, without affecting NIH. This highlights management concerns for patients with KTx and PVD. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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11 pages, 371 KiB

Open AccessArticle

Opinion and Sentiment Analysis of Palliative Care in the Era of COVID-19

by Megumi Inoue, Meng-Hao Li, Mahdi Hashemi, Yang Yu, Jahnavi Jonnalagadda, Rajendra Kulkarni, Matthew Kestenbaum, Denise Mohess and Naoru Koizumi

Healthcare 2023, 11(6), 855; https://doi.org/10.3390/healthcare11060855 - 14 Mar 2023

Cited by 1 | Viewed by 1264

Abstract

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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13 pages, 3453 KiB

Open AccessArticle

The Impact of the COVID-19 Pandemic on Kidney Transplant Candidate Waitlist Status across Demographic and Geographic Groups: A National Analysis of UNOS STAR Data

by Conner V. Lombardi, Jacob J. Lang, Meng-Hao Li, Abu Bakkar Siddique, Naoru Koizumi and Obi Ekwenna

Healthcare 2023, 11(4), 612; https://doi.org/10.3390/healthcare11040612 - 18 Feb 2023

Viewed by 1259

Abstract

The primary goal of this retrospective study is to understand how the COVID-19 pandemic differentially impacted transplant status across race, sex, age, primary insurance, and geographic regions by examining which candidates: (i) remained on the waitlist, (ii) received transplants, or (iii) were removed from the waitlist due to severe sickness or death on a national level. Methods: The trend analysis aggregated by monthly transplant data from 1 December 2019 to 31 May 2021 (18 months) at the transplant center level. Ten variables about every transplant candidate were extracted from UNOS standard transplant analysis and research (STAR) data and analyzed. Characteristics of demographical groups were analyzed bivariately using t-test or Mann–Whitney U test for continuous variables and using Chi-sq/Fishers exact tests for categorical variables. Results: The trend analysis with the study period of 18 months included 31,336 transplants across 327 transplant centers. Patients experienced a longer waiting time when their registration centers in a county where high numbers of COVID-19 deaths were observed (SHR < 0.9999, p < 0.01). White candidates had a more significant transplant rate reduction than minority candidates (−32.19% vs. −20.15%) while minority candidates were found to have a higher waitlist removal rate than White candidates (9.23% vs. 9.45%). Compared to minority patients, White candidates’ sub-distribution hazard ratio of the transplant waiting time was reduced by 55% during the pandemic period. Candidates in the Northwest United States had a more significant reduction in the transplant rate and a greater increase in the removal rate during the pandemic period. Conclusions: Based on this study, waitlist status and disposition varied significantly based on patient sociodemographic factors. During the pandemic period, minority patients, those with public insurance, older patients, and those in counties with high numbers of COVID-19 deaths experienced longer wait times. In contrast, older, White, male, Medicare, and high CPRA patients had a statistically significant higher risk of waitlist removal due to severe sickness or death. The results of this study should be considered carefully as we approach a reopening world post-COVID-19, and further studies should be conducted to elucidate the relationship between transplant candidate sociodemographic status and medical outcomes during this era. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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16 pages, 1222 KiB

Open AccessArticle

Evaluating State-Level Prescription Drug Monitoring Program (PDMP) and Pill Mill Effects on Opioid Consumption in Pharmaceutical Supply Chain

by Amirreza Sahebi-Fakhrabad, Amir Hossein Sadeghi and Robert Handfield

Healthcare 2023, 11(3), 437; https://doi.org/10.3390/healthcare11030437 - 03 Feb 2023

Cited by 6 | Viewed by 3242

Abstract

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study evaluates the impact of two categories of PDMP and Pill Mill regulations on the supply of opioid prescriptions at the level of dispensers and distributors (excluding manufacturers) using ARCOS data. The study uses a difference-in-difference method with a two-way fixed design to analyze the data. The study finds that both of the regulations are associated with reductions in the volume of opioid distribution. However, the study reveals that these regulations may have unintended consequences, such as shifting the distribution of controlled substances to neighboring states. For example, in Tennessee, the implementation of Operational PDMP regulations reduces the in-state distribution of opioid drugs by 3.36% (95% CI, 2.37 to 4.3), while the out-of-state distribution to Georgia, which did not have effective PDMP regulations in place, increases by 16.93% (95% CI, 16.42 to 17.44). Our studies emphasize that policymakers should consider the potential for unintended distribution shifts of opioid drugs to neighboring states with laxer regulations as well as varying impacts on different dispenser types. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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17 pages, 1726 KiB

Open AccessArticle

Volume–Outcome Relationship in Cancer Survival Rates: Analysis of a Regional Population-Based Cancer Registry in Japan

by Yoichiro Sato, Rena Kaneko, Yuichiro Yano, Kentaro Kamada, Yuui Kishimoto, Takashi Ikehara, Yuzuru Sato, Takahisa Matsuda and Yoshinori Igarashi

Healthcare 2023, 11(1), 16; https://doi.org/10.3390/healthcare11010016 - 21 Dec 2022

Cited by 1 | Viewed by 1317

Abstract

Background: There is limited data on the relationship between hospital volumes and outcomes with respect to cancer survival in Japan. The primary objective of this study was to evaluate the effect of hospital volume on cancer survival rate using a population-based cohort database. Methods: Using the Kanagawa cancer registry, propensity score matching was employed to create a dataset for each cancer type by selecting 1:1 matches for cases from high- and other-volume hospitals. The 5-year survival rate was estimated and the hazard ratio (HR) for hospital volume was calculated using a Cox proportional hazard model. Additional analyses were performed limited to cancer patients who underwent surgical operation, chemotherapy, and other treatments in each tumor stage and at the time of diagnosis. Results: The number of cases with complete data, defined as common cancers (prostate, kidney, bladder, esophagus, stomach, liver, pancreas, colon, breast, and lung), was 181,039. Adjusted HR differed significantly among hospital volume categories for the most common cancers except bladder, and the trends varied according to cancer type. The HR ranged from 0.76 (95%CI, 0.74–0.79) for stomach cancer to 0.85 (0.81–0.90) for colon cancer. Conclusions: This study revealed that a relationship may exist between hospital volume and cancer survival in Japan. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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13 pages, 817 KiB

Open AccessArticle

Racial Differences in Breastfeeding on the Mississippi Gulf Coast: Making Sense of a Promotion-Prevalence Paradox with Cross-Sectional Data

by John P. Bartkowski, Janelle Kohler, Xiaohe Xu, Tennille Collins, Jacinda B. Roach, Caroline Newkirk and Katherine Klee

Healthcare 2022, 10(12), 2444; https://doi.org/10.3390/healthcare10122444 - 03 Dec 2022

Cited by 1 | Viewed by 1717

Abstract

Breastfeeding is less prevalent among African American women than their white peers. Moreover, breastfeeding rates in the South lag behind those in other regions of the U.S. Consequently, various efforts have been undertaken to promote breastfeeding among groups for which this practice is less common. This study examines African American and white racial disparities concerning (1) exposure to breastfeeding promotional information and (2) reported prevalence of breastfeeding in primary social networks. The survey combines a randomly selected sample of adults representative of the population and a non-random oversample of African Americans in a predominantly rural tri-county area on the Mississippi Gulf Coast. An initial wave of 2019 Mississippi REACH Social Climate Survey data collected under the auspices of the CDC-funded REACH program (Mississippi’s Healthy Families, Mothers, and Babies Initiative; 2018–2023) is used to examine racial disparities in these two key outcomes for Mississippians in Hancock, Harrison, and Jackson counties. The results show that African American respondents are more likely to be exposed to breastfeeding promotional messages than their white counterparts. However, the reported prevalence of breastfeeding in African American respondents’ primary social networks is significantly lower than that indicated by their white peers. These paradoxical results underscore the limitations of promotional efforts alone to foster breastfeeding. While breastfeeding promotion is important, the reduction of racial disparities in this practice likely requires a multi-pronged effort that involves structural breastfeeding supports (e.g., lactation spaces, peer networking groups, and pro-breastfeeding employment policies and workplaces). This study provides a promising model of innovative methodological approaches to the study of breastfeeding while underscoring the complex nature of racial disparities in lactation prevalence. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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11 pages, 956 KiB

Open AccessArticle

Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models

by Norio Yamamoto, Shintaro Sukegawa and Takashi Watari

Healthcare 2022, 10(5), 892; https://doi.org/10.3390/healthcare10050892 - 12 May 2022

Cited by 3 | Viewed by 2685

Abstract

No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893–0.895) in all patients’ data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors’ loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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Review

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12 pages, 885 KiB

Open AccessReview

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

by Kelly H. Zou and Jim Z. Li

Healthcare 2022, 10(10), 1997; https://doi.org/10.3390/healthcare10101997 - 11 Oct 2022

Cited by 7 | Viewed by 2769

Abstract

Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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18 pages, 3688 KiB

Open AccessReview

A Bibliometric and Visualization Analysis of Motor Learning in Preschoolers and Children over the Last 15 Years

by Fei Xu, Jing Xu, Daliang Zhou, Hao Xie and Xuan Liu

Healthcare 2022, 10(8), 1415; https://doi.org/10.3390/healthcare10081415 - 28 Jul 2022

Cited by 1 | Viewed by 3500

Abstract

Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 years. The number of studies is constantly growing, with the United States and Australia, as well as other productive institutions and authors, at the leading edge. The dominant disciplines were Neurosciences and Neurology, Psychology, Rehabilitation, and Sport Sciences. The journals Developmental Medicine & Child Neurology, Human Movement Science, Physical Therapy, Neuropsychology, Journal of Motor Behavior, and Journal of Experimental Child Psychology have been the most productive and influential in this regard. The most common co-citations for clinical symptoms were for cerebral palsy, developmental coordination disorder, and autism. Research has focused on language impairment (speech disorders, explicit learning, and instructor-control feedback), as well as effective intervention strategies. Advances in brain mechanisms and diagnostic indicators, as well as new intervention and rehabilitation technologies (virtual reality, transcranial magnetic stimulation, and transcranial direct current stimulation), have shifted research frontiers and progress. The cognitive process is critical in intervention, rehabilitation, and new technology implementation and should not be overlooked. Overall, our broad overview identifies three major areas: brain mechanism research, clinical practice (intervention and rehabilitation), and new technology application. Full article

(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)

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